Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient CADNet for Classification of High-frequency Oscillations in Magnetoencephalography

Epilepsy is a chronic neurological disease, and locating the lesions precisely is crucial to the success of epilepsy surgery. The high-frequency oscillations (HFOs) in magnetoencephalography (MEG) of epileptic patients can be used to detect seizures. Due to the inefficient and error-prone operation of traditional HFOs detection, it is necessary to develop an approach for the detection of HFOs, which can automatically classify HFOs in MEG. In this paper, We proposed a novel deep learning-based CADNet for the classification of HFOs in MEG. First, we preprocessed acquired MEG data by short-time Fourier transform (STFT), and the extracted time-frequency domain information was applied for model training after pictorialism. Then, we captured the features from these images through convolutional neural network combined with multi-head self-attention, all these features were input into Dendrite Net for classification. We evaluated our model on MEG dataset, and the accuracy, precision, recall, and F1-score of the optimized model reached 0.97, 0.98, 0.97, 0.97. We compared the proposed CADNet with other deep learning models, the result demonstrates that our model outperforms others.
Remote Patient Monitoring System Using IoT and Artificial Intelligence: A Review

Remote patient monitoring system is perhaps the most well-known concept these days. With the advancement of medical healthcare innovation, the life expectancy of individuals has effectively expanded. People in rural and remote regions are yet struggling to acquire proficient medical care administrations because of the barrier of distance and absence of specialists. In the existing healthcare systems, even though some remote monitoring is available but they lack proper health sensors, accuracy and quality. This paper provides a comprehensive study on existing internet of things and artificial intelligence based remote patient monitoring systems which can witness in low-risk chances of illness and emergencies. The information assortment, analysis, challenges and monitoring issues in present health monitoring systems are also discussed. This helps to find future directions and scope of research in remote health monitoring system, and develop advanced healthcare technologies.
Snake Species Identification Using Deep Convolutional Neural Networks

Snakebite poisoning is one of the tropical diseases responsible for many deaths globally and particularly in developing countries. In most cases, identifying the species of the snake following the bite is a life-saving factor for snakebite poisoning patients. However, snake species identification is not a trivial task, it can be misleading and dangerous. That is why this study has investigated the use of Machine Learning approaches to automate snake specie identification. The study proposed a Deep Convolutional Neural Network (DCNN) model that is able to rapidly recognize the four snake species, namely, Naja mossambica, Dendroaspis polylepis, Bitis arietans, and Dispholidus typus. To train and test the model four different DCNN architectures were explored, namely, Xception, InceptionResNet version 2, Inception Version 4, and ResNet34, and then a performance comparison is presented. K-Fold Cross-Validation approach was applied to collect the performance results of each model. The experimental results showed that InceptionResNet version 2 achieved better performance with respect to the accuracy, 82.64%, and precision, 84.15%. In addition, the results showed that the InceptionResNet v2 model presents more accurate performance compared with the models reported in the literature.
An Efficient and Accurate 3D SLAM Method for Dynamic Environment

LiDAR-based 3D SLAM has always been one of the hotspots in the field of self-driving and mobile robots in the recent years. But how to build a robust and accurate map for a complex dynamic environment is still a challenging task, which attracts more and more attention in this community. In this paper, we proposed an efficient and accurate 3D SLAM method for complex dynamic environment, which mainly includes two stages. In the first moving object detection stage, an end-to-end full convolution semantic segmentation network (FCNN) is exploited to segment the potential moving objects accurately. Then the left point cloud is forward to the static SLAM module, which is based on direct point cloud registration method, the map is managed efficiently with the incremental kd-tree data structure. Additionally, an independent thread of the loop closure detection (LCD) based on the framework of multi-factor graph is adopted to further improve the accuracy and robustness of final outputs. With the elaborately design of the whole framework, the proposed method can work efficiently. The performance of the proposed method is validated with the benchmark dataset KITTI, the results show that by removing the dynamic objects, the stability and accuracy of SLAM can be greatly improved.
The Ossanna’s Theorem for the Analytical Determination of a Two-Bus System PV Curve and Voltage Collapse Point

In the technical literature, the PV curves of the electrical lines are generally computed by only considering their electrical parameters as lumped. This model approach allows finding PV curve formulations without a large computational burden. Notwithstanding, such approximations bring to large inaccuracies for long transmission lines (especially insulated cables), for distribution ones (high r/x ratios) and for low power factor supply conditions. Starting from the Ossanna’s theorem, novel and original PV curve and the voltage collapse point formulations for any electrical line are obtained. Moreover, PV curve analytical formulation considering the generator reactive power limits is formulated. Eventually, numerical results on the real-world case studies illustrate the differences with the proposed formulation with the classical ones found in the technical literature.
A Buck LED Driver with a Novel Average Current Mode Control

This paper presents a novel methodology to regulate the output current in CCM/DCM-operated buck converters to be used as post-regulators in LED lamp drivers. With this novel method only the current of the power switch needs to be sensed; regulation is accurate and insensitive to the input and output voltages and to the inductance value, with no need of external correction circuit nor calibration. The buck converter works with constant switching frequency, which enables an optimized inductor design. It is also possible to change the regulation setpoint in a continuous manner for LED lamp analog dimming.
A New Valid Angle Equation for PNF Measurements

A new valid far-field angle equation for rectilinear planar near-field measurements is presented. The new valid angle equation is derived by viewing the planar near-field to far-field transformation process as the generation of pseudo plane waves by a synthetic phased array. The synthetic phased array does not physically exist; rather, the array is formed during the post-processing of the planar near-field measurements. As part of this discussion, we present results from a numerical model, illustrating the total electric field present in the test zone due to the finite extent of the synthetic phased array. The new valid far-field angle equation accounts for the diffraction effects of the finite-sized synthetic array and uses the industry accepted test-zone magnitude ripple of +/- 0.5 dB to limit the valid far-field angle for a fixed scan plane size.
Prime decomposition of modular tensor categories of local modules of type D

Let $\mathcal{C}(\mathfrak{g},k)$ be the unitary modular tensor categories arising from the representation theory of quantum groups at roots of unity for arbitrary simple finite-dimensional complex Lie algebra $\mathfrak{g}$ and positive integer levels $k$. Here we classify nondegenerate fusion subcategories of the modular tensor categories of local modules $\mathcal{C}(\mathfrak{g},k)_R^0$ where $R$ is the regular algebra of Tannakian $\text{Rep}(H)\subset\mathcal{C}(\mathfrak{g},k)_\text{pt}$. For $\mathfrak{g}=\mathfrak{so}_5$ we describe the decomposition of $\mathcal{C}(\mathfrak{g},k)_R^0$ into prime factors explicitly and as an application we classify relations in the Witt group of nondegenerately braided fusion categories generated by the equivalency classes of $\mathcal{C}(\mathfrak{so}_5,k)$ and $\mathcal{C}(\mathfrak{g}_2,k)$ for $k\in\mathbb{Z}_{\geq1}$.
Secure Management of Healthcare Data in Fog and IoT Networks: A Short Survey on Existing Security Protocols

In recent years, multi-media data regarding various applications have emerged exponentially over the Internet of Things (IoT). Generally, these gathered data are fed to the cloud for storage and further processing. The fog layer is incorporated here which acts like a bridge and performs communication between the cloud and IoT devices. This fog-derived IoT network offers services by effectively analyzing and computing information gathered by IoT devices with the help of sensors. Though, it has two major challenges energy and security. Further, fog computing aims to minimize the overhead of cloud computing to provide network services to satisfy secure data transmission. Nowadays, various security protocols have been suggested to overcome the security challenges in the existing fog-driven IoT models. Moreover, among various applications, the healthcare application requires an efficient data management strategy with maximum security, and thus, this paper aims to study the various research works on healthcare data management in Fog and IoT networks with different authentication protocols. The existing research works on Foga-ssisted healthcare data management systems in IoT are categorized for guiding the upcoming researchers in this field. Moreover, this review discusses different authentication protocols, performance metrics analyzed for validation and diverse tools used for implementation. To conclude, the research challenges and gaps in the field of healthcare data management in fog and IoT networks are studied. This analysis further substantiates the requirement for a coherent technique for healthcare applications in fog computing.
Design of a Decoupled Sliding Mode Control for Four-Leg Distribution Static Compensator

In the conventional sliding mode control, the four-leg distribution static compensator (DSTATCOM) currents are controlled based on a current error in each phase. However, the four currents in a four-wire system can not be independently controlled variables and hence one of the four controllers is redundant in conventional schemes. Further, the current dynamics of a DSTATCOM converter are coupled through converter pole voltages in the natural reference frame. This leads to cross-coupling in the sliding variable equations with respect to four manipulated input variables. Considering the above points, in this paper, the current due to Thevenin’s equivalent load neutral-point voltage is considered as a fourth independent controlled variable and the corresponding system dynamic equations are presented. To get a decoupled feature, a new sliding surface function is structured. The performance of a DSTATCOM with the proposed control scheme under various operating conditions is validated through a detailed simulation study and experimental results, obtained from a laboratory prototype of four-leg DSTATCOM.
Challenges of Vehicle-Grid Integration as Modern Distributed Energy Implementation

Unpredictable and unmanaged Electric Vehicle (EV) charging together with intermittent solar generation remains a challenge in modern distributed energy implementation. Without the technology for harnessing EV charging to the benefit of the grid, there will be no market for grid services and little impact for aggregators. This paper provides systematic approaches for Vehicle-Grid Integrated microgrid planning. Several tools from governments and commercial simulation packages including Interruption Cost Estimate (ICE) Calculator, PVWatts, Storage Value Estimation Tool (StorageVETTM), Electrical Transient Analyzer Program (ETAP), and Real Time Digital Simulator (RTDS), are used to verify the design requirements and simulate the electric load changes using smart charging and Vehicle-to-Grid (V2G). The simulation results showed that load shaping by smart charging and V2G fattened undesirable ramps and halved the system’s peak load, which can be translated to significant cost savings for the grid operator.
Resilient Control for Time-Delay Systems in Cyber-Physical Environment Using State Estimation and Switching Moving Defense

Cybersecurity for complex systems operating in cyber-physical environment is becoming more and more critical because of the increasing cyber threats and systems' vulnerabilities. Security by design is quite an important method to ensure the systems' normal operations and services supply. For the aim of coping with cyber-attack affections properly, this paper studies the resilient security control issue for time-varying delay systems in cyber-physical environment with state estimation and moving defense approach. Time-varying delay factor induced by communication and network transmission, or data acquisition and processing, or certain cyber-attacks, is considered. To settle the cyber-attacks from the perspective of system control, a dynamic system model considering attacks is presented, and the corresponding switched control model with time-varying delay against attacks is formulated. Then the state estimator for system states is designed to overcome the problem that certain states cannot be measured directly. Estimated states serve as the input of the resilient security controller. Sufficient conditions of the stability of the observer and control system are derived out with the Lyapunov stability analysis method jointly. A moving defense strategy based on anomaly detection and random switching is presented, in which an optimization problem for calculating the proper switching probability of each candidate actuator-controller pair is given. Simulation experimental results are shown to illustrate the effectiveness of the presented scheme.
Fast AFM Imaging Based on Neural Network Compressed Sensing

Atomic force microscopy (AFM) realizes super-resolution imaging at micro-nano scale, but the time-consuming scanning imaging limits its measurement efficiency. Compression of AFM scanning points to improve the imaging speed has become a solution, and the imaging quality should also be guaranteed. In this work, the compressed sensing algorithm based on neural network is used as the reconstruction solver after compressed sampling of AFM data. The sample data of AFM database are incorporated into the network training set, which not only shortens the AFM scanning imaging time, but also ensures the high-resolution image reconstruction. Experimental results show that the network-based CS algorithm improves the peak signal noise ratio (PSNR) by 8.52 dB compared with the TVAL3 algorithm, and the minimum average reconstruction error rate (Err) is only 38.5 % of that of the TVAL3 algorithm. The reconstruction is fast, less than 0.5 s, and is less affected by the amount of sample data.
Sustainability through SF6 reduction on HV/E-HV cable accessories

The urgency to act on climate change has never been greater, and, in the absence of global regulation, companies are voluntarily stepping up to fill the gap. In the power cables industry, high focus is currently on the accessories, because of their extensive use of SF6 gas, as part of the equipment to carry out routine tests of joints and cable drums, and through their interface to the gas-insulated electrical equipment. The technical challenges connected to the SF6 reduction in the cable accessories are hereby presented, with the main R&D activities consequently generated.
Quality process to improve operating results: A study on potato production in Barranca, Peru

This research work focuses on developing the Quality Management process for potato-producing SMEs in Vinto Roncador. This work will be based on the objectives of the Value Stream Mapping, Standardized Work, and Good Agricultural Practices tools. The purpose of the proposal is to help these agri-food companies to increase their performance, profitability and sustainability to be competitive at the national and even global level. Finally, the conclusions and recommendations observed throughout the execution are detailed, from the key points of the development to the results obtained in it.
Noise Parameterization of Continuous Deep Reinforcement Learning for a Class of Non-linear System

Reinforcement learning (RL) is one of the most important algorithms for artificial intelligence. DDPG as continuous controller approach which can work at continuous and high dimensional data applies in this paper to solve nonlinear valve system. The aim of this paper is gaining analysis of the DDPG noise parameterization. Noise parameter addition is known to be able to increase the exploration ability of the algorithm. The noise parameterization is using the Ornstein-Uhlenbeck (OU) noise injection. This exploration investigation concerns to the algorithm’s performance. The evaluation measurement is based on the total reward to system during training. The result indicates that noise parameterization affects the performance of the algorithm. The comparisons show that the injection of OU noise for DDPG algorithm influences the total reward. The simulation find that the total reward that is achieved by DDPG with OU noise injection is higher than DDPG without OU noise injection at 317,810.
Stepped-Frequency CW RCS measurement in Semi-Anechoic Chamber

This paper presents the implementation of a broadband Radar Cross-Section (RCS) measurement system in a Semi-Anechoic Chamber (SAC) of Directed Energy Research Center, where fully anechoic conditions are unavailable, and consequently, spurious reflections are significant. A metallic trihedral corner reflector of 30 cm sides is used as a target, and background subtraction is performed to minimize noise influence in the measurement. A single target reference approach is performed using a sphere as the reference target, and its scattering response is shortly described. A time-domain gating algorithm is then implemented to remove the unwanted reflections and obtain the isolated response of the target. The measurements are performed in the X band as a function of the viewing angle for vertical transmit and receive polarization. The results are compared to numerical simulation using the Integral Solver (I-Solver) of CST.
DOA Estimation Method Based on Adaptive Threshold BIHT Algorithm

In the past ten years, the direction of arrival (DOA) estimate problem for infinite resolution analog to digital converters (ADC) has been successfully solved using the theory of compressed sensing for sparse signal reconstruction. However, in some applications, such as large-scale array systems, low-resolution ADCs are required to minimize hardware cost and complexity. This study examines the 1-bit compressed sensing DOA estimation problem based on 1-Bit compression and suggests an adaptive threshold binary iterative hard thresholding (BIHT) sparse reconstruction solution. Based on this, the 1-bit DOA estimate problem is expanded to include the plural domain and multi-snapshot scenarios. In the meantime, simulation tests are used to confirm the efficiency of the suggested strategy when applied to DOA estimation.
Solving a Multi-Objective Location-Routing Problem with Minimum Cost and Total Time Balance

In transportation activities, providing a workload balance for drivers and vehicles can contribute several beneﬁts for the ﬁrm. However, few attention has been paid to the importance of workload balance in location-routing problem (LRP) research. Thus, this study intends to present a mathematical model to solve the multi-objective LRP which addresses this issue. The proposed model considers two objective functions: (1) to minimize the total cost and (2) to balance the workload in distribution activities. The main purposes of this model are to obtain the optimal location of a distribution center, number of vehicles estab-lished, and delivery routes which satisfy both of these two objectives. Furthermore, to solve the model, this study proposes the non-dominated sorting genetic algorithm-II (NSGA-II) for several problem scenarios with diﬀerent numbers of customers. The experimental results show that the proposed algorithm performs well in terms of quality and computational time of the solution. cardinality, spread, convergence, uniformity of the NSGA-II, of quality indicators: (1) error ratio ( ER ) Mousavi to capture the ratio of Pareto solutions to the population, (2) modiﬁed maximum spread ( MS ) (see Adra and Fleming [1]) to capture the diversity of solutions, (3) mean-ideal distance ( MID ) (see Mousavi et al. [15]) to measure the convergence of solutions into ideal point (0,0), (4) Spacing metric ( SM ) (see Collette and Siarry [3]) to measure the uniformity between solutions, and (5) CPU time ( CP U ) required to run the
Can star products be augmented by classical physics?

It has been suggested that star products in phase-space quantization may be augmented to describe additional, classical effects. That proposal is examined critically here. Two known star products that introduce classical effects are: the generalized Husimi product of coarse-grained quantization, and a non-Hermitian damped star product for the harmonic oscillator. Following these examples, we consider products related by transition differential operators to the classic Moyal star product. We restrict to Hermitian star products, avoiding problems already pointed out for the original damped product. It is shown, however, that with such star products, augmented quantization is impossible, since an appropriate classical limit does not result. For a more complete study, we then also consider generalized, or local, transition operators, that depend on the local phase-space coordinates, as well as their derivatives. In this framework, one example of possible physical interest is constructed. Because of its limited validity and complicated form, however, it cannot be concluded that augmented quantization with local transition operators is practical.
A remark on the Hochschild-Kostant-Rosenberg theorem in characteristic p

We prove a Hochschild-Kostant-Rosenberg decomposition theorem for smooth proper schemes $X$ in characteristic $p$ when $\dim X\leq p$. The best known previous result of this kind, due to Yekutieli, required $\dim X<p$. Yekutieli's result follows from the observation that the denominators appearing in the classical proof of HKR do not divide $p$ when $\dim X<p$. Our extension to $\dim X=p$ requires a homological fact: the Hochschild homology of a smooth proper scheme is self-dual.
Quantising a generic family of billiards with analytic boundaries

A generic family of plane billiards has been discovered recently. The shape of the boundary is given by the quadratic conformal image of the unit circle, and is thus real analytic. For small deformations of the unit disc the billiard is a typical KAM system, but becomes ergodic or even mixing when the curvature of the boundary vanishes at some point. The Kolmogorov entropy has been calculated, and it increases with the deformation of the boundary. The author studies aspects of the quantum chaos for this billiard. He solves numerically the eigenvalue problem for the Laplace operator with Dirichlet's boundary condition. He examines the spectrum, and inspects the avoided crossings at which mixing of nearby states occurs. The variation of the nodal structure and of the localisation properties of the eigenfunctions is studied. In analysing the level spacing distribution he finds a continuous transition from the Poisson distribution towards the Wigner distribution. The exponent in the level repulsion law varies continuously along with a generic perturbation. For small perturbations it seems to be proportional to the square root of the perturbation parameter.