Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm

Electric vehicles (EVs) are the most important components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are the key barriers to EV adoption. To address the problem of prolonged charging time, the simple approach of developing a new charging station to enhance the charging capacity may not work due to the limitation of physical space and strain on power grids. Prediction of precise EV charging time can assist the drivers in effective planning of their trips to alleviate range anxiety during trips. Therefore, this study employed four different ensemble machine learning (EML) algorithms: random forest, extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. The prediction experiments were based on 2 years of real‐world charging event data from 500 EVs in Japan's private and commercial vehicles. The study emphasized predicting charging time for different charging modes, that is, normal and fast charging operations. The results indicate that EML models performed well under various scenarios, with the XGBoost model having the highest accuracy. Moreover, we also employ the newly developed Shapley additive explanation (SHAP) approach to tackle the non‐interpretability issues of the ML algorithm by interpreting the XGBoost model outputs. The obtained SHAP value plots demonstrated the nonlinear relationship between explanatory variables and EV charging time.

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

A sensitive and fast sandwich-type electrochemical SARS-CoV‑2 (COVID-19) nucleocapsid protein immunosensor was prepared based on bismuth tungstate/bismuth sulfide composite (Bi2WO6/Bi2S3) as electrode platform and graphitic carbon nitride sheet decorated with gold nanoparticles (Au NPs) and tungsten trioxide sphere composite (g-C3N4/Au/WO3) as signal amplification. The electrostatic interactions between capture antibody and Bi2WO6/Bi2S3 led to immobilization of the capture nucleocapsid antibody. The detection antibody was then conjugated to g-C3N4/Au/WO3 via the affinity of amino-gold. After physicochemically characterization via transmission electron microscopy (TEM), scanning electron microscopy (SEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS), cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) analysis were implemented to evaluate the electrochemical performance of the prepared immunosensor. The detection of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP) in a small saliva sample (100.0 µL) took just 30 min and yielded a detection limit (LOD) of 3.00 fg mL−1, making it an effective tool for point-of-care COVID-19 testing.

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Sensitive sandwich-type electrochemical SARS-CoV­2 nucleocapsid protein immunosensor.

Four chaotic maps are used herein as case study to design an embedded cryptosystem based on a pseudo-random number generator (PRNG). The randomness of the sequences is enhanced by applying the m o d 1023 function and verified by analyzing bifurcation diagrams, the maximum Lyapunov exponent, and performing NIST SP 800-22 and TestU01 statistical tests. The PRNG is applied in a simple algorithm for real-time RGB images encryption on a machine-to-machine (M2M) scheme, using message queuing telemetry transport (MQTT) protocol over WiFi network and through Internet. The cryptanalysis confirms that the proposed image encryption scheme is robust to resist most of the existing attacks, such as statistical histograms, entropy, key-space, correlation of adjacent pixels, and differential attacks. The implementation of the proposed cryptosystem is done using enhanced sequences from the Logistic 1D map, and it reaches a throughput of up to 47.44 Mbit/s using a personal computer with a 2.9 GHz clock, and 10.53 Mbit/s using a Raspberry Pi 4. As a result, our proposed embedded cryptosystem is suitable to increase the security in the transmission of RGB images in real-time through WiFi networks and Internet.

Real-time RGB image encryption for IoT applications using enhanced sequences from chaotic maps

Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm—A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches.

/pdf/a-cross-layer-approach-mac-net-with-updated-ga-mnug-cla-28l1ubxr.pdf

A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network

Speaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very difficult because emotions alter the voice characteristics of a person; thus, the acoustic features differ from those used to train models in a neutral environment. Therefore, speaker recognition models trained on neutral speech fail to correctly identify speakers under emotional stress. Although considerable advancements in speaker identification have been made using convolutional neural networks (CNN), CNNs cannot exploit the spatial association between low-level features. Inspired by the recent introduction of capsule networks (CapsNets), which are based on deep learning to overcome the inadequacy of CNNs in preserving the pose relationship between low-level features with their pooling technique, this study investigates the performance of using CapsNets in identifying speakers from emotional speech recordings. A CapsNet-based speaker identification model is proposed and evaluated using three distinct speech databases, i.e., the Emirati Speech Database, SUSAS Dataset, and RAVDESS (open-access). The proposed model is also compared to baseline systems. Experimental results demonstrate that the novel proposed CapsNet model trains faster and provides better results over current state-of-the-art schemes. The effect of the routing algorithm on speaker identification performance was also studied by varying the number of iterations, both with and without a decoder network. 

Emotional speaker identification using a novel capsule nets model

Intelligent reflecting surfaces (IRSs) are considered as a promising candidate enabling technology for future green wireless communication. Such intelligent surfaces have real-time reconfigurable properties that beamform the signal to a desired destination. In this paper, our objective is to study the performance optimization in an IRS-assisted wireless network. An array of reflecting elements is individually configured in IRS system set up, which is installed between the source and the destination to assist the communication between both without any obstacle. IRS-assisted network achieves significant performance gain as compared to wireless networks without IRS. However, there are severe practical challenges with IRSs including hardware impairments, size of IRS, and distribution of reflecting elements in the IRS. To tackle these issues, we analyzed the performance of the network for maximum achievable rates, minimum transmit power, and maximum energy efficiency (EE). The selection of optimal number of reflecting elements in the IRS enables huge savings in energy to achieve target rate with minimal power. We show that by incorporating optimal number of reflecting elements in IRSs, high EE can be achieved, which outperforms the direct transmission method. Moreover, we show the impact of increasing the number of reflecting elements on the trade-off between EE and achievable rates.

An optimized scheme for energy efficient wireless communication via intelligent reflecting surfaces

This paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.

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Adi Alhudhaif

Papers

Emotional speaker identification using a novel capsule nets model

An optimized scheme for energy efficient wireless communication via intelligent reflecting surfaces

Automatic Arrhythmia Detection Based on the Probabilistic Neural Network with FPGA Implementation

Hand gesture recognition framework using a lie group based spatio-temporal recurrent network with multiple hand-worn motion sensors

Artificial general intelligence-based rational behavior detection using cognitive correlates for tracking online harms