Imane Hachchane
email: hachchaneimane@gmail.com

Abdelmajid Badri

Aïcha Sahel

Yassine Ruichek

Video retrieval with CNN features

Keywords: Image Processing, Classification, Object Recognition, CNN, Faster R-CNN, Image-To-Video Instance Retrieval, Face Retrieval, Video Retrieval

Convolutional neural network features are becoming the norm in instance retrieval. This work investigate the relevance of using an of the shelf object detection network like Faster R-CNN as a feature extractor. We build an Image-to-video face retrieval pipeline composed of filtering and re-ranking that uses the objects proposals learned by a Region Proposal Network (RPN) and their associated representations taken from a CNN. Moreover we study the relevance of features from a finetuned network. The results obtained are very promising.

INTRODUCTION

Visual search applications especially video retrieval have gained a vast popularity recently due to the explosion of visual content that we are witnessing nowadays. This increase led to a proliferation of visual search applications like instance search. This is used to retrieve images or videos of a specific object from large databases. This work addresses a variant of this problem, the task of image-to-video instance retrieval witch is the task of identifying a video collection from a specific instance in a static image. Traditionally, image-to-video retrieval methods [START_REF] Arandjelovic | Three things everyone should know to improve object retrieval[END_REF]- [START_REF] Long | Fully Convolutional Networks for Semantic Segmentation[END_REF] are based on hand-crafted features (SIFT [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF], BRIEF [START_REF] Calonder | BRIEF: Binary Robust Independent Elementary Features[END_REF] , etc.) and not much effort has been put so far into the adaptation of deep learning techniques, such as convolutional neural networks (CNN). CNNs trained with large amounts of data can learn features generic enough to be used to solve tasks for which the network has not been trained [START_REF] Ng | Beyond short snippets: Deep networks for video classification[END_REF] .For image retrieval, in particular, many works in the literature [START_REF] Razavian | Visual Instance Retrieval with Deep Convolutional Networks[END_REF], [START_REF] Araujo | Large-Scale Video Retrieval Using Image Queries[END_REF] have adopted solutions based on standard features extracted from a pretrained CNN for image classification [START_REF] Oliveira Barra | Large scale content-based video retrieval with LIvRE[END_REF], achieving encouraging performances. In this paper we try to fill this gap by exploring the relevance of on-the-shelf and fine-tuned features of an object detection CNN for image-to-video face retrieval.

RELATED WORK

Most work in visual search focus on image to image retrieval, were we use a query image and a database of images [START_REF] Zheng | SIFT Meets CNN: A Decade Survey of Instance Retrieval[END_REF], [START_REF] Zhang | Query Specific Rank Fusion for Image Retrieval[END_REF] but this work focus on image to video retrieval were we search a database of videos using query images. But more precisely we are focusing on instance face retrieval. Face retrieval remains a challenging task because conventional image retrieval approaches, such as bag of words, are difficult to adapt to the face domain [START_REF] Herrmann | Fast face recognition by using an inverted index[END_REF]. This is mainly the result of using the traditional key point detection based descriptors like SIFT, that have tendency to fail due the smooth face surface. Early works, using a pretrained image classification convolutional neural network as a feature extractor, showed that fully connected layers for image retrieval were more suitable [START_REF] Babenko | Neural Codes for Image Retrieval[END_REF]. Razavian et al. [START_REF] Razavian | CNN Features off-the-shelf: an Astounding Baseline for Recognition[END_REF], improved the results by combining fully connected layers extracted from different image sub-much. Later on, new works found that using convolutional layers significantly outperform fully connected layers during image recovery tasks [START_REF] Razavian | CNN Features off-the-shelf: an Astounding Baseline for Recognition[END_REF], [START_REF] Salvador | Faster R-CNN Features for Instance Search[END_REF].

Many CNN-based object detection pipelines have been proposed, but we are more interested in the latest ones. Faster R-CNN [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF] created by Ren et al. it uses a Region Proposal Network (RPN) that removes the dependence of object proposals from older CNN object detection systems. In Faster R-CNN, RPN shares features with the object-detection network in [START_REF] Girshick | Fast R-CNN[END_REF] to simultaneously learn prominent object propositions and their associated class probabilities. Although the Faster R-CNN is designed for generic object detection, Jiang et al. [START_REF] Jiang | Face Detection with the Faster R-CNN[END_REF] Demonstrated that it can achieve impressive face detection performance specially when retrained on a suitable face detection training set [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF].

In this work we exploit the features of a state of the art pre-trained object detection CNN Faster R-CNN. We use his end-to-end object detection architecture to extract global and local convolutional features in a single forward pass and test their relevance for image-to-video face retrieval.

METHODOLOGY

CNN-based Representations

We explore the relevance of using CNN features for face image to video face retrieval. The query instance is defined by a bounding box above the query image. We use the features extracted from Faster R-CNN pre-trained models [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF] as our global and local features. Faster R-CNN has a region proposal network that give the locations in the image that have bigger probabilities of having an object, and a classifier that labels each of those object proposals as one of the classes in the learning dataset [START_REF] Salvador | Faster R-CNN Features for Instance Search[END_REF]. We will extract compact features from the activations of a convolutional layer in a CNN [START_REF] Salvador | Faster R-CNN Features for Instance Search[END_REF], [START_REF] Tolias | Particular object retrieval with integral max-pooling of CNN activations[END_REF]. Faster R-CNN is faster on a global and local scale. We build a global frame descriptor by ignoring all the layers that works with object proposals and extract features from the last convolutional layer. Considering the extracted activations of a convolution layer for a frame, we group the activations of each filter to create a frame descriptor with the same dimension as the number of filters in the convolution layer. We aggregate the activations of each window suggestion in the RoI Pooling layer to create regional descriptions [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF].

3.2

Video retrieval This section describes the three ranking strategies we used: Filtering step. We create image descriptors for query and database frames. At testing time, the descriptor of the query is compared to all items in the database, which are then ranked according to a similarity measure. At this stage, the entire frame is considered as a query. Spatial re-ranking. After the filtering step, the N upper elements are analyzed locally and re-ranked.

expansion (QE). We average the frame descriptors of the N higher elements of the first ranking with query descriptor to carry out a new search.

3.3

Fine-tuning Faster R-CNN Fine tuning the Faster R-CNN network allows as to obtain features specific to face retrieval and should help improve the performance of spatial analysis and re-ranking. To achieve this, we choose to fine-tune Faster R-CNN to detect the query faces to be retrieved by our system. The resulting networks will be used to extract better local and global representations, and will be used to perform spatial reranking.

EXPERIMENTS

4.1

Datasets exploited We evaluate our methodologies using the following datasets:

• YouTube Celebrities Face Tracking and Recognition Data (Y-Celeb) [START_REF] Kim | Face tracking and recognition with visual constraints in real-world videos[END_REF] : The dataset contains 1910 sequences of 47 subjects. All videos are encoded in MPEG4 at 25fps rate.

• YouTube Faces Database [START_REF] Wolf | Face recognition in unconstrained videos with matched background similarity[END_REF] : The data set contains 3,425 videos of 1,595 different people. All the videos were downloaded from YouTube. An average of 2.15 videos are available for each subject. The shortest clip duration is 48 frames, the longest clip is 6,070 frames, and the average length of a video clip is 181.3 frames.

The datasets used to finetune the network:

• FERET [START_REF] Phillips | The FERET database and evaluation procedure for face-recognition algorithms[END_REF]: 3528 images, including 55 Query images. A framing box surrounding the target face is provided for query images. • FACES94 [START_REF] Spacek | Faces94 a face recognition dataset[END_REF]: 2809 images 2809 images, including 55 Query images. A framing box surrounding the target face is provided for query images.

• FaceScrub [START_REF] Ng | A data-driven approach to cleaning large face datasets[END_REF]: 55127 images.

4.2

Experimental Setup We use the VGG16 architecture of Faster R-CNN to extract the global and local features. We chose the VGG16 architecture because it performs better. That has been showmen in previous works in the literature [START_REF] Salvador | Faster R-CNN Features for Instance Search[END_REF], [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF]were the capabilities of deeper networks achieve better performance. The global descriptors are extracted from the last convolution layer "conv5_3" and are of dimension 512. The local features are grouped from the Faster R-CNN RoI clustering layer. All experiments were performed on a Nvidia GTX GPU.

4.3

Off-the-shelf Faster R-CNN features We carried out a comparative study of the sum and maxpooling strategies of the image-wise and region-wise descriptors. Table 1 summarizes most our results. According to our experiments, the sum-pooling gives better performance than the max-pooling. It also shows the performance of Faster R-CNN with a VGG16 architectures trained on two different datasets (Pascal VOC and COCO ), VGG16 trained on COCO performed better because the dataset is bigger and more diverse . Moreover, it present the impact of spatial reranking and query expansion. Using the global features of Faster R-CNN on their own without any reranking strategy give the best results. Spatial reranking & QE had no impact or a negative one on the results

Table 1 Mean Average Precision (MAP) of pre-trained Faster R-CNN models with VGG16 archi-tectures. (P) and (C) denote whether the network was trained with

4.4

Fine-tuning Faster R-CNN We evaluate the impact of fine-tuning a pre-trained network on recovery performance with the query objects to retrieve. We chose to refine the model VGG16 Faster R-CNN, pre-trained with the objects of Pascal VOC, with two deferent datasets. The first network was refined using FERET and Faces94 datasets, we combine them to create one dataset. We modify the output layer in the network to return 422 class probabilities (269 people in the FERET dataset plus 152 people in the Faces94 dataset, plus one additional class for the background) and their corresponding bounded bound box coordinates [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF]. This new refined network will be called VGG(F-F). The second network was refined using FaceScrub dataset. we modify the output layer in the network to return 530 class probabilities (530 people, plus one additional class for the background) and their corresponding bounded bound box coordinates. Our second refined network will be called VGG(F-S) [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF] We kept the Faster R-CNN original parameters described in [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF], but due to the our smaller number of training sample we decreased the number of iterations from 80,000 to 20,000. We use the refined networks of the tuning strategy (VGG(F-F) & VGG(F-S)) on all datasets to extract image and region descriptors to perform a face retrieval. Those results are also presented in Table 2. The refined features slightly exceeded the raw feature in the spatial reranking and the QE stages. But still, the global features of Faster R-CNN from VGG16 trained on COCO used without any reranking strategy give the best results.

CONCLUSION

This article explores the use of features from an object detection CNN for image-to-video face retrieval. It uses Faster R-CNN features as global and local descriptors. We have shown that the common similarity metric give similar results. We also found that sum-pooling performs better than max-pooling in this case, and contrary to our previous work [START_REF] Hachchane | New Faster R-CNN Neuronal Approach for Face Retrieval[END_REF] fine tuning does not improve the results. In general we found that applying the similarity measure on the CNN feature gave the best results.

In future work we will work on reducing the feature extraction time.

Bibliographie

  Pascal VOC or Microsoft COCO images, respectively. With a comparison between sum and max pooling strategies. When indicated, QE is applied with M = 5

				Y-Celeb		YouTube Faces Database
	Network	Pooling
			Ranking	Reranking	QE	Ranking	Reranking	QE
	VGG16	max	0.888	0.860	0.550	0.892	0.877	0.882
	(P)	sum	0.915	0.846	0.600	0.897	0.886	0.891
	VGG16	max	0.911	0.888	0.522	0.892	0.878	0.889
	(C)	sum	0.926	0.807	0.512	0.903	0.882	0.896

Table 2

 2 Mean Average Precision (MAP) of pre-trained Faster R-CNN models with VGG16 archi-tectures. (F-S) AND (F-F) denote whether the network was trained with FaceScrub or Feret & Faces94 images, respectively. With a comparison between sumand max pooling strategies. When indicated, QE is applied with M = 5

				Y-Celeb		YouTube Faces Database
	Network	Pooling
			Ranking	Reranking	QE	Ranking	Reranking	QE
	VGG16	max	0.809	0.777	0.457	0.848	0.834	0.838
	(F-S)	sum	0.917	0.843	0.578	0.882	0.873	0.874
	VGG16	max	0.915	0.874	0.554	0.894	0.884	0.887
	(F-F)	sum	0.924	0.899	0.621	0.896	0.892	0.893


Soumaya Al Morabeti
email: soumaya.almorabeti@gmail.com

Mounir Rifi

Hanae Terchoune

Rectifier Circuit Designs for RF Energy Harvesting applications

Keywords: RF energy harvesting system, rectenna, microstrip rectifier circuit, diode, low pass filter

RF energy scavenging, commonly referred to as RF energy harvesting, is the capability of collecting ambient RF energy from antennas to supply power to electronic devices. The rectifier circuit is the key component of wireless energy harvesting system. Therefore, the development of efficient and compact rectifier circuit has become recently a vital research topic. This paper presents a state of the art and review of the recent designs of microstrip rectifier circuit used for RF energy harvesting applications at 2.45 GHz and 5.8GHz.

INTRODUCTION

Wireless power transfer and RF energy harvesting are an alternative technology that can reduce the dependency of the most wireless devices on non-autonomous power source. They have received great attention and known a successful impact in many real life applications since the last several decades, due to their fulfillment of remote power to electronic and electrical devices involving low power consumption, located in unreachable area or needing expensive maintenance costs [START_REF] Taybi | Design of A New 5.8 GHz RF-DC Rectifier Structure for Wireless Power Transmission[END_REF] [START_REF] Adel | High Sensitive and Efficient Circular Polarized Rectenna Design for RF Energy Harvesting at 5.8 GHz[END_REF].Rectenna is the most main element in RF energy harvesting system to harvest the wireless energy from the RF sources. The components of the rectenna system have traditionally been focused at 2.45 GHz. To decrease the aperture sizes without sacrificing component efficiency, the frequency of ISM band centered at 5.8 GHz has been investigated [START_REF] Benyetho | A New Fractal Multiband Antenna for Wireless Power Transmission Application[END_REF].The general block diagram of a rectenna is depicted in fig 1.This passive element contains a receiving antenna that collects and captures the radiated microwave energy from the transmitting antenna, and the rectifying circuit that converts these incoming energy into DC power. Usually, printed dipole and circular polarized microstrip patch antennas are the most used in rectenna system [START_REF] Jiménez Pérez | SUR-VEY OF ENERGY HARVESTING CIRCUITS: RE-SEARCH ISSUES AND CHALLENGE[END_REF].

fig 1 : The general block diagram of a rectenna

As highlighted in fig. 1, the rectifying circuit consists of a HF filter, rectifier (diode), DC filter and a load. The input HF filter preserve the antenna from the high order harmonics generated by the diode due to its nonlinear characteristics, and ensures the adaptation between the receiving antenna and the rectifying circuit. The diode rectifies the RF signal (AC current) and converts it into a DC signal. The output DC filter blocks all the high order harmonic frequencies produced by the rectifier in order to protect the load. These last one is a resistive element that presents the input impedance of the device to be powered. In the recent years, the challenge in designing a rectifier circuit is to ensure a compromise between a high output voltage and good power conversion efficiency for low input power applications .Thus, the main design methodology is: First, selection of an adequate energy harvesting circuit topology. Next, choose of an appropriate diode. Then, conception of a matching network (bandpass filter) and low pass filter. Finally, optimization and simulation of the overall circuit design using the EM simulation software. This review paper is organized as follows. First, the rectifier circuit theoretical background is introduced in section 2. In section 3, the related works and comparison of reviewed designs are presented .Finally, some propositions are suggested for our own design.

RECTIFIER CIRCUIT BACKGROUND

Topologies

The microwave rectifier can take several topologies depending on the configuration of the diodes used for rectification. The serial and shunt topologies are the most used in the literature .Furthermore, in order to improve the output DC voltage, the voltage doubler or voltage multiplier topology can also be used [START_REF] Sennouni | Advance and Innovation in Wireless Power Transmission Technology for Autonomous Systems[END_REF].These conventional rectifier circuit topologies are illustrated in fig 2. In the serial topology, the single diode is placed between the RF filter and the DC filter. In the shunt topology, the diode is connected in parallel between the filters. The diode is self-biased by the DC voltage that it generates. The single stage voltage multiplier circuit or voltage doubler topology can be seen as the combination of the two preceding topologies. It consists of two diodes, one mounted in serial and the other in parallel, to produce a larger output voltage.

In effect, the mono diode topologies are mainly preferable because of their ease of implementation, their high efficiency, their small size and their low cost. They offer the theoretical advantage of minimizing the losses in the diodes [START_REF] Christopher | MICROWAVE-ENERGY HARVESTING AT 5.8 GHZ FOR PASSIVE DE-VICE[END_REF]. However, the output voltage levels for both topologies are low. The voltage doubler topology provides a high output voltage, but a low RF-DC conversion efficiency compared to the mono diode topology.

The simulations performed in [START_REF] Hosain | Design and analysis of efficient rectifiers for wireless power harvesting in DBS devices[END_REF] prove that the voltage doubler circuit is more suitable for high input power levels applications (>20 dBm).The serial topology presents a good RF-DC conversion efficiency for low input power levels applications (around -10 dBm and 20 dBm).

Each of these topologies has its advantages and limitations. The choice of the suitable rectifier circuit topology is generally depends on the voltage required at the output , the available input power and the size constraint.

Diode

In designing a rectifier circuit with high RF-to-DC conversion efficiency, the choice of a proper diode is one of the most important factors since the diode is the main source of loss and its performance and character-istic determine the overall performance of the circuit [START_REF] Taybi | Design of A New 5.8 GHz RF-DC Rectifier Structure for Wireless Power Transmission[END_REF]. Zero bias Schottky diodes are usually used for high frequency rectifiers because of their high switching capacity , low barrier (high saturation current),low junction capacitance and low voltage threshold than the common PN diodes .In fact, the low threshold allows for more efficient operation at low powers, and the low junction capacitance increases the maximum frequency at which the diode can operate [START_REF] Christopher | Harvesting Wireless Power[END_REF]. . The second class is the p-type silicon characterized by low barrier and high . For low input power levels applications, the p-type Schottky diode is recommended since it provides a higher output voltage compared to the n-type [START_REF] Okba | Conception et réalisation de rectennas utilisées pour la récupération d'énergie électromagnétique pour l'alimentation de réseaux de capteurs sans fils[END_REF]. The most used commercial Schottky diodes are HSMS-285X (n-type ) and HSMS-286X (p-type ) [START_REF] Nintanavongsa | Design Optimization and Implementation for RF Energy Harvesting Circuit[END_REF].The simulations performed in [START_REF] Christopher | MICROWAVE-ENERGY HARVESTING AT 5.8 GHZ FOR PASSIVE DE-VICE[END_REF] shows that HSMS-285x has the largest efficiencies at the lowest powers. This characteristic is due to its low turn-on voltage. However, it is unable to function well at larger powers because of its small reverse breakdown voltage.

Energy conversion efficiency

The main characteristics that should be optimized when designing rectifier circuit are the DC output voltage and the RF-DC conversion efficiency. This last one refers to the ratio of the PDC power recovered at the output of the rectifier and the PRF power injected at the input of the rectifier using a microwave source. It is determined using the formula (1) :

 (%)=100. RF DC P P (1)
The PDC power is calculated as follows:

DC P = L DC R V 2 (2)
With RL is the load resistance, and VDC is the maximum DC voltage across the diode, limited by the reverse breakdown voltage Vbr by:

DC V = 2 br V (3)
The PRF power is calculated using the FRIIS transmission equation (4):

2 . . . . . 4            r G G P P r e e RF   (4)
It gives the received RF power as a function of the transmitted power Pe, the maximum gains of the transmitting Ge and receiving antennas Gr and the losses in the free space which depend on the frequency and the distance r between the two antennas .

In fact, a variety of loss mechanisms make it difficult to achieve high RF-DC conversion efficiency, especially in nonlinear devices such as the diodes [START_REF] Christopher | Harvesting Wireless Power[END_REF]. The maximum energy conversion efficiency of the rectifier circuit is limited by impedance matching, device parasitics, and harmonic generation. It generally depends on the microwave input power, the optimum connected load, the own junction voltage and breakdown voltage of the diode. The efficiency becomes quite low when the power is small or the load is not matched. When the input voltage to the diode is lower than the junction voltage or is higher than the breakdown voltage the diode does not show a rectifying characteristic [START_REF] Christopher | MICROWAVE-ENERGY HARVESTING AT 5.8 GHZ FOR PASSIVE DE-VICE[END_REF].Fig 4 illustrates the general relationship between the efficiency and losses in microwave energy conversion circuits as a function of input power [START_REF] Taybi | Design of A New 5.8 GHz RF-DC Rectifier Structure for Wireless Power Transmission[END_REF].

fig 4 : General Relationship between the efficiency and losses in microwave energy conversion circuits as a function of input power

At low input power region, the efficiency is small because the voltage swing at the diode is below or comparable with the diode turn-on threshold voltage (VT Effect). As the power continues to increase , the efficiency increases and levels off with the generation of higher order harmonics. At high input power region, the efficiency sharply decreases because the voltage swing at the diode exceeds the breakdown voltage (Vbr Effect) of the diode [START_REF] Christopher | Harvesting Wireless Power[END_REF]. The critical input power where the breakdown effect becomes dominant is expressed as

L br R V 4 2 .
In addition, the RF-DC conversion efficiency strongly depends on the characteristics and internal parameters (Vbr,VT,Rs, Cj, Vj ) of the used diode, , as well as the value RL of the load. The simulations performed in [START_REF] Christopher | MICROWAVE-ENERGY HARVESTING AT 5.8 GHZ FOR PASSIVE DE-VICE[END_REF][8] prove that :

 As the turn-on threshold voltage VT is decreased, the energy conversion efficiency at a given power increases. So a low turn-on threshold is required for efficient and low input power applications.  When the series resistance Rs increases, the energy conversion efficiency decreases. So a low value of Rs means that it is low losses in the diode and therefore a high efficiency.  As the junction tension Vj is decreased, the RF-DC conversion efficiency increases.  As the junction capacitance Cj, is decreased, the RF-DC conversion efficiency increases. Cj limits the maximum frequency for which a diode can operate.  When the resistance of the load RL is low, the efficiency is high. Hence, an optimization must be done to determine the optimum RL.

Impedance matching network

When the rectifier circuit is not properly matched to the receiving antenna, a part of the incident power from the antenna will be reflected back to the environment and not absorbed. Then the available power for rectification is automatically reduced. Thus, it is necessary to design an impedance matching network in order to match correctly the input impedance between the circuit and the receiving antenna [START_REF] David | Microwave Engineering[END_REF]. The most frequently used matching networks are the L network, π network, and T network .The choice of the suitable adaptation network depends on the objective sought.If it is to simply make a single-band adaptation, the L-network is sufficient. If other functions are added to the adaptation such as the harmonic rejection (Band-pass HF filter). T or π networks will be favored in this case.π type matching circuit attains wider bandwidth than L type matching network [START_REF] Hosain | Design and analysis of efficient rectifiers for wireless power harvesting in DBS devices[END_REF].

An adaptation circuit can be composed of lumped elements such as capacitors and inductors or distributed elements such as stubs and microstrip lines [START_REF] David | Microwave Engineering[END_REF]. However, at high frequencies, the distributed technology is the most applied [START_REF] Douyere | Méthodologie de Synthèse et D'optimisation de Radiopile Bornée à un Domaine de Fonctionnement[END_REF].In fact, at microwave frequencies, the lumped components perform poorly due to parasitic reactance. In order to pass from the lumped elements to the distributed elements, we can use the microstrip lines and stubs in open circuit or in short circuit. An open circuit stub in microstrip technology is equivalent to a parallel-mounted capacitor, and a shortcircuit stub is equivalent to a parallel inductor [START_REF] Kim | Compact microstrip lowpass filter using shunt open stubs and coupled slots on ground plane[END_REF].

DC filter

The DC filter is a low-pass filter that is the most often composed of capacitors placed in parallel with the load resistance. The filtering capacitor is the part of the rectifier that will block the fundamental signal and the harmonics downstream of the diode. The cutoff frequency must be lower than that of the fundamental signal. The value of the filtering capacitor is determined from the following equation:

C= fc . . 2 1  (5)
In general, the design of microstrip low pass filters involves two main steps. The first one is to select an appropriate low pass prototype (lumped-element filter design). The choice of the type of response, including passband ripple and the number of reactive elements (filter order), will depend on the required specifications [START_REF] Jia-Shen | Microstrip Filters for RF/Microwave Applications[END_REF]. Chebyshev and Butterworth are the most popular low pass prototypes in microstrip filters design. Chebychev filter tolerates a slight ripple in the bandwidth, but has a better rejection than the Butterworth filter. The next step is to find an appropriate microstrip realization that approximates the lumped element filter. The low pass filter is widely implemented with stubs (shunt, radial) or stepped impedance lines [START_REF] Kim | Compact microstrip lowpass filter using shunt open stubs and coupled slots on ground plane[END_REF].

RELATED WORKS

In the literature, many kinds of Microstrip rectifier circuit designs at 5.8 GHz and 2.45 GHz for RF energy harvesting system have been proposed. This section presents a brief review of the recent designs.

Circuits at 2.45GHz

The paper [START_REF] Takhedmit | A 2.45-GHz low cost and efficient rectenna[END_REF] reported a RF-DC rectifier circuit based on modified bridge rectifier with four Schottky diodes. A maximum efficiency of 52 % and an output DC voltage of 3.64 V have been reached over an optimal resistive load of 1050Ω at the input power of 10 dBm. The presented circuit device doesn't need neither input HF filter nor bypass capacitor.

In [START_REF] Takhedmit | Compact and efficient 2.45GHz circularly polarized shorted ring-slot rectenna[END_REF], a RF-to-DC rectifier circuit has been optimized at 2.45 GHz for an input power of 100 mW.the proposed circuit is based on a commercial zero bias Schottky diode HSMS-2850 ,and it is designed at the back side of the antenna in order to reduce the size of the rectenna .The output DC voltage and overall efficiency are respectively higher than 1.1 V and 56% over a resistive load of 2500Ω .

In [START_REF] Adel | Efficient Rectenna Design Incorporating New Circularly Polarized Antenna Array for Wireless Power Transmission at 2[END_REF] ,the authors have developed a microwave rectifier circuit ,which consisted of a short-ended Tmicrostip line matching network, a Schottky diode HSMS-2820, and an output low pass filter.The circuit provided a high conversion efficiency of 65.8% and a DC output voltage of 7.02V at 20dBm input power for an optimum load resistor of 750 Ω.

The paper [START_REF] Chuc | Design and Fabrication of Rectifying Antenna Circuit for Wireless Power Transmission System Operating at ISM Band[END_REF] proposed a microwave double voltage rectifier with two Schottky diodes HSMS2820.A maximum efficiency of 71.5% and a DC voltage output of 10.75 V are reached at 22dBm of input power. The optimal load resistance was around 810. A RF-DC rectifying circuit based on a Schottky diode HSMS2820, with a series topology,have been developed in [START_REF] Taybi | A new design of high output voltage rectifier for rectenna system at 2.45 GHz[END_REF].It provided an important value of output voltage of 11.23V at 20 dBm of input power .The optimal load resistance was around 2 k.

Table 1 shows the results comparison of the presented rectifier circuits at 2.45 GHz. It can be noticed that the rectifier circuit proposed in [START_REF] Taybi | A new design of high output voltage rectifier for rectenna system at 2.45 GHz[END_REF], has presented the best performances with a high output voltage of 11.23V, with just one rectification diode. This circuit is judged to be compact, low cost and efficient.

Circuits at 5.8 GHz

The authors in [START_REF] Adel | High Sensitive and Efficient Circular Polarized Rectenna Design for RF Energy Harvesting at 5.8 GHz[END_REF], have developed a microstrip rectifier that combined series and shunt configurations. It constituted of an input matching circuit (low pass filter, two Schottky diodes HSMS2852 and HSMS2850,and an output filter ( three fan shaped stubs).An output DC voltage of 2.2 V and a RF-to-DC conversion efficiency of more than 70% were achieved with an optimum load resistance of 6 kΩ for 0 dBm microwave incident power level.

In [START_REF] Sennouni | Advance and Innovation in Wireless Power Transmission Technology for Autonomous Systems[END_REF], a RF-to-DC rectifier circuit with a modified doubler voltage topology was developed for low incident power .The rectifier circuit consisted of an impedance matching network (a T-junction with short end), two Schottky diode HSMS-2862, and three parallel capacitors of 200 pF of each one ,have used as a DC low pass filter and placed in parallel with the resistance R. The rectifier exhibited an output DC voltage of more than 2.53V, 2.1 V and 0.61 V when the input power was around 0 dBm,-2dBm and -10 dBm with the optimum load of 7.5 KΩ.

In [START_REF] Taybi | Design of A New 5.8 GHz RF-DC Rectifier Structure for Wireless Power Transmission[END_REF], a RF-DC rectifier circuit has been proposed using microstrip lines technology, with a voltage multiplier topology including four schottky diodes HSMS2820 and another diode in series with the load resistance (160Ω )in order to improve the sensitivity. This diode was acted as a variable resistor due to its current dependence in the junction resistance. The conversion efficiency and the output voltage reached respectively the values of 47% and 12V for an input power of 30 dBm.

In [START_REF] Suri | A 5.8 GHz Inset-Fed Rectenna for RF Energy Harvesting Applications[END_REF], the authors have implemented a rectifier circuit with a Greinacher Voltage Doubler topology, using Schottky diode HSMS-285C.The matching circuit consisted of inductors and capacitors lumped together. A load of 1500 ohm was used. The proposed rectifier circuit yielded a maximum output voltage of 3.34 V and a conversion efficiency of 74.38% when the input power was around 10 dBm. The rectifier circuit proposed in [START_REF] Yang | A 5.8 GHz Circularly Polarized Rectenna with Harmonic Suppression and Rectenna Array for Wireless Power Transfer[END_REF] ,was integrated on the back side of the antenna substrate in order to minimize the profile of the rectenna.The Schottky diode HSMS-286c double tube was used in series to increase the output DC voltage. The DC pass filter consists of a 100 pF choke and a section of microstrip line. A maximum efficiency of 66.2 % and an output DC voltage of 6.33 V have been measured with a 900 Ω load at the input power of 18 dBm.

Table 2 summarizes the performances of these presented rectifier circuit designs. We can deduce that the rectifier circuit proposed in [START_REF] Adel | High Sensitive and Efficient Circular Polarized Rectenna Design for RF Energy Harvesting at 5.8 GHz[END_REF] has achieved a good efficiency of 70%, but a very low output DC voltage of 2.2 V at 0 dBm .It is need to be further improved.

Synthesis

Based on the review gained in the previous sections ,it has been found that the main challenge in designing a rectifier circuit is to insure a compromise between a high output voltage ,good power conversion efficiency, small size and low cost ,especially for low input power applications. To achieve this purpose, some suggestions may be useful:  If the RF energy harvesting application required a larger voltage, the use of voltage doubler and voltage multiplier configurations can increase enormously the DC output voltage.  If the size constraint is paramount, the use of series topology is preferred.  Integration of rectifier circuit on the back side of the antenna substrate can minimize the profile and the size.  It is recommended to use schotkky diode HSMS-285x at 2.45GHz and HSMS-286x at 5.8GHz.  Optimization of the rectifier circuit using harmonic balance analysis and ADS simulator.

In this study, we have performed a roundup of rectifier circuit, by approaching its different topologies and the critical points to consider in order to design a compact and efficient rectifier circuit. Furthermore, we have reviewed the recent and advanced designs developed in microstrip technology at 5.8GHz and 2.45 GHz, for RF energy harvesting applications at low input power. As a perspective, conception, simulation and implementation of our own rectifier circuit.

fig 2 :

 2 fig 2 : The conventional rectenna topologies: (a) series; (b) shunt; (c) single stage voltage doubler

  fig 3 : The equivalent electrical circuit of a Schottky diode

Table 1 :

 1 Comparison between the rectifier circuit designs at 2.45GHz

					
	Ref	Topol-ogy	Schottky Diode	Input power	(%) Vo (V)	RL (Ω)
		modi-
	[19]	fied	_	10	52	3.6 105
		bridge		dBm			0
		rectifier
	[20]	_	HSMS-	100	56	1.1 250
			2850	mW	%		0
	[21]	_	HSMS-	20	65.	7.0	750
			2820	dBm	8	2
	[22]	double	HSMS-	22	71. 10.

Table 2 :

 2 Comparison between the rectifier circuit designs at5.8GHz

					
	Ref	Topol-ogy	Schottky Diode	Input power	(%) Vo (V)	RL (Ω)
	[2]	series-	HSMS-
		shunt	2852,HS	0 dBm 70	2.2 600
			MS-2850				0
	[5]	modfied	HSMS-	0			750
		doubler	2862	dBm	_	2.5	0
		voltage
	[1]	voltage	Five of	30
		multi-	HSMS-	dBm	47	12 160
		plier	2820
	[15]	Greinac
		her	HSMS-	10	74	3.3	150
		Voltage	285C	dBm		4	0
		Doubler
	[16]	_	HSMS-	18	66.	6.3	900
			286C	dBm	2	3