Graça Almeida
email: mdg.almeida@fct.unl.pt

Fernando Melicio
email: fmelicio@deea.isel.ipl.pt

Carlos Chastre
email: chastre@fct.unl.pt

José Fonseca

Displacement Measurements with ARPS in T-Beams Load Tests

Keywords: Digital Image Correlation, Block Motion Estimation, image processing

The measurement of deformations, displacements, strain fields and surface defects in many material tests in Civil Engineering is a very important issue. However, these measurements require complex and expensive equipment and the calibration process is difficult and time consuming. Image processing could be a major improvement, because a simple camera makes the data acquisition and the analysis of the entire area of the material under study without requiring any other equipment like in the traditional method. Digital image correlation (DIC) is a method that examines consecutive images, taken during the deformation period, and detects the movements based on a mathematical correlation algorithm. In this paper, block-matching algorithms are used in order to compare the results from image processing and the data obtained with linear voltage displacement transducer (LVDT) sensors during laboratorial load tests of T-beams.

Introduction

Since the 80's, several works about digital image correlation techniques are under development in order to obtain an accurate knowledge of the displacement field [START_REF] Chu | Applications of Digital.Image-Correlation Techniques to Experimental Mechanics[END_REF][START_REF] Sutton | Determination of displacements using an improved digital correlation method[END_REF][3]. The general idea behind digital image processing techniques is to calculate the displacement field or the strain field without contact and using a simple low cost camera. The research aims to reduce the computation time and to increase the accuracy of the system. When conventional methodology is used the number of the measured points takes a huge importance because they increase the hardware, the time to get the setup ready and the costs. Using image analysis techniques the density of the measured points can be very high. As an example, a trivial image of 1024 by 1024 pixels can be used to obtain a continuous information field with more than 4000 analysis points.

In our previous work [START_REF] Almeida | Displacement Estimation of a RC Beam Test based on TSS algorithm[END_REF] the three-step search (TSS) [START_REF] Barjatya | Block Matching Algorithms for motion Estimation[END_REF] algorithm was studied, especially the simple and efficient (SES) algorithm. A partnership between researchers from the Civil Engineering Department and the Electrical Engineering Department, of the Universidade Nova de Lisboa (UNL) made possible to explore the traditional methodology used in Civil Engineering measurements, which requires a complex and expensive sensorial setup and a very complex calibration process. In this work we compare the results of the traditional approach with the results obtained by digital image processing techniques.

In this paper the purpose is to use the adaptive rood pattern search (ARPS) and compare it with the data obtained with physical sensors [START_REF] Carvalho | 3rd fib International Congress. 2010[END_REF]. In this paper only two concrete beams are compared. Although concrete has by itself a good texture a speckle pattern was applied to the T-beams surfaces in order to get an easier image processing (in this paper a random dot pattern was applied). The work developed so far shows that resolution versus specimen dimensions, focal length, distance between camera and specimen, distortion and the speckle pattern are some of the factors that influence the most the measurements error. In order to choose the most promising correlation technique several tests were conducted, such as the comparison of different block matching algorithms and the use of the edge detection techniques. As shown in the previous work, [START_REF] Almeida | Displacement Estimation of a RC Beam Test based on TSS algorithm[END_REF] the TSS algorithm shows to be more adequate than edge detection techniques. However, some research questions such as what kind of correlation technique is more efficient or how to increase the accuracy of the system without increasing computation time are still open. Another open issue is what should be the relationship between the entire image and the number of target points. The comparison between the image processing results and the information obtained by the LVDT sensors will be used to establish this relationship.

In [START_REF] Carolin | Photographic strain monitoring for civil engineering[END_REF] a non-touching strain measurement method that covers a pre-defined area is presented. In this work several tests were carried out on RC beams with a span of 4.5 m and the results were compared to the traditional electrical strain gauges. A speckle pattern correlation was used and a photo was taken every 30kN until rupture. In this work a camera film was used and the film was scanned with a high quality scanner. The picture was divided into 128x128 sub-pictures and then subjected to a threshold. The centre of gravity of the resulting black & white picture was then calculated. With this methodology and the speckle correlation it was possible to find the same sub-picture in the second loading condition and therefore estimate the deformation. Despite the good results obtained by this methodology it is significantly dependent on the threshold value and therefore varying with the illumination conditions, speckle pattern and image acquisition parameters. In our work we look for a more stable and conditions independent solution for this problem.

Contributions to Sustainability

Image processing analysis is used in many situations for earth observations such as resource managements, soils mapping, water resources, etc. The measurements made by image processing simplify the maintenance of local materials, the waste or loss of sensors and human resources are best applied.

With measurements without contact, fewer materials are used which implies reduction of costs and energy. In our tests the same system could analyze several materials with no duplication of the sensors. So it is possible to contribute to a better world with reduction of plastics, iron, and energy.

ARPS -Block Motion algorithm

The basic idea of block motion estimation is to divide the current image into a matrix of blocks and then compare these blocks with the previous image in order to calculate the displacement vectors. The current block is searched in the previous image in a delimited search area, p pixels around the current block. In our tests a block takes a square side of 16 and the search parameter p is 7.

Block-based motion estimation assumes that objects move in a translational movement. In (Fig. 1) it is possible to see an example of two images that could be compared. At left is the non deformed image and at right the deformed image. It is also possible to see the grid of 16 x 16 pixels and the random pattern that was applied. The methodology for estimation of displacement uses the intensity of the pixel and with the unique pattern in each sub-region makes it possible to find the displacement. The match block algorithm chosen is based on the least cost. The cost function normally used is the mean square error (MSE) or the mean absolute difference (MAD). In this work the MAD function was used in order to save computation time.

In most cases adjacent blocks have similar motions. The block on the immediate left, above, above-left and above-right of the current block are the most important to calculate the predicted the motion vector (MV) [START_REF] Yao Nie | Adaptive Rood Pattern Search for Fast Block-Matching Motion Estimation[END_REF]. Four types of region of support could be used (Fig. 2). In this paper the type D was chosen because it requires less memory. For the initial search, the ARPS algorithm evaluates the four endpoints in a symmetrical rood pattern plus the predicted MV. The four arms of the rood pattern are of equal length (Fig. 3). The size of the rood pattern is equal to the length of the predicted motion vector (i.e. the motion vector of the immediate left of the current block). In each search points is necessary to compute the MAD function. The size of the rood pattern, Γ,

is calculated in (1), [ ] 2 ) ( 2 )
+ = = Γ → (1)
The square and the root square operations drawn in (1) require a lot of computation time. Therefore instead of (1) it is possible to use a simplification that only requires the highest magnitude of the two components of the predicted MV (2).

{ }

, 2 ) ( ) ( y predicted x predicted MV MV Max = Γ (2)
When it is not possible to apply the type D of the ROS, the value 2 is chosen for the size of the arm length (i.e. Γ=2). The minimal matching error (MME) point found in the current step will be re-positioned as the new search center of the next search iteration until the MME point is found as the center of the fixed pattern.

Results

Several load tests have been carried out in order to compare the results obtained by the image processing techniques with the information acquired by a classical measurement system. These tests [START_REF] Carvalho | 3rd fib International Congress. 2010[END_REF] uses two reference T-beams and three T-beams strengthened with different FRP techniques. In this paper only the results from TSC1 and HB2 are used. The image acquisition conditions for these tests are shown on Table 1. Each image is divided in sub regions of 16 x 16 pixels (~4.4 mm x 4.4mm). The TSC1 beam was used as reference without any additional flexural reinforcement and the HB2 beam was reinforced with 3 bonded GFRP sheet layers. The T-beams had a 3m span by 0.3m heights and were tested until rupture in a 4point bending test system. All the tested beams followed a monotonic loading history. The deflection control was granted by seven standard 100mm LVDT, displayed along the longitudinal direction of the beam. At the mid-span, a wirecontrolled transducer (500mm) was used together with the LVDT (100mm) in order to obtain the results of larger deformation, if needed during the post-collapse of the test. The data from the LVDT at the mid-span was used for the comparison with the data obtained with the image system analysis.

The image acquisition for TSC1, and HB2 was done with a digital Cannon EOS 400D camera with a resolution of 3888x2592 and two spots of 500W each guaranteed artificial light. The artificial lightning was used in order to maintain a constant light environment. All the images were captured on RAW format and then convert to TIFF format for image processing with MATLAB.

Before the data acquisition it was necessary to make the preparation for the image acquisition system (Fig. 4) and the sensor data system acquisition. The T-beam was initially prepared with an underlying painting of white mate ink and then with a superimposed random speckle pattern manually painted using a large brush with mate black ink. All the digital measurements are done at a distance without any particular or long calibration, having a low cost support, and they are easy to implement in the T-beam test setup.

The photos were taken with intervals of 30 seconds. In first image of the test, the area of interest is over and at the end is down (Fig. 5). In some load tests the displacement could be large. The digital image processing system compares two adjacent images in order to evaluate the displacement. These results are compared with the results acquired with one of the real sensors used and this evaluation can be seen in a graph of the displacement versus time (d vs. t).

In a non-contact strain measurement it is possible to analyze a small area of interest and a large one with the same hardware conditions. Moreover, this technique can be compared with non-linear software based on the finite element analysis (FEA) in order to corroborate the strain fields in the T-beam structure.

The strain map and the grid vector of the movement vector displacement were done for the small part associated with the LVDT at the mid-span (Fig. 6). The displacement vector diagram shows the compressive vertical load applied to the specimen. In order to know the implication of the entire T-beam we have also done the strain map of the entire T-beam.

The ARPS algorithm was used in the region of the image associated with the position of the LVDT, for each experiment (TSC1 and HB2), it is possible to visualize the evaluation of the displacement versus time (d vs. t) (Fig. 7). The abrupt jump indicates that a major crack is found and in this situation the image processing has some problems. The error, before crack situation, was 0.4 mm for TSC1 and 4.3 mm for HB2. The error can be reduced if we reduced the interval between photos.

The evaluation of force versus displacement (F vs. t) is also shown (Fig. 8) and it is also possible to visualize that our system is close to the real sensors.

Conclusions

The comparison between the measurements from LVDTs sensors and the measurements obtained from image processing techniques revealed to be very similar. Thus, the image processing technique used seems to be a very promising technique for measurement displacements with a much lower investment and much faster and easier setup.

The results obtained in this study shows that it is possible to continue and explore this subject and test the whole area of a T-beam in order to compare with the remain LVDTs sensors. Moreover, a strain field of the T-beam in study can be reproduced and compared to other techniques of modeling structures such as FEA, in order to calibrate and validate the image processor technique. The research will eventually provide software that enables a real time monitoring of the RC structure during the experimental test. In spite of using median and high pass filter amongst others preprocessing image technique, the results does not show a significant improvement.

It is important in future work to analyze the influence of the regular pattern and also increase the resolution of the image acquisition in order to obtain more precise results.

Fig. 1 .

 1 Fig. 1. At left is a no deformed image and at right is the deformed image; images are subdivide in sub region of 16x16 pixels.

Fig. 2 .

 2 Fig. 2. Regions of support: the blocks market by "O" are on each case the current block and the blocks market with a star are used for predicted the MV.

Fig. 3 .

 3 Fig. 3. Symmetrical shape of Adaptive Rood Pattern with four search points locating at the four vertices.

Fig. 4 .

 4 Fig. 4. The preparation of the image acquisition system (left) and data sensor system (right).

Fig. 5 .

 5 Fig. 5. Images of the test: the first image (left) and the last image (right).

Fig. 6 .

 6 Fig. 6. Representation of displacement map (left) where the dark regions indicated larger displacements, the vector diagram (centre) shows the flow of deformation and with the same system is also possible to compute the complete displacement map (right).

Fig. 7 .

 7 Fig. 7. Evaluation of displacement vs. time with results from image processing (** point) and LVDT sensor (++ point): TSC1 at left and HB2 at right.

Fig. 8 .

 8 Fig. 8. Evaluation of the force vs. displacement: TSC1 at left and HB2 at right. Values from sensor are market with (++) and image processing with (**).

Table 1 .

 1 T-Beam image acquisition condition: resolution and the number of photos.

	T beam designation	Resolution	Number of Photos
	TSC1	36 pixel/cm	52
	HB2	35 pixel/cm	27


Rui Antunes
email: rui.antunes@estsetubal.ips.pt

Fernando V Coito

Hermínio Duarte-Ramos

A Linear Approach towards Modeling Human Behavior

Keywords: Human Dynamics Modeling, Identification, Human-Machine Interfaces, Manual Tracking Systems, Machine Adaptation

The human operator is, no doubt, the most complex and variable element of a Mechatronics system. On simpler manual control tasks, a linear model may be used to capture the human dynamics, however experiences on human operator response during pursuit manual tracking tasks, show that the dynamics of the human operator appear to depend on the specific task that the subject is asked to perform. This means that a unique truly human model cannot be completely achieved. Rather, a different set of models, each for a certain class of task, seems to be needed. This ongoing PhD work introduces several approaches on the human operator dynamic characteristic modeling and identification procedures, which may be useful for developing improved "humetronic" systems, i.e. human-machine systems which may be able to adapt themselves to the skill level of humans, aiming, with reduced effort, to achieve for best performance and safety.

Introduction

Nowadays we rely on many different mechatronics equipments and gadgets for carrying out our way of life, and we are well aware of the limitations with which machines can efficiently perform controlled manual tasks, because unfortunately these machines usually do not change regardless of the human operator's skill.

Current research aimed to improve performance and safety on man-machine systems is increasing. In an ordinary human-machine interface the human operator controls the machine by performing various manual control tasks. While performing the activity the operator is also learning it, and its skill is increasing. Hence, it seems natural the need for designing human-oriented machines which, in a smart way, may improve the assistance and performance with its human users.

Automatic Control versus Human-in-the-Loop (HIL) Control

Automatic control applications usually do not consider that the human factor inevitably belongs into the resulting closed-loop, and in an automatic control process human is still often considered remissness.

Human-in-the-loop (HIL) control is a starting approach to integrate the human model into the already assortment of electrical and mechanical factors involved in a closed-loop man-machine control design.

The Human Adaptive Mechatronics (HAM) Assist Control Concept

An important goal today is to improve the design of next generation adaptive human-oriented machines, which will have the ability to intelligently cooperate with its human users. These machines should be able to actively adapt according to the skills of their operators, being capable to evaluate human's global performance. A human adaptive mechatronics assist control system [START_REF] Harashima | Human Adaptive Mechatronics -Interaction and Intelligence[END_REF], [START_REF] Habib | Human Adaptive and Friendly Mechatronics (HAFM)[END_REF], [START_REF] Kado | Control System for Skill Acquisition -Balancing Pendulum based on Human Adaptive Mechatronics[END_REF] identifies the operator based on his acquired actions in the process, and an assist controller is then iteratively conceived to improve the operator's performance according to its skill. Fig. 2 shows a HAM assist control application example [START_REF] Suzuki | Assist Control and its Tuning Method for Haptic System[END_REF], where the machine dynamics is settled by tuning its virtual model from previously estimation of human's skill.

Modeling Human Behavior

Human models are important to design a machine system to be controlled by an

Human H(s) b) c) Process P(s) Human H(s) a)
operator. The model is not aimed to closely replicate the operator behavior, but to provide sufficient information for the design of closed-loop control system incorporating the human behavior. Thus, in spite the fact that humans are non-linear time-varying complex systems, linear models are used as a way to capture some of the relevant characteristics of the human operator. This allows the use of standard control design techniques, combined with supervisionary controls approaches to tackle non-linearity and time variations.

To obtain linear models a number of experiments is required. Such experiments must be designed so as to minimize undesired effects like operator learning and prediction of the task or action signal saturation.

The first studies in human modeling were inspired by the demand for pilot models during the Second World War, and since that time there had been made many experiments, especially in pursuit and compensatory manual tracking tasks. One of the earliest studies of the human operator acting as a linear servomechanism was done in 1947 by Tustin, who proposed that there might be a linear law that could represent most of the operator's behavior. More recently new identification theories, methods and tools became available, striving human modeling to achieve significant progress.

In this paper several approaches are introduced on the human dynamic modeling and identification methods, and experimental procedures, for developing new human-oriented machines, which aim for best performance and safety, reducing also operator's effort. Although it must be stressed that whatever approach is taken to the description of human behavior, it only will capture a fraction of it, as human operator comprises the complex element in a man-machine system.

Contribution to Sustainability

Human-oriented machines help to create more sustainable services and products, without compromising human and environmental safety, as we are all challenged to produce and recycle more with fewer resources.

Essentially, three necessary phases are required for designing HAM systems: 1) Estimate human control characteristics; 2) Quantify the overall skill;

3) Design the assist-control human-machine system. The first phase can be considered both as a signal processing and a system identification problem, which implies developing theoretical and experimental procedures for obtaining the human controller from measured response data. On simpler manual control tasks a linear model can capture relatively well the human's behavior 1 . However, the model depends on the task and the process to be controlled, and simultaneously on many other factors, both personal and environmental, such as concentration, training, disturbances, comfort, workload, saturation, intermittency, fatigue, etc. The second phase for HAM design uses the experimental data obtained to quantify the overall operator skill, by comparing between an individual and its ideal response. Comparing ideal and identified parameters can also provide more accurate skill quantification. Next, according to the skill level obtained, an assistcontrol system (which is able to change the "amount of assistance") is finally implemented, based on an ideal model and on the acquired skill data.

In this work special focus is given on the first step of the Human Adaptive Mechatronics project, i.e. on the methods and procedures that can be derived for estimating the human control characteristics. We propose that the systemic notion of task dependence must be employed to model a human-machine system. New schemes for deriving task-dependent characteristics are explored though nonparametric and parametric identification methods.

Different approaches to the human identification problem are presented: we started by transient analysis (impulse and step response), obtained from pursuit manual tracking experiments. A special method to estimate the operator's delaytime was investigated. Structured Auto Regressive with eXogenous (ARX) inputoutput data parametric models where employed to obtain human-machine linear models from manual 1-D tracking experiences. Finally, the improved frequency analysis method is described in detail for modeling operator dynamics. An experimental setup that allowed the implementation of a human operator control task has also been developed, implemented and tested. The experimental procedures deployed for estimating the human-machine dynamics had an important role in this work, and where also described in detail.

State-of-the-Art / Related Literature

There have been in the past many studies covering the human operator dynamics modeling, especially within aeronautical engineering and flight control systems. Since the late 1940's that many experiments of human pilots performing target tracking tasks where studied. Important theories have been first introduced by McRuer et al., for manual tracking tasks using random references. A quasi-linear model was developed for describing human behavior during compensatory tracking tasks. In the 1960s and the 1970s modeling the human controller made great progresses [START_REF] Gaines | Linear and Nonlinear Models of the Human Controller[END_REF]. The McRuer's et al. linear crossover model [START_REF] Mcruer | Human Pilot Dynamics in Compensatory Systems[END_REF] and the optimal control model of Baron and Kleinman (applied for compensatory tracking) where proposed. The Baron's optimal control model was further developed by Tomizuka for preview tracking. In 1974 Shinners proposed the use of ARMA (autoregressive moving-average) models, obtained from tracking tasks.

In the 1990s Kawato postulated that the human's control has feedback and feedforward structures, and through the process of learning he changes from feedback to feedfoward (in the Feedback Error Learning model [START_REF] Ito | Internal model visualized[END_REF] the cerebellum acquires a model of the machine, as an inverse model in the feedforward path). Wolpert and Kawato improved the Feedback Error Learning model to a module selection and identification control (MOSAIC) [START_REF] Suzuki | Human skill elucidation based on gaze analysis for dynamic manipulation[END_REF], expanding the inverse model into a controller and the forward model into a predictor. Latest developments include using optimal control model (OCM) design techniques (by minimizing a certain performance index), assuming that the human operator performs the manual task in an optimal way. Particle swarm optimization (PSO) is a promising method for obtaining the OCM parameters from experimental data. ARX models (linear approach) and adaptive-network-based fuzzy inference tools have been recently studied. A new hybrid fuzzy-ARX modeling method [START_REF] Celik | Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques[END_REF] has also been recently developed for predicting the human operator control actions.

Research Contribution and Innovation

This work introduces several methodologies on the human-machine dynamic modeling and identification procedures, which can be applied on the development of improved HAM systems. In general, for execution of complicated tasks the human response does not follow linear behavior. However, for simpler servo/regulator control tasks, a linear model can be employed to capture most of the operator's dynamic characteristics.

Linear models can be obtained by physical (mathematical) modeling, and through system identification techniques based on measured data. The first approach is rather involved and time consuming, lying far beyond the objectives in hand. Hence, this work is focused on the system identification problem, i.e., on how to estimate a human-machine system model from the observed input/output data. Parametric identification methods are techniques to estimate parameters for pre-defined model structures, by finding through numerical search the parameters values that minimize the differences between the model's output and the measured data. ARX parametric identification methods were used in this work to model the human-machine dynamics. Nonparametric identification methods allow obtaining the operator behavior without the need for a pre-defined parameterized model structure. We have employed also nonparametric methods in this work, by analyzing the human operator transient and frequency responses on manual tracking tasks.

ARX Model Estimation

Auto-Regressive with eXogenous terms (ARX) models are well suited for modeling linear systems, due to its high potential and simplicity. The most common ARX structure is the linear difference equation:

1 1 ( ) ( 1) ( ) ( ) ( 1

) na nb y t a y t a y t na b u t nk b u t nk nb

+ -+ + - = - + --+ K K . ( 1
)
The present output y(t) is related to a number of past inputs u(t-k) and past outputs y(t-k). na equals the number of poles and nb the number of zeros. nk is the time delay inherent to the system. Typical estimation methods used for obtaining a and b parameters are the least-squares, the Kalman filter, and the instrumental variable method.

Transient Response Analysis

The dynamic properties of a linear model can be investigated by analyzing its transient response. For example, system time delays, time constants and static gain can be computed through the obtained impulse and step responses. Also, the right previous selection of the delay nk in the ARX model structure may be crucial for obtaining good identification results. Finally, the identification of low order linear models can also be easily confirmed from transient (step and impulse) analysis experiments.

Time-delay Estimation

Time-delay [START_REF] Boer | Estimation of Time-Varying Delay Time in Nonstationary Linear Systems: An Approach to Monitor Human Operator Adaptation in Manual Tracking Tasks[END_REF] is an important human factor that affects operator performance.

Although human is considered a nonlinear system, many studies (such as proposed by Ragazzini) support that in a simple motor task the operator behavior can be sufficiently identified by a linear model plus a finite time-delay (as a result of the neuromuscular and central nervous latencies, and also due to other human dependent and environmental factors). The time-delay parameter is inherent to the ARX method, through the sampling shift. Therefore, human operator time-delay estimation can be further confirmed throughout the ARX identification procedure.

The Improved Frequency Analysis Method

Another nonparametric identification method is based on the frequency response, i.e. on how a linear dynamic model would react to certain sinusoidal inputs. In a LTI system, if we let the input u(t) be a sinusoid of a certain frequency, then the output y(t) will also be a sinusoid of the same frequency. However, the amplitude and the phase may be different. If we consider a one-dimensional normalized input signal of duration T, to be tracked, x(t), built from a sum of N sinusoids at fixed multiple frequencies, the correspondent output y(t) and the I/O response for each frequency may be obtained through the following diagram:

0 0 1 1 ( ) sin( ) ( ) sin( ) N N k k k k k k k x t x a t y t y b t ω ω ϕ = = = + ⇒ = + + ∑ ∑ . ( 2, 3)
( ) sin( )cos ( ) sin 2 k k T k C C k k k k k b T y T b t tdt y T ω ϕ ω ϕ = + ⇔ = ∫ (4,5) (6,7) (8)
cos k t ω × × ∫ ∫ sin k t ω ( ) k C y t ( ) k S y t ( ) sin( ) k k k k y t b t ω ϕ = + 0 ( ) sin( )sin ( ) cos 2 k k T k S S k k k k k b T y T b t tdt y T ω ϕ ω ϕ = + ⇔ = ∫ 2 2 2 ( ) ( ) k k C S k b y T y T T = + and ( ) arctan ( ) k k C k S y T y T ϕ         = 0 0 0 y K x = .
which corresponds to the resulting human-machine closed-loop frequency response and static gain 0 K (for a previous input offset 0

x ). From the closed-loop experimental data, an open-loop human-machine model can be obtained, by inverse manipulation.

Discussion of Results and Critical View

This section presents obtained results from the experiments that where conduced for estimating human-machine models, in 1-D pursuit manual tracking tasks.

Experimental Procedures

For the transient analysis, two sets of five tracking samples (each lasted 2 minutes, and with at least 6 steps/impulses) were performed for a same operator with no history of neurological disease. To ensure human memorization or fatigue does not influence results, a minimum 15 minute rest was imposed between trials. The impulse and step inputs were visually recognized through large led indicators. In concerning ARX modeling, a set of four tracking tasks where conduced, with T=120 seconds duration each, comprising a white noise low-pass (H(s)) filtered input signal.

) 9
To obtain a human-machine linear model from the experimental data, a procedure was proposed. In system identification the adequate choice of the sampling rate is important. Sampling to fast leads to poor modeling. However, the use of an incorrect value for pure time-delay can mask system behavior. So the procedure to choose both time-delay and sample rate is as follows:

1. The signals are oversampled, at 100 Hz.

2. The first 0.5 seconds of each test are discarded because it corresponds to the low-pass filter initial transient. 3. For each choice for time-delay the signals are decimated before an ARX model is computed. From the point of view of discrete time models this corresponds to non-integer delay. 4. Taking the quadratic deviation between the true output and its estimated value as a performance index, the best combination for (time-delay, sampling rate, model structure) is chosen. 5. Continuous time equivalents of the time-delay and ARX model are separately obtained, and combined into a single continuous time model. Frequency analysis experimental procedures and results fall beyond the size of this paper, and were already described in detail in previous work [START_REF] Antunes | Using Human Dynamics to Improve Operator Performance[END_REF], [START_REF] Antunes | Human-Machine Control Model Approach to Enhance Operator Skills[END_REF].

Experimental Results

Obtained step and impulse response data showed that pure time-delay is unrelated with the number of trials (or human experience). Hence, for the identification of the operator's behavior a simple compensation data shift can be included in the correspondent model. Saturation and nonlinearity characteristics can also be found at the maximum limits, and near the joystick's origin, and the impulse tests where made with a 0.2 offset, to avoid the mechanical nonlinearities near the origin. As for ARX modeling, the best fit corresponds to the model: work, these obtained models depend on the type and shape of the input tracking signal (the same occurs with the estimated time-delay). It was also verified that the time-delay characteristic (for a same individual) is almost constant regardless the skill level or experience. Hence, pure time-delay can be detached for identification purposes.

For future work, and to be even more applicable, the linear identification methods described should be also accomplished from real-time collected task execution data.

Fig. 1 .

 1 Fig. 1. a) Human-machine system block diagram. Comparison between automatic b) and human-in-the-loop control c).

Fig. 2 .

 2 Fig. 2. Human adaptive mechatronics assist control block diagram.

Fig. 3 .

 3 Fig. 3. Frequency analysis block diagram for each k-multiple frequency.

  0

Fig. 4 .

 4 Fig. 4. A manual tracking time-trial using Logitech's Extreme 3D Pro. 8-bit analog Joystick.

Fig. 5 .

 5 Fig. 5. Step (left) and impulse (right) responses, at 100Hz sampling rate.

Table 1 .

 1 Pure time-delay (in seconds), on five manual tracking step and impulse tasks.

	Impulse		delay (s)
	1 -10	0.43 0.37 0.36 0.39 0.41 0.46 0.39 0.42 0.38 0.51
	11 -20	0.57 0.43 0.41 0.40 0.46 0.45 0.44 0.41 0.42 0.58
	21 -30	0.44 0.41 0.46 0.46 0.44 0.49 0.54 0.39 -	-
	Step		delay (s)
	1 -10	0.43 0.42 0.45 0.45 0.46 0.4	0.4	0.42 0.42 0.42
	11 -20	0.41 0.39 0.44 0.46 0.47 0.44 0.42 0.37 0.43 0.51
	21 -30	0.34 0.52 0.5	0.43 0.4	0.58 0.39 0.45 0.49 0.41

The Crossover and the Proportional Derivative are generalized low order linear human models, well described in the literature.

The human-machine estimated time-delay is 280 ms, while the average timedelay obtained from the impulse and step analysis was, respectively, 440 ms and 437 ms.

Although impulse response analysis is a simple and fast open-loop experimental method, it has some disadvantages, due to augmented saturation, nonlinearities and human fatigue. Step response analysis confirmed the same time-delay values that were obtained in the impulse response experiments. These methods, along with ARX model estimation are still limited modeling techniques, which need additional validation. Frequency analysis can be used to obtain an accurate open-loop LTI model, from a sum of sinusoidal input signals. It is a closed-loop experimental method. Hence, to capture the relevant human-machine dynamic characteristics, the obtained models need to be converted to open-loop ones.

Conclusions and Further Work

This work investigated the linear modeling methods potential for identifying the human-machine controller characteristics, as required for the design of Human Adaptive Mechatronics (HAM) systems.

An experimental human-machine LabVIEW interface setup has been developed and implemented for collecting data from human operators, as they performed pursuit manual tracking tasks with an analog joystick. As confirmed in previous


Manuela A Vieira

Paula Louro

Miguel Fernandes

João Costa

Optical Transducers Based on Amorphous Si/SiC Photodiodes

Keywords: Amorphous Si/SiC photodiodes, photonic, optoelectronic, image sensors, demultiplexer devices, optical amplifiers

Amorphous Si/SiC photodiodes working as photo-sensing or wavelength sensitive devices have been widely studied. In this paper single and stacked a-SiC:H p-i-n devices, in different geometries and configurations, are reviewed. Several readout techniques, depending on the desired applications (image sensor, color sensor, wavelength division multiplexer/demultiplexer device) are proposed. Physical models are presented and supported by electrical and numerical simulations of the output characteristics of the sensors.

Introduction

The Tunable optical filters are useful in situations requiring spectral analysis of an optical signal. A tunable optical device is a device for wavelength selection such as an add/drop multiplexer (ADM) which enables data to enter and leave an optical network bit stream without having to demultiplex the stream. They are often used in wavelength division multiplexing (WDM) systems [START_REF] Bas | Fiber Optics Handbook, Fiber, Devices and Systems for Optical Communication[END_REF]. WDM systems have to accomplish the transient color recognition of two or more input channels in addition to their capacity of combining them onto one output signal without losing any specificity (wavelength and bit rate) Only the visible spectrum can be applied when using polymer optical fiber (POF) for communication [START_REF] Randel | Advanced modulation techniques for polymer optical fiber transmission[END_REF]. So, the demand of new optical processing devices is a request.

Contribution to Sustainability

These sensors are different from the other electrically scanned image sensors as they are based on only one sensing element with an opto-mechanical readout system. No pixel architecture is needed. The advantages of this approach are quite obvious like the feasibility of large area deposition and on different substrate materials (e.g., glass, polymer foil, etc.), the simplicity of the device and associated electronics, high resolution, uniformity of measurement along the sensor and the cost/simplicity of the detector. The design allows a continuous sensor without the need for pixellevel patterning, and so can take advantage of the amorphous silicon technology. It can also be integrated vertically, i. e. on top of a read-out electronic, which facilitates low cost large area detection systems where the signal processing can be performed by an ASIC chip underneath.

In this paper we present results on the optimization of different multilayered a-SiC:H heterostructures for spectral analysis in the visible spectrum. A theoretical analysis and an electrical simulation are performed to support the wavelength selective behavior.

Device configuration and spectral analysis

The semiconductor sensor element is based on single or stacked a-SiC:H p-i-n structures using different architectures, as depicted in Fig. 1. All devices were produced by PE-CVD on a glass substrate. The simplest configuration is a p-i-n photodiode (NC11) where the active intrinsic layer is a double layered a-SiC:H/a-Si:H thin film. In the other the active device consists of a p-i'(a-SiC:H)-n / p-i(a-Si:H)-n heterostructures. To decrease the lateral currents, the doped layers (20 nm thick) of NC12 have low conductivities and are based on a-SiC:H [START_REF] Vieira | [END_REF].

NC12

Fig. 1 Device configuration.

Deposition conditions are described elsewhere [4,5]. Full wavelength detection is achieved based on spatially separated absorption of different wavelengths. The blue sensitivity and the red transmittance were optimized, respectively, using a thin a-SiC:H front absorber (200 nm) with an optical gap of 2.1 eV and a thick a-SiH back absorber (1000 nm) with an optical gap around 1.8 eV. The thicknesses of both absorbers result from a trade-off between the full absorption of the blue light in the front diode and the green light across both. As a result, both front and back diodes act as optical filters confining, respectively, the blue and the red optical carriers, while the green ones are absorbed across both [6]. The devices were characterized through spectral response at 1 kHz and photocurrentvoltage measurements. To test the sensitivity of the device under different electrical and optical bias three modulated monochromatic lights channels: red (R: 626 nm; 51µW/cm 2 ), green (G: 524 nm; 73µW/cm 2 ) and blue (B: 470nm; 115µW/cm 2 ) and their polychromatic combinations (multiplexed signal) illuminated separately the device. The generated photocurrent was measured under positive and negative voltages (+1V<V<-10V), with steady state red (λ R= 626 nm;Φ R =102 µW/cm 2 ), green (λ G= 524 nm; Φ G =71 µW/cm 2 ) and blue (λ B= 470 nm; Φ B =293 µW/cm 2 ) optical bias and without it (Φ R,G,B =0).The semiconductor sensor element is based on single or stacked a-SiC:H p-i-n structures using different architectures, as depicted in Fig. 1.

In Fig. 2, for NC11 and NC12 it is displayed the spectral photocurrent under different applied voltages. Data show different behaviors when single and double structures are compared. In the single configuration the electrical field is asymmetrically distributed across the graded i'i -layer leading to a collection efficiency that is only dependent on the light depth penetration across it. In the double, the contribution of both front and back diodes are evident and the effect of the internal p-n junction crucial on the device functioning and future applications.

Wavelength (De)Multiplexer Device

In optical communications different wavelengths (color channels) which are jointly transmitted must be separated (demultiplexed) to decode the multiplexed information. Fig. 3 displays, under positive and negative bias, the multiplexed signals, acquired with NC12 device, due to the simultaneous transmission of three independent bit sequences, each one assigned to one of the red (R: 626 nm), green (G: 524 nm) and blue (B: 470nm) color channels. At the top of the figure, the optical signal used to transmit the information is displayed to guide the eyes on the different ON-OFF states.

To recover the transmitted information (8 bit per wavelength channel, 2000bps) the multiplexed signal, during a complete cycle (0<t<T), was divided into eight time slots, each corresponding to one bit where the independent optical signals can be ON [START_REF] Bas | Fiber Optics Handbook, Fiber, Devices and Systems for Optical Communication[END_REF] or OFF (0). As, under forward bias, the device has no sensitivity to the blue channel (Fig. 2), the red and green transmitted information can be identified from the multiplexed signal at +1V. The highest level corresponds to both channels ON (R&G: R=1, G=1), and the lowest to the OFF-OFF stage (R=0; G=0). The two levels in-between are related with the presence of only one channel ON, the red (R=1, G=0) or the green (R=0, G=1) (see horizontal labels in Fig. 3). To distinguish between these two situations and to decode the blue channel, the correspondent sub-levels, under reverse bias, have to be analyzed. From Fig. 2b, it is observed that the green channel is more sensitive to changes on the applied voltage than the red, and that the blue only appears under reverse bias. So, the highest increase at -8V corresponds to the blue channel ON (B=1), the lowest to the ON stage of the red channel (R=1) and the intermediate one to the ON stage of the green (G=1) (vertical labels in Fig. 3). Using this simple algorithm and MATLAB as programming environment, the independent red, green and blue bit sequences can be decoded, in real time, as: R [00011011], G [001101010] and B [00101011] in agreement with the optical signals used to transmit the information.

Optical Bias Controlled Wavelength Discrimination

In Fig. 4a the spectral photocurrent at different applied voltages is displayed without and under red, green and blue background irradiation. In Fig. 4b the ratio between the spectral photocurrents under red, green and blue steady state illumination and without it (dark) are plotted.

When an external electrical bias (forward or reverse) is applied to a double pin structure, it mainly influences the field distribution within the less photo excited sub-cell: the back under blue irradiation and the front under red steady bias [8]. Under negative bias the blue bias enhances the spectral sensitivity in the long wavelength ranges and quenches in the short wavelength range. The red bias has an opposite behavior; it reduces the collection in red/green wavelength ranges and amplifies the blue one. The green optical bias only reduces the spectral greenish photocurrent keeping the other two almost unchangeable. no bias,

λ L =650 nm λ L =470 nm λ L =470 nm V=+1V no bias λ L =650 nm λ L =524 nm λ L =470 nm
λ L =650 nm λ L =524 nm λ L =470 nm V=-8V no bias, λ L =650 nm λ L =470 nm λ L =470 nm

Green channel

Photocurrent (a. To analyze the self bias amplification under transient conditions and uniform irradiation, three monochromatic pulsed lights (R, G and B input channels illuminated separately NC12 device. Steady state red, green and blue optical bias was superimposed separately and the photocurrent generated measured at -8V and +1 V.

In Fig. 5a, Fig. 5b and Fig. 5c the signal is displayed for each monochromatic channel separately and in Fig. 5d for the bit sequence shown on the top of figure.

Results show that when an optical bias is applied it mainly enhances the field distribution within the less photo excited sub-cell. Even at high frequencies the blue irradiation amplifies the red (α R =2.25) and the green (α G =1.5) channels and quenches the blue one (α B =0.8). The red bias has an opposite behavior, it reduces the red and green channels and amplifies de blue (α R =0.9, α G =0.5, α B =2.0). The green optical bias reduces the green channel (α G =0.75) keeping the other two almost unchangeable. This optical nonlinearity makes the transducer attractive for optical communications and can be used to distinguish a wavelength, to read a color image, to amplify or to suppress a color channel or to multiplex or demultiplex an optical signal. In Fig. 5c the green channel is tuned through the difference between the multiplexed signal with and without green optical bias

Electrical Model

The silicon-carbon pi'npin device is a monolithic double pin photodiode structure with two red and blue optical connections for light triggering (Figure 6a). Based on the experimental results and device configuration an electrical model was developed [START_REF] Vieira | Amorphous and Polycrystalline Thin-Film Silicon Science and Technology[END_REF]. Operation is explained in terms of the compound connected phototransistor equivalent model displayed in Figure 6 b. Good agreement between experimental and simulated data was observed. The expected levels, under reversed bias, and their reduction under forward bias are clearly seen (Figure 3). If not triggered ON by light the device is nonconducting (low levels), when turned ON it conducts through different paths depending on the applied voltage (negative or positive) and trigger connection (Q 1 Q 2 or both).

Under negative bias (low R 1 ) the base emitter junction of both transistors are inversely polarized and conceived as phototransistors, thus taking advantage of the amplifier action of neighboring collector junctions, which are polarized directly. This results in a charging current gain proportional to the ratio between both collector currents (C 1 /C 2 ). The device behaves like a transmission system able to store, amplify and transport all the minority carriers generated by the current pulses, through the capacitors C 1 and C 2 . Under positive bias (high R 1 ) the device remains in its non conducting state unless a light pulse (I 2 or I 2 +I 4 ) is applied to the base of Q 2 . This pulse causes Q 2 to conduct because the reversed biased n-p internal junction behaves like a capacitor inducing a charging current across R 2 . No amplification effect was detected since Q 1 acts as a load and no charges are transferred between C 1 and C 2 .

The optical amplification under transient condition also explains the use of the same device configuration in the Laser Scanned Photodiode (LSP) image and color sensor [9]. Here, if a low power monochromatic scanner is used to readout the generated carriers the transducer recognize a color pattern projected on it acting as a color and image sensor. Scan speeds up to 10 4 lines per second are achieved without degradation in the resolution. V=-8V

Φ L =0 λ RL =626nm λ GL =524nm λ BL =470nm λ S =470 nm
Photocurrent (µ)A Time (ms) Fig. 7 LSP color sensor.

For the color image sensor only the red channel is used (Fig. 6, I 2 ≠0, I 1 =I 3 =I 4 =0). To simulate a color image at the XY position, using the multiplexing technique, a low intensity moving red pulse scanner (Φ S , λ S ), impinges in the device in dark or under different red, green and blue optical bias (color pattern, Φ L, λ RGB , L, Φ L >Φ S ). Fig. 7 displays the experimental acquired electrical signals. The image signal is defined as the difference between the photocurrent with (light pattern) and without (dark) optical bias. Without optical bias (Φ L =0) and during the red pulse, only the minority carriers generated at the base of Q 2 by the scanner, flow across the circuit (I 2 ) either in reverse or forward bias. Under red irradiation (red pattern, Φ≠0, λ RL ) the baseemitter junction of Q 2 is forward bias, the recombination increases reducing I 2 thus, a negative image is observed whatever the applied voltage. Under blue (Φ≠0, λ BL ) or green (Φ≠0, λ BL ) patterns irradiations the signal depends on the applied voltage and consequently, on R 1 . Under negative bias an optical enhancement is observed due to the amplifier action of adjacent collector junctions which are always polarized directly. Under positive bias the device remains in its non conducting state, unless the red pulse (I 2 , dark level) is applied to the base of Q 2. No amplification occurs and the red channel is strongly reduced when compared with its value under negative voltage. Under blue irradiation, the internal junction becomes reverse biased at +1 V (blue threshold) allowing the blue recognition. The behavior under a green pattern depends on the balance between the green absorption into the front and back diodes that determines the amount of charges stored in both capacitors. Under negative bias both the green component absorbed either in the front (blue-like) or at the back (redlike) diodes reaches the output terminal while for voltages at which the internal junction n-p becomes reversed (green threshold), the blue-like component is blocked and the red-like reduced. So, by using a thin a-SiC:H front absorber optimized for blue collection and red transmittance and a back a-Si:H absorber to spatially decouple the green/red absorption, the model explains why a moving red scanner (probe beam) can be used to readout RGB the full range of colors at each location without the use of a pixel architecture.

Conclusions

Different architectures based on silicon carbon pin devices were studied both theoretically and experimentally. Results show that when the doped layers has low conductivities and are based on a-SiC:H material the devices are optical and voltage controlled and can be used as optical filter, amplifier or multiplexer /demultiplexer devices in the visible range.

Fig. 2

 2 Photocurrent (µA)

Fig. 3

 3 Photocurrent(µA)

  Photocurrent (nA)

Fig. 5

 5 Photocurrent (a.u.)

  Photocurrent (µA)