Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensing-Assisted Neighbor Discovery for Vehicular Ad Hoc Networks

In this paper, we propose a sensing-assisted neighbor discovery algorithm that utilizes the sensing capability of radar to improve the efficiency of neighbor discovery for vehicular ad hoc networks (VANETs). To store and manage the sensing information of radar, we design the sensing neighbor list (SNL) by analogy with the communication neighbor list (CNL). For vehicle mobility, we build a vehicle-to-vehicle (V2V) state evolution model and use extended Kalman filtering (EKF) to predict, track, and update the kinematic parameters of nodes, which are stored in the SNL. Specifically, the conversion relationship between CNL and SNL is implemented by the designed SNL based neighbor discovery (SBND) algorithm. Numerical simulation results show that the performance of the proposed algorithm is significant in terms of vehicle tracking and communication overhead reduction.
Flexible Hierarchical Multi-Beam Search for mmWave massive MIMO Systems

In this study, we proposed a hierarchical multi-beam search algorithm along with a hierarchical codebook to efficiently estimate the angle-of-departures (AoDs) and angle-of-arrivals (AoAs) and design the precoder and decoder in millimeter-wave massive MIMO systems with a uniform linear array. Existing hierarchical codebooks were designated for the case with antenna numbers being a power of two. We propose a more flexible codeword-generation algorithm applicable to the case with antenna number equals to any composite numbers. Moreover, we proposed a least-square based refinement to raise the accuracy of the estimated AoDs and AoAs with lower training overhead. It shows through the simulation results that the proposed algorithm achieves a comparable unsuccess rate compared with an existing method, yet demands lower training cost and complexity. It also shows that the gap in spectral efficiency with the proposed multi-beam search algorithm and perfect CSI case is insignificant.
Wi-Nod: Head Nodding Recognition by Wi-Fi CSI Toward Communicative Support for Quadriplegics

Recently, the studies of wireless device-free human sensing technology have dramatically advanced with enabling a variety of applications, from activity recognition to vital sign monitoring. In this paper, we propose Wi-Nod which leverages the Wi-Fi Channel State Information (CSI) to detect head nodding gestures for each Morse code symbol based on time-frequency features for accurate recognition accuracy in multi-human context environment. The system consists of three basic modules: data collection, data preprocessing, and learning part based on the inception model. The model was trained to perform the head movement detection based on the CSI spectrogram collected by the ESP32 nodes. We evaluated the performance of the system on four different data sets collected in two different sessions. Our system achieves over 95% recognition accuracy that reveals the feasibility of Wi-Nod system for real-life deployment.
RALI: Increasing Reliability in LoRaWAN through Repetition and Iteration

For a seamless deployment of the Internet of Things (IoT), lightweight self-organizing solutions are needed to overcome the challenges of IoT, mainly stemming from their constrained nature. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via pure ALOHA-type access. In fact, the conception of LoRaWAN advocates simplicity in order to reduce drastically the battery consumption. This simplicity in the contention access method severely degrades reliability. To address that shortcoming, the adoption of Successive Interference Cancellation (SIC) in conjunction with packet repetition has been lately considered as a promising solution for the particular setting of IoT. However, the proposed solutions were devised for synchronised systems and particular care is needed to fit these solutions for the asynchronous LoRaWAN networks. In this paper, we put forward such a tailored mechanism, coined RALI (Repetition in ALOHA for LoRaWAN with Iteration), assess its performances analytically and demonstrate through extensive simulations its notable efficiency.
Intelligent Reflecting Surfaces-Aided Mixed FSO/RF Communication System

Free space optics (FSO) communication offers various advantages like higher bandwidth, higher data rates, etc., when compared with its counterpart, i.e. radio frequency (RF) communication. Despite that the FSO link is adversely affected due to atmospheric turbulence, obstructions between transmitter and receiver, and pointing errors. In this work, we propose a mixed FSO/RF system based on decode-and-forward (DF) relaying technique consisting of a FSO-based transmitter node (i.e. source), a relay node, which is capable of receiving FSO signal and transmitting RF signal, and an RF-based receiver node (i.e. destination). Furthermore, both FSO and RF links are aided with intelligent reflecting surfaces (IRS). The main goal of the proposed system model is to increase the coverage and overall system reliability of the existing mixed FSO/RF system. Specifically, we investigate the performance of the proposed system by deriving the closed-form expressions for outage probability and average bit error rate (BER) in terms of multivariate Fox’s H-function without using central limit theorem. Moreover, the numerical results are shown along with some insightful discussions. From numerical results, it is deduced that IRS-assisted mixed FSO/RF system performs better than the existing mixed FSO/RF systems without IRS. Finally, analytical results are verified by performing Monte-Carlo simulations.
dToF LIDAR System Using Addressable Multi-Channel VCSEL Transmitter, 128x80 SPAD Sensor, and ML-Based Object Detection for Adaptive Beam-Steering

Light detection and ranging (LiDAR) technology is increasingly being deployed in vehicles for advanced driving assistance system (ADAS) and is a potential critical technology for autonomous driving in the future. Currently, hybrid solid-state LiDARs based on micro-electro- mechanical system (MEMS) or rotating mirrors are the most mature solutions [1]. Unfortunately, these hybrid LiDARs still exhibit high risk of damage due to their limited mechanical reliability in harsh environments across many years of operation. Furthermore, these solutions require highly accurate calibration due to the complex optical alignment, resulting in higher production cost. Compared with these mechanical solutions, solid-state flash LiDARs without any moving parts are easier to mass-produce, more robust, less costly, and exhibit smaller form-factor, allowing flash-LiDAR to be embedded in multiple places around the automobile. Nevertheless, the detection distance of a single-flash LiDAR is short due to the smaller optical power limited by the laser eye-safety requirement, VCSEL thermal heating, and long-term VCSEL reliability [2]. Furthermore, for some applications, the distance of only one or two nearest objects needs to be accurately measured, and therefore empty parts of the field-of-view (FOV) are of limited interest. Unfortunately, single-flash LiDARs capture the point cloud of the entire FOV within each frame, resulting in high power consumption.
An Analysis of Multicasting Optimisation Mechanisms for Intelligent Edge Computing with Low-Power and Lossy Networks

This work studies the built-in multicast model in Contiki OS to provide the basis of a comparative evaluation for a new optimisation model using Radio Duty Cycling (RDC) mechanism. A significant amount of energy is consumed at the edge node executing various multicast routing protocols in Low-Power and Lossy Networks (LLN). The optimisation of the routing protocol and selection of an efficient multicast transmission model has the potential to reduce energy consumption in Edge Computing (EC) enabled LLN. With the precise objective of reducing energy consumption, this paper utilises a well-known RDC technique in multicast communication scenarios. To this end, a series of experiments are conducted to evaluate the performance of the existing RDC mechanisms proposed in the literature. The evaluation results are then utilised to develop an efficient RDC-based multicast transmission model. The comparative performance analysis reveals a 23.7% reduction in the RDC rate compared to the traditional model, consequently improving the energy consumption of EC-enabled LLN.
A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this article, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: Grow-Shrink (GS), Incremental Association MB (IAMB), and Interleaved IAMB (Inter-IAMB). Our implementations are available as part of an open-source software called ramBLe, and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC’21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.
Optimized Inter-Turn Short Circuit Fault Diagnosis for Induction Motors using Neural Networks with LeLeRU

Induction Motor (IM) restoration costs and downtime can be decreased by early Inter-turn short circuit fault (ISCF) detection. Due to the controller’s innate desire to generate an adjusted set of currents actually below fault conditions, fault detection of electric motors driven by an inverter with a model predictive control (MPC) algorithm becomes more difficult in inverter-driven applications. We suggest a novel actuation method in this contribution using the switching sequences produced by the Finite Control Set Model Predictive Controller (FCS-MPC) for ISCF of IM. based on diagnostics from neural networks (NN). Hence, no extra sensors or equipment are required for fault detection. This paper proposes a novel procedure for ISCF fault location of IM based on Neural Networks with Learnable Leaky ReLU (LeLeLU) function.
Spectrum Occupancy Prediction based on adaptive Recurrent Neural Networks

This paper investigates the prediction of spectrum usage by wireless local area networks based on recurrent neural networks (RNNs). The prediction results can be used to enhance the spectrum efficiency in dynamic spectrum sharing, and accuracy of spectrum sensing in cognitive radio networks. Observed time series of duty cycle (DC), which indicates the spectrum usage trend, is utilized to predict the future DC by the RNN. At first, we reveal a drawback of prediction by a single RNN, and this approach is denoted by a conventional approach in this paper. Specifically, the prediction results may have a significant biased error if the observed DCs are biased to either high or low values. For this problem, we propose the DC prediction with two RNNs and each of them designed for high DC case and low DC case, respectively. The prediction algorithm at first identifies the state of current DC either high or low. Then, the RNN for the identified state is performed for an accurate DC prediction. Numerical evaluations based on comprehensive measurement experiments of spectrum usage have presented that the proposed DC prediction can improve the accuracy of DC prediction compared to the conventional approach.
Resonant Based DC-DC Converter for Fast EV Battery Charging Applications Using Novel Adder Architecture - Analysis, Design & Resonant Components Tolerance Effects

Consistent efforts are being made to enable electrification of the transport industry for a cleaner environment. Though there has been substantial increase in electric vehicles (EVs); lack of adequate and quick charging points are proving to be major stumbling blocks to the wide spread adoption of electric vehicles. This paper presents a novel multi-module series connected dual active bridge resonant DC-DC converter with a unique adder configuration for obtaining wide output voltage (150V-920V) for EV battery charging applications. The system is conceived with the ability to connect multiple modules in parallel to facilitate the fast charging. A detailed analysis is performed to select components and also to briefly highlight the adverse effects of component tolerances in these multi-module systems. The manuscript suggests a simple modification in the temporal characteristics of waveform generated by the converter for addressing the component variation problem. A scaled down experimental prototype (voltage range of 42V-223V and power range of 0. 2kW-0.875kW) showing 95.5% peak efficiency is built to demonstrate proof of concept.
Lightweight Wireless Sensing Through RIS and Inverse Semantic Communications

Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.
Enhancing 6 nm CD Patterned Defect Classification with TSOM and CNNs

The rapid growth of the semiconductor industry has made it increasingly critical to detect and classify increasingly smaller patterned defects with high certainty. Machine learning has made significant contributions to this task. However, the literature shows that successfully classifying defect types at 6 nm CD with 100% accuracy using high-volume-manufacturing compatible optical imaging methods is challenging. In this study, we demonstrate that through-focus scanning optical microscopy (TSOM) images, combined with a convolutional neural network (CNN), can enhance the successful classification of patterned defects at a 6 nm CD. We use simulated optical images at 193 nm illumination wavelength for this purpose. Our results show that all eight variations of the defects, including a 3 nm difference in size in one dimension which is over 50 times smaller than the illumination wavelength used, can be classified with 100% accuracy. Depending on the defect type, we observe potential gains in the optical signal of over 200% for the TSOM images compared to the fixed-focus images used conventionally, which may have contributed to the successful classification. These findings suggest that TSOM images in combination with CNN hold promise for defect detection and classification in the semiconductor industry.
Setting Groups-based Overcurrent Relays Coordination Considering Distributed Generations Uncertainties

Distributed generations (DGs) are widely connected to the electrical power grid to mitigate the overall system losses. Each DG contains a number of parallel units. The uncertainty due to planned or forced outage of the DG units changes the generation capacity, leading to overcurrent relays (OCRs) miscoordination in the distribution network. In this paper, new multiple optimal setting groups (SGs) are determined for OCRs that would satisfy protection coordination under various DGs capacity. In the first step, the proposed hybrid method uses k-means to classify different capacities of DGs into some clusters, whose number is equal to the number of SGs. The short-circuit current changes in each cluster; accordingly the OCRs coordination problems is modeled as an interval linear programming in the second stage, and optimal settings are determined for each setting group. The recommended technique is applied to the distribution section of the IEEE 14-bus system with two synchronous DGs. The outputs of the simulations and analysis have been done in PowerFactory and MATLAB software, respectively, indicate optimal coordination of relays for all DGs’ generation levels.
Second Harmonic Generation from Metal Nano-Particle Resonators: Numerical Analysis On the Basis of the Hydrodynamic Drude Model

A detailed computational study of the wavelength-dependent efficiency of optical second-harmonic generation in plasmonic nanostructures is presented. The computations are based on a discontinuous Galerkin Maxwell solver that utilizes a hydrodynamic material model to calculate the free-electron dynamics in metals without any further approximations. Besides wave-mixing effects, the material model thus contains the full nonlocal characteristics of the electromagnetic response, as well as intensity-dependent phenomena such as the Kerr effect. To be specific, two prototypical nanostructures are studied in depth with the help of two independent computer codes. For an infinitely long metal cylinder, it is found that the spectral position of linear particle plasmon modes (dipolar modes, higher-order modes, and, for frequencies above the plasma frequency also bulk plasmon modes) and their associated relative strengths for scattering and absorption both at the fundamental and second-harmonic wavelengths largely con.
The impact of data fragmentation on processing time modeling in semiconductor manufacturing : *Topic: IE: Industrial Engineering

Given their complexity, semiconductor manufacturing processes are highly fragmented. Hundreds of different products are processed by complex machines (so-called cluster tools) under different settings. In their turn, cluster tools are divided into different modules (generally into cassette, process, and transfer modules). Modeling semiconductor manufacturing processes for each of the resulting processing paths (i.e., combinations of modules, machine settings, machines, etc.) becomes intractable. This paper tackles the issue of data fragmentation in modeling the processing times associated with a Chemical-Mechanical Planarization (CMP) machine. Based on a cloud of available observations, the variability of processing times is modeled with different granularities. The distribution characteristics of empirical processing time data are determined via a moment-based approximation. Through an illustrative study conducted on a benchmark dataset, this paper quantifies statistically the value (resp. loss) induced by a fine (resp. macroscopic) representation of the processing times of a CMP machine.
Review and Future Developments of Wound Field Synchronous Motors in Automotive

Electric motors are increasingly spreading in the automotive sector, following the progressive electrification of transportation aimed to reduce CO2 emissions and increase sustainability. Most of the employed motors are permanent magnets (PM) based and these materials are subjected to price fluctuations. In addition, they are not the key for sustainability, especially when referring to rare earth materials. An alternative to the classic PM motors is the wound field synchronous machine (WFSM) which, thanks to the absence of magnets, represents an inherent more sustainable solution. In addition, this machine presents higher controllability and fault tolerance compared to their PM counterpart, which on the other hand features higher torque density and efficiency values. While WFSMs have been widely adopted and studied as power generators in grid-connected and isolated power plants, their use as traction motors is limited and under investigation. This paper aims to resume the state of the art of WFSMs and to analyze the challenges of its employability in the automotive field. Possible future developments, contextualized considering the authors' vision, will be also discussed.
Hybrid Beamforming for Outage-Minimization in Frequency Selective Millimeter-Wave Channels

We propose a hybrid beamforming for coordinated multi-point (CoMP) transmission using orthogonal frequency division multiplexing (OFDM) over millimeter-wave (mmWave) channels to combat random propagation path blockages. In particular, a sum-of-outage-probability minimization problem with manifold constraints is formulated, which designs the hybrid beamformers, the data rate allocation, and the power allocation over subcarriers jointly to meet the prescribed data rate requirement. A new block stochastic learning mechanism exploiting prior knowledge of the path blockages is also introduced to solve the problem efficiently. Numerical results confirm the effectiveness of the proposed approach in minimizing the outage probability of users according to their target rate. Furthermore, these results also show that the proposed hybrid CoMP transmission only with a few radio frequency (RF) chains and knowledge of blockage probabilities achieves comparable outage performance to a fully digital CoMP transmission alternative with perfect knowledge of instantaneous path blockages.
The Study on Cooling Load Forecast of an Unit Building using Neural Networks

The electric power load during the summer peak time is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. The method of forecasting the cooling load using neural network is also suggested. The daily cooling load is mainly dependent on actual temperature and humidity of the day. The simulation is started with forecasting the temperature and humidity of the following day from the past data. The cooling load is then simulated by using the forecasted temperature and humidity data obtained from the simulation. It was observed that the forecasted data were closely approached to the actual data.