Miracle-dz/UniFAR-FTUs huggingface dataset:
Second-Order Control Volume Finite Element Method for Self-Heating Effects Simulation of Semiconductor Devices

A higher-order control volume finite element method for triangular elements is proposed to simulate the self-heating effects of semiconductor devices. In the scheme, the currents of carrier continuity equations are expressed by the second-order accurate fluxes on the triangle edges incorporating second-order vector basis functions. Although the developed scheme is not monotone in theory, the numerical results reveal that the higher-order discretization approach is exceptionally robust and accurate, and the usage of coarser mesh is allowed compared with the first order method.
An RGB/Infra-Red camera fusion approach for Multi-Person Pose Estimation in low light environments

Enabling collaborative robots to predict the human pose is a challenging, but important issue to address. Most of the development of human pose estimation (HPE) adopt RGB images as input to estimate anatomical keypoints with Deep Convolutional Neural Networks (DNNs). However, those approaches neglect the challenge of detecting features reliably during night-time or in difficult lighting conditions, leading to safety issues. In response to this limitation, we present in this paper an RGB/Infra-Red camera fusion approach, based on the open-source library OpenPose, and we show how the fusion of keypoints extracted from different images can be used to improve the human pose estimation performance in sparse light environments. Specifically, OpenPose is used to extract body joints from RGB and Infra-Red images and the contribution of each frame is combined by a fusion step. We investigate the potential of a fusion framework based on Deep Neural Networks and we compare it to a linear weighted average method. The proposed approach shows promising performances, with the best result outperforming conventional methods by a factor 1.8x on a custom data set of Infra-Red and RGB images captured in poor light conditions, where it is hard to recognize people even by human inspection.
Wide Band Cross Polarization Converting Metasurface Based on Circular Split Rings Resonators

A compact wideband cross polarization conversion metasurface is presented, whose structure consists of double slit circular split ring resonators (CSRRs) imprinted on a metal backed Rogers dielectric substrate. This presented metasurface has the potential for polarization conversion of x- or y-polarized incident waves into their orthogonal counterparts. Cross polarization conversion (CPC) is attained with a ratio of 71% within the 0.5-dB bandwidth, ranging from 8.52 to 17.93 GHz. Simulation results show that the polarization conversion efficiency is approximately equal to 100% in the three studied frequency bands of interest. Moreover, the responses of the proposed metasurface in both the x and y polarizations are the same, which is due to the symmetric structure of the CSRRs. The proposed structure is compact and it has a unit cell size of 0. 32λ0 and thickness of 0. 11λ0 at the center frequency of the CPC bandwidth.
Two-stage DC/DC converter modeling method based on harmonic equivalent circuit

When modeling harmonic state space for complex systems, descriptive equations cannot be written due to the increase of state variables. In this paper, a harmonic model of the sub-module is established to obtain the harmonic equivalent circuit of the entire system and simplify the circuit by using harmonic impedance instead of the periodic switching behavior. Then, a method for determining the fundamental frequency of the harmonic state equation under the background of multi-switching frequency is proposed. The circuit derives a harmonic state space model of a two-stage DC/DC converter. Simulations show that, the harmonic equivalent circuit model has higher accuracy and is of higher value in the study of frequency coupling of multi-stage DC/DC converters in the context of DC distribution networks.
Accurate Stride Length Estimation via Fused Radio and Inertial Sensing

Stride length estimation has various applications, ranging from pedestrian tracking to individual healthcare. It is usually achieved by inertial sensing, which, however, suffers from large errors due to the noisy readings on the low-cost commodity sensors and unconstrained human walking. Different from prior methods that explore inertial sensors only, in this paper, we present a fused radio and inertial sensing design that estimates fine-grained stride length. Our approach incorporates recent advances in WiFi sensing that underpins walking distance estimation at centimeter accuracy from radio signals. We then present a novel step detection algorithm using inertial sensor readings, which not only counts steps but also reports the time information of every detected step. The proposed algorithm then fuses the time-annotated distance estimates and steps to derive the stride length. The evaluation on a large public dataset shows that our step counting algorithm yields an error of 3%. Furthermore, experiments on commodity hardware with eight users demonstrate an error of about 2 cm in stride length estimation.
Knowledge-Based Machine Learning Boosting for Adversarial Task Detection in Mobile Crowdsensing

Mobile Crowdsensing (MCS) leverages Sensing as a Service paradigm to contribute to the Internet of Things ecosystems through non-dedicated sensing capabilities of smart mobile devices. Distributed and non-trusted nature of MCS systems are vulnerable against various threats for the devices, MCS platforms, as well as the participating devices that provide sensory data services. Out of the many threats, submission of fake tasks may lead to drained resources at the participating devices, and clogged sensing server resources at MCS platforms. In this paper, classical machine learning (ML) performance is boosted by knowledge-based methods and sequential feature selection which is proposed for the first time against fake tasks submission to MCS platforms. Prior Knowledge Input and Prior Knowledge Input with Difference exploit AdaBoost and Decision Tree methods as initial accuracy to improve the accuracy of learning the legitimacy of submitted tasks to MCS platforms. Moreover, Sequential Feature Selection is implemented to investigate further improvements for the detection of task legitimacy in MCS campaigns. Intelligently selected 5 features amongst 10 possible features and implementation of knowledge-based methods boost the accuracy of machine learning performance from 93.67% to 97.37% for AdaBoost, and from 92.28% to 97.58% for Decision Trees.
Exemplifying Computational Thinking Scenarios in the Age of COVID-19: Examining the Pandemic's Effects in a Project-Based MOOC

The rapid, global spread of COVID-19 has led to an unprecedented rise in enrollments in online learning experiences among learners of all ages. In this article, we explore the impact of the global pandemic on a massive open online course, Problem Solving Using Computational Thinking, with a particular focus on the topics learners chose for their final projects. The Computational Thinking MOOC was designed using a project-based learning approach and aims to provide learners with an introduction to the “big ideas” of computational thinking using a range of case studies that encompass topics such as airport surveillance, epidemiology, and human trafficking. Beyond observing a sharp increase in enrollment and engagement at the time the pandemic began, we discuss ways in which the course's project-based pedagogy allowed learners to bring their present experiences and concerns together with the course's subject matter in order to meet the learning objectives for the course. Many learners chose to address aspects of the pandemic in the course's final project and applied ideas about computational thinking to peer-graded assignments that conveyed an individualized sense of importance and urgency. We assert that this approach, along with the inclusion of a timely epidemiology case study, enabled learners to more deeply internalize the role that computational thinking can play in their own lives and in society as a whole.
High-Dimensional Variability Analysis via Parameters Space Partitioning

A stochastic collocation-based method is proposed for variability analysis in the presence of relatively large number of stochastic parameters. The method is based on the node tearing concept where the original space of stochastic parameters is decomposed into a combination of lower dimension subspaces. Pertinent numerical results are presented to validate the efficiency and accuracy of the proposed technique.
Adaptive Sensing Algorithm for IoT Applications with Data and Temporal Accuracy Requirements

This paper proposes an adaptive sensing algorithm for long-term IoT applications. The objective is to satisfy data and temporal accuracy requirements while prolonging the lifetime of battery-powered devices with energy-hungry transmission modules. The algorithm is based on the Send-on-Delta (SoD) technique combined with a GM(1,1) prediction and considers a moving temporal window and outliers removal. Numerical results show the superiority of our algorithm with respect to a linear approximation. The effectiveness of the proposal is demonstrated in terms of adaptability, accuracy, and reduction of data transfer. This is of particular relevance for applications requiring long sensing periods and high sampling rate.
Learnings from the Swiss Second-Hand Car Market for E-Mobility

E-mobility market adoption has yet to cross the chasm between the market groups of early adopters and early majority. This paper uses the innovative approach of analyzing the Swiss second-hand car market to quantify the success of e-mobility. Firstly, we gather data from the largest Swiss e-commerce platform to derive car usage patterns. Secondly, we deduce a technical metric to qualify the functioning of the market. Thirdly, the patterns are used to quantify the electric car market and outline success factors for e-mobility. The results show that for e-mobility, crossing the chasm is brand-dependent. Only some car brands have succeeded so far.
Virtual Reality based Telerobotics Framework with Depth Cameras

This work describes a virtual reality (VR) based robot teleoperation framework which relies on scene visualization from depth cameras and implements human-robot and human-scene interaction gestures. We suggest that mounting a camera on a slave robot’s end-effector (an in-hand camera) allows the operator to achieve better visualization of the remote scene and improve task performance. We compared experimentally the operator’s ability to understand the remote environment in different visualization modes: single external static camera, in-hand camera, in-hand and external static camera, in-hand camera with OctoMap occupancy mapping. The latter option provided the operator with a better understanding of the remote environment whilst requiring relatively small communication bandwidth. Consequently, we propose suitable grasping methods compatible with the VR based teleoperation with the in-hand camera. Video demonstration: https://youtu.be/3vZaEykMS_E.
An Interval Power Flow Method based on Linearized DistFlow Equations for Radial Distribution Systems

With consideration of the radial structure of most distribution systems, this paper proposes an interval power flow method based on linearized Distflow equations. In the proposed method, the calculation of interval power flow solution has been modeled as an optimization problem, and the uncertainties derived from the randomness of distributed renewable resources as well as electric vehicle loads are properly involved. The proposed method is more high-efficiency than the existing methods since its linear property, and the 33-bus distribution system as well as a practical urban distribution system is employed to verify its effectiveness.
Randomized View Reconciliation in Permissionless Distributed Systems

In a <italic>sybil attack</italic>, an adversary creates many fake identities/nodes and have them join the system. Computational puzzles have long been investigated as a possible sybil defense: nodes that fail to solve the puzzle in time will no longer be accepted by other nodes. However, a malicious node can behave in such a way that it is accepted by some honest nodes but not other honest nodes. This results in different honest nodes having different <italic>views</italic> on which set of nodes constitute the system. Such <italic>view divergence</italic>, unfortunately, breaks the overarching assumption required by many existing security protocols. Partly spurred by the growing popularity of Bitcoin, researchers have recently formalized the above <italic>view divergence</italic> problem and proposed interesting solutions (which we call <italic>view reconciliation protocols</italic>). All existing view reconciliation protocols so far have a similar <inline-formula> <tex-math notation="LaTeX">$\Theta (N)$ </tex-math></inline-formula> time complexity, with <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> being the number of honest nodes in the system. As this paper’s main contribution, we propose a novel view reconciliation protocol whose time complexity is only <inline-formula> <tex-math notation="LaTeX">$\Theta ({\ln N}/{\ln \ln N})$ </tex-math></inline-formula>. To achieve such an exponential improvement, we aggressively exploit randomization. The hidden constant factor in the asymptotic complexity of our protocol, however, is considerably larger than in previous protocols. Concrete numerical comparisons show that our protocol is more suitable for large-scale systems, while existing protocols are better for smaller-scale systems.
Optimal Design of an Axisymmetric Electromagnetic Actuator

This paper presents a detailed procedure for the optimization of the electromagnetic actuator. Based on the accurate electromagnetic field distribution obtaining from the finite element analysis, the electromagnetic force on the mover is accurately computed with the Maxwell stress tensor method. With accurately obtained electromagnetic force, the particle swarm optimization method is applied to achieve the maximum electromagnetic force under the geometry and current density limitations. Two different optimized candidates are obtained and it is shown that they are fit for different applications.
Tradeoff Between Ergodic Energy Efficiency and Spectral Efficiency in D2D Communications Under Rician Fading Channel

This paper aims at investigating the tradeoff between ergodic energy efficiency (EE) and spectral efficiency (SE) for device-to-device (D2D) communications underlaying cellular networks. Assuming average-based resource allocation, we propose a multiobjective optimization problem (MOOP) approach to deal with the EE and SE maximization problem. Specifically, the formulated MOOP results in the maximization of the total ergodic sum rate of the D2D users and the minimization of the total average transmit power of the D2D transmitters. Two practical scenarios are considered, i.e., a limited interference scenario, which includes the sparsely deployed scenario, and a densely deployed scenario, where for each scenario the maximum achievable EE and SE are derived. Specifically, considering the limited interference scenario, an upper bound is imposed on the received interference across different D2D communications, and closed-form expressions for the optimal power allocation, ergodic sum rate, and maximum EE are obtained. In the general, densely deployed scenario, however, the cross D2D network interference needs to be taken into account. We provide an optimal solution for the power control based on sequential fractional programming to tradeoff between complexity and performance gain. Simulation results unveil an interesting tradeoff to strike a balance between EE and SE.
NARX-Wavelet Based Active Model for Removing Motion Artifacts from ECG

The continuous monitoring of the ECG (Electrocardiogram) signal has proved its potential for early detection of several life-threatening conditions in recent times. So the healthcare professional’s community is strongly emphasizing on developing low cost and efficient real-time ECG monitoring devices for continuous monitoring and predictive detection of life-threatening conditions which can be addressed at an early stage, before its lethal onset. In recent times it is observed that real-time ECG monitoring systems gained popularity due to their practical designs and comfortable attachments which do not interfere with patient/subject mobility as monitoring is performed in their natural environment, so due to motion artifacts or several other artifacts their outputs degrade at the analysis end. This paper addresses a serious issue of motion artifact filtering using a NARX-wavelet active model, which showed significant non-erroneous output as the filtering or de-noising is achieved through wavelet-based sampling rate independent technique and the motion artifacts are addressed by using NARX neural networks based on predictive noise cancellation in active mode.
Tractable Inference and Observation Likelihood Evaluation in Latent Structure Influence Models

Latent Structure Influence Models (LSIMs) are a particular kind of Coupled Hidden Markov Models (CHMMs). Against CHMMs, LSIMs overcome the exponential growth of state-space parameters by considering the influence model for coupled Markov chains. Nevertheless, the exact inference in LSIMs requires exponential complexity. We propose a new recursive formulation to compute marginal forward and backward parameters by <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(T{(NC)}^{2})$</tex-math></inline-formula> instead of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(TN^{\text{2}\,C})$</tex-math></inline-formula> for <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula> channels of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> states apiece observing <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> data points. This formulation is derived systematically and carefully to increase the inference accuracy. Furthermore, a solution is presented for the evaluation problem of LSIMs based on the proposed marginal forward parameter. This solution is essential in statistical multi-channel time-series classification. The results show that the proposed algorithm is generally more accurate and reliable than other existing algorithms. Novelties in deriving the marginal backward parameter plays an important role in this superiority. The Hellinger distance is computed between the proposed and exact forward and one-slice parameters for various simulation scenarios. Distances are small enough, indicating that the proposed inference algorithm is sufficiently close to exact inference for various channels, hidden state numbers, and other parameters. Statistical multi-channel time-series classification is also considered for both proposed and exact algorithms. Classification results are almost similar, indicating that the proposed approximate inference is proper and acceptable in the classification task. Finally, the iEEG dataset's parameter learning indicates that the proposed inference algorithm leads to a higher log-likelihood than the existing algorithms.
A Multivariate Time Series Prediction Schema based on Multi-attention in recurrent neural network

In the past decades, various approaches have been proposed to address the time series prediction problem, among which nonlinear autoregressive exogenous (NARX) models achieve great progresses in one-step time prediction. Although NARX models are capable of capturing long-term dependence of the time series data, the impact of associated attributes lacks enough attention. To cope with this issue, in this paper we propose a Multi-Attention algorithm based Recurrent Neural Network (RNN) to perform multivariate time series forecasting. In the first stage, given a raw multivariate time series segment, we obtain both relevant encoder hidden state and encoder hidden state of the associated attribute by employing input-attention and self-attention respectively. In the second stage, we use temporal-convolution-attention neural network to process the encoder hidden states and capture long-range temporal patterns. Finally, extensive empirical studies tested with four real world datasets (NASDAQ100, SML2010, Gas Sensor Array Temperature Modulation and Air Quality) demonstrate the effectiveness and robustness of our proposed approach.
NeuralPot: An Industrial Honeypot Implementation Based On Deep Neural Networks

Honeypots are powerful security tools, developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network attracting malicious activity and securing the rest of the network entities. Since an increase in intrusions has been observed lately, more advanced security systems are necessary. In this paper a new method of adapting a honeypot system in a modern industrial network, employing the Modbus protocol, is introduced. In the presented NeuralPot honeypot, two distinct deep neural network implementations are utilized to adapt to network Modbus entities and clone them, actively confusing the intruders. The proposed deep neural networks and their generated data are then compared.
A Design Flow for Synthesis of Quasi Delay Insensitive Combinational Circuits

Nowadays, synchronous digital circuits design in MOS technology can cause difficulties, this is due to the clock signal. The asynchronous paradigm presents interesting features focused on to solve the clock signal problem. An important class of the asynchronous paradigm is the known Quasi Delay Insensitive (QDI) circuits that can also be used for critical applications, because they are robust to temperature variations, to wire and gate delays, and to supply voltage variation, having low electromagnetic emissions and low noise. QDI combinational circuits are designed as a block of functions whose indicability shows the robustness of the circuit communication with the environment. This paper presents an architecture based on basic gates that do not use C-elements to implement QDI functions. In addition, an approach to the synthesis of QDI function blocks and a disjoint logic minimization method (DSOP) for simple output. The new architecture as the DSOP method has been tested for a set of benchmarks. We can highlight the area reduction of up to 64.3% compared to literature QDI architectures.
Enabling DSRC and C-V2X Integrated Hybrid Vehicular Networks: Architecture and Protocol

Emerging Vehicle-to-Everything (V2X) applications such as Advanced Driver Assistance Systems (ADAS) and Connected and Autonomous Driving (CAD) requires an excessive amount of data by vehicular sensors, collected, processed, and exchanged in real-time. A heterogeneous wireless network is envisioned where multiple Radio Access Technologies (RATs) can coexist to cater for these and other future applications. The primary challenge in such systems is the Radio Resource Management (RRM) strategy and the RAT selection algorithm. In this article, a Hybrid Vehicular Network (HVN) architecture and protocol stack is proposed, which combines Dedicated Short-Range Communication (DSRC) technology-enabled ad hoc network and infrastructure-based Cellular V2X (C-V2X) technologies. To this end, we address the design and performance evaluation of a distributed RRM entity that manages and coordinates Radio Resources (RR) in both RATs. Central to distributed RRM are adaptive RAT selection and Vertical Handover (VHO) algorithms supported by two procedures. (1) Measurement of Quality of Service (QoS) parameters and associated criteria to select the suitable RAT according to the network conditions. (2) Dynamic communication management (DCM) via implementing RR-QoS negotiation. The simulation results show the effectiveness of the proposed architecture and protocol suite under various parameter settings and performance metrics such as the number of VHOs, packet delivery ratio, and throughput, and latency.