Miracle-dz/UniFAR-FTUs huggingface dataset:
From Klein Heidelberg to Modern Multistatic Passive Radar

Passive radar technology is currently entering the maturity stage. The rapid development of ground-based passive radars can be seen around the world. Technology is cheap and reliable, and a further step will be to apply the passive radar to mobile platforms – surface, sea, air-borne and perhaps also space-borne. This paper presents the history of the passive radar, and the present stage of the development of the abovementioned technology in military and civilian applications.
On the Reliability and Flexibility of FPGAs for Fault Tolerance in Sectored Networked Control Systems

As Networked Control Systems grow more complex in industrial applications, moving network modules and cabling decrease overall system reliability. This paper presents an FPGA-based fault tolerance technique to reduce cabling and increase the overall reliability of such a system. A sectored sensor-to-actuator networked control system architecture with sensor-level sift-out modular redundancy is modeled and analyzed. The fault models considered in this study are Single Event Upsets and hard failures. A reliability analysis is then conducted to evaluate the reliability of each block in the system and study the overall system reliability. A generic reliability analysis is presented to investigate the flexibility of the fault tolerance technique and a case study demonstrates the reliability improvements over a system that does not utilize FPGAs and cable reduction.
Novel Measures for Thermal Management of Silicon Photonic Optical Phased Arrays

Silicon photonics has become a promising technology for fabrication of compact, highly integrated systems. When dealing with phase sensitive silicon photonic components, however, temperature control and in particular, reduction of temperature gradients across phase sensitive components becomes crucial. Solid-state optical beam steering using optical phased arrays (OPAs), is an exemplary system, which could be miniaturized by using silicon photonics and where temperature control is of utmost importance. In order to reduce the system cost and size as well as to increase the steering velocity, it is desired to replace mechanical beam steering systems by solid-state steering systems. Due to their compatibility with CMOS fabrication processes, silicon photonics based OPAs are promising candidates to become the next generation beam steering solution. However, due to the large thermo-optic coefficient of silicon, these phased arrays are extremely sensitive to temperature. Thermal management of silicon-photonic based OPAs requires not only heat dissipation of integrated active components, but also reduction of temperature gradients across the phased array. Here a novel concept for thermal management of silicon photonic OPAs is proposed and analyzed using finite-element simulations. The proposed concepts show a formidable efficiency in improving the heat dissipation as well as in reducing temperature gradients across the array.
Teaching Hardware Implementation of Neural Networks using High-Level Synthesis in Less Than Four Hours for Engineering Education of Intelligent Embedded Computing

This paper presents the motivation and design of a mini-course to teach hardware implementation of neural networks using high-level synthesis (HLS) in less than four hours for engineering education of intelligent embedded computing. By standing on the shoulders of giants, the combination of the real-world problem, decoding the process of neural networks hardware design and using HLS as hands-on lab, the students are able to not only pick up the underlying concepts of digital system design naturally but also implement a real working neural networks hardware accelerator in person. Thus, the main contribution of the work is to facilitate the engineering education of hardware design more engaging and practical.
Open-Phase Operating Modes in High Voltage Distribution Networks

The open-phase operation modes which arise during the breakdowns of the wires of overhead power transmission lines, incomplete phase commutations, broken contacts, and for other reasons are not always accompanied by high emergency currents sufficient for the operation of relay protection. Experience of operation shows that in complex electric networks at wire breakage of transmission lines there are operating modes that lead to non-selective operation of relay protection and excessive power cuts. The analysis of processes occurring in open-phase modes, determining dependencies of currents from various factors, and the reaction of relay protection is considered in the article.
FPGA-Based Hardware Implementation of Computationally Efficient Multi-Source DOA Estimation Algorithms

Hardware implementation of the proposed direction of arrival (DOA) estimation algorithms based on Cholesky and LDL decomposition is presented in this paper. The proposed algorithms are implemented for execution on a field programmable gate array (FPGA) as well as a PC (running LabVIEW) for the multiple non-coherent sources located in the far-field region of a uniform linear array (ULA). Prototype testbeds built using the national instruments (NI) universal software radio peripheral (USRP) software defined radio (SDR) platform and Xilinx Virtex-5 FPGA are originally constructed for the experimental validation of the proposed algorithms. The results from LabVIEW simulations and real-time hardware experiments demonstrate the effectiveness of the proposed algorithms. Specifically, the implementation of the proposed algorithms on a Xilinx Virtex-5 FPGA using the LabVIEW software clarifies their efficiency in terms of computation time and resource utilization, which make them suitable for the real-time practical applications. Moreover, the performance comparison with the QR decomposition-based DOA algorithms as well as similar FPGA-based implementations reported in the literature is conducted in terms of the estimation accuracy, computation speed, and FPGA resources consumed.
Application of Big Data in Education Data Mining and Learning Analytics-A Literature Review

The usage of learning management systems in education has been increasing in the last few years. Students have started using mobile phones, primarily smart phones that have become a part of their daily life, to access online content. Student's online activities generate enormous amount of unused data that are wasted as traditional learning analytics are not capable of processing them. This has resulted in the penetration of Big Data technologies and tools into education, to process the large amount of data involved. This study looks into the recent applications of Big Data technologies in education and presents a review of literature available on Educational Data Mining and Learning Analytics.
Optimal Power Flow of a Power System Incorporating Stochastic Wind Power Based on Modified Moth Swarm Algorithm

The combined heat and power (CHP) generator not only generates electrical power but also generates heat energy in a single process, which decreases the emission level significantly. The integration of wind power into the power system will lead to an impact on the economic operation of the system as well as the bus voltage and transmission losses. In this paper, a formulation of optimal power flow (OPF) problem of a power system incorporating stochastic wind power is presented. To solve this problem, a modified version of the moth swarm algorithm (MMSA) is proposed. Three objective functions, which are the minimization of operating cost, the minimization of transmission power loss, and the voltage profile improvement, are considered in this paper. To minimize the operating cost, the direct, overestimation, and underestimation costs of wind power units are considered. Two test systems are considered to prove the effectiveness and the superiority of the proposed MMSA in comparison with other methods. The comparison with other methods proves the efficiency and the superiority of the proposed MMSA.
Low-Noise Integrated Potentiostat for Affinity-Free Protein Detection With 12 nV/rt-Hz at 30 Hz and 1.8 pArms Resolution

This letter presents a low-noise integrated potentiostat for affinity-free molecular detection in applications for personalized medicine. The affinity-free sensing technique uses a digital classifier to identify molecules through unique vibrational signatures. The sensing mechanism relies on coherent interference of electron wave functions at the interface between a nanoscale working electrode and a liquid electrolyte. Coherence at the sensing interface is enabled by low-noise feedback, which reduces the effective temperature of the electrons. The described three-channel potentiostat IC uses chopping and correlated double sampling to achieve an input-referred voltage noise of 12 nV/rt-Hz at 30 Hz and a current resolution of 1.8 pArms with 0.5-s averaging time. Each channel consumes 5 mW and occupies 0.41 mm2 in 65-nm CMOS.
Heterogenic Multi-Core System on Chip for Virtual Based Security

The paper describes the process of coding information in the heterogenic multi-core system on chip for virtual-based security designed For image processing, signal processing and neural networks emulation. The coding of information carried out in assembly language according to the GOST. This is an implementation of the GOST – a standard symmetric key block cipher has a 64-bit block size and 256-bit key size.
Signal-Based Approach to EMG-Sensor Fault Detection in Upper Limb Prosthetics

Electromyography (EMG) signals have been extensively used as a control signal in robotics, rehabilitation and health care. Creating an prosthetic hand requires many steps, beginning with a way for the prosthesis to detect the EMG signal. Unfortunately, signal detection may cause some troubles. Among them there are sensor faults that may lead to serious distortion of the signal and malfunction of the prosthetic hand. This paper is devoted to the development of algorithmic software that allows to detect faults of EMG sensors of prosthetic hand. To solve the problem modules of fault detection and signal correction were developed. These modules not only carefully supervise records from individual EMG electrodes, but, if necessary, replace them in systems if one or more signals are violated. This approach is based on signal processing methods with low computational complexity, that allows to reduce hardware requirements.
Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting

Electric load forecasting has always been a key component of power grids. Many countries have opened up electricity markets and facilitated the participation of multiple agents, which create a competitive environment and reduce costs to consumers. In the electricity market, multi-step short-term load forecasting becomes increasingly significant for electricity market bidding and spot price calculation, but the performances of traditional algorithms are not robust and unacceptable enough. In recent years, the rise of deep learning gives us the opportunity to improve the accuracy of multi-step forecasting further. In this paper, we propose a novel model multi-scale convolutional neural network with time-cognition (TCMS-CNN). At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. In addition, we design an innovative time coding strategy called the periodic coding strengthening the ability of the sequential model for time cognition effectively. At last, we integrate MS-CNN and periodic coding into the proposed TCMS-CNN model with an end-to-end training and inference process. With ablation experiments, the MS-CNN and periodic coding methods had better performances obviously than the most popular methods at present. Specifically, for 48-step point load forecasting, the TCMS-CNN had been improved by 34.73%, 14.22%, and 19.05% on MAPE than the state-of-the-art methods recursive multi-step LSTM (RM-LSTM), direct multi-step MS-CNN (DM-MS-CNN), and the direct multi-step GCNN (DM-GCNN), respectively. For 48-step probabilistic load forecasting, the TCMS-CNN had been improved by 3.54% and 6.77% on average pinball score than the DM-MS-CNN and the DM-GCNN. These results show a great promising potential applied in practice.
An RGBD-SLAM with Bi-directional PnP Method and Fuzzy Frame Detection Module

The core technology to mobile robots is referred to as Simultaneous Localization and Mapping (SLAM). It has been wildly investigated on improving pose estimate accuracy and mapping consistency. This paper proposes several algorithms on the target. Firstly, a fuzzy frame evaluation and filtering strategy based on OpenCV is proposed to prevent fuzzy frames from affecting pose estimation. Secondly, a Bi-directional PnP-RANSAC method is proposed to estimate the pose between frames, in which more matching information is exploited, and the consistency of results can be verified to obtain more accurate pose results. Furthermore, for the consideration of efficiency, a GPU acceleration algorithm named SiftGPU [1] is applied in the feature extraction and matching process to improve real-time performance. Finally, a complete RGB-D SLAM framework is built to realize the above algorithms. Experiments have been conducted in the lab environment and dataset [2], producing global consistent dense 3D environment representation and accurately estimated trajectory, demonstrating the effectiveness of the proposed methods.
Une approche interactive pour la transcription rythmique dans OpenMusic

Nous presentons un systeme pour la transcription rythmique integre dans l'environnement de composition assistee par ordinateur OpenMusic. La transcription rythmique consiste a traduire une suite d'evenements dates vers une representation pulsee et structuree liee a la notation musicale traditionnelle. Notre systeme privilegie les interactions avec l'utilisateur afin de trouver un equilibre satisfaisant entre differents criteres, en particulier la precision de la transcription et la lisibilite des partitions produites. Ce systeme s'appuie sur une approche uniforme, utilisant des representations hierarchiques de la notation de durees sous forme d'arbres de rythmes, et des algorithmes efficaces pour l'enumeration paresseuse de solutions de transcription. Sa mise en oeuvre se fait via une interface dediee permettant l'exploration de l'espace des solutions, leur visualisation et leur edition locale, avec une attention particuliere portee sur le traitement des appoggiatures.
A Deep Learning Approach for Extracting Polarity from Customers’ Reviews

Due to the expansion of social networks and media such as Tweeter, Facebook, LinkedIn, and different weblogs, and the great increase in information sharing and comments, Which typically are in the form of text data, big enough to be recognized as big data. , and with respect to the importance of these data for the analysis of customers’ priorities, needs and their attitudes toward different products, finding and extracting data from their comments, are the primary goals of this research. To serve this purpose, this research has used deep learning approach, and multilayer neural network methods in order to extract the polarity of customers’ opinions and comments in two domains of products/services ranging from restaurant to laptop.The findings of this study indicate that the proposed model using the potencies of the long short-term-memory networks, is able to determine the comments’ polarity with 85 % and 84.62 % precision for restaurant and laptop domains respectively, in such a way that the results are relatively more accurate than the results of other methods
Learning-Based Model Predictive Control for Autonomous Racing

In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard. One major issue in autonomous racing is that accurate vehicle models that cover the entire performance envelope of a race car are highly nonlinear, complex, and complicated to identify, rendering them impractical for control. To address this issue, we employ a relatively simple nominal vehicle model, which is improved based on measurement data and tools from machine learning.The resulting formulation is an online learning data-driven model predictive controller, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior. To improve the vehicle model online, we select from a constant in-flow of data points according to a criterion reflecting the information gain, and maintain a small dictionary of 300 data points. The framework is tested on the full-size AMZ Driverless race car, where it is able to improve the vehicle model and reduce lap times by $ {\mathbf{10}{\%}}$ while maintaining safety of the vehicle.
IoT Resource Access utilizing Blockchains and Trusted Execution Environments

We consider IoT resources with a Trusted Execution Environment (TEE) and propose a model to provide trusted resource access that is linked to blockchain payments, ensuring the integrity and confidentiality of the IoT data. The model is built on the widely used OAuth 2.0 open authorization framework, which provides delegated authorization for IoT resources. We utilize hash-lock and time-lock mechanisms to cryptographically link trusted resource access, provided by the IoT resource’s TEE, to authorization grants and blockchain payments. The model is implemented in the OP-TEE open source port for the Raspberry Pi that uses ARM’s TrustZone and is evaluated on the Rinkeby public Ethereum testnet.
Minimal order observer based H∞ controller design based on overbounding approximation method

In this paper, the minimal order observer based H∞ controller design is studied. The state space representation of the minimal order observer based controller is complicated and involves the product and quadratic terms of the decision variable matrices, i.e., the state feedback and observer gains. To make the design problem as a bilinear matrix inequality (BMI) problem, we represent the minimal order observer based controller by a descriptor representation, where the decision variable matrices are affinely involved. This descriptor representation enables us to design controllers by applying the overbounding approximation method for general BMI problems. The effectiveness of the proposed design procedure is demonstrated by a numerical example.
Blind Detection Techniques for Non-Cooperative Communication Signals Based on Deep Learning

The performance of existing signal detection methods depends heavily on the amount of prior information acquired by the sensor of interest. Therefore, to improve cognitive radio-based detection in low-signal-to-noise (SNR) environments, we propose a deep learning method-based passive signal detection. A convolution neural network (CNN) and the long short-term memory (LSTM) approach are used to extract the frequency and time domain features of the signal. Our method can detect signal when little to none prior information exists. The simulation experiments verify the probability of detection for our method. The results show that our method is about 4.5–5.5 dB better than a traditional blind detection algorithm under different SNR environments.
A Review of Mobile Learning Considerations in Open Distance Learning and Access to Higher Education

The advent of the ubiquitous mobile technology, the ever-changing interactive multimedia resources, and the mobility nature of the adult learners has galvanized the world-wide synergy of working towards the establishment of a global knowledge economy. This paper was informed by review of wide collection of literature that established how the mobile learning considerations in Open and Distance Learning influence access to higher education. The paper explains what; the everpresent nature of mobile learning characteristics, the andragogical factors, the organizational considerations, and the regulatory framework in Open and Distance Learning influence the access to higher education. The paper establishes that higher education institutions must consider improving technological infrastructure and incompatibility of mobile devices with learning management systems, provide modern mobile devices, improve instructors’ andragogical skills on m-learning, deal with instructors and students concerns and provide motivations incentives for m-learning.
Fault Tolerance Techniques for Distributed, Parallel Applications

This chapter gives an overview of techniques used to tolerate failures in high-performance distributed applications. We describe basic replication techniques, automatic rollback recovery and applicationbased fault tolerance. We present the challenges raised specifically by distributed, high performance computing and the performance overhead the fault tolerance mechanisms are likely to cost. Last, we give an example of a fault-tolerant algorithm that exploits specific properties of a recent algorithm.
Full Mean Square Performance Bounds on Quaternion Estimators for Improper Data

Novel physical insights are provided into the mean square performance bounds of quaternion-valued widely linear (WL), semi-widely linear (SWL), and strictly linear estimators for the generality of quaternion-valued Gaussian data. This is achieved by first defining three kinds of complementary mean square errors (CMSEs) of these estimators and by further exploiting the corresponding degrees of <inline-formula><tex-math notation="LaTeX">$\mathbb {H}$</tex-math></inline-formula>-improperness (second-order noncircularity). Next, the investigation of the bounds of the attainable CMSEs by these classes of estimators shows that only a joint consideration of the proposed CMSE analysis and the standard MSE analysis provides enough degrees of freedom for a detailed account of the MSE performance. The so-established framework for the analysis of estimators for <inline-formula><tex-math notation="LaTeX">$\mathbb {H}$</tex-math></inline-formula>-improper data is shown to be capable of measuring error power distribution for each data channel, an important finding, which is not possible to obtain through the standard MSE analysis only. Simulations in the system identification setting support the analysis.
Task Offloading for Vehicular Fog Computing under Information Uncertainty: A Matching-Learning Approach

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize stable and reliable task offloading under information uncertainty remains a critical challenge. In this paper, we propose a matching-learning-based task offloading algorithm to address this challenge. First, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Second, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading algorithm by combining matching theory and upper confidence bound (UCB) algorithm. Simulation results demonstrate that the proposed algorithm can achieve bounded deviation from the optimal performance without the global information.