The term of Intellectual Property Rights (IPR) refers to the legal protections attached to works of creativity, such as image, books, movies, and music. The purpose of IPR is to ensure that the people responsible for creating anything new have the opportunity to profit from their labor. Different forms of IPR protection exist, such as: patents, copyrights, trademarks, etc. Our comparison presents some solutions for IPR based on image encryption that might improve the security of online communities with regards to intellectual property of images. It can be used to help ensure and that multimedia content is used legally under the terms of the copyright license selected by the content creators. Copyright issues can be found and reported more quickly with its help. In any case, multimedia files are crucial to the functioning of the modern internet. Encrypting this information is a great way to keep it safe. It will soon be an integral part of the safeguards protecting the integrity of the internet.

Information security in data storage and transmission is increasingly important. On the other hand, images are used in many procedures. Therefore, preventing unauthorized access to image data is crucial by encrypting images to protect sensitive data or privacy. The methods and algorithms for masking or encoding images vary from simple spatial-domain methods to frequency-domain methods, which are the most complex and reliable. In this paper, a new cryptographic system based on the random key generator hybridization methodology by taking advantage of the properties of Discrete Cosine Transform (DCT) to generate an indefinite set of random keys and taking advantage of the low-frequency region coefficients after the DCT stage to pass them to a subsystem consisting of an Reversible Logic Gate (RLG) group to obtain the secret keys that are passed to Rivest Shamir Adleman (RSA) to finish encrypting the image. The results indicate that the proposed method has the ability to generate a very large set of highly complex and secure secret keys that can be used later in the encryption stage. Moreover, the number and complexity of those keys will change each time the image is changed, and this represents the contribution of the proposed method. They experienced no time loss throughout the encryption and decryption processes when using RLG, which indicates that the proposed system did a good job in making different keys from the same image. And it differs in the strength of the key from one image to another, depending on the nature of the color imge.

Multiple sclerosis (MS) is an autoimmune malady of the central nervous system. To interpret this disease and its progression, magnetic resonance imaging (MRI) is increasingly exploited. This paper focuses on the segmentation and the detection of MS lesions by cerebral magnetic resonance imaging. Lately, machine learning (ML) techniques have been used in biomedicine and computer science to make diagnostic and analytical decisions to fight multiple sclerosis. We propose the SVM approach to analyze MRI images for the medical application of Multiple Sclerosis. The subject of this study is to assess this approach on a real public database, the proposed technique has been tested in the BraTS 2020 dataset which contains 369 denoting MRI images of patients associated with MS. Each one includes 4 MRI images modalities which are T1 weighting (T1), T2 weighting (T2), Fluid Attenuated Inversion Recovery (FLAIR) and T1 with gadolinium-enhancing contrast (T1EC). This paper is concerned with verifying the effectiveness of this algorithm by comparing these results against the reference performed by specialists, we further compared our segmentations with those of FSL tools, Freesurfer and statistical Parametric Mapping (SPM) since these methods are considered to be the best tools for brain segmentation. The propound approach is validated by using Python program.

Magnetic resonance imaging (MRI) has quickly established itself as the reference imaging tool for the management of patients suffering from multiple sclerosis (MS), both for the diagnosis and the follow-up of the evolution and evaluation of the impact of new therapies. The treatment of multiple sclerosis does not cure the disease, but it slows its progression and can help to space out attacks. In this paper, tumor segmentation is treated as a problem of classification using the Ant Colony optimization algorithm (ACO) combined with a proposed protocol based on BrainSeg3D tools. Many studies and many existing approaches tend the multiple sclerosis (MS) which is a chronic inflammatory anomaly of the central nervous system. The aim of this work is to evaluate and to verify the effectiveness of the proposed protocol on a public longitudinal database which contains 20 MS patients. This study is concerned with comparing these results against the ground truth performed by two experts and against other methods namely Dissimilarity Map (DM) creation and segmentation in terms of Dice Similarity Coefficient (DSC).

Co-design embedded system are very important step in digital vehicle and airplane. The multicore and multiprocessor SoC (MPSoC) started a new computing era. It is becoming increasingly used because it can provide designers much more opportunities to meet specific performances. Designing embedded systems includes two main phases: (i) HW/SW Partitioning performed from high-level (eclipse C/C++ or python (machine learning and deep learning)) functional and architecture models (with virtual prototype and real prototype). And (ii) Software Design performed with significantly more detailed models with scheduling and partitioning tasks algorithm DAG Directed Acyclic Graph and GGEN Generation Graph Estimation Nodes (there are automatic DAG algorithm). Partitioning decisions are made according to performance assumptions that should be validated on the more refined software models for ME block and GGEN algorithm. In this paper, we focus to optimize a execution time and amelioration for quality of video with a scheduling and partitioning tasks in video codec. We show how they can be modeled the video sequence test with the size of video in height and width (three models of scheduling tasks in four processor). This modeling with DAG and GGEN are partitioning at different platform in OVP (partitioning, SW design). We can know the optimization of consumption energy and execution time in SoC and MPSoC platform.

Actually, digital television has taken a long time to take root, both in its context of basic processing and coding, and in the context of broadcasting and transporting the processes associated with this branch of modern technology. It is known that this recorded delay is mainly due to the difficulty encountered in basic digital processing techniques and digital image storage. Some solutions are proposed to provide digital picture reception in hand devices. Three main standards are approved: Digital Video Broadcasting-Handheld DVB-H (Europe), Media Flo (USA) and Digital Multimedia Broadcasting DMB (Asia). They are using the same transport stream of nominal TV or DAB platform. We propose in this work an original idea to insert digital video in GSM interfaces without modify or change any parameters of the norm. Then, the new proposed solution allows achieving this aim using directly GSM channels. The main task is to make reduction of the bit rate coming from the video encoder using H265 advanced norm by interposing a new design “Brewer-buffer”. The outputs of this new proposed element will be directed to the time division multiple access TDMA frames of the GSM norm in order to make the RF channels. To restore the digital video our receiver must be equipped by the symmetric component “Invert brewer-buffer”. In fact, we will propose also, a new architecture of the receiver used in this application of hand-held video reception. We specify the originality of the proposed solution and demonstrate the fundamental architecture. For the simulation and the implementation of the main circuits proposed in this work, we have based on the VHDL language.

In the last decades, image processing has become an interesting task in the field of medical imaging. one of the most important medical imaging techniques used for diagnosis is the magnetic resonance image (MRI) which is considered to be a very useful medical tool for detecting the tumor progression of multiple sclerosis MS. The segmentation of brain tumors from MRI images is an effective technique. Several recent approaches for segmentation and classification of MRI sequences have been proposed for the automatic detection of MS outliers. This paper illustrates an algorithm based on the meta-heuristic “Ant Colonies Optimization ACO” for the segmentation of MRI images. We propose to apply the technique of ant colonies to estimate the segmentations of brain MRI images from a novel MR datasets of 30 MS patients, which were acquired with a 3T MR scanner with conventional sequences in order to optimize their overall rendering and to compare with consensual segmentation. The submitted algorithm is evaluated by using MATLAB GUI program.

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

Today, many researches and many existing methods treat the multiple sclerosis (MS) which is a chronic inflammatory pathology of the central nervous system. To detect multiple sclerosis outliers, we should have a diagnosis by magnetic resonance imaging (MRI) with different sequences. For this purpose, some recent methods of segmentation and classification of MRI sequences have been proposed for the automatic detection of MS tumors. In this paper, we propose a new protocol based on the existing and novel semi-automated lesions segmentation methods of BrainSeg3D. This protocol is applied on a novel MR databases of 30 MS patients, which were acquired with a 3T MR scanner with conventional sequences in order to evaluate our test with the obtained Consensus segmentation. The obtained results show an improvement comparant to those proposed in the literature.

Several techniques have been proposed to adapt multimedia and telecommunication applications to resource constraints. These techniques are mostly implemented at the application layer and make simplistic assumptions about the system resources communication and they are often unconvinced to the co-design in embedded system SoC and MPSoC. Moreover, they often assume that the data codec video characteristics and their processing needs are stationary, which is not true in practice especially in hardware for the real target FPGA. In fact, data streams in codec video are highly dynamic and may also experience concept drift, thereby requiring continuous online adaptation of the throughput and quality to each processing task in video and frame. Hence, existing solutions for multimedia and telecommunication applications are often too conservative or too aggressive, especially in video codec. To address these limitations, complexity in video test, quality of frame, precision, send video with a fast time especially in medical applications, airport and military flight problems. we propose offline algorithm scheduling which minimize the execution time (i.e., troughput and output quality of video and frame) of medical and military applications under real time and resources constraints. Our algorithm scheduling uses offline and online reinforcement learning techniques. Moreover, our scheduler is able to detect concept drifts and to smoothly adapt the scheduling strategy (DAG algorithm). Our experiments realized on a chain of tasks modeling real-life streaming application demonstrate that our scheduler is able to learn the scheduling policy and to adapt it such that it minimizes the execution time and optimize the precision, quality of frame as the video codec characteristics are dynamically changing. The strategy of scheduling algorithm is to order the tasks in processor or different processors. This approach remade tow problem in static scheduling algorithm with video and frame processing (the physiological data of a patient and allows in cases of urgent problems to trigger an alarm remotely controlled by an expert to intervene quickly in case of emergency. The key of contribution in this research are the modeling and scheduling tasks in ME blocks in video codec with new approach automatic, implementation and developpement this algorithms virtual Platform “OVP”, we work with tow codec video (H264 and H265) in ME block.

Today, communications security, i.e. the discipline of preventing unauthorized interceptors from accessing telecommunications in an intelligible form, while still delivering content to the intended recipients, is a main issue in our modern society especially. In this paper, attention is drawn to the importance and relevance of optical correlation techniques for detection and tracking people. In order to be efficient, these techniques need pre- or post-processing steps to take into account the environmental conditions. The aim of this work is to improve the performance of the optical correlation method, based on a new decision process in order to reduce the false detection rate. To realize this, we propose a method using a VanderLugt correlator with a phase-only filter for face recognition using two criteria for decision making based on the values of the peak-to-correlation energy and the energy distribution in different parts of the correlation plane. In the three-step algorithm, the first stage consists by dividing the correlation plane into nine equal sub-planes. In the second stage the energy of each sub-plane is computed, while in the last stage the classification criterion is realized and the recognition rate is calculated. Numerous tests were performed using the Pointing Head Pose Image Database. They show the effectiveness of the method in terms of face recognition detection rate without pre-processing phase and with 0% false detection.

Several techniques have been recently proposed to adapt video codec H264 applications to existing many core platforms. Among these techniques, the generation and automatic online of DAG algorithm : GGEN methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various video codec H264 tasks depending on dynamically changing data video codec characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single Motion Estimation ”ME” block input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new automatic and efficient methodology and associated algorithms for online directed acyclic graph-efficient scheduling of ME block applications with multiple streams on many core systems with resource constraints. Moreover, our scheduler is able to detect overlapping of the tasks, the communications problems between the tasks and to smoothly adapt the scheduling strategy. Our experiments realized on a chain of tasks modeling ME block application demonstrate that our scheduler is able to learn the scheduling policy and to adapt it such that it minimizes the targeted Time To Market TTM as the ME block characteristics in video codec are dynamically changing in Multi-Processor System on Chip ”MPSoC” and System on Chip ”SoC” system.

The aims of this paper is to propose a novel method for solving problem face detection and tracking system based in four algorithms: block-matching (BMA), Meanshift, Camshift and Kalman filter. Firstly, BMA is applied to the different sequential test as a preprocessing stage to detect faces. Then, the face tracking system are with three modules: Meanshift, Camshift and Kalman filter. This scheme gives a better face detection and tracking. Our work increases the performance and other criteria values in the embedded system. The human is more and more interested in producing intelligence which is one of the most impressive features of natures. Researchers are trying to make intelligent machine that have various capabilities. Building a machine or robot is probably one of the most challenging problems which humans are trying to solve. Recently, many projects have started with the purpose of learning machines to track some particular objects. One of the most challenging applications in computer vision is tracking objects efficiently in video sequence. Though progress has been accomplished, the best algorithms are far from reaching the speed and the performance of system. Object or multi-object tracking (face, human, car, etc.) is a fundamental problem that merits particular attention, since it is the key to solve a number of computer vision applications.

This paper describes an implementation system tracking and detection object and multi-object. We trait the different tracking algorithms of moving objects in video sequences. The tracking a particular precise object, robust, efficient, reliable and fast is a difficult problem to solve. Hence, it is a very essential for computer vision applications. For this reason, tracking objects in video sequences is a very active area of research since the 1970. It has attracted many people in the area of research for use in a variety of applications. The tracking is often the first step in an analysis of the activities, detection, behavior, interactions and relationships between objects of interest. Many methods of tracking objects have been proposed and developed. Tracking is the estimation and analysis of the trajectories of an object in the frame plane by moving in an frame where video sequence. Most of the motion objects tracking algorithms take input frames captured by a fixed camera to give at output a video sequence. However, there are other algorithms that take as input a video sequence to provide a video at output containing the tracked objects. These algorithms perform a first step of the detection objects in order to determine which of the pixels of the current frame which belong to the background of the sequence and which represent the motion objects. The problem of object tracking can be expressed in terms of detection of the object in each frame of the video sequence. The set of applications (security systems, military systems, intelligent transportation systems, video conferencing, surveillance, etc.) can be addressed. So, the lightness of the systems also lets you open recent applications such as human-machine interface and/or the human-robot interface, where the sensors can be embedded on a mobile robot. This usually requires the exploitation of object tracking techniques and algorithms in motion that will register and implement on FPGA targets that are introduced in these interfaces. We can cite as an example then the main existing algorithms in the literature as the block-matching, the KLT, the Kalman filter, the Meanshift and the Camshift.

With reference to our recent publications, [1] [2] [3], in which we proposed a new idea for data, collection and organization in an isolated photovoltaic, site and generating a text message process that will be, entrusted to an FM broadcasting band. We claim to, make in this article, the essential elements to ensure, the proper conduct of the RF procedure in the, presence of the “newborn” telemetry proposed, process. For this, we installed a complete chain that, contains a signal generator, a simple transmission, line, a tuned FM modulator, an RF power amplifier, and a measuring system composed by a network, analyzer and a spectrum analyzer. We will check the, "S" parameters variations of the new facility in the, presence of a simulated telemetry stream.

The main purpose of this work is to put together the benefits of the optical correlator which is VunderLaugt (VLC) correlator method for face recognition applications. With this aim in mind, we compare the performances of VLC correlator based on the fast Fourier transform (FFT) with the optical Fourier Transform (NO_FT) based on Fraunhofer diffraction. To achieve this goal, numerical implementation of the optical FT is needed. In this paper, we suggest and validate an all-numerical implementation of a VLC correlator with optical FT. Different tests using the Pointing Head Pose Image Database (PHPID) and considering faces with vertical and horizontal rotations were performed. The receiving operating characteristics (ROC) curves show that the optical FT simulating the Fraunhofer diffraction leads to better performances than the FFT.

By reference to our recent paper [1] and in order to elaborate a new telemetry network destined to supervise a photovoltaic (PV) fields, using RDS subscriber (Radio Data System) we continue to study various aspects of the transmission. So, in this paper, we gathered all eventualities of distortion and interference between different components of signals in order to draw conclusions about our telemetry process transmission. Total harmonic distortion THD and inter-modulation between signals are the main parameters considered in this simulation.

There are many approaches for designing filters to be used with optical correlation techniques that presents a good method to carry out instantly a decision on the target form. In this paper, we are interested on filters designs since their important role for target tracking and identification. Considering a Vanderlugt correlator (VLC), we discuss the basis of existing filters in the literature, and we carry out a performance analysis of the above filters in terms of Receiver Operating Characteristic (ROC) and peak-to-correlation energy (PCE). Simulation results prove that POF is more appropriate for optical correlator to be used for face recognition.