Miracle-dz/UniFAR-FTUs huggingface dataset:
Special-Purpose Processors for Determining the Hamming Distance between Signals: Theoretical Basis, Methods, and Structures

The scope of application of the theory of pattern recognition based on statistical estimates in the Hamming space is investigated. The prospect of using special image recognition processors as components of computing tools is formed. The main provisions of the theory of pattern recognition based on estimates of the Euclidean distance in Hamming space are presented. Circuit design solutions of special-purpose processors for determining the Hamming distance between analog signals are proposed, iIt is presented the technical solutions of the ADC used in such special processors. Developed the structure of a two-channel ADC of parallel type with the source codes of Haar TNB, which is characterized by high speed.
An In-Laboratory Comparison of FocusBand EEG Device and Textile Electrodes Against a Medical-Grade System and Wet Gel Electrodes

In this study, FocusBand (FocusBand Technologies, T 2 Green Pty Ltd.) textile electrodes were tested against medical-grade wet electrodes to investigate the possible differences in skin-electrode interface behavior and EEG signals’ quality. In vivo electrical impedance spectroscopy and simultaneous forehead EEG measurements were performed with ten healthy subjects. In addition, the FocusBand device was tested in a stand-alone manner against a medical-grade reference system using similar test measurements. Compared to wet electrodes, textile electrodes had higher median absolute skin-electrode impedances five minutes after the attachment, but this difference decreased to 50–55 % of the initial during the first 60 minutes on all measured frequencies (1–1000 Hz). Textile and wet electrodes produced highly consistent EEG signals at forehead Fp1 and Fp2 locations. From those, Fp1 signals were more consistent in terms of normalized cross-correlations and agreement of the relative spectral powers. A stand-alone comparison showed that the FocusBand device can be used to record forehead biopotential signals, but the quality was not as consistent as with the medical-grade system. Based on impedance characteristics, a recording made using FocusBand textile electrodes may be more susceptible to artifacts than recording made using the medical-grade wet electrodes. However, FocusBand textile electrodes can be used, after a short stabilization period, to reliably record forehead EEG signals with a quality almost equal to that reached with medical-grade wet electrodes.
Radio-over-Fiber Systems with 1-bit Digital Modulation for 5G/6G Indoor Wireless Communication

This paper introduces the radio-over-fiber systems with the two novel 1-bit digital modulators: a phase-modulated-clocking envelope ΔΣ modulator and an all-digital outphasing modulator. By these modulators, radio-over-fiber systems are realized with commercially available optical devices for digital communication systems in low cost. The simulation results show that these modulators are verified to comply with the 3GPP ACLR specifications for 100 MHz OFDM signal.
Neural network computing using a large-area vertical-cavity surface-emitting laser

High-performance computing hardware is crucial for advanced neural network (NN) schemes. Photonics promises strong advantages in terms of parallelism, yet until now scalable and integrable concepts are scarce and partially rely on exotic substrates. The majority of large scale and parallel photonic NN demonstrations are neither standalone nor autonomous [1] , usually lacking fundamental NN constituents or requiring substantial interaction with a classical electronic computer. In this contribution, we implement a fully parallel photonic reservoir computer based on the spatially distributed modes of an efficient and fast semiconductor laser [2] . Crucially, all neural network connections are realized in hardware, and our laser-based and fully parallel NN comprising ~100 neurons produces results without pre-or post-processing.
Development of an Unmanned Aerial System for Maritime Environmental Observation

We developed an octocopter-based unmanned aerial system (UAS) to monitor remote environments. The system contains various environmental monitoring sensors for measuring wind speed/direction, temperature, relative humidity, atmospheric pressure, fine dust, and multispectral and RGB data. The UAS consists of an unmanned aerial vehicle (UAV), a ground control station, and a server. We studied its electrical, mechanical, and software (SW) configurations. Specifically, we developed hardware (HW) and SW to control the yaw and gimbal pitch directions of the UAV conveying multispectral and RGB cameras. To prevent the obtained solar reflectance from affecting the multispectral and RGB data and to improve the quality of the obtained data, we maneuvered the yaw so that it would always deviate 135° from the solar azimuth angle, with the sun at its rear. Managing the yaw direction involves controlling the UAV based on a micro air vehicle link message-based robot operating system (ROS). To safely test the UAS performance before the maiden flight in an ocean area, we first evaluated PX4 and ROS-based SW through indoor software-in-the-loop simulation (SILS) via the Gazebo tool. Subsequently, we used an F450 UAV and an actual maritime UAV to sequentially perform pre-flight and flight experiments. This paper explains the system operation scenario, UAS component, and simulation and experimental results. The results reveal that the average yaw angle error during the mission, $\bar {\left |{ \theta _{y,des}-\theta _{y} }\right |}$ , is approximately 8°, and the average pitch angle of the gimbal during the mission, $\left |{ 50^{\circ }-\left |{ \bar {\theta _{c}} }\right | }\right |$ , is less than 5°.
Non-Clifford Gate on Gottesman-Kitaev-Preskill Encoded Optical Qubits with Nonlinear Feedforward

Achieving a fault-tolerant universal quantum computer with an optical system is one of the goals of quantum optics. For that purpose, the most promising candidate is the Gottesman-Kitaev-Preskill (GKP) encoding of qubits in the continuous-variable system [1] . In order to perform universal computation with GKP qubits, the current open problem is the realization of non-Clifford operations, which require non-Gaussianity. In the original GKP’s paper where [1] , two different approaches are suggested to implement a non-Clifford T gate, i.e. $\hat T = \left| {{0_{\text{L}}}} \right\rangle \left\langle {{0_{\text{L}}}\left| { + {{\text{e}}^{i\frac{\pi }{4}}}} \right|{1_{\text{L}}}} \right\rangle \left\langle {{1_{\text{L}}}} \right|$ , where |0 L 〉 and |1 L 〉 are the logical bases of the GKP qubits. The first approach is using a cubic phase gate, a non-Gaussian operation, combined with Gaussian operations. In this context, the cubic phase gate has been widely developed with nonlinear feedforward as a key technology [2] , [3] . In recent research, however, it is shown that this approach is not optimal for GKP qubits [4] . Hence, the second approach, which is based on the magic state injection method [5] , is more promising.
A Fairness-Driven Resource Allocation Scheme Based on a Weighted Interference Graph in HetNets

One of the most important 5G features is their support for heterogeneous networks (HetNets). Complementing the classic macrocell base stations (MBS), femtocell base stations (FBS) are beneficial in terms of extensive coverage, including indoor, and enhancement of capacity. Unfortunately, FBSs performance in 5G HetNets is affected by complex cross-tier and co-tier interferences, causing reduced quality of service (QoS) and unfairness among users. This paper proposes an innovative resource allocation (RA) algorithm for interference mitigation (IM) based on graph coloring techniques to improve QoS and inter-user fairness. The proposed algorithm, named Weighted Edge-Weighted Vertex Interference Mitigation (WEWVIM), employs a weight to the directed edge corresponding to the interference strength from nearby base stations (BSs) and a weight to every vertex, indicating the color with the smallest interference or higher transmission rate. A region of interest (ROI) is formed to find the interfering BSs. Simulation results show that WEWVIM outperforms existing schemes in terms of fairness and QoS, including throughput, packet loss ratio (PLR), delay, and jitter.
Forecasting turbulence in a passive resonator with supervised machine learning

Predicting complex nonlinear dynamical systems has been even more urgent because of the emergence of extreme events such as earthquakes, volcanic eruptions, extreme weather events (lightning, hurricanes/cyclones, blizzards, tornadoes), and giant oceanic rogue waves, to mention a few. The recent milestones in the machine learning framework offer a new prospect in this area [1] , [2] . For a high dimensional chaotic system, increasing the system’s size causes an augmentation of the complexity and, finally, more nodes in the network. Here, we propose a new supervised machine learning strategy to forecast bursts occurring in the turbulent regime of a fiber ring cavity. Indeed, we have recently demonstrated [3] that this system can continuously transform a stable periodic pattern with a well-defined frequency comb into a turbulent state via a spatiotemporal intermittency mechanism. Figure 1a ) shows an illustration of this transition. Even though a turbulent evolutions, an interesting feature is the persistent long-range correlation in the dynamics [3] . The system can then be seen as adjacent subdomains. Owing to this feature, instead of predicting the whole system, we use appropriate tools of chaos theory to identify recurrent picture in the past at the same location of the bursts. Figure 1b ) shows the statistics on the bursts (red) and the profiles detected at a previously computed time lag (blue). We have taken advantage of the apparent causality to make an association precursors-pulses. Knowing when and where the bursts may emerge, a fair question is " What is coming? ". On the other hand, as shown in Figure 1c ), no correlation can be found between the profile (amplitude and size) of the precursors of the corresponding pulse. However, using the pulses/precursors pairs to perform supervised learning with a recurrent neural network, we have obtained a high correlated map between the actual observation and the prediction, as can be seen in Figure 1d ). As our strategy deals with intrinsic characteristic quantities of the dynamical behavior, the pre-trained network can trigger the forecasting even for a more extensive system, provided that all the other parameters are the same.
Convergence of Broadcast and Broadband Performance Evaluation of Layered Division Multiplexing for 5G and Beyond

Further evolved Multimedia Broadcast Multicast Service (FeMBMS) was introduced in 3GPP Release 14 to provide standalone ultra-high-definition multicast/broadcast services over larger distances. Release 16 made significant improvements to FeMBMS with support for large inter site distance up to 125 km, support for high mobility up to 250 km/h, and enhancements to the cell acquisition subframe. However, the capabilities, efficiency, and service delivery flexibility of this system still lags behind dedicated broadcasting systems such as ATSC 3.0. Our previous works had shown that in an LTE infrastructure, incorporating layered division multiplexing (LDM) can deliver broadcast and broadband services in one RF channel to achieve higher spectral efficiency. The newest 5G standards bring many changes to the 3GPP specifications. Therefore, this study investigates the performance and capabilities of LDM for delivering mixed broadcast and broadband services for 5G and beyond. Simulations show that in an LDM enabled 5G broadcast priority network, a broadcast service and a near full capacity broadband service can be delivered using the same time frequency resource.
Edge Intelligence (EI)-Enabled Malware Internet of Things (IoT) Detection System

With the development of 5G networks, IoT devices are increasingly used in the industrial and household fields. Due to the characteristics of the CPU multi-architecture system of IoT devices, the traditional signature-based and single-architecture based detection methods is not effective are for detecting cross-architecture malware. To solve this problem, we propose a cross-architecture IoT malware detection system based on Graph Attention Networks (GAT). We employ the CFG extracted from the binary executable file as the graph structure and Opcode and PSI as the feature attributes of the graph nodes. Through GAT, we obtain the neighborhood features of each node, and assign different weights to different nodes in the neighborhood and finally complete detection. Different training phases are allocated to the edge and cloud center for execution to improve system performance and protect user data privacy. The experimental results of par show that the detection accuracy of our system reaches 99.67%. Compared with existing detection methods, we obtained the best accuracy.
Access Control for a Connected Vehicle Ecosystem

In recent years, vehicles became a complex network of ECUs and sensors that communicate with each other and their environment. Vehicle data from these connected vehicles is processed and shared with multiple 3rd party applications via cloud vehicle data platforms or aftermarket products for added value. The rise of connected vehicles has also opened attacks on services that tamper with data. Thus, an increased risk of security and privacy in the automotive domain has to be mitigated. In this paper, we present a vehicle data use case to identify the requirements and challenges for security and privacy in the automotive domain. Furthermore, we conduct a literature review of access control models and evaluate them against the identified requirements. Based on our findings, we conclude that the Attribute Based Access Control (ABAC) model fulfils most of our vehicle specific requirements.
A Low-Complexity Hybrid Precoding Scheme for mmWave MIMO Systems with Dynamic Subarrays

Hybrid precoding is a credible solution to address the trade-off between spectral efficiency and power consumption in mmWave massive MIMO systems. Though traditional hybrid precoding with fixed architectures has reached its limits, the flexibility of hybrid precoding has not been fully exploited, which gives insight to improve the performance further. In this paper, we adopt a novel architecture called dynamic-subarray, where the connection between radio-frequency (RF) chains and antennas can be dynamically changed by switches. We propose a low-complexity scheme to design the hybrid precoder with dynamic subarrays. Simulation results demonstrate that our proposed method has an excellent performance on the achievable rate and energy efficiency even with lower complexity.
Impacts of Electric Vehicle Charging under Cold Weather on Power Networks

Deep decarbonisation of the transportation requires widespread adoption of electric vehicles (EVs). Currently, the dominant energy storage technology for EVs is lithium based batteries which are designed to work under mild ambient temperatures (e.g. 21 Celsius). However, most cities with high EV penetration experience cold winter months when the performance of EVs is significantly degraded. In this paper, we present an impact assessment of cold weather EV charging on the power networks by reviewing existing literature on empirical studies related to battery performance, EV driving range, and charger characteristics. Two potential issues are identified. First, charging EVs at low temperatures significantly increases distribution network harmonics, hence limits the number of EVs that can be charged at the same time. Second, more frequent charging of EVs increases demand from the grid. To quantify this, a Monte Carlo based simulation is developed for the case of UK and results show that nearly 450 MW of extra generation is needed to cushion impacts of cold weather charging of 11 million vehicles. The problems pertinent to temperature effects on EV charging require greater attention as EVs are becoming the main mode of transport in the next decade.
Code Polymorphism Meets Code Encryption: Confidentiality and Side-channel Protection of Software Components

In this article, we consider that, in practice, attack scenarios involving side-channel analysis combine two successive phases: an analysis phase, targeting the extraction of information about the target and the identification of possible vulnerabilities, and an exploitation phase, applying attack techniques on candidate vulnerabilities. We advocate that protections need to cover these two phases to be effective against real-life attacks. We present PolEn, a toolchain and a processor architecture that combine countermeasures to provide an effective mitigation of side-channel attacks: As a countermeasure against the analysis phase, our approach considers the use of code encryption; as a countermeasure against the exploitation phase, our approach considers the use of code polymorphism, because it relies on runtime code generation, and its combination with code encryption is particularly challenging. Code encryption is supported by a processor extension such that machine instructions are only decrypted inside the CPU, which effectively prevents reverse engineering or any extraction of useful information from memory dumps. Code polymorphism is implemented by software means. It regularly changes the observable behaviour of the program, making it unpredictable for an attacker, hence reducing the possibility to exploit side-channel leakages. We present a prototype implementation, based on the RISC-V Spike simulator and a modified LLVM toolchain. In our experimental evaluation, we illustrate that PolEn effectively reduces side-channel leakages. For the protected functions evaluated, static memory use increases by a factor of 5 to 22, corresponding to the joint application of code encryption and code polymorphism. The overhead, in terms of execution time, ranges between a factor of 1.8 and 4.6.
Thermally reconfigurable loss in a passive optical cavity

Thermo-optic effect is the simplest and most cost-effective method for reconfiguring an optical cavity, but fails to tune the optical loss in an ordinary cavity. By embedding an interferometer within an optical cavity, we demonstrate thermo-optic tuning of the quality factor from 3000 to 10000 and extinction ratio from ~0 dB to 25 dB in a completely passive silicon micro-ring resonator. This is an important step towards realizing cost-effective reconfigurable photonic devices in purely passive waveguide platforms.
Disentangling 3D/4D Facial Affect Recognition With Faster Multi-View Transformer

In this paper, we propose MiT: a novel multi-view transformer model1 for 3D/4D facial affect recognition. MiT incorporates patch and position embeddings from various patches of multi-views and uses them for learning various facial muscle movements to showcase an effective recognition performance. We also propose a multi-view loss function that is not only gradient-friendly, and hence speeds up the gradient computation during back-propagation, but it also leverages the correlation associated with the underlying facial patterns among multi-views. Additionally, we offer multi-view weights that are trainable and learnable, and help substantially in training. Finally, we equip our model with distributed performance for faster learning and computational convenience. With the help of extensive experiments, we show that our model outperform the existing methods on widely-used datasets for 3D/4D FER.
Modeling of Subdiffusion Impedance in Multilayer Nanostructures

Mathematical model of the subdiffusion impedance in multilayer nanostructures is considered on the basis of the Cattaneo-type diffusion equation in fractional derivatives. The Nyquist diagrams are calculated for different values of subdiffusion coefficient and time parameter (time for which the flow delays concerning the gradient of charge carriers concentration). A qualitative agreement is obtained for the GaSe system with encapsulated β-cyclodextrin.
Turbulence control by non-Hermitian potentials

We propose a method to control turbulence by the introduction a non-Hermitian spatiotemporal modulation, based on the energy redirection from unstable to stable modes by the unidirectional coupling induced by these potentials and so, influence and slow down the excitation cascade of turbulence. The phenomenon of the turbulence is, and has been for a long time an interesting and challenging topic for scientist. Plenty of turbulent systems follow a quite general scenario, the excitation in a continuous system imposed on a large spatial scale, decreases in scale due to nonlinear wave-mixing processes until finally entering into a dissipative range, on a small spatial scale. Turbulence bridges these two spatial scales and results in an energy cascade through the wavenumbers. Turbulence appears in a wide range of disciplines, from fluids to business and optics. For this last case, turbulence represents one of the main inconvenience for laser source. These sources loss stability when power is increased through a instability of spatial character, the Modulation Instability (MI). Some techniques have been proposed to reduce and suppress the MI [1] . Aside, the introduction of non-Hermitian potentials has shown an asymmetric or even unidirectional coupling between modes allowing different spatial effects [2] – [3] .
Harmonic Emission Limit Allocation Using VDE AR-N 4130: Application and Adaptation of Experiences from IEC TR 61000-3-6

Increased installation of power cables and the intensified roll-out of power electronic equipment in the extra high voltage level pose challenges for network operators to assess network perturbations and power quality issues. In order to regulate harmonic levels in electric networks, grid operators are required to assign harmonic emission limits to the grid connection users. This ensures the compliance with the compatibility levels of the transmission grid. In this paper, the allocation procedure of harmonic current limits according to the novel German application rule VDE AR-N 4130 is compared with the approach from technical guideline IEC TR 61000-3-6. Key differences between both approaches are detected and presented on the basis of a case study. A restrictive component formulated in IEC method is elaborated, which is decisive for the differences occurring. Based on this finding, an adaptation of the allocation principle according to VDE is proposed. This adaptation enhances the allocation of comparable harmonic current emission limits.
Security Awareness Scheme of Edge Computing in IoT Systems

As edge computing has been widely used in IoT (Internet of Things) systems, the security has become one of important issues for IoT. Because of a large amount of private information stored in edge computing devices, it makes edge computing devices attractive to various kinds attacks. To deal with this challenge, this paper proposes a security awareness scheme for edge computing devices in IoT system. Test results show that the proposed approach can improve services-oriented security situation of IoT systems based on edge computing.
Recent Progress and Perspectives of Intra-Oscillator High Harmonic Generation Using Thin-Disk Lasers

High harmonic generation (HHG) in a gas target is the most common approach for table-top coherent extreme-ultraviolet (XUV) light generation. Progressing from kilohertz to megahertz repetition rate XUV sources is of high interest for reducing acquisition times and enabling XUV frequency comb applications [1] .