UWB Indoor Radio Propagation Modelling in Presence of Human Body Shadowing Using Ray Tracing Technique

This paper presents a ray-tracing method for modelingUltra Wide Bandwidth indoor propagation channels. Avalidation of the ray tracing model with our indoor measurementis also presented. Based on the validated model, the multipathchannel parameter like the fading statistics and root mean squarerms delay spread for Ultra Wide bandwidth frequencies aresimply extracted. The proposed ray-tracing method is basedon image method. This is used to predict the propagation ofUWB electromagnetic waves. First, we have obtained that thefading statistics can be well fitted by log normal distributionin static case. Second, as in realistic environment we cannotneglect the significant impact of Human Body Shadowing andother objects in motion on indoor UWB propagation channel.Hence, our proposed model allows a simulation of propagationin a dynamic indoor environment. Results of the simulation showthat this tool gives results in agreement with those reported inthe literature. Specially, the effects of people motion on temporalchannel properties. Other features of this approach also areoutlined

Stochastic and balanced distributed energy-efficient clustering (SBDEEC) for heterogeneous wireless sensor networks

Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Many new protocols are specifically designed for these raisons where energy awareness is an essential consideration. The clustering techniques are largely used for these purposes.In this paper, we present and evaluate a Stochastic and Balanced Developed Distributed Energy-Efficient Clustering (SBDEEC) scheme for heterogeneous wireless sensor networks. This protocol is based on dividing the network into dynamic clusters. The cluster’s nodes communicate with an elected node called cluster head, and then the cluster heads communicate the information to the base station. SBDEEC introduces a balanced and dynamic method where the cluster head election probability is more efficient. Moreover, it uses a stochastic scheme detection to extend the network lifetime. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) and than the Distributed Energy-Efficient Clustering (DEEC) in terms of network lifetime. In the proposed protocol the first node death occurs over 90% times longer than the first node death in DEEC protocol and by about 130% than SEP.Postprint (published version

Traffic Signs Detection and Recognition System in Snowy Environment Using Deep Learning

A fully autonomous car does not yet exist. But the vehicles have continued to gain in range in recent years. The main reason? The dazzling progress made in artificial intelligence, in particular by specific algorithms, known as machine learning. These example-based machine learning methods are used in particular for recognizing objects in photos. The algorithms developed for the detection and identification must respond robustly to the various disturbances observed and take into account the variability in the signs’ appearance. Variations in illumination generate changes in apparent color, shadows, reflections, or backlighting. Besides, geometric distortions or rotations may appear depending on the viewing angle and the panels’ scale. Their appearance may also vary depending on their state of wear and possible dirt, damage. In this work, to improve the accuracy of detection and classification of sign road partially covered by snow, we use the Fast Region-based Convolutional Network method (Fast R-CNN) model. To train the detection model, we collect an image dataset composed of multi-class of road signs. Our model can simultaneously multi-class of a road sign in nearly real-time

No Communication Nodes Synchronization for a Low Power Consumption MAC Protocol in WSN based on IR-UWB

Synchronization is an important issue in multi hops Wireless Sensor Networks (WSN). Such networks are known by their limited resources of energy, storage, computation, and bandwidth. In addition if the networks entities are deployed with high density, it makes the synchronization mandatory for these networks. Impulse Radio Ultra Wide Band (IR-UWB) technology is a promising solution for this kind of networks due to its various advantages such as its robustness to severe multipath fading even in indoor environments, its low cost, low complexity, and low power consumption. To exploit the specific features of this technology, we need a convenient MAC protocol. WideMac was presented as a new low power consumption MAC protocol designed for WSN using IR-UWB transceivers. Because of the luck of synchronization in this protocol, this paper presents a solution for the synchronization problem especially in the case of no communication between the Network’s nodes. To implement and evaluate the proposed synchronization mechanism, we used MiXiM platform under OMNet++ Simulator

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

A Cognitive Radio (CR) is an intelligent wireless communication system, which is able to improve the utilization of the spectral environment. Spectrum sensing (SS) is one of the most important phases in the cognitive radio cycle, this operation consists in detecting signals presence in a particular frequency band. In order to detect primary user (PU) existence, this paper proposes a low cost and low power consumption spectrum sensing implementation. Our proposed platform is tested based on real world signals. Those signals are generated by a Raspberry Pi card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type).  RTL-SDR dongle is used as a reception interface. In this work, we compare the performance of three methods for SS operation: The energy detection technique, the Artificial neural network (ANN) and the support vector machine (SVM). So, the received data could be classified as a PU or not (noise) by the ED method, and by training and testing on a proposed ANN and SVM classification model. The proposed algorithms are implemented under MATLAB software. In order to determine the best architecture, in the case of ANN, two different training algorithms are compared. Furthermore, we have investigated the effect of several SVM functions. The main objective is to find out the best method for signal detection between the three methods. The performance evaluation of our proposed system are the probability of detection and the false alarm probability . This Comparative work has shown that the SS operation by SVM can be more accurate than ANN and ED

Call Admission Control Optimization in 5G in Downlink Single-Cell MISO System

The main goal ofNew Radio 5G (NR) mobile technology is to support three generic service categories, each with very specific requirements. The first category is enhanced Mobile Broadband (eMBB), the second category relates to massive Machine-Type Communications (mMTC), and the third category relates to ultra-Reliable Low Latency Communications (URLLC). The slicing of the radio part of 5G network access network has greatly contributed to the emergence of these three categories of service with different qualities of service. This division therefore enabled the network to reserve the necessary resources for each category of services, orthogonally, and according to the performance required. In this article, we have dealt with the problem of Call Admission Control (CAC) in 5G networks where we have considered the case of the only two categories eMBB and uRLLC, which their users are served by a single cell. We calculated the maximum eMBB users admitted into the system with guaranteed data rate, while allocating power, bandwidth, and beamforming directions to all uRLLC users whose latency requirements and reliability are always guaranteed. We only considered the downlink communication, and we used the case of the multiple-input single-output (MISO) system. This CAC problem is formulated as a minimization problem l0 which is known as NP-hard problem. We therefore chose to use Sequential Convex Programming (SCP) to find a suboptimal solution to the problem

Hybrid Deep Learning Vision-based Models for Human Object Interaction Detection by Knowledge Distillation

People hope that computers can be in constant intelligence development. Just like humans, they can ”see” the world and ”recognize” a visual event. We propose an approach based on computer vision methods to recognize Human-Object interaction(HOI). The technique stands on aggregating significant contextual features Human-Object interactions and scene recognition. We design a branch architecture consisting of the main branch for HOI detection and a supplementary branch for scene recognition. We explore the deep learning models through the knowledge distillation method and the Cross Branch Integration mechanism for encoding models into graph neural network architecture. We construct a knowledge graph to merge between high-level context information. When trained collaboratively, those models allow computing efficiency, strong context knowledge

Parallel Genetic Algorithm Decoder Scheme Based on DP-LDPC codes for industrial IoT scenarios

The new concept of Industry 4.0 has been developed: it includes both Internet of Things (IoT) structure and the local networks that are still needed to carry out real-time tasks. Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances when solving large optimization problems. This article proposes a decoder based on parallel Genetic Algorithms (PGAD) for Decoding Low Density Parity Check (LDPC) codes. The proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency, the best performances are obtained for Ring Crossover (RC) as a type of crossover and the tournament as a type of selection. Furthermore, the performances of the new decoder are improved using Multi-criteria method. For the LDPC code, simulation results showed that our Proposed PGAD exceeds the sum-product by a gain of 1.5 dB at BER = 10-4, and the PGAWS exceeds the sum-product by 2.5 dB

Optimization of UHF RFID five-slotted patch tag design using PSO algorithm for biomedical sensing systems

In this paper, a new flexible wearable radio frequency identification (RFID) five-shaped slot patch tag placed on the human arm is designed for ultra-high frequency (UHF) healthcare sensing applications. The compact proposed tag consists of a patch structure provided with five shaped slot radiators and a flexible substrate, which minimize the human body’s impact on the antenna radiation performance. We have optimized our designed tag using the particle swarm optimization (PSO) method with curve fitting within MATLAB to minimize antenna parameters to achieve a good return loss and an attractive radiation performance in the operating band. The PSO-optimized tag’s performance has been examined over the specific placement in some parts of the human body, such as wrist and chest, to evaluate the tag response and enable our tag antenna conception in wearable biomedical sensing applications. Finally, we have tested the robustness of this tag by evaluating its sensitivity as a function of the antenna radiator placement over the ground plane or by shaping the ground plane substrate for the tag’s position from the human body. Our numerical results show an optimal tag size with good matching features and promising read ranges near the human body

Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products