Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of EEG signals based on time-frequency analysis and spiking neural network

Electroencephalogram (EEG) is one of the most effective and essential tools for analyzing and diagnosing epilepsy. However, there is a challenging task for detecting seizures from EEG signals, which is due to the non-stationary nature of EEG signals. This paper proposes a novel automatic EEG signal recognition method to assist epilepsy detection. Specifically, the multi-wavelet basis function (MWBF) expansion method is first adopted to construct a time-varying autoregressive (TVAR) model of EEG signals, and a robust ultra-orthogonal least squares (UOLS) algorithm aided by derivative information of EEG signals is then utilized for model structure detection; besides, the power spectral density (PSD) estimation method is applied to extract high-resolution time-frequency features; particularly, to fully exploited the spatiotemporal information of the extracted features, features were fed into the spiking neural networks (SNN) for classification. Experimental results on a widely-used benchmark dataset show that proposed methods outperform other related methods in terms of classification performance.
QCNN Inspired Reconfigurable Keyword Spotting Processor With Hybrid Data-Weight Reuse Methods

The keyword spotting (KWS) system is one of the most important interfaces between humans and machines since it is usually the start of automatic speech recognition and natural language processing techniques. However, for KWS hardware, it is still a problem to make one specified chip both low power and high performed under multiple scenarios, such as in meeting rooms, on different traffic or in parks and so on, for different scenarios own wide range signal-noise-ratios (SNRs). The problem leads to the requirements of balanced design between KWS system accuracy and the hardware cost under various noise types and levels. To overcome the balanced design and tradeoff problems, a complete KWS processor including an Mel-Frequency Cepstrum Coefficients (MFCC) feature extractor and a quantized Convolutional Neural Network (QCNN) accelerator is proposed for wide SNR range and low-power KWS in this paper. Firstly, the approach to quantize CNNs into QCNNs with high accuracy is proposed with considerations of hardware-software tradeoff. With the tradeoff of KWS system accuracy and hardware cost, the 4bit/8bit dual-working-mode strategy is proposed to keep low hardware cost and high accuracy under different scenarios. To be specific, the training, tuning and validating of the CNNs and QCNNs are taken with the dataset of 10 keywords chosen from the Google Command Speech Dataset (GCSD). Secondly, a serial FFT based MFCC extractor is implemented with low power and small footprint. Finally, with a novel hybrid reuse strategy of input data and network weight, a reconfigurable and approximate computing based QCNN accelerator is designed. Implemented and verified under TSMC 22nm ULL technology, with the area of $1.42mm^{2}$ , the QCNN accelerator can achieve $5.26~\mu \text{W}/9.08~\mu \text{W}$ power consumption in 4bit/8bit work mode with accuracy of 88% and 93% respectively, which is superior to the state-of-the-art processors.
3D Human Pose Estimation With Generative Adversarial Networks

3D human pose estimation from a monocular RGB image is a challenging task in computer vision because of depth ambiguity in a single RGB image. As most methods consider joint locations independently which can lead to an overfitting problem on specific datasets, it’s crucial to consider the plausibility of 3D poses in terms of their overall structures. In this paper, we present Generative Adversarial Networks (GANs) for 3D human pose estimation, which learn plausible 3D human body representations by adversarial training. In GANs, the generator regresses 3D joint positions from a 2D input and the discriminator aims to distinguish the ground-truth 3D samples from the predicted ones. We leverage Graph Convolutional Networks (GCNs) in both generator and discriminator to fully exploit the spatial relations of input and output coordinates. The combination of GANs and GCNs promotes the network to predict more accurate 3D joint locations and learn more reasonable human body structures at the same time. We demonstrate the effectiveness of our approach on standard benchmark datasets (i.e. Human3.6M and HumanEva-I) where it outperforms state-of-the-art methods. Furthermore, we propose a new evaluation metric distance-based Pose Structure Score (dPSS) for evaluating the structural similarity degree between the predicted 3D pose and its ground-truth.
GDTF: Generalized Detection Theoretic Framework for T-Wave Alternans Analysis

Detection and estimation of t-wave alternans (TWA) in presence of indispensable physiological artifacts is still a challenging task, as in most of the cases, the signal of interest resides well below the noise levels. In this paper, a generalized detection theoretic framework (GDFT) is proposed for the detection and estimation of TWA from the stress test ECG signal. The analytical foundations, TWA signal modeling, and finally simulations of nine TWA detectors and estimators belonging to median match filtering, empirical mode decomposition (EMD) based match filtering, and generalized likelihood ratio test (GLRT) for GDTF are presented. GLRT schemes require noise statistics for parameter estimation and are computationally efficient. GLRT detectors outperform all the detectors including the benchmark spectral method by ≥ 2 dB for a broad spectrum of SNR ranging from −15 dB to 30 dB under Gaussian noise. EMD based strategies also outperform spectral method under Gaussian and Laplacian noise by ≥ 1 dB.
Visual Analytics Indicators for Mobility and Transportation

Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.
Physical-Layer Security for Mobile Users in NOMA-Enabled Visible Light Communication Networks

In view of the secrecy requirements of visible light communication (VLC) and the massive connectivity demand of future communication, this work investigates physical-layer security (PLS) for a VLC network based on nonorthogonal multiple access (NOMA). To date, however, research on PLS in NOMA-enabled VLC networks considers only users in a static state. The movements of users make it imperative to dynamically allocate optical access points (OAPs) and transmit power to mobile users (MUs). Such a resource allocation problem is transformed into the problem of dynamically allocating power in this paper. Thus, joint secure communication and power allocation optimization is formulated to maximize the network secrecy performance in each time frame subject to the constraint of the maximum power of OAPs and the constraint of power allocation based on NOMA among the associated MUs at each OAP. The formulated joint optimization problem is generally nonconvex because the logarithmic subtraction operation exists in the secrecy capacity, and we cannot directly find the optimal solution. A hierarchical power allocation algorithm is naturally proposed based on an iterative security-aware water-filling approach and the optimality conditions of Karush-Kuhn-Tucker. Convergence and effectiveness are investigated for the presented power allocation algorithms through simulations. The simulation results show that the network sum secrecy capacity depends on the number of MUs, the characteristics of the optical transceiver, and the number of OAPs deployed in the room.
Fundamentals of Integrated Transformers: From Principles to Applications

Magnetic transformers are conventionally used for distributing and converting electrical energy and managing high electrical powers. They are typically large and bulky, such that one may wonder whether they can go along with integrated circuits at all. However, their use in integrated circuits is absolutely ubiquitous [1]-[13]. They are used to implement fully integrated power amplifiers, frequency synthesizers, complete wireless transceivers, and power management circuits. They are indeed very useful, as they offer galvanic isolation to realize ac coupling while also allowing one to implement single-ended to differential conversion and signal or power combining.
Unreined Students or Not: Modes of Freedom in a Project-Based Software Engineering Course

Software engineering courses include practical and theoretical elements that give many options for pedagogical combinations among them. In this paper, we report on two different pedagogical approaches for an undergraduate, introductory project-based software engineering course with more than 500 students working in collaborative scrum teams. We call one approach ‘Every Student is an Innovator’, and the other ‘No Student Left Behind’. This SE course has been long-running, with stable learning objectives and content. However, from one year to another, we radically changed the pedagogical approach of the course along several dimensions, among them the technical framework, software tools, project topic, mentor roles, assessment form and frequency, feedback and degree of student innovativeness. We report on the perceived challenges, detailed changes, the anticipated effects on the course learning outcomes. The results showed that innovativeness and fun need freedom and flexibility with processes and technology. However, strict design requirements and systematic guidance ensure fulfillment of learning objectives. Analyzing student and staff feedback, we find that both approaches lead to students using more time than intended and worrying about unknown assessment criteria.
Invoice Code Recognition System Based on FPGA Acceleration

Aiming at the problem of invoice code identification, this article focuses on the embedded system for identifying invoice codes. FPGA has the characteristics of large data throughput, flexible development, and high real-time performance in image processing. Therefore, based on the DE10- LITE development board, this article mounts an FPGA hardware accelerator in the ARM soft core. An invoice code identification system was built through the method of software and hardware collaborative design. The hardware realizes image data acquisition, preprocessing and other functions, and the software part realizes the function of projection segmentation and recognition. Meanwhile, the different coupling modes of accelerator in the heterogeneous system are compared with each other. The verification results on the hardware development platform show that the system could quickly and accurately identify the invoice code.
Optical Properties of Silicon Nanowhiskers Obtained by Metal-Assisted Chemical Etching under Gamma Irradiation

The results of a study of the optical properties of porous silicon nanostructures obtained by metal-assisted chemical etching of γ-quanta irradiated with low doses during their formation are discussed. The total reflection from the obtained samples depends not only on the irradiation dose of the forming layer, but also on the dose of preliminary irradiation of the initial silicon wafer by in situ control. By studying the Raman spectra of the samples, it was confirmed that changes in the spectra take place both due to the effect of small doses of γ-quanta on the initial silicon wafers and due to the in situ effect of gamma radiation on the forming SiNWs. We observed a shift in the wavelength of the photoluminescence maximum from 600 nm to 750 nm with a change in the irradiation of the initial silicon wafer from 0 to 40 kR at the same dose of irradiation of the forming porous layer.
Induction Motor Fault Diagnosis Using Graph-Based Semi-Supervised Learning

In this paper, a graph-based semi-supervised learning (GSSL) method is proposed for fault diagnosis of direct online induction motors using stator current and vibration signals. A 0.25 HP induction motor under healthy, single- and multi-fault conditions is tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the motor are recorded simultaneously under steady-state operation in each test. Features for machine learning are extracted from the raw experimental stator current and vibration data using the discrete wavelet transform (DWT). Three GSSL algorithms, local and global consistency (LGC), Gaussian field and harmonic function (GFHF), and greedy-gradient max cut (GGMC), are used in the paper. It is found that both stator current and vibration signals perform well for one individual fault diagnosis using GSSL algorithms, but for classification of a combination of five different faults, the stator current outperforms the vibration signal significantly. Among the three GSSL algorithms, GGMC shows better classification results over LGC and GFHF for both stator current and vibration signals.
Precision Coupon Targeting with Dynamic Customer Triage

Coupon is a powerful tool for promotional marketing and customer targeting. Improving customer conversion effects by optimally allocating coupons to the right customers, however, is a nontrivial task. In particular, offering coupons to all customers may not be cost effective while offering coupons to randomly selected customers may not lead to optimal effects. In this paper, we address challenges in precision coupon targeting with a customer triage framework which would ration coupons precisely to customers who will be the most promising commercial conversions. Specifically, first we use random user-experiments to quantify the coupon effects of our interest. Then, we model the observation from user-experiments to predict the expected coupon effects on all customers. With the predicted coupon effects, we develop a joint decision strategy for customer triage to target customers with the most influential coupons under budget constraints. The joint decision strategy can dynamically balance exploitation and exploration in the decision process spanning multiple decision periods. Implemented on a real-world online takeout service system, our results show significant improvements in comparison with alternative approaches.
Mapping Diversity for High Bandwidth Efficiency CCK in Underwater Acoustic Channels

This paper presents an optimal design of QAM-CCK modulation based on maximizing mapping diversity for ARQ transmission scheme, aiming at improving the unsatisfying performance of QAM-CCK in underwater channels with single transmission. In order to achieve the optimal BER performance, this paper first derives the pair-wise error probability after re-transmission and then obtains the optimal construction of QAM-CCK by minimizing the target function. Simulation results demonstrate that optimum desigend QAM-CCK achieves 2 dB SNR gain compared to randomly selected QAM-CCK when BER equals $10^{-4}$. Furthermore, optimum desigend QAM-CCK achieves 5 dB SNR gain compared to randomly selected QAMCCK when BER equals $10^{-5}$ under tested acoustic channels from UNet06 sea experiments.
Blockchain-Based Verifiable Tracking of Resellable Returned Drugs

An enormous amount of drug supply is regularly wasted due to several reasons related to incorrect prescription, purchase of unnecessary quantities, drug intolerance, allergy, or interactions. On the other hand, these medicines may be needed by patients who cannot afford to purchase them. To address this challenge, we propose a fully decentralized solution that governs the return and redistribution of unused drugs that are fit for usage. Our approach exploits the decentralized blockchain technology, smart contracts, and decentralized storage systems such as the Interplanetary File Systems (IPFS). We develop a system that provides the ability for customers as well as pharmacies to return re-usable, resellable drugs for donation or resale at a lower price. The proposed scheme tracks the production of drugs from manufacturers to traders that subsequently sell them to customers. Unused drugs can be returned after approval by specialized entities and then redistributed. In particular, we present the decentralized system architecture and implement a prototype on a test Ethereum blockchain platform to present the suggested workflow. Further, we provide system evaluation focused on assessing system functionality, performance, execution cost, and security of smart contracts and their robustness. We have also made our smart contracts code publicly available on Github. 1.1https://github.com/MazenDB/ReturnedDrugs
Honeybee Algorithm for Content Delivery Networks

The rapid changes and increase of modern and cloud-ready services “on-demand” increase the utilization of Content Delivery Networks (CDNs) to deliver service and content to end-users efficiently. In order to minimize the communication cost and the average waiting time, it is necessary to send the end-users' requests to the best available servers. In this paper, we design and implement a Honeybee algorithm that adapts quickly to possible servers' downtime to avoid communication delays. We then compare it to other algorithms available in the literature. Finally, the evaluation is performed using various scenarios with networking issues, such as single server failures or natural disasters consisting of multiple server issues.
Path Planning for Solar Rechargeable Aircraft

When designing unmanned aerial vehicles on solar panels, the main criteria is the possibility of continuous flight, since the main area of application of this type of aircraft is flight at low altitudes. Accordingly, there is an urgent problem, already after the design stage, this is the optimal flight planning in order to fulfill the assigned mission. This article will consider approaches to how it is possible to optimize flight trajectory planning based on two large factors: taking into account the impact of meteorological obstacles (for example, such as clouds and rain, where there is a loss of solar energy, strong winds, which require a lot of energy consumption), as well as local correction of the aircraft position with a permissible deviation from the flight path and the use of maneuvering to increase energy production of solar power system. This is accomplished by adjusting the heading angle and altitude.
Content Delivery Networks - Q-Learning Approach for Optimization of the Network Cost and the Cache Hit Ratio

With an increasing demand for web content delivery, it is necessary to optimize the CAPEX and OPEX costs of the Content Delivery Networks. Ideally, all web content to be requested should be stored in local cache nodes at all times. However, the content demand varies across space and time. In this paper, we propose a Content Delivery Network model that allows us to choose the best trade-off between costs and cache hit ratio.
Modelling User Influence and Rumor Propagation on Twitter using Hawkes Processes

Understanding the spread of rumors on online social networks (OSNs) is crucial for designing strategies to detect and mitigate rumor propagation. Previous studies analysing rumor propagation have focused on summarising the static measurements of propagation-based information cascades. But static features are unable to capture the dynamic nature of information propagation across time. In this paper, we employed two generative models, Multivariate Hawkes process (MHP) and marked Hawkes process (marked HP) to model user influence and the dynamics of rumor propagation. Using the MHP model, we were able to derive a novel measurement of user influence in information propagation, namely, the influence rate. We then employed the marked HP model and considered various mark measurements including the proposed influence rate to provide new insights into differentiating between rumor and non-rumor propagation, and among different types of rumor propagation. Our analysis on Twitter rumor datasets clearly showed that users play different roles (i.e., possess different influence rates) across different categories of source tweets. Moreover, different categories of source tweets have different patterns of diffusion. In particular, rumor cascades typically attracted more influential users at the early stage of cascades, and they are more likely to generate more retweets than non-rumor cascades. Among different types of rumors, false rumors diffused faster than true rumors.
A Test of Cross-border Magnetic Induction Communication from Water to Air

In the Underwater Sensor Network (UWSN), the cross-border communication between the underwater sensor carrier and the water data center has important practical significance. At present, under-water acoustic communication and sea surface relay is mostly adopted by the cross-border communication, but the communication performance will be challenged by the complex propagation characteristics of mixed channels near the sea surface. The magnetic induction field can directly propagate across the air-water interface and is insensitive to time-varying, multipath channels. This paper analyzes the propagation characteristics of the seaair transboundary magnetic induction field and develops a transboundary magnetic induction communication test system and conducts a transboundary communication test from underwater to air. The test results show that cross-border magnetic induction communication is feasible and reliable from water to air.
Gamified Internet of Things Testing within a Virtual Learning Environment — towards the Interactive Simulation Game “IoTCityLab”

The number of connected devices increases continually. The Internet is omnipresent in nearly all areas of daily life, both in private and professional spheres. Humans and institutions are especially concerned about security and usability of the large number of connected objects. This is also a big challenge for professionals in the field of software and hardware testing. They are forced to extend their knowledge to provide quality assurance of connected devices and environments. The qualification requirements are constantly changing. For this reason, the virtual simulation game IoTCityLab will be developed. The target audience are both experienced software testing professionals as also students at universities and vocational trainees who plan a career in the Internet of Things domain. It is a multi-player game in which teams act as in real projects while they will gain experience and knowledge through application. The simulation game is composed of several modules on several approaches of Internet of Things testing. In the first game module the players need to set up a test automation environment for testing an autonomous vehicle in an agile team. In each individual game module the focus is on testing of elements which are part of a smart city infrastructure. Each individual game module focuses on a particular stage of development. By playing the game modules, the players have the opportunity to accompany the proceedings in the implementation of a smart city infrastructure and experience testing and quality assurance in this field interactively.