Miracle-dz/UniFAR-FTUs huggingface dataset:
SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals

Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions, hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis of SZ by visual inspection is subjective, difficult, and time-consuming. Electroencephalogram (EEG) signals are widely used to detect the SZ as they reflect the conditions of the brain. Conventional machine learning methods involve many lengthy manual steps, such as decomposition, feature extraction, feature selection, and classification. In this article, automated identification of SZ is proposed using a combination of time–frequency analysis and convolutional neural network (CNN) to overcome the limitations of feature extraction-based methods. Three press button tasks are analyzed to segregate normal subjects and SZ patients. The EEG signals are subjected to continuous wavelet transform, short-time Fourier transform, and smoothed pseudo-Wigner–Ville distribution (SPWVD) techniques to obtain scalogram, spectrogram, and SPWVD-based time–frequency representation (TFR) plots, respectively. These 2-D plots are fed to pretrained AlexNet, VGG16, ResNet50, and CNN. We have obtained an accuracy of 93.36% using the SPWVD-based TFR and CNN model. In comparison to the benchmark AlexNet, ResNet50, and VGG16 networks, the developed CNN model with four convolutional layers not only requires fewer learnable parameters but also is computationally efficient and fast. This clearly indicates that our proposed method combining the SPWVD-CNN model has performed better than the state-of-the-art transfer learning techniques. Our developed model is ready to be tested with more EEG data and can aid psychiatrists in their diagnosis.
Research on the visualization method of two dimensions underwater flow simulation

The simulation and visualization of the flow field integrated into the rendering of the underwater scene is the key direction of the research work in the field of augmented reality in recent years. The underwater scene visualization framework of this article includes two parts: two dimensions flow simulation and visualization rendering. The flow field simulation is based on the NS equation solution, and the solution of the fluid dynamics equation is also the most computationally intensive. Therefore, use CUDA to write multiple kernel functions to solve the N-S equation multiple times. This method avoids the time overhead of setting up and accessing textures. Using CUDA can control the characteristics of a variety of fast memory (shared memory, texture memory share memory, texture memory), and the algorithm can be executed more efficiently. The visualization framework of underwater flow simulation adopts OpenGL and GLSL. The final experimental results show that better visual effects can be obtained with more than 80 iterations.
Abnormal data detection of power marketing audit based on DSR-Net

With the gradual realization of the goal of "full coverage, full collection and full cost control" of the power consumption information acquisition system established by the State Grid Corporation of China, the smart grid with the basic technical characteristics of informatization, automation and interaction has entered the stage of full completion. Therefore, the application of power big data technology has become an inevitable requirement for the intelligent development of the power industry, and power data anomaly detection has always been the top priority. Compared with traditional methods, this method is more able to collect the same feature relationship between data, and has strong generalization ability. After experimental comparison, the F1 score evaluation index of DSR-Net is 92.56, which is 9.33% higher than the traditional support vector machine algorithm.
NS-FDN: Near-Sensor Processing Architecture of Feature-Configurable Distributed Network for Beyond-Real-Time Always-on Keyword Spotting

Always-on keyword spotting (KWS) that detects wake-up words has been the indispensable module in the voice interaction system. However, the ultra-low-power embedded devices put forward strict requirements on energy consumption, latency, and recognition accuracy of KWS. In this work, we propose a near-sensor processing architecture of feature-configurable distributed network (NS-FDN) for always-on KWS applications. The proposed distributed network adapts to the flexible keywords demands in the actual scene by splitting the conventional single network into distributed sub-networks. We design a channel-independent training framework to improve the recognition accuracy of distributed networks. The speech features are evaluated and the redundancy is reduced in NS-FDN, which can also configure the speech features to further reduce the computing complexity and improve processing speed. For deeper optimization, we implement a 65nm-process prototype chip with near-sensor mixed-signal processing architecture avoiding energy-consuming analog-to-digital converter. By improving the system, algorithm, and hardware designs of the KWS, our co-optimized architecture eliminates the energy consumption bottleneck long-standing in conventional KWS systems and achieves state-of-the-art system performance. The experiment results show that NS-FDN achieves 31.6% energy consumption savings, 1.6 times memory savings, 57 times speedup, and 3.4% higher recognition accuracy compared with the state of the art.
Practical Microgrid Protection Solutions: Promises and Challenges

Microgrids and inverter-based resources (IBRs) offer an exciting promise of clean, renewable, and resilient energy. However, these emerging technologies pose a new set of challenges due to simulated inertia, limited overload current contributions, firmware-based inverter behavior, and more. This article explains some of these challenges and shares several common techniques used to overcome them with programmable protective relays (PPRs).
Computer Vision for Sensed Images Approach in Extremely Harsh Environments: Blast Furnace Chute Wear Characterization

Measurements and characterization for extremely harsh environments require accurate approach especially by means of image-based computer vision. Because of harsh conditions, such as high temperature, pollution, turbulences, radioactive exposure, high energy, direct measurements through conventional sensors are not easy even with recent sensing technologies. Live and/or shortest time-delayed sensing, by means of imaging, can come to help to overcome the aforementioned constraints. The paper outilines the use of sensed images for characterizing the effects of high temperatures, at the inlet of a blast furnace, during the discharge of materials using a chute. This latter is subject to wear due to chemico-physical reactions at around 350-450 °C. Given the specific application related to the harsh environment, two algorithms are comparatively proposed and updated for the purposes of the paper; they are based both on computer vision, namely monadic technique and conventional neural network. For the first technique, virtual sensors have been introduced within the image thanks to sinogram and backprojection subtechniques. The results highlight the effects of the environment on the layers of anti-wear compounds applied on the chute, then they permit to understand the chute life-cycle. Quantitative percentage of material detection has been included as well as specific metrics for machine learning expression.
Secure Coding for the Gaussian Wiretap Channel with Sparse Regression Codes

We consider the Gaussian wiretap channel (WTC), where two legitimate users communicate in the presence of a passive eavesdropper. Sparse Regression Codes (SPARCs) are capacity achieving codes for the AWGN and have been introduced by Joseph and Barron. In this paper, we draw parallels between standard wiretap encoding techniques, based on random binning, and the nested structure that exhibit and propose a coding scheme which achieves a level of information-theoretic security and reliability simultaneously. In particular, we consider SPARCs defined via an i.i. d. Gaussian dictionary as well as with a Bernoulli designed matrix and show that results are the same in both cases. Finally, we discuss how a concatenation with an outer code can be applied to our proposed secure encoding scheme and bound the probability of message error.
Access Strategies for Network Caching

Having multiple data stores that can potentially serve content is common in modern networked applications. Data stores often publish approximate summaries of their content to enable effective utilization. Since these summaries are not entirely accurate, forming an efficient access strategy to multiple data stores becomes a complex risk management problem. This paper formally models this problem as a cost minimization problem, while taking into account both access costs, the inaccuracy of the approximate summaries, as well as the penalties incurred by failed requests. We introduce practical algorithms with guaranteed approximation ratios and further show that they are optimal in various settings. We also perform an extensive simulation study based on real data and show that our algorithms are more robust than existing heuristics. That is, they exhibit near-optimal performance in various settings, whereas the efficiency of existing approaches depends upon system parameters that may change over time, or be otherwise unknown.
To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?

We study how the amount of correlation between observations collected by distinct sensors/learners affects data collection and collaboration strategies by analyzing Fisher information and the Cramer-Rao bound. In particular, we consider a simple setting wherein two sensors sample from a bivariate Gaussian distribution, which already motivates the adoption of various strategies, depending on the correlation between the two variables and resource constraints. We identify two particular scenarios: (1) where the knowledge of the correlation between samples cannot be leveraged for collaborative estimation purposes and (2) where the optimal data collection strategy involves investing scarce resources to collaboratively sample and transfer information that is not of immediate interest and whose statistics are already known, with the sole goal of increasing the confidence on an estimate of the parameter of interest. We discuss two applications, IoT DDoS attack detection and distributed estimation in wireless sensor networks, that may benefit from our results.
Accurate Offset Angle Detection Strategy for Wireless Charging Coils Based on Electronic Compasses

In order to achieve high-efficiency transmission in the electric vehicles’ wireless power transfer system, it is important to ensure that the transmitting and receiving coils are properly aligned. Only if the real-time offset position of the two coils is obtained can the driver align the two coils. This paper proposes a novel offset angle detection strategy for wireless charging coils based on electronic compasses, aiming at the angle offset between transmitting and receiving coils. An electronic compass is placed in the transmitting coil and the receiving coil, respectively. In this process, the exact angle positioning of the charging coil is realized with the aid of geomagnetic and the application of vector theory to determine the offset angle between the two coils. Finally, an experimental prototype has been constructed. The experimental results indicate that the positioning error of the coil angle is within 1 °. This method’s measurement process is simple, and the deviation angle between the coils can be directly and conveniently calculated by examining the electronic compass output, which helps guide to align the transmitting coil with the receiving coil.
Urban Driving Games With Lexicographic Preferences and Socially Efficient Nash Equilibria

We describe Urban Driving Games (UDGs) as a particular class of differential games that model the interactions and incentives of the urban driving task. The drivers possess a “communal” interest, such as not colliding with each other, but are also self-interested in fulfilling traffic rules and personal objectives. Subject to their physical dynamics, the preference of the agents is expressed via a lexicographic relation that puts as first priority the shared objective of not colliding. Under mild assumptions, we show that communal UDGs have the structure of a lexicographic ordinal potential game which allows us to prove several interesting properties. Namely, socially efficient equilibria can be found by solving a single (lexicographic) optimal control problem and iterated best response schemes have desirable convergence guarantees.
Private Investors Participation in Long-Term Distribution Network Planning

Distribution network planning is one of the challenging issues for the network planner. However, in recent years the integration of renewable energy resources has increased the planning complexity. On the other hand, the high investment cost of these resources is a significant barrier for the distribution companies. Therefore, in this paper, a distribution network planning framework to assess the private investor's participation is proposed. The proposed model includes two layers where in the first layer a common network planning according to different levels of load reduction is performed, and then in the second layer, the optimum site and size of photovoltaic (PV) and energy storage system (ESS) as renewable resources is determined. Meanwhile, payback period years as an economic index is considered to evaluate the profitability of the plan from the investor’s point of view. The proposed model is tested and implemented to a 54-bus distribution test system and the result illustrates the cooperation of utility and private investors in the long-term distribution network planning problem.
Case analysis and model construction of the impact of supply chain relationship quality on growth performance in the start-up stage of new ventures

At present, the entrepreneurial market of small and medium-sized enterprises in China is increasingly active. However, in order to achieve sustainable growth and development, these new ventures cannot obtain competitive advantage in the market competition only relying on their own strength and resources, and even affect the survival of enterprises. External relationship has become an important way for new ventures to obtain supplementary resources and promote the growth and development of enterprises. Based on this, this paper uses the typical case analysis method to understand how the supply chain relationship quality growth performance in the initial stage of new ventures. This also provides some experience and reference for the new venture how to build the appropriate supply chain relationship quality according to its growth stage, and adopt the matching relationship management mode.
Finite Element Modeling Of Finned Double-Pass Solar Air Heaters

Modeling of three-dimensional (3D) finned double-pass solar air heater using the finite element method (FEM) based simulation program COMSOL Multiphysics is proposed and compared with the mathematical model. This model is used to analyze and simulate the performance of a finned double pass solar air heater. A non-isothermal conjugate heat transfer is used for coupling between heat transfer in the solid domain and fluid flow domain. The turbulent flow (k-ω) and a high Reynolds number are applied for high accuracy results. The results are presented in terms of temperature and velocity distribution along with the solar air heater, outlet air temperature, and thermal efficiency of the finned double-pass solar air heater. The FEM simulation results are compared with mathematical results as well as validated with the experimental results. The comparison results in percentage relative error between the experimental results and FEM simulation results is less than between the experimental results and mathematical model results for the outlet temperature and thermal efficiency.
Design of High Performance HMRPD Network on Chip Interconnect for Neuromorphic Architectures

An emerging neural network consists of larger core chips that rely on the network-on-chip (NoC) based interconnection to control the massive volume of inter-neuron traffic. The NoC interconnect renders high flexibility as it solves one of the critical challenges of reconfigurable neural-networks in hardware implementation. In this paper, a novel two-dimensional (2-D) hybrid of mesh-ring with partial diagonal link (HMRPD) topology has been proposed for neuromorphic architectures. The proposed architecture has been examined in terms of average distance, hope count, and core count of application from source to destination. The analysis shows that the proposed HMRPD topology has low communication costs, high scalable due to small diameter, average distance, and high bandwidth. Finally, a comparison has been performed with the existing topology for neural-network hardware accelerators. Moreover, the proposed HMRPD topology reduces latency and improves the throughput of neural information processing.
General hardware multicasting for fine-grained message-passing architectures

Manycore architectures are increasingly favouring message-passing or partitioned global address spaces (PGAS) over cache coherency for reasons of power efficiency and scalability. However, in the absence of cache coherency, there can be a lack of hardware support for one-to-many communication patterns, which are prevalent in some application domains. To address this, we present new hardware primitives for multicast communication in rack-scale manycore systems. These primitives guarantee delivery to both colocated and distributed destinations, and can capture large unstructured communication patterns precisely. As a result, reliable multicast transfers among any number of software tasks, connected in any topology, can be fully offloaded to hardware. We implement the new primitives in a research platform consisting of 50K RISC-V threads distributed over 48 FPGAs, and demonstrate significant performance benefits on a range of applications expressed using a high-level vertex-centric programming model.
Automatic Fruit Detection System using Multilayer Deep Convolution Neural Network

Agriculture has become an important thing in everyday life. Among this, fruits are a great thing in every day life. Classification of fruits based on their accuracy is a decent approach to all the fruit sellers. There is many parallelism between apple and cherry and various kinds of similarities are present in many types of fruits, so the classification plays an important role. However, there are troubles in fruit classification using machine learning algorithms like Support Vector Machine (SVM) and Convolutional Neural Network(CNN). So, the methods of CNN, pooling layers and fully connected network have been applied to overcome the problems. The CNN and pooling layers have been applied to extract the features of the fruits. To expose this scheme, various fruits such as Apple, Blueberry, Cherry, Grape blue, Guava, Kiwi, Lemon, Papaya, Strawberry, Plum, Tomato and Mango are considered. By implementing this project, the accuracy of fruit classification is increased.
Selection of Deep Neural Network Models for IoT Anomaly Detection Experiments

This research is about selection of deep neural network models for anomaly detection in Internet of Things network traffic. We are experimentally evaluating deep neural network models using the same software, hardware and the same subsets of the UNSW-NB 15 dataset for training and testing. The assessment results are quality metrics of anomaly detection and the time spent on training models.
Hyper-Plane Sliding Mode Control of Non-Minimum Phase Grid-Connected Zeta Converter

In this paper, a hyper-plane sliding mode controller (SMC) is presented for regulate of dc-link voltage of the grid-connected Zeta converter along with control of ac current of the grid. To unified analyze of the system, averaged state-space model of the grid-connected Zeta converter is presented. Although from point of structure view, the system is seen as a single-input single-output system, based on control strategy, it is a multi-input multi-output (MIMO) system. Due to nonlinear characteristic of the converter and unstable-zero dynamic problem which lead to non-minimum phase of the capacitor voltages, hyper-plane SMC is used to indirect regulate of the dc-link voltage by inductors’ currents. Therefore, by considering the nonlinear operation of the system, a two-stages controller based on unified model of the system is designed to insure the converter stability in wide range of output variables. To validate the performance of the presented controller, the system is simulated by MATLAB/Simulink software and results are shown to prove the controller accuracy.
Optimal Supervisory Control for Flexible Manufacturing Systems Model With Petri Nets: A Place-Transition Control

Supervisory control for discrete event systems that is widely studied in the literature to tackle the Petri net model’s deadlock occurrence of flexible manufacturing systems. A supervisory structure constructed using control places imposed restrictions on the concurrent operation in the systems, which degrade the supervisor’s efficiency. This study proposed a new method to construct a supervisory structure using combine control places and control transitions to ensure flexible manufacturing systems’ smooth operation. Place-Transition controller is designed for each concurrent process of the systems. Three algorithms are proposed in this method. The algorithm 1 computes the loop markings at each process of the Petri net model. Algorithm 2 is used to sort the deadlock markings based on the concurrent processes of the Petri net model. The Transition-Place controller and Place-Transition controller are designed using algorithm 3. Transition-Place controller creates extra states in the reachability graph, which is called dump markings. Place-Transition controller projects the deadlock markings to the loop markings via dump markings. The number of states created by the Transition-Place controller depends on the number of concurrent states in the Petri net model. The final supervisory structure controls the deadlock occurrence of flexible manufacturing systems with zero restrictions of systems operation. This proposed method is efficient as it retains all the states generated in the controlled Petri net model.
Improved Model Predictive Control Methods with Natural Capacitor Voltage Balancing for the Four Level-Single Flying Capacitor (4L-SFC) Inverter

In this paper, two control schemes based on finite control set-model predictive control (FCS-MPC) are proposed for a reduced-capacitor multilevel converter, referred to as four level-single flying capacitor (4L-SFC) converter. By using the notion of reconstructing phase voltage levels, natural capacitor voltage balancing can be achieved for the 4L-SFC converter, which is not fulfilled by the PI-based conventional modulation methods. Besides, two proposed strategies present a weighting factor-less approach to achieve fixed switching frequency, lower current distortion, and reduced ripple of capacitor voltages as well as lightening the computational burden. A single-objective cost function based on voltage vector error is defined in both methods to accomplish the reference current tracking. The second proposed method employs modulated model predictive control (M2PC) to guarantee a well-concentrated harmonic spectrum and lower current distortion compared to the first strategy. Simulation studies are conducted to validate and compare the performance of the proposed methods in terms of steady-state and transient-state response.