RozitaTeymourzadeh,Bright, S I Grace

Optimised Toolbox for the Design of Rotary Reluctance Motors

Keywords: Motor, GUI , CIM, Rotary Reluctance, Toolbox I

The operation of reluctance machine is highly affected due to the attraction-repulsion principle. The problem has been identified as early over past several decades. Numerous researchers present their work by addressing the issues on the machine design aspects. However it is very tedious and time consuming as the design procedure involves higher analytical derivations and calculations. This paper attempts to present a graphical user interface toolbox to use for the design of reluctance motors. The developed toolbox help the user a simple design procedure for a rotary reluctance motor. The GUI also calculates the analytical values of the aligned, unaligned and intermediate inductance values of the particular design as this gives a comprehensive results on the analytical evaluation of the inductance at various rotor positions. Cyclic Integration Method (CIM) is used for the evaluation of the designed tool box for validation.

INTRODUCTION

The design of motor in general is a complex procedure and hence requires an easy and understandable design method and tool for the designer. Various CAD has being developed for the design of electrical machines. Most have being developed using MATLAB m-file while others have used GUIDE, Simulink and java. Designs have being made mostly for DC machines, AC machines, Induction motors and transformers [START_REF] Marchand | Graphics and GUIs with MATLAB[END_REF]. Work on CAD design of reluctance motor is gaining popularity as increase in the need to develop new magnetic design of reluctance motor is on the rise [START_REF] Krishnan | Switched Reluctance Motor Drives: Modeling, Simulation, Analysis, Design, and Applications[END_REF][START_REF] Praveen | Design of Switched Reluctance Motors and Development of a Universal Controller for Switched Reluctance and Permanent Magnet Brushless DC Motor Drives[END_REF]. Reluctance motors have simplicity in construction, high fault tolerance and makes use of no magnets in its magnetic design. Torque ripple are generated due to the fringing flux that develop just before the overlap position of the stator and rotor teeth. Therefore it is highly important to study the behaviour of the reluctance machine near to the saturation. The study of the inductance slope during the rising edge from unaligned to aligned decides the motoring torque. Variations on the physical dimension of the machine helps to achieve a smoother torque profile with reduced ripples [START_REF] Krishnan | Switched Reluctance Motor Drives: Modeling, Simulation, Analysis, Design, and Applications[END_REF][START_REF] Aravind | Design of reluctance motors for low speed applications[END_REF].

II. DESIGN TOOL

The design of reluctance motor is a complex procedure and hence requires an easy and understandable design method and tool for the designer. This is the reason the GUIDE tool of MATLAB/SIMULINK is used for the design. This tool is picked with preference to m-file because of its user friendly interface compared to m-file been used. The user of m-file has to understand the codes written by the programmer while the user of GUIDE need not worry about the codes used. In designing the GUI, the basic principle involved is taking inputs into the GUI and then an output based on the computational codes fixed into the m-file of the GUI.

Fig. 1: Introductory GUI

The introductory GUI above makes use of a pushbutton that links the user to the rotary SRM design.The first graphic tool is the rotary SRM design guide. The mechanical design parameters for the motor take input such as the bore diameter, length, pole arcs of both the stator and rotor and the capacity values of the required motor under design. These inputs are then used to calculate the other dimensions of the motor. From these, the average torque is evaluated. If the average torque is not equal or greater than the required torque, the user is able to vary the inputs until this is achieved. This in turn helps the user be able to compare various structures of the motor it gives the required torque. Also included in this is the costing analysis for the motor. The developed tool for the machine design for the rotary configuration is as shown in Fig. 2.

Fig. 2: Rotary SRM Design GUI

The analytical inductance evaluation is used for the inductance profile GUI. The inductance GUI takes input parameters of Number of stator poles (P s ) and rotor poles (P r ), stator pole arc (β s ), rotor pole arc (β r ), air gap length (l g ), bore diameter (D), stack length (L), shaft diameter (D sh ), stator back iron thickness (b sy ), height of stator pole (h s ) and rotor pole (h r ), turns per phase (T ph ).The second graphic tool has the analytical evaluations of the inductance at various rotor positions. The user is able to see results for the inductance at fully unaligned, aligned and intermediate positions. Graphical representation of the BH curve of the material used, the inductance profile graph, the graph of torque versus rotor position and the flux linkage versus current graph can be analyzed from the result section as shown in Fig. 2.

Fig. 3: Inductance Profile GUI

III. MACHINE DESIGN

Design procedures of reluctance machine are documented comprehensively in [START_REF] Krishnan | Switched Reluctance Motor Drives: Modeling, Simulation, Analysis, Design, and Applications[END_REF], [START_REF] Praveen | Design of Switched Reluctance Motors and Development of a Universal Controller for Switched Reluctance and Permanent Magnet Brushless DC Motor Drives[END_REF] and this is used as reference for this research work. However the designed values have been verified and validated by introducing an analytical approach method as Cyclic Integration Method (CIM). The cyclic integration method involved the triangulation principle normally employed for the finite element analysis. But in this approach a triangulation on the vector potential is calculated for a small strip of area and later integrated to the whole radial surface. This calculated value is then integrated once again in the circumferential surface of the area enclosed. The optimal performance of this method has been validated by comparing the results with the finite element design and analysis tool [START_REF] Aravind | Design of reluctance motors for low speed applications[END_REF] The design procedure involves the design of the entire layout of the machine including the physical dimensions such as the diameter, length, rotor size and other standard parameters. The machine specifications of a SRM includes the required power output in horse power (hp), speed (rpm), allowable peak current (A), available dc supply voltage etc., From this value the required torque output can be calculated using the equation: [START_REF] Krishnan | Switched Reluctance Motor Drives: Modeling, Simulation, Analysis, Design, and Applications[END_REF] where, T req -torque required (N.m) N -Speed of the motor (rev/min) This helps to know the actual torque that is required as provided by the machine specifications. When the average torque calculated is equal to or greater than the required torque, the machine is according to specifications and the user can then use the dimensions for the motor parts to fabricate the motor. The average torque of reluctance motor is derived from the inductance difference between the fully unaligned rotor position and fully aligned rotor position. The equation linking the torque generated, rate of change in inductance and magnitude of current is:

(

) 2

IV. CYCLIC INTEGRATION METHOD

To verify that the inductance profile calculations are correct, the cyclic integration method is used to calculate both the unaligned and aligned inductance. The cyclic integration method involves the design of the motor using CAD to determine the angles and limits of the integration and also iterating the magnetic flux density of the stator pole so that the specified magneto motive force (mmf) is derived. However this method of cyclic integration does not take into account the effect of the leakage flux.

MMF = F = T ph * I p (3)
Once the mmf is calculated the permeance for an entire bounded region is calculated as:

P radial = (4)
where r -radius of the region enclosed L -stack length of the machine ul -the upper limit ll -the lower limit

Then the total reluctance for the selected face is calculated using (5) where θ -the angle subtended by the bounded region After the total reluctance is derived the magnetic flux density of the stator pole is derived using the formula: Test performace design for the machine is carried out using the developed GUI and an 8/6 reluctance machine design is presented and the inductance values are evaluated using analytical method for checking for the optimisation of the presented toolbox. The GUI inductance profile is test using the inputs in below:

Fig. 8: Inputs for Inductance Profile

The results for the fully unaligned and aligned positions are as shown in Fig. 9 below. This shows the inductance for each flux path, the flux density and the reluctance.

Fig. 9: Results for fully unaligned and aligned positions

The total unaligned and Unaligned inductance are as shown in FIG. 10.

Fig. 10: Total unaligned and aligned Position

To verify the values the cyclic integration method based on the CAD design for the same parameters of the designed machine is performance. The result of the aligned inductance from the cyclic integration method is = 64mH

The comparison between analytical (GUI) and the cyclic integration method is as shown in the table below: Percentage difference between the values for unaligned inductance is 6.31% while that for aligned is 4.14%. The difference in values comes as a result of assumptions in the calculations. For example, the cyclic integration method does not take into account the leakage flux and the integration path is based on assumption. The fully unaligned position for this motor takes place between 0° and 10° rotor pole position to stator pole position. To get a more accurate result, more regions can be selected and their inductance derived. The final inductance then takes the sum of all regions and average value can be derived by the numbers of regions taken. In general, from the error calculations, the percentage difference is seen to be minimal and the GUI results verified to be accurate. Since the inductance have being calculated and verified, the torque of the machine can now be computed. The average torque is derived based on uniform current in the rising edge of the inductance profile. The average torque is calculated by: ( The design output of the motor gives the required torque, back iron thickness, rotor and stator height, pole width, turns per phase, MMF, peak current, aligned and unaligned inductance of the motor and finally the average torque. If the average torque generated does not give the required output, the user is required to change some inputs parameters such as the stator and rotor pole arc. This helps move the average torque closer to the required torque. When this is achieved, the motor can then be said to have been designed. The above results compare the design for two separate inputs. As seen from results the average torque calculated is less than the required torque in Fig 17 . In this case, the input parameters where then varied, changing the pole arcs. From results B, it is seen that the average torque is greater than the required torque. This shows that the design of the SRM parameters is accurate at this stage. The design of the motor using CAD is as shown in Fig. 19.

Fig. 4 :Fig. 5 :

 45 Fig. 4: Flowchart for machine designThe graphical user interface based on the above design then gives the values of the inductance, magnetic flux density, reluctance for each flux path for fully aligned, unaligned and various intermediate rotor positions. For optimize the performance of the machine designed and to give the user on the motoring slope the total unaligned and aligned inductance is generated by calculations. Form the data the designer can identify and understand the performance comparisons by varying the design values.

Fig. 6 :

 6 Fig. 6: Analytical magnetic design calculationsThe calculation area for the various position under fully aligned, partially aligned and unaligned conditions are presented in Fig.7. However the flux lines assumed here is of concentric circles and the limitation on the choice of circle is limited with respect to the high numeric computations involved.

Fig. 11 :Fig. 12 :

 1112 Fig. 11: Flux maps at fully unaligned PositionThe unaligned inductance of a reluctance motor takes place from zero degrees (0°) or (180/P r ) angle of rotor pole from stator pole and ((180/P r ) -(βs + β r )). With reference to the example; the rotor positions at fully unaligned position fall within 30° and 10° from rotor poles to stator poles. This gives two distinct regions for the unaligned inductance which are calculated and the average taken as the inductance of the SRM machine at fully unaligned position. The limits of integration are 15.37 and 0 for part a and 17.52 and 7.89 for part b. The inductance values derived from these are: Unaligned Inductance for part a = 16.6mH Unaligned Inductance for part b = 4.7mH Total Unaligned Inductance = (part a + part b)/2 = 10.7mH

  where T a v -average torque δw m -total work done P r-number of rotor poles P s -number of stator polesThe total work done is calculated by subtracting the work done in the unaligned position from that of in the aligned positions. The work done is calculated by multiplying the flux linkage and the corresponding current.

Fig. 13 :

 13 Fig.13: Torque result

Fig

  Fig. 14: SRM machine specification

Fig. 19 :

 19 Fig. 19: CAD of 8/6 SRM

TABLE 1 Analytical & GUI Comparisons

 1

		GUI (mH)	Analytical (mH)
	Aligned inductance	66.7652	64
	Unaligned inductance 11.4217	10.7

VIII. ACKNOWLEDGMENT

The authors wish to record CERVIE, UCSI University for supporting this research work.


Rachel Bawden
email: rachel.bawden@limsi.frrico.sennrich

Rico Sennrich

Alexandra Birch
email: a.birch@ed.ac.ukbhaddow@inf.ed.ac.uk

Barry Haddow

M F Pl

Pl

Evaluating Discourse Phenomena in Neural Machine Translation

For machine translation to tackle discourse phenomena, models must have access to extrasentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50% accuracy on our coreference test set and 53.5% for coherence/cohesion (compared to a non-contextual baseline of 50%). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multiencoding and decoding of two sentences leads to the best performance (72.5% for coreference and 57% for coherence/cohesion), highlighting the importance of target-side context.

Coreference (%)

Coherence/cohesion (%)

Introduction

Machine translation (MT) systems typically translate sentences independently of each other. However, certain textual elements cannot be correctly translated without linguistic context, which may appear outside the current sentence. The most obvious examples of context-dependent phenomena problematic for MT are coreference [START_REF] Guillou | Incorporating Pronoun Function into Statistical Machine Translation[END_REF], lexical cohesion [START_REF] Carpuat | One translation per discourse[END_REF] and lexical disambiguation (Rios [START_REF] Gonzales | Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings[END_REF], an example for each of which is given in (1-3). In each case, the English element in italic is ambiguous in terms of its French translation. The correct translation choice (in bold) is determined by linguistic context (underlined), which can be outside the current sentence. This disambiguating context can be source or target-side; the correct translation of anaphoric pronouns it and they depends on the gender of the translated antecedent (1). In lexical cohesion, a translation may depend on target factors, but may also be triggered by source effects and linguistic mechanisms such as repetition or alignment (2). In lexical disambiguation, source or target information may provide the appropriate context (3).

(1) The bee is busy. // It is making honey.

L'abeille[f] est occupée. // Elle[f]/#il[m] fait du miel.

(2) Do you fancy some soup? // Some soup? Tu veux de la soupe? // De la soupe/#du potage?

(3) And the code? // Still some bugs... Et le code ? // Encore quelques bugs/#insectes...

Recent work on multi-encoder neural machine translation (NMT) appears promising for the integration of linguistic context [START_REF] Zoph | Multi-source Neural Translation[END_REF][START_REF] Libovický | Attention Strategies for Multi-Source Sequence-to-Sequence Learning[END_REF]Jean et al., 2017a;[START_REF] Wang | Exploiting Cross-Sentence Context for Neural Machine Translation[END_REF]. However models have almost only been evaluated using standard automatic metrics, which are poorly adapted to evaluating discourse phenomena. Targeted evaluation, in particular of coreference in MT, has proved to be time-consuming and laborious [START_REF] Guillou | Incorporating Pronoun Function into Statistical Machine Translation[END_REF].

In this article, we address the evaluation of discourse phenomena for MT and propose a novel contextual model. We present two hand-crafted, discourse test sets designed to test models' capacity to exploit linguistic context for coreference and coherence/cohesion for English to French translation. Using these sets, we review contextual NMT strategies trained on subtitles in a high-resource setting. Our new combination of strategies outperforms previous methods according to our targeted evaluation and the standard metric BLEU.

Evaluating contextual phenomena

Traditional automatic metrics are notoriously problematic for the evaluation of discourse in MT [START_REF] Hardmeier | Discourse in Statistical Machine Translation[END_REF]; discursive phenomena may have an impact on relatively few word forms with respect to their importance, meaning that improvements are overlooked, and a correct translation may depend on target-side coherence rather than similarity to a reference translation.

Coreference has been a major focus of discourse translation, spurred on by shared tasks on crosslingual pronoun prediction (Guillou et al., 2016;[START_REF] Loáiciga | Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction[END_REF]. Participants were provided with lemmatised versions of reference translations,1 in which pronoun forms were to be predicted. Evaluation in this setting (with the use of reference translations) was possible with traditional metrics, because the antecedents were fixed in advance. However there are at least two disadvantages to the approach: (i) models must be trained on lemmatised data and cannot be used in a real translation setting, and (ii) many of the pronouns did not need extra-sentential context; easier gains were seen for the pronouns with intrasentential antecedents and therefore the leaderboard was dominated by sentence-level systems. Guillou and Hardmeier's (2016) pronoun translation test suite succeeds in overcoming some of these problems by creating an automatic evaluation method, with a back-off manual evaluation. Manual evaluation has always been an essential part of evaluating MT quality, and targeted translation allows us to isolate a model's performance on specific linguistic phenomena; recent work using in-depth, qualitative manual evaluation [START_REF] Isabelle | A Challenge Set Approach to Evaluating Machine Translation[END_REF][START_REF] Scarton | A Quantitative Analysis of Discourse Phenomena in Machine Translation[END_REF] is very informative. [START_REF] Isabelle | A Challenge Set Approach to Evaluating Machine Translation[END_REF] focus on specially constructed challenging examples in order to analyse differences between systems. They cover a wide range of linguistic phenomena, but since manual evaluation is costly and time-consuming, only a few examples per phenomenon are analysed, and it is difficult to obtain quick, quantitative feedback.

An alternative method, which overcomes the problem of costly, one-off analysis, is to evaluate models' capacity to correctly rank contrastive pairs of pre-existing translations, of which one is correct and the other incorrect. This method was used by [START_REF] Sennrich | How Grammatical is Characterlevel Neural Machine Translation[END_REF] to assess the grammaticality of character-level NMT and again by Rios [START_REF] Gonzales | Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings[END_REF] in a large-scale setting for lexical disambiguation for English-German. The method allows automatic quantitative evaluation of specific phenomena at large scale, at the cost of only testing for very specific translation errors. It is also the strategy that we will use here to evaluate translation of discourse phenomena.

Our contrastive discursive test sets

We created two contrastive test sets to help compare how well different contextual MT models handle (i) anaphoric pronoun translation and (ii) coherence and cohesion.2 For each test set, models are assessed on their ability to rank the correct translation of an ambiguous sentence higher than the incorrect translation, using the disambiguating context provided in the previous source and/or target sentence. 3 All examples in the test sets are hand-crafted but inspired by real examples from OpenSubtitles2016 [START_REF] Lison | OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles[END_REF] to ensure that they are credible and that vocabulary and syntactic structures are varied. The method can be used to evaluate any NMT model, by making it produce a score for a given source sentence and reference translation.

Our test sets differ from previous ones in that examples necessarily need the previous context (source and/or target-side) for the translations to be correctly ranked. Unlike the shared task test sets, the ambiguous pronouns' antecedents are guaranteed not to appear within the current sentence, meaning that, for MT systems to score highly, they must use discourse-level context. Compared to other test sets suites, ours differs in that evaluation is performed completely automatically and concentrates specifically on the model's ability to use context. Each of the test sets contains 200 contrastive pairs and is designed such that a non-contexual baseline system would achieve 50% accuracy.

context:

Oh, I hate flies. Look, there's another one! current sent.: Don't worry, I'll kill it for you.

context:

Ô je déteste les mouches. Regarde, il y en a une autre ! correct:

T'inquiète, je la tuerai pour toi. incorrect:

T'inquiète, je le tuerai pour toi.

context: Ô je déteste les moucherons. Regarde, il y en a un autre ! correct:

T'inquiète, je le tuerai pour toi. incorrect:

T'inquiète, je la tuerai pour toi.

context: Ô je déteste les papillons. Regarde, il y en a un autre ! semi-correct: T'inquiète, je le tuerai pour toi. incorrect:

T'inquiète, je la tuerai pour toi.

context: Ô je déteste les araignées. Regarde, il y en a une autre ! semi-correct: T'inquiète, je la tuerai pour toi. incorrect:

T'inquiète, je le tuerai pour toi. Coreference test set This set contains 50 example blocks, each containing four contrastive translation pairs (see the four examples in Fig. 1).

The test set's aim is to test the integration of target-side linguistic context. Each block is defined by a source sentence containing an occurrence of the anaphoric pronoun it or they and its preceding context, containing the pronoun's nominal antecedent. 4 Four contrastive translation pairs of the previous and current source sentence are given, each with a different translation of the nominal antecedent, of which two are feminine and two are masculine per block. Each pair contains a correct translation of the current sentence, in which the pronoun's gender is coherent with the antecedent's translation, and a contrastive (incorrect) translation, in which the pronoun's gender is inversed (along with agreement linked to the pronoun choice). Two of the pairs contain what we refer to as a "semi-correct" translation of the current sentence instead of a "correct" one, for which the antecedent in the previous sentence is strangely or wrongly translated (e.g. flies translated as araignées "spiders" and papillons "butterflies" in Fig. 1). In the "semi-correct" translation, the pronoun, whose translation is wholly dependent on the translated antecedent, is coherent with this translation choice. These semi-correct examples assess the use of target-side context, taking into account previous translation choices.

Target pronouns are evenly distributed according to number and gender with 50 examples (25 correct and 25 semi-correct) for each of the pronoun types (m.sg, f.sg, m.pl and f.pl). Since there are only two possible translations of the current sentence per example block, an MT system can only score all examples within a block correctly if it correctly disambiguates, and a non-contextual baseline system is guaranteed to score 50%.

context:

What's crazy about me? current sent.: Is this crazy? Source:

context:

Qu'est-ce qu'il y a de dingue chez moi ? correct:

Est-ce que ça c'est dingue ? incorrect:

Est-ce que ça c'est fou ?

Target:

context: What's crazy about me? current sent.: Is this crazy? Source:

context:

Qu'est-ce qu'il y a de fou chez moi ? correct:

Est-ce que ça c'est fou ? incorrect:

Est-ce que ça c'est dingue ?

Target: Example block from the coherence/cohesion test: alignment.

context:

So what do you say to £50? current sent.: It's a little steeper than I was expecting.

Source:

context:

Qu'est-ce que vous en pensez de 50£ ? correct:

C'est un peu plus cher que ce que je pensais. incorrect:

C'est un peu plus raide que ce que je pensais.

Target:

context: How are your feet holding up? current sent.: It's a little steeper than I was expecting.

Source:

context:

Comment vont tes pieds ? correct:

C'est un peu plus raide que ce que je pensais. incorrect:

C'est un peu plus cher que ce que je pensais.

Target:

Figure 3:

Example block from the coherence/cohesion test: lexical disambiguation.

Coherence and cohesion test set Coherence and cohesion concern the interpretation of a text in the context of discourse (i.e. beyond sentence level). De [START_REF] De | Introduction to Text Linguistics[END_REF] define the dichotomous pair as representing two separate aspects: coherence relating to the consistency of the text to concepts and world knowledge, and cohesion relating to the surface formulation of the text, as expressed through linguistic mechanisms. This set contains 100 example blocks, each containing two contrastive pairs (see Figs. 2 and3). Each of the blocks is constructed such that there is a single ambiguous source sentence, with two possible translations provided. The use of one translation over the other is determined by disambiguation context found in the previous sentence. The context may be found on the source side, the target side or both. In each contrastive pair, the incorrect translation of the current sentence corresponds to the correct translation of the other pair, such that the block can only be entirely correct if the disambiguating context is correctly used.

All test set examples have in common that the current English sentence is ambiguous and that its correct translation into French relies on context in the previous sentence. In some cases, the correct translation is determined more by cohesion, for example the necessity to respect alignment or repetition (Fig. 2). This means that despite two translations of an English source word being synonyms (e.g. dingue and fou, "crazy"), they are not interchangeable in a discourse context, given that the chosen formulation (alignment) requires repetition of the word of the previous sentence. In other cases, lexical choice is determined more by cohesion, for example by a general semantic context provided by the previous sentence, in a more classic disambiguation setting as in Fig. 3, where the English steeper is ambiguous between French cher "more expensive" and raide "sharply sloped". However, these types are not mutually exclusive and the distinction is not always so clear.

Contextual NMT Models

In order to correctly translate the type of phenomena mentioned in Sec. 1, translation models need to look beyond the sentence. Much of the previous work, mainly in statistical machine translation (SMT), focused on post-edition, particularly for anaphoric pronoun translation (Guillou et al., 2016;[START_REF] Loáiciga | Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction[END_REF]. However, corefer-ence resolution is not yet sufficient for high quality post-or pre-edition [START_REF] Bawden | Cross-lingual Pronoun Prediction with Linguistically Informed Features[END_REF], and for other discourse phenomena such as lexical cohesion and lexical disambiguation, detecting the disambiguating context is far from trivial.

Recent work in NMT has explored multi-input models, which integrate the previous sentence as an auxiliary input. A simple strategy of concatenating the previous sentence to the current sentence and using a basic NMT architecture was explored by [START_REF] Tiedemann | Neural Machine Translation with Extended Context[END_REF], but with mixed results. A variety of multi-encoder strategies have also been tested, including using a representation of the previous sentence to initialise the main encoder and/or decoder [START_REF] Wang | Exploiting Cross-Sentence Context for Neural Machine Translation[END_REF] and using multiple attention mechanisms, with different strategies to combine the resulting context vectors, such as concatenation [START_REF] Zoph | Multi-source Neural Translation[END_REF], hierarchical attention [START_REF] Libovický | Attention Strategies for Multi-Source Sequence-to-Sequence Learning[END_REF] and gating (Jean et al., 2017a).

Although some of the models were evaluated in a contextual setting, for example on the crosslingal pronoun prediction task at DiscoMT17 (Jean et al., 2017b), certain strategies only appear to give gains in a low-resource setting (Jean et al., 2017a), and, more importantly, there has yet to be an in-depth study into which strategies work best specifically for context-dependent discursive phenomena. Here we provide such a study, using the targeted test sets described in Sec. 2 to isolate and evaluate the different contextual models' capacity to exploit extra-sentential context. We test several contextual variants, using both a single encoder (Sec. 3.1) and multiple encoders (Sec. 3.2).

NMT notation All models presented are based on the widely used encoder-decoder NMT framework with attention [START_REF] Bahdanau | Neural Machine Translation by Jointly Learning to Align and Translate[END_REF]. At each decoder step i, the context (or summary) vector c i of the input sequence is a weighted average of the recurrent encoder states at each input position depending on the attention weights. We refer to the recurrent state of the decoder as z i . When multiple inputs are concerned, inputs are noted x

Single-encoder models

We train three single-source models: a baseline model and two contextual models. The baseline model translates sentences independently of each other (Fig. 4a). The two contextual models, described in [START_REF] Tiedemann | Neural Machine Translation with Extended Context[END_REF], are designed to incorporate the preceding sentence by prepending it to the current one, separated by a <CONCAT> token (Fig. 4b). The first method, which we refer to as 2-TO-2, is trained on concatenated source and target sentences, such that the previous and current sentence are translated together. The translation of the current sentence is obtained by extracting the tokens following the translated concatenation token and discarding preceding tokens. 5 The second method, 2-TO-1, follows the same principle, except that only source (and not target) sentences undergo concatenation; the model directly produces the translation of the current sentence. The comparison of these two methods allows us to assess the impact of the decoder in producing contextual translations.

Multi-encoder models

Inspired by work on multi-modal translation [START_REF] Caglayan | Multimodal Attention for Neural Machine Translation[END_REF][START_REF] Huang | Attention-based Multimodal Neural Machine Translation[END_REF], multiencoder translation models have recently been used to incorporate extra-sentential linguistic con-5 Although the non-translation of the concatenation symbol is possible, in practice this was rare (<0.02%). If this occurs, the whole translation is kept.

text in purely textual NMT [START_REF] Zoph | Multi-source Neural Translation[END_REF][START_REF] Libovický | Attention Strategies for Multi-Source Sequence-to-Sequence Learning[END_REF][START_REF] Wang | Exploiting Cross-Sentence Context for Neural Machine Translation[END_REF]. Unlike multi-modal translation, which typically uses two complementary representations of the main input, for example a textual description and an image, linguistically contextual NMT has focused on exploiting the previous linguistic context as auxiliary input alongside the current sentence to be translated. Within this framework, we encode the previous sentence using a separate encoder (with separate parameters) to produce a context vector of the auxiliary input in a parallel fashion to the current source sentence. The two resulting context vectors c

(1) i and c

(2) i are then combined to form a single context vector c i to be used for decoding (see Fig. 4c). We study three combination strategies here: concatenation, an attention gate and hierarchical attention. We also tested using the auxiliary context to initialise the decoder, similar to [START_REF] Wang | Exploiting Cross-Sentence Context for Neural Machine Translation[END_REF], which was ineffective in our experiments and which we therefore do not report in this paper.

Attention concatenation The two context vectors c

(1) i and c

(2) i are concatenated and the resulting vector undergoes a linear transformation in order to return it to its original dimension to produce c i (similar to work by [START_REF] Zoph | Multi-source Neural Translation[END_REF]).

c i = W c [c (1) i ; c (2) i ] + b c (1)
Attention gate A gate r i is learnt between the two vectors in order to give differing importance to the elements of each context vector, similar to the strategy of [START_REF] Wang | Exploiting Cross-Sentence Context for Neural Machine Translation[END_REF].

r i = tanh W r c (1) i + W s c (2) i + b r (2) c i = r i W t c (1) i + (1 -r i ) W u c
(2) i

(3)

Hierarchical attention An additional (hierarchical) attention mechanism [START_REF] Libovický | Attention Strategies for Multi-Source Sequence-to-Sequence Learning[END_REF] is introduced to assign a weight to each encoder's context vector (designed for an arbitrary number of encoders).

e (k) i = v b tanh W b z (i-1) + U (k) b c (k) i + b e (4) β (k) i = exp e (k) i K k =1 exp e (k ) i
(5)

c i = K k=1 β (k) i U (k) c c (k) i (6)

Novel strategy of hierarchical attention and context decoding

We also test a novel strategy of combining multiple encoders and decoding of both the previous and current sentence. We use separate, multiple encoders to encode the previous and current sentence and combine the context vectors using hierarchical attention. We train the model to produce the concatenation of the previous and current target sentences, of which the second part is kept, as in the contextual single encoder models.

Experiments

Each of the multi-encoder strategies is tested using the previous source and target sentences as an additional input (prefixed as S-and T-respectively) in order to test which is the most useful disambiguating context. Two additional models tested are triple-encoder models, which use both the previous source and target (prefixed as S-T-).

Data

Models are trained and tested on fan-produced parallel subtitles from OpenSubtitles20166 [START_REF] Lison | OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles[END_REF]. The data is first corrected using heuristics (e.g. minor corrections of OCR and encoding errors). It is then tokenised, further cleaned (keeping subtitles ≤80 tokens) and truecased using the Moses toolkit [START_REF] Koehn | Moses: Open Source Toolkit for Statistical Machine Translation[END_REF] and finally split into subword units using BPE [START_REF] Sennrich | Neural Machine Translation of Rare Words with Subword Units[END_REF]. 7 We run all experiments in a high-resource setting, with a training set of ≈29M parallel sentences, with vocabulary sizes of ≈55k for English and ≈60k for French.

Experimental setup

All models are sequence-to-sequence models with attention [START_REF] Bahdanau | Neural Machine Translation by Jointly Learning to Align and Translate[END_REF], implemented in Nematus [START_REF] Sennrich | Nematus: a Toolkit for Neural Machine Translation[END_REF]. Training is performed using the Adam optimiser with a learning rate of 0.0001 until convergence. We use embedding layers of dimension 512 and hidden layers of dimension 1024. For training, the maximum sentence length is 50. 8 We use batch sizes of 80, tied decoder embeddings and layer normalisation.

The hyper-parameters are the same for all models and are the same as those used for the University of Edinburgh submissions to the news translation shared task at WMT16 and WMT17. Final models are ensembled using the last three checkpointed models. Models that use the previous target sentence are trained using the previous reference translation. During translation, baseline translations are used. For the targeted evaluation, the problem does not apply since the translations that are being scored are given.

Results and Analysis

Overall translation quality is evaluated using the traditional automatic metric BLEU [START_REF] Papineni | BLEU: A Method for Automatic Evaluation of Machine Translation[END_REF] (Tab. 1) to ensure that the models do not degrade overall performance. We test the models' ability to handle discursive phenomena using the test sets described in Sec. 2 (Tab. 2). The models are described in the first half of Table 1: #In is the number of input sentences, the type of auxiliary input of which (previous source or target) is indicated by Aux., #Out is the number of sentences translated, and #Enc is the number of encoders used to encode the input sentences. When there is a single encoder and more than one input, the input sentences are concatenated to form a single input to the encoder.

Overall performance

Results using the automatic metric BLEU are given in Tab. 1. The models are tested on four different genres of film: comedy, crime, fantasy and horror. 9 Scores vary dramatically depending on the genre and the best model is not always the same for each of the genres. Contrary to intuition, using the previous target sentence as an auxiliary input (prefix T-) degrades the overall performance considerably. Testing at decoding time with the reference translations did not significantly improve this result, suggesting that it is unlikely to be a case of overfitting during training. The highest performing model is our novel S-HIER-TO-2 model with more than +1 over the baseline BLEU on almost all test sets. There is no clear second best model, since performance depends strongly on the test set used.

Targeted evaluation

Tab. 2 shows the results on the discourse test sets.

Coreference The multi-encoder models do not perform well on the coreference test set; all multiencoder models giving at best random accuracy, as with the baseline. This set is designed to test the 9 Each of the test sets contains three films from that genre, with varying sizes and difficulty. The number of sentences in each test set is as follows: comedy: 4,490, crime: 4,227, fantasy: 2,790 and horror: 2,158. model's capacity to exploit previous target context. It is therefore unsurprising that multi-encoder models using just the previous source sentence perform poorly. It is possible that certain pronouns could be correctly predicted from the source antecedents, if the antecedent only has one possible translation. However, this non-robust way of translating pronouns is not tested by the test set. More surprisingly, the multi-encoder models using the previous target sentence also perform poorly on the test set. An explanation could be that the target sentence is not being encoded sufficiently well in this framework, resulting in poor learning. This hypothesis is supported by the low overall translation performance shown in Tab. 1.

Two models perform well on the test set: 2-TO-2 and our S-HIER-TO-2. The high scores, particularly on the less common feminine pronouns, which can only be achieved through using contextual linguistic information, show that these models are capable of using previous linguistic context to disambiguate pronouns. The progressively high performance of these models can be seen in Fig. 5, which illustrates the training progress of these models. The S-T-HIER-TO-2 model (which uses the previous target sentence as a third auxiliary input) performs much worse than S-HIER-TO-2, showing that the addition of the previous target sentence is detrimental to performance. Whilst the Table 2: Results on the discourse test sets (% correct). Results on the coreference set are also given for each pronoun class. CORR. and SEMI correspond respectively to the "correct" and "semi-correct" examples. The best, second-and third-best results are highlighted by decreasingly dark shades of green. results for the "correct" examples (CORR.) are almost always higher than the "semi-correct" examples (SEMI), for which the antecedent is strangely translated, the TO-2 models also give improved results on these examples, showing that the target context is necessarily being exploited during decoding.

These results show that the translation of the previous sentence is the most important factor in the efficient use of linguistic context. Combining the S-HIER model with decoding of the previous target sentence (S-HIER-TO-2) produces some of the best results across all pronoun types, and the 2-TO-2 model performs almost always second best.

Coherence and cohesion Much less variation in scores can be seen here, suggesting that these examples are more challenging and that there is room for improvement. Unlike the coreference examples, the multi-encoder strategies exploiting the previous source sentences perform better than the baseline (up to 53.5% for S-CONCAT). Yet again, using the previous target sentence achieves near random accuracy. 2-TO-2 and 2-TO-1 achieve similarly low scores (52% and 53%), suggesting that if concatenated input is used, decoding the previous sentence does not add more information.

However, combining multi-encoding with the decoding of the previous and the current sentences (S-HIER-TO-2) greatly improves the handling of the ambiguous translations, improving the accu- racy to 57%. Extending this same model to also exploit the previous target sentence (S-T-HIER-TO-2) degrades this result, giving very similar scores to T-HIER and is therefore not illustrated in FIgure 5. This provides further support for the idea that the target sentence is not encoded efficiently as an auxiliary input and adds noise to the model, whereas exploiting the target context as a bias in the recurrent decoder is more effective.

How much is the context being used?

Looking at the attention weights can sometimes offer insights into which input elements are being attended to at each step. For coreference resolution, we would expect the decoder to attend to the pronoun's antecedent. The effect is most expected when the previous target sentence is used, but it could also apply for the previous source sentence when the antecedent has only one possible translation. Unlike Tiedemann and Scherrer (2017), we do not observe increased attention between a translated pronoun and its source antecedent. Given the discourse test set results, which can only give high scores when target-side context is used, the contextual information of the type studied in this paper seems to be best exploited when channelled through the recurrent decoder node rather than when encoded through the input. This could explain why coreference is not easily seen via attention weights; the crucial information is encoded on the decoder-side rather than in the encoder.

Conclusion

We have presented an evaluation of discourselevel NMT models through the use of two discourse test sets targeted at coreference and lexical coherence/cohesion. We have shown that multiencoder architectures alone have a limited capacity to exploit discourse-level context; poor results are found for coreference and more promising results for coherence/cohesion, although there is room for improvement. Our novel combination of contextual strategies greatly outperfoms existing models. This strategy uses the previous source sentence as an auxiliary input and decodes both the current and previous sentence. The observation that the decoding strategy is very effective for the handling of previous context suggests that techniques such as stream decoding, keeping a constant flow of contextual information in the recurrent node of the decoder, could be very promising for future research.

Figure 1 :

 1 Figure 1: Example block from the coreference set.

  Figure 2:

  k is the input number and j the input position. Likewise, when multiple encoders are used, c (k) i refers to the k th context vector where k is the encoder number. In the following section, all W s, U s and bs are learned parameters.

Figure 4 :

 4 Figure 4: The baseline model and the two contextual strategies tested (single and multi-encoder).

Figure 5 :

 5 Figure 5: Progression of % correctly ranked examples (from the coreference test set) during training.

Table 1 :

 1 Results (de-tokenised, cased BLEU) of the ensembled models on four different test sets, each containing three films from each film genre. The best, second-and third-best results are highlighted by decreasingly dark shades of green.

		System Description			BLEU ↑
		Aux.	#In #Out #Enc.	Comedy Crime Fantasy Horror
	Single-encoder, non-contexual model
	BASELINE		1	1	1	19.52	22.07	26.30	33.05
	Single-encoder with concatenated input
	2-TO-2	src	2	2	1	20.09	22.93	26.60	33.59
	2-TO-1	src	2	1	1	19.51	21.81	26.78	34.37
	Multi-encoder models (+previous target sentence)
	T-CONCAT	trg	2	1	2	18.33	20.90	24.36	32.90
	T-HIER	trg	2	1	2	17.89	20.77	25.42	31.93
	T-GATE	trg	2	1	2	18.25	20.76	25.55	32.64
	Multi-encoder models (+previous source sentence)
	S-CONCAT	src	2	1	2	19.35	22.41	26.50	33.67
	S-HIER	src	2	1	2	20.22	21.90	26.81	34.04
	S-GATE	src	2	1	2	19.89	22.80	26.87	33.81
	S-T-HIER	src, trg	3	1	3	19.53	22.53	26.87	33.24
	Multi-encoder with concatenated output
	S-HIER-TO-2	src	2	2	2	20.85	22.81	27.17	34.62
	S-T-HIER-TO-2	src, trg	3	2	3	18.80	21.18	27.68	33.33

This was to avoid pronoun forms being trivial to predict from the morphological inflections of other forms within the sentence, an unrealistic setting for MT output.

The test sets are freely available at https://diamt. limsi.fr/eval.html.

We acknowledge that in reality, the disambiguating context is not guaranteed to be in the previous sentence (cf.Guillou (2016, p. 161), for the distribution of intra-and intersentential anaphoric pronouns). However it is important to first judge in a controlled way whether or not models are actually capable of using extra-sentential linguistic context at all, before investigating longer distance context.

The choice to use only nominal antecedents and only two anaphoric pronouns it and they is intentional in order to provide a controlled environment in which there are two contrasting alternatives for each example. This ensures that a non-contextual baseline necessarily gives a score

of 50%, and also enables us to explore this simpler case before expanding the study to explore more difficult anaphoric phenomena.

http://www.opensubtitles.org

90,000 merge operations with a minimum theshold of 50.

76 when source sentences are concatenated to the previous sentence in order to keep the same percentage of training sentences as for other models.

Acknowledgments

Rico Sennrich has received funding from the Swiss National Science Foundation (SNF) in the project CoNTra (grant number 105212 169888). This project has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements 644333 (SUMMA) and 644402 (HimL).


Member IEEE/IET Rozita Teymourzadeh
email: rozita@ucsi.edu.my

Memtode Jim Abigo

Member IET Mok Vee Hong

Characteristic Analysis of 1024-Point Quantized Radix-2 FFT/IFFT Processor

Keywords: DFT, IDFT, Fast Fourier Transform (FFT), IFFT, quantized, floating point, Radix I

The precise analysis and accurate measurement of harmonic provides a reliable scientific industrial application. However, the high performance DSP processor is the important method of electrical harmonic analysis. Hence, in this research work, the effort was taken to design a novel high-resolution single 1024-point fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) processors for improvement of the harmonic measurement techniques. Meanwhile the project is started with design and simulation to demonstrate the benefit that is achieved by the proposed 1024-point FFT/IFFT processor. Pipelined structure is incorporated in order to enhance the system efficiency. As such, a pipelined architecture was proposed to statically scale the resolution of the processor to suite adequate trade-off constraints. The proposed FFT makes use of programmable fixed-point/floating-point to realize higher precision FFT.

INTRODUCTION

Discrete Fourier Transform (DFT) is amongst the most fundamental operations in digital signal processing. However, the widespread uses of DFTs make its computational requirements an important issue. The direct computation of the DFT requires approximately N 2 operations where N is the transform size. The breakthrough of Cooley-Tukey (CT) FFT comes from the fact that it reduces the complexity to an order of Nlog 2 N operations. The FFT is therefore an efficient algorithm to compute the DFT and its inverse (IDFT). It has several applications in the field of signal processing including the real-time processing of wireless time-domain and frequency-domain signals especially for use in Orthogonal Frequency Division Multiplexing (OFDM) systems such as Digital Video Broadcasting (DVB), Digital Subscriber Line (xDSL) and WiMAX (IEEE 802. [START_REF]IEEE Standard for radix-independent floating-point arithmetic[END_REF]) [START_REF] Meletis | High Speed Pipeline Implementation of Radix-2 DIF Algorithm[END_REF][START_REF] Curtis | High Performance Digital Signal Processing[END_REF][START_REF] Ismail | VLSI Implementation and Performance Analysis of Efficient Mixed-Radix 8-2 FFT Algorithm with Bit Reversal for the Output Sequences[END_REF][START_REF] Diaz De Cerio | Efficient FFT on Torus Multicomputers: A Performance Study[END_REF]. These applications require large-point FFT processing, such as 1024/2048/8192point, FFTs for multiple carrier modulation. Many FFT algorithms based on the CT decomposition such as radix-2 2 , radix-2 3 , radix-4, radix-(4+2), prime-factor as well as split-radix algorithms, have been proposed using the complex mathematical relationship to reduce the hardware complexity [START_REF] Curtis | High Performance Digital Signal Processing[END_REF][START_REF] Ismail | VLSI Implementation and Performance Analysis of Efficient Mixed-Radix 8-2 FFT Algorithm with Bit Reversal for the Output Sequences[END_REF]. For example, in [START_REF] Diaz De Cerio | Efficient FFT on Torus Multicomputers: A Performance Study[END_REF] one butterfly unit is used for all computations and N+N.log 2 N clock cycles are required for the computation of the FFT. A second implementation approach is for speed demanding applications, where one butterfly unit is used for each decimation stage of a radix-2 FFT [START_REF] Su | A Grouped Fast Fourier Transform Algorithm Design for Selective Transformed Outputs[END_REF]. A pipeline architecture based on the constant geometry radix-2 FFT algorithm, which uses log 2 N complex-number multipliers (more precisely butterfly units) and is capable of computing a full N-point FFT in N/2 clock cycles has been proposed in 2009 [START_REF] Saeed | Efficient FPGA Implementation of FFT/IFFT Processor[END_REF]. All these developments have introduced their own disadvantages, in addition to the age-long finite word-length effects of digital circuitry [START_REF] Greene | A Parallel 64K Complex FFT Algorithm for the IBM/Son/Toshiba Cell Broadband Engine Processor[END_REF][START_REF] Saeed | Efficient FPGA Implementation of FFT/IFFT Processor[END_REF][START_REF] Satorius | Finite Word length Implementation of a Megachannel Digital Spectrum Analyzer[END_REF]. This paper thus, uses the pipeline architecture [START_REF] Teymourzadeh | On-Chip Implementation of High Resolution High Speed Floating Point Adder/Subtractor with Reducing Mean Latency for OFDM[END_REF] to propose a model for the analysis of important design constraints like the finite word-length effects and amount of resolution needed to achieve the appropriate SNR [START_REF] Saeed | Efficient FPGA Implementation of FFT/IFFT Processor[END_REF][START_REF] Satorius | Finite Word length Implementation of a Megachannel Digital Spectrum Analyzer[END_REF][START_REF] Pel-Yun | Automatic IP Generation of FFT/IFFT Processors with Word-Length Optimization for MIMO-OFDM Systems[END_REF] for the desired design needs using the statistical tools for the analysis of a range of feasible resolution.

II. ARCHITECTURE DEVELOPMENT

A. Algorithm development of the decimation-in-time (DIT) radix-p FFT

The DFT of an N-point sequence x[n] is given by: For k = 0, 1, 2,...,N-1

Where .

Consider the general formula of the DIT Radix-p FFT as follows:

for k = 0,1,2,…,N/p-1 and R = 0,1,2,…,p-1. Using the above decomposition, the DFT can be reduced successively to N/p p-point DFTs. In general, this process can be repeated m times and therefore there are totally m stages in the implementation of the DFT.

B. Parallel Architecture

The computational structure of a butterfly unit is shown in Fig. 1. It is the fundamental computational of the parallel architecture

) (k x e k N W ) ( ) ( ) ( k X W k X k X o k N e   ) ( ) ( ) 2 / ( k X W k X N k X o k N e    1  ) (k x o + + Fig. 1. Radix-2 butterfly unit
The butterfly unit requires a complex multiply and two complex additions. Therefore, it takes a total of (N/2) Log 2 N complex multiplies and Nlog 2 N complex additions to compute all N-point DFT samples. An 8-point Radix-2 DIT FFT requires N/2 butterfly units per stage for all m stages [START_REF] Chang | Integer FFT Optimized Coefficient Sets[END_REF][START_REF] Wang | Hybrid Word-Length Optimization Methods of Pipelined FFT Processors[END_REF][START_REF] Shannon | Communication in the presence of noise[END_REF][START_REF] Sripad | Necessary and sufficient condition for quantization errors to be uniform and white[END_REF][START_REF] Kontro | Floating-point arithmetic in signal processing[END_REF]. For larger butterflies (N > 2 6 ), the processor becomes extremely complex and slow. Hence, a simpler and faster architecture is then required. Therefore, the proposed system was designed and simulated by MATLAB software. Fig. 2, shows the overall pipelined system structure and its designed control signals.

C. Pipeline Architecture

The same butterfly unit can perform the N/2 butterfly operations computed in every stage sequentially. Since the two inputs of a next butterfly unit of a stage are provided from the output of the butterfly unit of the previous stage at different time points, a shuffling unit is inserted between two successive butterfly units in order to route these outputs to the corresponding inputs of the next stage. To increase the system efficiency in the Radix butterfly algorithm, the pipeline registers are located after each addition, subtraction blocks that is the end of each stage. Hence, the pipeline butterfly algorithm keeps the final result in the register to be transferred to next step by the next calculation cycle. However the measurement of system efficiency after applying pipeline structure only can be evaluated after the hardware implementation. Fig. 3 shows the inner layer of proposed FFT processor design where pipelining is applied in signal input logic block. In the proposed design, excluding the input stage, the rest of stages consist of the twiddle factors, the shuffling unit and a floating-point quantize model. The interval of the quantize unit for each stage is preset statically and this is used to vary the bit-resolution of the processor. Fig. 5 shows the flowchart of overall system operation while quantizing and pipelining are applied. The IFFT computation uses the same fundamental Radix-2 DIT Butterfly unit. However, the input is scaled by the factor of N (1024). These discrete input values are then sent through the processor stage, which performs the same operation except that the conjugate of the twiddle factors are used instead. The output stage simply compares the results of the proposed FFT/IFFT Processor with the idle FFT/IFFT processor and their difference is observed as system error that will be analysed in the next chapter.

III. STATISTICAL THEORY OF QUANTIZATION A. Uniform Quantization

One would expect that quantization has a similar effect on functions of the amplitude as sampling has on functions of time. Quantization is an operation on signals that is represented as a "staircase" function. Each input value is rounded toward the nearest allowable discrete level. The probability of each discrete output level equals the probability of the input signal occurring within the associated quantum band [START_REF]IEEE Standard for radix-independent floating-point arithmetic[END_REF]. For example, the probability that the output signal has the value zero equals the probability that the input signal falls between

, where q is the quantization box size [START_REF] Saeed | Efficient FPGA Implementation of FFT/IFFT Processor[END_REF]. Fig. 6 shows the model of quantizing. The quantize error (h) is given as

h = x -Q(x) (3)
If x and h are real, with probability density function (PDF) as P x (.), then the quantization error variance is

= (4) (5) (6)
where q is the quantization interval, b is the number of bits. Quantization noise is defined as the difference between the output and input of the quantized signal. Since the quantized unit is designed in the proposed processor to enhance the calculations, Fig. 7 illustrates a plot of the error versus the number of bits for the uniform quantized, while Fig. 8 shows a comparison of the mean, standard deviation as well as variance of the uniform quantization model achieved by the proposed FFT Processor. The probability of getting a given error value is the sum of probabilities of all the quantization boxes. The uniform quantize model performs uniform quantization on the signal input and thus, a linear signal is required at the input. Fixedpoint numbers are considered linear since the Radix point remains fixed. However, this research was focused on floating-point numbers hence; the response of the uniform quantize to floating point input was observed. As result the quantization noise was increased. Equation ( 6) gives an expression for the SNR of the quantizer using the ratio of the variances of the input to noise.

B. Non-Uniform Quantization

The uniform quantization model is usually not used for floating-point quantization due to the overall non-uniform characteristic of the latter. Quantization of floating-point numbers is carried out only on the mantissa hence; it is more relevant to consider the relative error ε caused by the quantization process. The relative error defined in terms of the numerical values of the quantized floating-point number Q(x) = 2 e Q(M) and the un-quantized number x = 2 e M is given as [START_REF] Greene | A Parallel 64K Complex FFT Algorithm for the IBM/Son/Toshiba Cell Broadband Engine Processor[END_REF] It is possible however, to represent the floating-point quantizer using a combination of a compressor, a uniform quantize and an expander. Fig. 9 shows the non-uniform quantized model. = q 2 /6 = (0.167) 2 -2b (8) An expression for the variance is shown in [START_REF] Saeed | Efficient FPGA Implementation of FFT/IFFT Processor[END_REF]. The variance from the floating-point quantization equals half that obtained from the uniform quantization which is a generally preferred characteristic. Fig. 10 determines that the stability of the

Q x Q(x)
Compressor Q Expander processor performance such that no variation occurs when the number of bits is 7 bit, unlike that of the uniform quantization which attains this stability at bit position eight 8, as shown in Fig. 7. That is the advantage of the system while modeling the floating-point structure.

Fig. 10 Error variance of the non-uniform quantizer

In addition, bit position 2 of Fig. 10 gives minimum swing before stability, contrary to that of Fig. 7 which occurs at bit position 3. This minimum swing gives a false minimum error position and can be used for less sensitive applications in which minimum error is not important.

Fig. 11 illustrates the comparison between mean, standard deviation when number of bit increased.

IV. DISCUSSION

Statistical parameters like the mean, standard deviation and variance were used to analytically develop expressions for the variation in error as a function of the quantization interval. These parameters were also known to have relationships with the SQNR. The percentage error of the non-uniform quantization generally decreased with an increase in the quantizer interval. As such, the SQNR is increased with respect to the quantization step size. The same general trend was observed in the uniform quantization, as well as the FFT and IFFT results. Fig. 12 shows the error variation when the input data increased. The general trend observed from the results indicates that the measured dispersion can only be valuable when they are used alongside the mean since the mean actually provides the benchmark for understanding the decreasing trend. Fig. 13 shows the mean standard deviation and variance when the number of bit increased. Therefore, the variance is decreased as quantization interval increased. Hence, the variance is inversely proportional to the percentage error, and as such, inversely proportional to the SQNR. This provides experimental proof to the theoretical models given earlier and provides a benchmark for the tradeoff between the SQNR and the resolution. As shown in Fig. 14 the error variation also decreased as bit resolution increased.

CONCLUSION

The 1024-point radix-2 FFT/IFFT processor is designed and simulated using MATLAB Simulink toolbox. The proposed processor satisfies the FFT size requirement of the 1024points for a quantized pipeline structure. The percentage error and all the measured dispersion were found to decrease as the bit-resolution increased. This shows how the SQNR improves with bit-resolution. Although the power requirement for such SQNR systems are high, the proposed architecture provides an ease in the trade-off decision between the SQNR, power requirement and bit-resolution of the Radix-2 FFT/IFFT processor.

Fig. 2 .

 2 Fig. 2. Proposed pipelined system algorithm

Fig. 4 .

 4 Fig. 4. Ten-Stage 1024-point pipeline processor

Fig. 5 .

 5 Fig. 5. Proposed overall system operation

Fig. 6 .

 6 Fig. 6. Uniform Quantize Model

Fig. 7 .

 7 Fig. 7. Error of the Uniform Quantization

Fig. 8

 8 Fig. 8 Measured dispersion of uniform quantize

Fig. 9 .

 9 Fig. 9. Non-uniform quantization model

Fig. 11

 11 Fig. 11 Comparison of the measured dispersion of non-uniform quantization

Fig. 12 .

 12 Fig. 12. Error variation of the FFT processor

Fig. 13 .

 13 Fig. 13. Measured dispersion of FFT/IFFT processor

Fig. 14 .

 14 Fig. 14. Error variation of IFFT processor


Waleed Ahmed

A L Garidi

Norsuzlin Bt

Mohad Sahar

Rozita Teymourzadeh
email: rozita@ucsi.edu.my

Ceng Member

Planar Dipole Antenna Design At 1800MHz Band Using Different Feeding Methods For GSM Application

Keywords: Antenna, GSM, Planner, Dipole, VSWR, MMIC, Microstrip I

This research work focuses on the design and simulation of planar dipole antenna for 1800MHZ Band for Global System Mobile GSM application using Computer Software Technology CST studio software. The antenna is structured on fire resistance FR4 substrate with relative constant of 4.3 S/m. Two types of feeding configuration are designed to feed the antenna in order to match 50 Ω transmission lines which are via-hole integrated balun and quarter wavelength open stub. The via-hole is capable to provide maximum return loss of -25dB, bandwidth of 18.4% and the voltage standing wave ratio (VSWR) of 1.116 V at optimum dimension of length 59mm and width 4mm; the bandwidth is improved 25% to 30% by extending the width of the antenna 8 mm to 10 mm followed by deterioration of return loss value to -15dB. While the open stub at length of 67 mm, width of 6 mm and height 1.6mm will provide max return loss of -47.88dB and bandwidth of 17% with VSWR 1.008 << 2. Way that the antenna substrate has influenced the performance of the antenna. The lower relative constant will result the higher return lows, narrower bandwidth and better radiation pattern in trade-off the resonant length Via-hole and then the quarter wave open stub are most convenient for practical implementation.

INTRODUCTION

Antenna is playing significant role in the wireless communication systems. Radio frequency and Microwave has direct effect on antenna for trance-receiving the signals and antenna will transmit it through the electro-magnetite wave in free space [START_REF] Balanis | Antenna theory Analysis and design[END_REF]. In order to design antenna in the bandwidth of 900MHz -1800 MHz in the industry of cellular network such as GSM network, the IEEE Standard [START_REF] Balanis | Antenna theory Analysis and design[END_REF] (Std 145-1983) is approached accordingly.

The trends of the mobile phone technology has been dramatically decreased the weight and size of the communication equipment. Microstrip dipole antennas consist of a thin sheet of low loss insulating called the dielectric substrate and it is completely covered with a metal on one side called the ground plane.

From the other side is partly metalized where the circuit of antenna is printed [START_REF] Garg | Microstrip antenna Design handbook[END_REF]. In particular, planar dipole-type exhibits many attractive features, such as a simple structure, inexpensive easy integration with monolithic microwave integrated circuits (MMIC), low-profile, comfortable to planar and non-planar surfaces. Therefore, it works best on air and portable application. Micro-strip dipole moment is attractive because they basically own large feature like simple analysis and manufacturing and its attractive radiation pattern particularly low between-polarized rays.

II. ANTENNA GEOMETRY DESIGN

The desired frequency that the dipole antenna is designed to operate on is 1800MHz GSM band. The substrate material has been used is FR4 with dielectric constant of 4.3 thickness of 1.6mm.

The length of the antenna is the parameter that controls the resonant frequency. As result, the length is treated as half wave dipole, which is given by the following formula:

2   L (1) F C   (2)
Where L is dipole length, λ is the wavelength, C is the speed of light in free space and F is the frequency of operation [START_REF] Jamaluddin | Microstrip dipole antenna analysis with different width and length at 2.4 GHz[END_REF].

Table 1 shows the range of the dimensions parameters and table 2 presents the calculated values. However, the speed of the signal when it propagates in free space varies as it propagates in medium and that is because of the effective dielectric constant of the microstrip substrate associated with fringing fields [START_REF] Pozar | Microwave Engineering 3 rd edition[END_REF]. Therefore, the above equations have been modified to the following:

  1 . 0 05 . 0   W 20 05 . 0   h w Length (L)  48 . 0  L 5 . 0  W T Height (H)  02 . 0  h 5 . 0  h T Thickness (T)   T 1  r 
(3) (4) [START_REF] Li | Dual-Band And Wideband Design of A Printed Dipole Antenna Integrated With Dual-Band Balun[END_REF] In most practical design, the wavelength of the printed dipole is treated as approximately in medium with equivalent to the average of that in free space, which is considered as alternative method for better determination of the length. Besides, the antenna parameters differ widely for different types of materials [START_REF] Li | Dual-Band And Wideband Design of A Printed Dipole Antenna Integrated With Dual-Band Balun[END_REF]. The feeding method is designed using integrated microstrip balun.

Planar antennas are integrated with other microwave circuitry. Therefore, the feeding techniques need to be designed and analyzed properly to reach between a good antenna performance and efficient circuit design. The word balun is a narrowing for "balanced to unbalanced" [START_REF] Li | Dual-Band And Wideband Design of A Printed Dipole Antenna Integrated With Dual-Band Balun[END_REF]. Table 3 has the practical parameters of the antenna being designed. At 1800 MHz band the antenna wavelength is 89.9 mm given by (3), the corresponding resonant length is 67mm for dipole with open stub and 60mm for dipole with via-hole by ( 4) and [START_REF] Li | Dual-Band And Wideband Design of A Printed Dipole Antenna Integrated With Dual-Band Balun[END_REF]. Fig. 1 shows the open stub feeding while Fig. 2 illustrates the dipole antenna with via-hole feed.

PRINCIPLES OF BALUN OPERATION AND DESIGN

The main objective of balun design is to provide the system with balance transition between the antenna and its feeding circuit. However, as the rest of the system was then overturned as one part of antenna attached to external plate while the other connects to the internal connector. On the edge attached to the shield, the current can pass through over the outside of the coaxial cable. Baluns changes the tent onto the antenna ports the same magnitude on each but across stage, these tensions caused the same amount of current flow to the outside of the coaxial cable. This type of arrangement will let the feed point to have the same phase as the point of the top shield [START_REF] Kuo | A Study of Planar Printed Dipole Antennas For Wireless Communication Application[END_REF].

Fig. 1 shows quarter wavelength open stub feeds the dipole strips by creating virtual short circuit across the center of the dipole. This arrangement provides further possibilities for reactance compensation of the balance load [START_REF] Chuang | Design Analysis of a 2.4-GHz Polarization-Diversity Printed Dipole Antenna With Integrated Balun and Polarization Switch Circuit For WLAN and Wireless Communication Application[END_REF]. The special feature of this method is that it does not require physical direct connection top of substrate. Therefore, it is commonly used to feed microstrip dipole antenna because it requires no soldered or plated through connection [START_REF] Saxena & Kanaujia | Design And Simulation of A Dual Band GapCoupled ANnular Ring Microstrip Antenna[END_REF].

IV. SIMULATION RESULT

A. Quartered wavelength open stub method

The result shows the s-parameter as function of frequency.

The plot of S11 shown in Fig. 3 is used to determine whether the antenna is a single band and operating at the desired resonant frequency also the bandwidth can be calculated for

  f c  4 3  L 3 2  L
the corresponding frequency band. It provides bandwidth of 17% of the resonance frequency with maximum return loss of -47.88 dB as shown in Fig. 3. Fig. 4 illustrates how the resonant frequency is shifted down as the length reduces while Table 4 is a summary of the resulting measurements of different lengths. The balance feeding is illustrated by the value of VSWR in Fig. 5 while Fig. 6 shows the radiation pattern of the antenna.

B. Via-hole feeding method

The dipole radiator is modeled on the top of substrate and adopted with the integrated balun to form a ground plane as a return bath for the current distributions. Since this feeding is through via from the top to the bottom physically connecting the feed line to the center of the dipole to make the formation of center fed the simulations results differs for different widths of the antenna and Table 5 summarize the differences as well as Fig. 8. Besides, the antenna is resonating at 1.8GHz as shown in Fig. 7 with sufficient balance between antenna and its feeding circuit which is determined by the value of VSWR in Fig. 9. From Fig. 10, it can be seen that the antenna has omnidirectional radiation pattern. Since the polarization of the antenna is horizontal, the Eplane coincides with the azimuth plane and the H-field coincides with elevation plane as shown in Fig. 11 and Fig. 12. The new features in this design in comparison to previous research works is that it has planar structure which can easily integrated to array form to provide greater directivity and high efficiency without causing substantial power losses in the feeding circuit since each element is match to its feeding circuit impedance. In term of bandwidth, this antenna has bandwidth range from 18.4% to 25% while previous search has bandwidth from 11.53% to 13.22% [START_REF] Jamaluddin | Microstrip dipole antenna analysis with different width and length at 2.4 GHz[END_REF]. This advantage is a result of choosing the right configuration of the feeding circuit. V. DISCUSSION Throughout the entire simulation results it has been seen how the antenna performance behaves as function of its geometry parameters. Besides, the feeding technique is important factor that makes substantial effects on the simulation results.

First, the dipole with integrated via-hole balun feed is being used as balance to unbalanced transformer and impedance transformer from coaxial line to the two printed dipoles strip via physical connection. This method is capable to provide us with a bandwidth of 18.4% With maximum return loss of -25dB as shown in Fig. 8 at length =59 mm, width=4 mm and height=1.6 mm. the bandwidth can be increased to 24.5% by changing the width of the antenna from 4mm to 8mm. However, the size will increase and other impacts such as reducing the return loss and increasing the value of VSWR.

In term of practical implementation, it is considered one of the most common suitable due to its compact size and acceptable performance but it requires soldering which may cause junction radiation. The second part is about open stub integrated balun. This method does not require direct connection between the feed line and the dipole. Therefore, a virtual short circuit is created between the open stub and the dipole strip making a center fed current distribution [START_REF] Chuang | Design Analysis of a 2.4-GHz Polarization-Diversity Printed Dipole Antenna With Integrated Balun and Polarization Switch Circuit For WLAN and Wireless Communication Application[END_REF].

The optimized dimension for optimum performance are at length=65 mm, width=6 mm and height=1.6mm. The optimum results are bandwidth of 17 percent with minimum VSWR1.008 v also maximum return loss of -47.88dB. The radiation pattern of this method is unidirectional and has main lobe directivity of 2 dB in the elevation plane with angular width of 89 deg and has the same magnitude on the azimuth plane. For implementation, it is very convenient and suitable since it does not require soldering though it has narrow bandwidth but again it depends on the application requirements.

V. CONCLUSION

This proposed work focuses on the design and simulation of planar dipole antenna for 1800MHz Band for GSM application using CST studio software. The antenna was structured on FR4 substrate with relative constant of 4.3. Two types of feeding configuration have been designed to feed the antenna in order to match 50 ohm transmission line.

These types are via-hole integrated balun, and quarter wavelength open stub. As results, the dimension of the antenna and simulation results such as return loss, bandwidth and voltage standing wave ratio (VSWR) has been analyzed. For instance, the via-hole is capable to provide maximum return loss of -25dB, bandwidth of 18.4% and VSWR of 1.116 v at length of 59 mm and width 4 mm; it is bandwidth can be improved to 25% and 30%. While the open stub at length of 67mm, width of 6mm and height 1.6mm can provide max return loss of -47.88dB and bandwidth of 17% with VSWR 1.008 << 2 and also has better radiation pattern.

Fig. 1 .

 1 Fig. 1. Dipole with quarter wavelength open stub feed

Fig. 3 .Fig. 6 .

 36 Fig. 3. Return Loss Plot at 1800MHz of antenna

Fig. 8 .

 8 Fig. 8 . Return Loss for different widths of the antenna

Fig. 11 .

 11 Fig. 11. E-field Radiation at 1.8GHz

TABLE I DESIGN

 I

		RECOMMENDATION AND RESTRICTION [2]
	Parameters Symbol	Recommendation	Restriction
	Width (W)

TABLE II CALCAULATED

 II

				VALUES OF ANTENNA PARAMETERS
	Material Type			mm	L-mm	w-mm h-mm
	FR4	4.3	2.65	104	52	6.25	2.08
	Arlon AD300	3	2	118	59	7.08	2.36
	Rogers RT5880	2.2	1.6	132	65.88	7.92	2.64

TABLE III DIMENSIONS

 III OF QUARTER WAVLENGTH AND VIA-HOLE

	Parameter	Specifications
	PCB Substrate	FR-4, h=1.6mm, =4.3, tanδ= 0.002.
	Dipole arm	L= 67mm, W=6mm, gap g = 3mm
	Microstrip Balun	L= 22mm, L=3mm, w=5mm, w= 3mm
	Ground Plane	L = 25mm, w= 15mm.
	Feed Line	L=25mm, w=3mm, Open stub=25mm
	Dipole arm	Length L= 60mm, Width W=6mm, gap g = 3mm.
	Via-hole	R= 0.375mm
	III.

TABLE IV COMPARISON

 IV

			FOR DIFFERENT LENGTHS AT W=6 mm
	Length Z11	VSWR RL	BW	Directivity
	63mm	45-i 4	1.1013	-26dB	16%	1.7dB
	65mm	48-i 2.2	1.05	-32dB	16.5%	1.8dB
	67mm	51-i0.216	1.0081	-47.dB	17%	2dB

TABLE V COMPARED

 V

			RESULTS FOR DIFFERENT WIDTHS
	Width	Impedance	VSWR	Return Loss	Bandwidth %
	8mm	43.614	1.255	-18.95dB	24.5%
	7mm	44.43	1.216	-20.22 dB	22.5%
	6mm	45.3	1.18	-21.6 dB	20.6%
	5mm	46.17	1.17	-22.74	19.7%

Fig. 7. Return Loss 1800MHz of antenna


CEng. IEEE Rozita Teymourzadeh
email: rozita@ucsi.edu.my

Iet Member

Amirrize Alpha

Smart Novel Computer-based Analytical Tool for Image Forgery Authentication

Keywords: Authentication, Image processing, Classification, GUI, BPNN I

This paper presents an integration of image forgery detection with image facial recognition using black propagation neural network (BPNN). We observed that facial image recognition by itself will always give a matching output or closest possible output image for every input image irrespective of the authenticity or otherwise not of the testing input image. Based on this, we are proposing the combination of the blind but powerful automation image forgery detection for entire input images for the BPNN recognition program. Hence, an input image must first be authenticated before being fed into the recognition program. Thus, as image security identification and authentication requirement, any image that fails the authentication/verification stage is not to be used as an input/test image. In addition, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented.

INTRODUCTION

Face pattern recognition is an exciting area of research as a result of scientific issue and possible social, legal and security application [START_REF] Kumar | A Back Propagation Based Face Recognition Model, Using 2D Symmetric Gabor Features[END_REF]. Two approaches to 2-D features identification are base on position and based on geometric approach [START_REF] Bolme | The CSU Face Identification Evaluation System: Its Purpose, Features and Structure[END_REF].

In 1987, Sirovich and Kirby [START_REF] Sirovich | A Low Dimensional Procedure for the Characterization of Human Faces[END_REF] illustrated in their approach that a particular facial images reasonably represents in terms of the maximum coordinates branded as Eigen image. It was observed that an acceptable picture of a face could be reconstructed from the specification of gray levels at 214 pixel locations. However, they showed that in actual construction, only 40 numbers with admixtures of Eigen pictures can be characterized a face. [START_REF] Wu | Human face profile recognition by computer[END_REF] were used 24 measurements in fiducially brands profile double facial image. The proposed system has been reduced by recognizing performance when more image in the database because there are not enough to differentiate features that identify a user.

Wu and Haung

Then, Turk and Penland [START_REF] Turk | Face recognition using eigenfaces[END_REF] introduced the Eigen faces method that achieved a remarkable acknowledgement speed, but the speed drastically reduced, when against resizing. Wiskott [6] took advantage of scale information to stage the information obtained from Gaber wavelet transform the facial images.

Later on, in 2006, Ahonen and Hadid [START_REF] Ahonen | Face recognition with Local Binary Patterns: Application on Face Recognition[END_REF] commenced the local binary pattern (LBP). This operator is for measuring the structure information for LAN of gray scale image. Because of direct interactions, indifference to the lights, ability to retrieve picture detail, the user can get the model own region which are more favorable.

Gallagher [START_REF] Gallagher | Detection of linear and cubic interpolation in JPEG compressed images[END_REF] analyzed a set of images and proposed that image forgery most often includes some form of geometric transformation.

The various research work had been completed [START_REF] Hu | Application of Rough Sets to Image Pre-Processing for Face Detection[END_REF][START_REF] Mehryar | Automatic landmark detection for 3D face image processing[END_REF][START_REF] Kim | Implementation of image processing and augmented reality programs for smart mobile device[END_REF][START_REF] Hou | Cubic splines for image interpolation and digital filtering[END_REF][START_REF] Heechul Han | Virtual out of focus with single image to enhance 3D perception[END_REF][START_REF] Lu | Regularized Discriminate Analysis For the Small Sample Size Problem in Face recognition[END_REF] to reduce the efficiency with variations in illumination, image size, size of database, percentage error. In addition, inability to detect altered images for more realistic recognition still is challenging task.

Hence, this paper proposes an efficient passive evaluation tool for authenticity of input images before recognition process stage, furthermore, the adjustment of illumination of images using contrast stretching and image histogram equalization techniques will be performed. In this proposed research work, the quality percentage is adjustable in order to achieve high performance (as low as 2% error rate) with a dual database approach. The test image is reduced to a lower 1-D dimensional vector, which represents distinguishing features of the test image.

II. SYSTEM DESIGN FUNDAMENTAL

The focus of the image authentication stage (Fig. 1) is the presence of the image re-sampling trace and/or interpolation signal. A derivative operator on the variance of the image will detect these traces. Later, the signal is processed through radon transformation that results in a periodic signal embedded or completely imposed on the image spectrum graph [START_REF] Gallagher | Detection of linear and cubic interpolation in JPEG compressed images[END_REF]. Hence, interpolated signal and re-sampled signal will be ready to function.

A. Interpolated Signal:

There are two main stages in geometric transform [START_REF] Popescu | Exposing digital forgeries by detecting traces of re-sampling[END_REF][START_REF] Ramakrishnan | On re-sampling detection and its application to image tampering[END_REF]. In the first stage, a spatial transformation of the physical re-arrangement of pixels in the image is performed and can be represented by a transformation function T.

X΄ = T X (X,Y) Y΄ = T Y (X,Y) (1)
As earlier stated, the geometric operations that are common with most forgeries are re-scaling, rotation and skewing. Hence, the importance of detection will be traced wit utilization of affine transformations.

The general equations for affine transformations are given by;-

X΄ = a 0 + a 1 x + a 2 y Y΄ = b 0 + b 1 x + b 2 y
The second stage is the interpolation. The image brightness of the processed image is allocated using low pass interpolation filter. If the Nyquist condition is met, the range of F(w) do not overlap in the Fourier domain the original signal f(x) can be rebuilt entirely [START_REF] Hou | Cubic splines for image interpolation and digital filtering[END_REF].

Combining the derivative theorem with the convolution theorem leads to the conclusion that by convolution of f k with derivative kernel D n (w), it is possible to reconstruct the nth derivative of the image f(x). The result of interpolated operation of f w (x) is denoted by;

(3) By assuming the constant variance random process, then the variance of which is var {D n {f w }(x)} as a function of x is given by; [START_REF] Wu | Human face profile recognition by computer[END_REF] Similarly, the covariance is represented as;

(5) Now, by assuming that ϑ is an integer, it can be noticed that; [START_REF] Wiskott | Face Recognition by Elastic Bunch Graph Matching[END_REF] Thus, var{D n {f w }(x)} is periodic over x with period ∆x that ∆x is the sampling step. 4), it is clear that different interpolators will change the structure of the original signal in different ways. The resulting periodic variance function computed using equation ( 4) for the nearest-neighbor interpolation. Hence, marks inserted in this interpolating are easy to spot using derived operator for them [START_REF] Hou | Cubic splines for image interpolation and digital filtering[END_REF]. It is continuous but its first derivative is discontinuous. Cubic interpolate using a third-order extrapolation polynomial kernel. Then, the n th derivative of the investigated 2 signal b(x,y), D n {b(x,y)} is computed.

C. Radon Transformation:

Radon transform is applied for traces of affine transformation. The Radon transformation computes projections of magnitude of D n {b(x,y)} along specified direction determination by an angle θ .

The projection is a line integral in a certain direction. This line integral is expressed as; [START_REF] Gallagher | Detection of linear and cubic interpolation in JPEG compressed images[END_REF] By assuminy that; [START_REF] Hu | Application of Rough Sets to Image Pre-Processing for Face Detection[END_REF] It is possible to represent the Radon transform in the following way; [START_REF] Mehryar | Automatic landmark detection for 3D face image processing[END_REF] (2)

To compute the Radon transformation, pixels are divided into four sub pixels and each sub pixel is projected separately. The Radon transformation is computed at angles θ from 0 to 179 degrees, in 1 degree increments. Hence, the output of this is 180 1-D vectors, δθ (θ is the orientation of the X' axis counterclockwise from the x-axis). the corresponding auto covariance sequences of δθ contain a specific strong periodicity, if the investigated signal has been re-sampled. The auto covariance sequences of θ is computed as; [START_REF] Gallagher | Detection of linear and cubic interpolation in JPEG compressed images[END_REF] The project target is to determine the image being investigated has undergone affine transformation. Hence, we focus only on the strongest periodic patterns present in the auto covariance R δθ.

D. Image Recognition:

It is difficult to recognize the face with ranking issue and classification process. Hence, the proposed technique used artificial nerve cells for training classifier. In recent years, BPNN system [START_REF] Kumar | A Back Propagation Based Face Recognition Model, Using 2D Symmetric Gabor Features[END_REF] makes use of fractal encoding method. It presented as input to the BPNN for identification purposes. However, in order to complete image authentication and recognition in the research work, four stages are introduced.

III. PROPOSED SYSTEM STRUCTURE

The proposed research work deals with an integration of four phases. The initial phase deals with image verification (originality authentication) with neural network pre-processing training and testing phases. Fig. 2 shows the flowchart performance of this research work that will be discussed in four stages.

A. Image verification (Phase 1):

The image verification phase is performed using detecting traces of image tampering such as re-sampling or interpolation or both. In this phase, a test/input process involves derivative operators and radon transformation. This process produces a periodic pattern in the image spectrum if the test image is forged or it produces simple impulse signals if the test image is still original. Meanwhile, It is observed that both image spectra forms could be overlapped that produces a form of image tampering. However, the proposed system specification allows selecting only original test or input images.

B. Image-preprocessing (Phase 2):

The image preprocessing as second phase is ready to function. In the pre-processing phase, time effective preprocessing is performed in order to make image data best fit for neural network input. Average filtering is applied and contrast of the image is enhanced through histogram equalization process. Then the size of image will be reduced so as to make it small but effective and most suitable for neuronal processing stage. Before moving images into the neural processing phase, all images data (test image and training database images) undergo a process of vectorization that is conversion of 2-D images into 1-D vectors. This conversion is due to neural network requires 1-D vectors for processing and conversion is performed using principal component analysis (PCA) and 2-D discrete cosine transform. Figure 3 shows the vectorization of one original image.

C. Neural Network Training (Phase 3):

The third phase is the neural network training. The structure of the proposed neural network is based upon multi-layers that are input, hidden and output layer. Pre-processed images in the vector format are presented as input to the neural network, which contains 400 neurons in the input layer and 90 neurons in the hidden layer. The number of neurons in the hidden layer is determined by experiences and guesswork considering optimal performance.

Figure 3. Image vectorization

As a final point, production level contains nerve cells that are the number of objects is being considered. The algorithm for effective use of a neural networks and reduces the slope of errors through changing weight and offset continued with impetus. The neural network is trained upon some set of images and tested upon different set of images.

In the proposed method, neural network uses BPNN algorithm for error computation and new weight calculation for each neuron link. It returns the output of each level, extract the mean square error (MSE) and spread it back if it is not close to the target. The response of the neural network is reliant upon weights, biases and transfer functions.

The transfer functions make use of in the feed forward BPNN in intermediate, input and output layer. Fig. 4 shows the training graph of neural network while the system will find the original image.

D. Testing stage (Phase 4):

The fourth phase is the testing of the neural network. Images for testing are applied to the trained neural network along with the already trained database images for calculating the percentage accuracy and error. These spectra are resulted from original and forged images respectively. The difference in spectrum is seen as a kind of sinusoidal periodic pattern, which replaces or overlaps the original impulse signals or spikes of an original image. These are the result of obtaining the first and second derivative of the auto covariance of the images. The auto covariance is obtained using the radon transformation through 0 to 179 degrees. Hence, it is a proved for efficiency of proposed system to detect the authenticity of images. The major integral part of the proposed analytical tool is the capability to extract a face from a full image and run it through any given database for a matching or equivalent face image. Fig. 7 illustrates the recognition of input image while different position is applied.

Figure 7. Image recognition

The high-speed performance is determined by the proper selection of the number of hidden neurons. The larger amount of hidden neurons results the faster network converges. Fig. 8 shows the time consuming required when the hidden layers in neural network processor are increased. Figure 8. Neurons in hidden layer vs. execution time Fig. 9 shows the error percentages of proposed analytical tools when the number of imaged are increased. As shown in Fig. 9, it is found that with increasing the number of subjects, performance error that is percentage of recognition with utilizing PCA and BPNN approach will decrease slightly. Face recognition as a classification problem does not take into consideration the originality of the test image and thus can always output a matching image for even a forged input image. It is therefore, important to first verify the authenticity of any test image. A single smart BPNN image processing for face image recognition is only highly accurate with small number of subjects (images) and requires a lowering of the image resolution for a whole recognition task as a way of reducing the computational complexity. Further work, can be done by further or better integration of both the image authentication phase with the rest of the neural network process such that we can eliminate the need for a human choice whether or not to proceed with the recognition of an input image after verification. Thus if an input image is found to have been forged or tampered with, the system automatically rejects it and asks for an original of the rejected image before proceeding with the recognition task. A database of unlimited sizes, format and dimensions of train images is created and used as a source of target input for the neural network. The project was designed and investigated and it was found that the resolution improved by ±2% error rate when number of database images applied is less than 20 images.

  It is verified the periodicity by the following; -Sampled Signal: From equation (

Figure 2 .

 2 Figure 2. Proposed system performance flowchart

Figure 4 .

 4 Figure 4. Neural network training curve In testing phase, just like in the training phase, incoming images undergo all the pre-processing stage and are made available to the network for evaluation.This test images (extracted face image) is then processed using the neural network analytic tool. After a number of iterations by the network through each image in the database, the error reduces based on the gradient descent-learning rule, the set error goal is reached and a matching image to test image is found as shown in the results session.

Figure 8 .Figure 6 .

 86 Figure 8. Original image spectrum

Figure 9 .

 9 Figure 9. Recognition percentage vs. number of subjects V. CONCLUSION

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