Long-range wide area network (LoRaWAN) is a promising low-power network standard that allows for longdistance wireless communication with great power saving. LoRa is based on pure ALOHA protocol for channel access, which causes collisions for the transmitted packets. The collisions may occur in two scenarios, namely the intra-spreading factor (intra-SF) and the inter-spreading factor (inter-SF) interference. Consequently, the SFs assignment is a very critical task for the network performance. This paper investigates a smart SFs assignment technique to reduce collisions probability and improve the network performance. In this work, we exploit different architectures of artificial neural networks for detecting collisions and selecting the optimal SF. The results show that the investigated technique achieves a higher prediction accuracy than traditional machine learning algorithms and enhances the energy consumption of the network.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication (AmBC) play major roles to enhance spectrum efficiency in wireless communication systems. Besides, the AmBC provides good reinforcement for the current trend toward dispensing batteries for battery-free Internet-of-Things (IoT) devices. In this paper, we propose a symbiotic battery-free IoT system, that exploits the downlink transmission of a NOMA multiplexing enabled cellular network, to permit an IoT spectrum-efficient uplink communication. The IoT backscatter device (BD) performs a symbiotic radio (SR) relation with the cellular source to power its communication by intelligently reflecting the received power. We derive a closed-form expression of the ergodic capacity (EC) of the BD transmission and tight approximations of the ECs of the cellular source transmission, where all channels undergo Nakagami-m fading. Additionally, we validate the analytical results obtained using Monte-Carlo simulations. The influences of several system parameters such as power allocation factor, reflection coefficient, and channels’ severity factors have been investigated. Finally, the performance of the proposed system is compared with a benchmark OMA-based system to highlight the achievable performance improvement.

A document by Zahraa Abdallah . Click on the document to view its contents.

The structural stability of damascene inter- connects for back-end-of-line (BEOL) technologies is an- alyzed using a combination of force-field based molecu- lar dynamics simulations and finite-element modeling. The candidate metals analyzed are- ruthenium, cobalt, tung- sten, and copper. Cohesive traction and normal bonding energy are calculated using force-field based molecular dynamics simulations and then fed as input to a finite- element analysis (FEA) tool, where their dependence on physical dimensions of the interconnect lines is studied. The characteristics of these metals are compared against the characteristics of copper. The parameters studied for the BEOL structures are sidewall angle, aspect ratio, inter- nal stress of the metal, and modulus of elasticity of the di- electric material around the metal at 18 nm pitch. Generally, lower aspect ratio and higher modulus of elasticity of the dielectric results in structures that show a lower tendency to collapse. Intrinsic stress of the metal and side wall angle have a minor impact on the overall stability. Based on this study, cobalt is most stable alternate metal amongst Ru, Co, and W.

Generative algorithms such as GANs are at the cusp of next revolution in the field of unsupervised learning and large-scale artificial data generation. However, the adversarial (competitive) co-training of the discriminative and generative networks in GAN makes them computationally intensive and hinders their deployment on the resource-constrained IoT edge devices. Moreover, the frequent data transfer between the discriminative and generative networks during training significantly degrades the efficacy of the von-Neumann GAN accelerators such as those based on GPU and FPGA. Therefore, there is an urgent need for development of ultra-compact and energy-efficient hardware accelerators for GANs. To this end, in this work, we propose to exploit the passive RRAM crossbar arrays for performing key operations of a fully-connected GAN: (a) true random noise generation for the generator network, (b) vector-by-matrix-multiplication with unprecedented energy-efficiency during the forward pass and backward propagation and (C) in-situ adversarial training using a hardware friendly Manhattan’s rule. Our extensive analysis utilizing an experimentally calibrated phenomological model for passive RRAM crossbar array reveals an unforeseen trade-off between the accuracy and the energy dissipated while training the GAN network with different noise inputs to the generator. Furthermore, our results indicate that the spatial and temporal variations and true random noise, which are otherwise undesirable for memory application, boost the energy-efficiency of the GAN implementation on passive RRAM crossbar arrays without degrading its accuracy.

Wireless Neural Interfaces are minimally invasive untethered links between the brain tissue and silicon platforms. Even though these interfaces have been envisioned for many biomedical applications, it is unclear how the ultimate technology will be, due to the lack of studies that support spatially distributed networks. In this paper, we contribute to the first step towards a networked wireless neural interfaces scenario by addressing the distributed power allocation through adaptable beamforming by varying the acoustic beam lateral resolution. Our technique provides selectivity and depthness of neural interface nodes inside the Brain's neocortex. Our results show improvements in average power transfer efficiency for sparser beams compared to narrower ones for a randomly placed network of implantable devices with 15 nodes within a 4mm2 space in the neocortex. Lateral resolution indeed achieves selective of the devices, the average efficiency was non-constant throughout the network allowing devices to operate in different time scales suitable for asynchronous networks. Enabling a functioning network is essential for providing an impactful interface with the Brain to allow future usage of this technology in a diverse set of neurological diseases theranostics.

Cybersecurity researchers always ignore the “people” behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— “How to solve cyberspace security issues via social engineering?” We briefly review existing frameworks for social engineering attacks and pro?pose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four “guidelines” for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field.

In the past few years, 3D imaging technology has received a lot of attention. Time-of-Flight (ToF) cameras based on the indirect ToF principle calculate depth information by measuring the phase shift between the emitted periodic signal and the reflected signal. In this work, we aim to study the effect of dirt on 3D imaging systems quantitatively. To this end, ten sheets with different transmittances, between 50% and 100% are used. To the best of our knowledge, for the first time we provide standardized methods for evaluating the effect of dirt on protective covers in the depth estimation of “flash lidar”. The experiments have shown that the depth measurements obtained using the multi-frequency estimation method are more accurate in the presence of dust than the single-frequency estimation method.

The performance of a classification model significantly depends on the degree to which the support of each data class overlaps. Successfully distinguishing classes is difficult if the support is similar while classes differ. In the one-class classification (OCC) problem, wherein the data comprise only a single class, classifier performance is significantly degraded if the population support of each class is similar. In this study, we propose a preprocessing algorithm that enhances classifier performance by utilizing macro information that is most easily obtainable in these two problem situations. The algorithm aims to improve classifier performance by reprocessing the given data into data with mitigated class imbalance through raking and sampling techniques. This improvement in performance is demonstrated by comparing representative classifiers used in the existing OCC problem with traditional binary classifier models, unavailable on the single-class dataset.

Abstract—In this contribution a novel dielectric radio frequency (RF) applicator cell with an open aperture exceeding the beam waist for terahertz (THz) transmission is presented. Dielectric heating equations are shown that explain the relation of losses and temperature of the material under test. Next to fundamental considerations, we are presenting 3D-EM simulations and measurement results of a manufactured RF applicator cell that shows excellent properties within the frequency range from 10MHz to 1 GHz.

In this work, we examine the potential of Physical Unclonable Functions (PUFs) that have been implemented on NAND Flash memories using programming disturbances to act as sustainable primitives for the purposes of lightweight cryptography. In particular, we investigate the ability of such PUFs to tolerate temperature and voltage variations, and examine the current shortcomings of existing NAND-Flash-memory PUFs that are based on programming disturbances as well as how these could potentially be addressed in order to provide more robust and more sustainable security solutions. This work was accepted for and presented at the Workshop on Sustainability in Security, Security for Sustainability, which took place on 18 March 2022 and was co-located with the 25th Design, Automation and Test in Europe Conference & Exhibition (DATE 2022).

Rest tremor is a most common symptom of Parkinson’s Disease (PD), with diagnosis and severity estimation often being hindered by subjectivity and limitations of existing methods. Additionally, treatment strategies for PD face challenges in monitoring symptom fluctuations with clinical methods, like the MDS-UPDRS and motor diaries, suffering from subjectivity, limited sensitivity, and infrequent sampling, potentially impacting treatment effectiveness. Hence, methods that can yield explainable biomarkers that accurately describe properties of PD rest tremor, while accounting for the presence of ongoing treatments, such as Deep Brain Stimulation (DBS) and medication, are important for integration in accurate AI-powered wearable systems. To that end, a Higher Order Spectrum (HOS)-based analysis to extract features from index finger velocity recordings of 16 PD patients is proposed. Two different scenarios are implemented for characterizing and classifying treatment (medication/DBS) effectiveness and tremor severity (Low-/High- Amplitude (LAT/HAT)), by means of statistical tests and a leave-one-subject-out cross-validation classification scheme, respectively. The proposed analysis resulted in area under the Receiver Operating Characteristics curve (AUC) score of 0.94 for the Medication treatment classification, 0.71 for the DBS treatment and 0.83 for LAT/HAT prediction. Our results demonstrate that the proposed approach can effectively assess the influence of medication and DBS and recognize rest tremor severity. Finally, our HOS-based methodology enables the establishment of new rest tremor classes, based on its nonlinearity and allows for new insights about the dynamic nature of the resting tremor production system.

Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMGsignals as part of the control strategy have increased in the last decade. In this review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched from July-September 2020: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,327 papers were initially assessed and 117 were finally included in this review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated large variations were found across studies between the different control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

In many sea-exploration scenarios, a wireless optical communication link between an onshore station and a floating marine structure or between a floating buoy and an underwater vehicle is required. In order to establish high-quality wireless optical communication, the effect of sea-waves must be taken into consideration. In this work, we investigate the influence of surface water waves having various amplitudes, frequencies and waveforms on the quality of an analog speech signal transmitted over a wireless optical communication link. Such investigation is also carried out on an encrypted speech signal. The quality of the speech signals is determined by utilizing a deep learning neural network that is trained to recognize ten speech commands and produces classification probabilities. In our experiments, either the detector or the light source is non-stationary as a result of the water-waves, thus distorting the received analog signal. We also show a method of deriving the frequency of the water waves by using a remote tracking unit based on micro-electro-mechanical systems (MEMS) mirrors.

This is app based gaming tournament platform. In this paper, We introduce Esports is a type of video game sport competition that is frequently arranged into multiplayer video games, especially amongst professional players, either individually or as a team.

Includes synthetic data processing and real data (IRIS community Wavefield experiment) processing examples.

Delayed signal cancellation (DSC) is a popular method to separate the fundamental frequency positive- and negative-sequence components from unbalanced three-phase signals. Conventional DSC can separate the sequence components using a quarter-cycle delay. Fast DSC tools can achieve the same with less than a quarter-cycle delay. However, neither conventional nor fast DSC can handle DC offset without requiring additional delayed signals. This letter addresses this issue by proposing a modified DSC to estimate the sequence components with DC offset rejection and having arbitrarily fast convergence speed, i.e., low memory requirement. Two equidistant delayed samples of the measured grid voltages/currents are required to implement the proposed technique and can easily be applied in a phase-locked loop (PLL). Comparative experimental results demonstrate the suitability of the proposed approach over other DSC methods.

GatePass Generation and Management System is a software mobile application. The Project “Gate Pass Generation and Management System” is itself tells that is to generate gate pass for regular and for visitor, students, teachers, employees and customers. It maintains the record of visitor and various activities of the user in the system. This can be used to replace our manual visitor process and then give them passes and management process through our system. Gate pass Generation and Management system is available for use of various visitor, students, teachers, employees and customers and it is easy to use and manage. This system is defined as a security system. It’s main aim to secure colleges from outside visitors, the department and the various working institute. Gate pass Generation and management system help the organization as well as a visitor to manage the Gate passes through QR code generator in easy way