Miracle-dz/UniFAR-FTUs huggingface dataset:
Open-Source RISC-V Processor IP Cores for FPGAs — Overview and Evaluation

Advances in semiconductor miniaturization are an important driver for Field Programmable Gate Arrays (FPGAs) since their invention in the 1980s. The increasing number of available on chip logic resources on one hand and on the other hand a decrease in part costs let the FPGA market grow steadily in recent years. It comes thus at no surprise that more and more microprocessors are integrated into programmable logic devices as they represent the central functionality in many digital systems. In parallel to these technological developments the open-source hardware community grew steadily in the last two decades. More than hundred open-source CPU cores can thus be found and selecting a core for a design project has to be done with care. In this work we thus want to focus on open-source 32-bit CPU IP cores suitable for FPGAs and which support the upcoming free and open RISC-V instruction set architecture that has some interesting advantages when compared to commercial CPU cores (as will be outlined in the paper). An overview on available projects and activities will be given and evaluation results for a selection of cores will be presented.
Load Frequency Control in Connected Power System including Distributed Energy Resources using Iterative Gradient Method

In this paper, load frequency control problem for power system is discussed. In the proposed method, we consider power system model including generator and battery. Also, saturation is taken into account as a constraint on the input to the model for generator and battery. Then, a cost function considering the storage capacity constraint is formulated. The connected power system cooperatively determines control input that minimize the cost function and keep the power system stable. Furthermore, we propose to consider the economic dispatch methodology considering the suppression of the fuel cost in power system. Therefore, load frequency control and economic dispatch methodology considering constraints of generator and battery are proposed in this paper. Finally, we confirm the effectiveness of our proposed method through numerical simulation.
Self-Powered Wireless Sensor for Fault Diagnosis of Wind Turbine Planetary Gearbox

This paper proposes a wind turbine planetary gearbox (PGB) fault diagnosis method based on a self-powered wireless sensor. The proposed wireless sensor, which consists of a piezoelectric energy harvester, a power management circuit, a microcontroller unit (MCU), a radio-frequency (RF) module, and an accelerometer, can acquire the vibration signals of wind turbine PGB by the accelerometer. The piezoelectric energy harvester utilizing vibration environment is optimized as a power supply for the proposed wireless sensor, including the MCU, RF module, and accelerometer. An ac–dc converter combined with a low-dropout voltage regulator is developed to provide stable dc voltage for the proposed wireless sensor. Stacked denoising autoencoder (SDAE) shows excellent performance in learning robust features from the noised signal. Thus, in this paper, the SDAE method is adopted to learn robust and distinguishable features from measured signals. Then, the least squares support vector machine (LSSVM) is employed to classify features extracted by the SDAE. Both the SDAE and LSSVM are optimized by quantum particle swarm optimization (QPSO). The experimental results show that the presented power supply can generate 3.3-V dc voltage, which ensures regular operation of the rest of the wireless sensor. The proposed wireless sensor can achieve a reliable communication distance of 40.8 m in the test environment. Furthermore, the SDAE approach and LSSVM show excellent performance in feature extraction and fault diagnosis, respectively. The experimental results indicate that the proposed method is effective in terms of fault diagnosis for the wind turbine PGB.
High Level Synthesis Introduction To Chip And System Design

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Remote Monitoring of Rehabilitation of Patients of Traumatology and Orthopedic Profile

Increasing number of locomotor apparatus injuries and diseases causes a high requirement for treatment and following rehabilitation one component of which is exercise therapy (ET). At present, a large number of patients need ET, properly selected exercises taking into account individual characteristics. It is quite easy to check correctness of such exercise performance in in-patient and out-patient clinics. However, in cases when the patient is not under the direct supervision of a doctor, control of the regularity and accuracy of performed exercises becomes impossible. Purpose of the present paper is to develop the smartphone application for patients with knee-joint pathology that allows monitoring of the patient of traumatology and orthopedics profile in the out-patient clinic on the basis of three-axes smartphone accelerometer and software.
3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

3D surface matching by local feature descriptors is a fundamental task in 3D object registration, recognition, and retrieval. In view of 2D projected descriptors’ information compression and 3D directly voxelized descriptors’ sensitivity to density variation and boundary, this paper first proposes a voxel-based buffer-weighted binary descriptor, named VBBD. After local surfaces of detected keypoints are voxelized, each voxel’s buffer region is established, and the buffer-weighted Gaussian kernel density is calculated. If the current voxel’s buffer-weighted density is larger than its local surface’s average density, the voxel is labeled 1, otherwise, it is 0. The proposed descriptor has several merits: 1) direct acquisition of 3D information without projection, the neighbor information is less compressed. 2) voxels are labeled and binarized according to the buffer-weighted density, which improves the robustness to boundary effect, noise and density variation. Based on the VBBD, a global optimal surface matching method based on a Kuhn_Munkres (KM) algorithm is adopted. The strategy has several advantages: 1) by calculating the descriptors of large-scaled surfaces which are down sampled, the number of the point cloud is reduced, and meanwhile, large-scaled information is obtained; and 2) KM algorithm is adopted to find matching pairs to achieve final maximum weight sum of all matching pairs, which can efficiently avoid local optimum. The experimental results show that, compared with other state-of-the-art descriptors, the VBBD has better descriptiveness and robustness, and the surface matching strategy by the VBBD can achieve both high recall and precision.
Development of an Effective 3D VR-Based Manipulation System for Industrial Robot Manipulators

Along with fast progress in their ability and flexibility, more robots are introduced for automation, and applied for more complicated tasks. Consequently, it demands more versatile and advanced manipulative devices for task manipulation and teaching. We thus propose a novel manipulation system based on virtual reality (VR). This system is intended for achieving 3D manipulation in a real-time manner. VR is recognized for capable of providing immersive feeling for the user. Meanwhile, it also faces the challenge in making the VR environment as close as the real one. To tackle this, we propose building a point cloud model of the real environment and integrating it with the virtual one. Meanwhile, to ease the load of the user during manipulation, we also develop a user-friendly interface, including a mechanism that can adjust the resolution of the command according to task complexity. Experiments are conducted to evaluate system’s performance, and questionnaires administered for acquiring user’s feedback.
Analysis of Current Predictive Control Algorithm for Permanent Magnet Synchronous Motor Based on Three-Level Inverters

In some high-precision control applications, such as defense industry and computerized numerical control machine tools, fast and stable electromagnetic torque response is required to ensure the high dynamic performance of the system, while traditional PI control often cannot meet its requirements. For this purpose, a predictive control algorithm based on the deadbeat control algorithm is proposed in order to improve the performance of the motor current loop. In order to solve the problem that the conventional deadbeat control algorithm has a large dependence on system parameters and low robustness, this paper proposes an improved deadbeat control scheme for the permanent magnet synchronous motor based on the three-level inverters. The scheme is based on the second theorem of Lyapunov stability. The improved deadbeat control algorithm can achieve a good output waveform when the switching frequency is not high and the response speed is fast. The robustness of the system is improved, and there are good characteristics in reduced torque ripple. Compared to the traditional PI regulators, the improved deadbeat control can quickly track the current commands without overshoot and oscillation and suppress torque ripple. The simulation and experimental results show that the improved deadbeat control proposed in this paper has good dynamic and static performance.
Decision-based Algorithm for Robust 2D-DoA Estimation with L-shaped Array

The L-shaped array structure is considered as an attractive alternative to estimate entire direction of arrival (both azimuth and elevation). Most of the techniques developed around this specific array structure assume a small elevation angle range (70 to 90 degrees) which is not enough realistic in the context of small cells environment. In this paper, we propose a decision fusion rule of a parallel 2-D direction of arrival (DOA) estimation of shifted signal. The performance of the proposed algorithm is investigated through numerical simulations and compared with the 2D propagator method estimator. The impact of angular spread on the precision of the DOA estimation approaches is also considered. Our algorithm accurately estimates the DOA over a wide range of elevation angle (10 to 90 degrees).
Convolutional Neural Networks for blind decoding in Sparse Code Multiple Access

Sparse code multiple access (SCMA) has attracted growing research interests in order to meet the targets of the next generation of wireless communication networks. Since it relies on non-orthogonal multiple access (NOMA) techniques, it is considered as a promising candidate for future systems that can improve the spectral efficiency and solve the problem of massive user connections. In this paper, the basic concept of SCMA is introduced, including SCMA encoding, codebook mapping, and SCMA decoding. The major challenge of SCMA is the very high detection complexity. Then, a novel strategy for blind decoding based on convolutional neural networks is proposed. Through simulations, we showed that our proposed scheme outperforms conventional schemes in terms of both BER and computational complexity, where 0.9 dB improvements can be achieved.
Modeling Bilevel Programs in Pyomo.

We describe new capabilities for modeling bilevel programs within the Pyomo modeling software. These capabilities include new modeling components that represent subproblems, modeling transformations for re-expressing models with bilevel structure in other forms, and optimize bilevel programs with meta-solvers that apply transformations and then perform optimization on the resulting model. We illustrate the breadth of Pyomo’s modeling capabilities for bilevel programs, and we describe how Pyomo’s meta-solvers can perform local and global optimization of bilevel programs.
Design and Implementation of Controller for EHPS of Intelligent Electric Bus

In order to meet the needs of bus electrification and intellectualization, a controller for the steering power system of an intelligent electric bus is studied in this paper. The contents mainly include: based on the structural principle and control strategy of the EHPS system, Luenberger observer is designed, which is applied to the sensorless control system of PMSM. Then, a controller with STM32F103zet6 chip as its core is designed and manufactured. The hardware design of the controller includes the minimum system circuit, power supply circuit, signal acquisition circuit, integrated driver module circuit, CAN communication circuit, fault and high temperature alarm circuit of the single chip computer; combining the design of each hardware part, the controller of EHPS system is made. In the aspect of software implementation, the control decision-making part, a motor drive control part, fault diagnosis, and transmission part are mainly designed. Finally, the bench test and vehicle test of the controller are carried out. The purpose of the bench test is to test the control effect of the controller on the motor. The bench test results show that the target speed response of the motor is fast and the output is stable. The controller developed in this paper realizes the control of PMSM. The purpose of the real vehicle test is to test whether the controller meets the actual application requirements of the steering power system of the vehicle. The actual vehicle test results show that the steering wheel’s torque decreases obviously, and the steering power system has good power-assisting performance when the EHPS system controller works. With the increase of vehicle speed, the power-assisting torque decreases accordingly. EHPS system realizes the dynamic change of power-assisting and meets the requirements of the conventional power-assisting mode of the car. In addition, the controller can realize the active steering control by controlling the electric power assistant device, which provides a platform for the realization of lane auxiliary maintenance, automatic parking, unmanned driving, and other functions.
Selfoptimization Modes of Electric Grids with Renewable Energy Sources Using the Principle of Least Action

The article devoted creating optimal conditions for the integration of renewable energy source (RES) into electric grids, which were designed with power from centralized generation at large power plants. The possibility of simultaneous optimal functioning of electric grids and renewable energy sources is considered. The complex criterion of optimality is the economic mode of power sources, as well as the minimum losses of electricity during its transmission. To optimize the modes of electricity grids with renewable energy sources, the principle of the least action is used. Optimal control of modes is carried out by the active-adaptive system.
Forecasting Consumption of Electrical Energy Using Time Series Comprised of Uncertain Data

A lot of forecasting methods and models have been proposed by researchers for different applications with the main issue to improve their accuracy. Electrical loads and energy consumption forecasting is one of the most important task in power system operation and planning. However, in some cases we need to solve this problem in the absence of reliable and adequate historical data. In this regard paper presents the development of fuzzy time series for electrical energy consumption forecasting. The experimental data of monthly energy consumption is used to demonstrate the model's effectiveness.
A Machine Learning-based Approach for The Prediction of Electricity Consumption

Balancing the power supply and demand is one of the most fundamental and important problems for the operation and control of any electric power grid. There are multiple ways to guarantee the supply-demand balance, but in this research we focus on one specific method to facilitate it namely the prediction of electricity consumption, which is widely used by utility companies or system operators. It is known that this prediction is challenging because of many reasons, for example, inexact weather forecasts, uncertain consumers’ behaviors, etc. Hence, analytical and linear models of electricity consumption might not be able to deal with such issues well. This paper therefore presents a machine learning-based approach to predict electricity consumption, in which an improved radial basis function neural network (iRBF–NN) is proposed, whose inputs are time sampling points, temperature, and humidity associated with the consumption. The parameters of this iRBF–NN are sought by solving an optimization problem where four types of cost functions are used and compared on their performances and computational costs. Afterward, the derived model is employed to predict the future electricity consumption based on the hourly forecasts of temperature and humidity. Finally, simulation results for realistic data in Tokyo are presented to illustrate the efficiency of the proposed approach.
Power Saving Extension for the NDN-Based GIF Protocol for the Internet of Things

Power consumption and network lifetime optimization is an essential performance objective for the IoT networks. Since the devices in this type of networks are mostly battery powered with a limited battery life, energy saving strategies becomes vital for such systems operation. Typically, the standard sleep mode of the 802.15. 4 protocol is used in order to reduce the power usage of the sensors in an IoT network. However, enabling the sleep mode function in an IoT device that utilizes the Named Data Networking (NDN) paradigm is challenging. In this paper, we propose a power saving extension for the Geographic Interest Forwarding (GIF) protocol. In the proposed E-GIF scheme, a cross-layer approach is used, which allows direct communication between protocols at nonadjacent layers. More precisely, the interactions between the MAC and the Routing layers are fully exploited in order to integrate the sleep mode into the forwarding decision of the sensor nodes that uses the NDN protocol stack. To validate the proposed scheme, we extend the ndnSIM simulator to support wireless multihop communication. Simulation experiments confirm the viability and the effectiveness of the proposal.
Optimal Fault-tolerant Control for A Class of Nonlinear Systems by Using Zero-sum Differential Game*

In this paper, an optimal fault-tolerant control (FTC) method using zero-sum differential game is proposed for a class of nonlinear systems with actuator faults. Firstly, it is reveals that the FTC problem is equivalent to a zero-sum differential game problem, in which the controller and the fault are regarded as two opposite players. Secondly, the Nash-equilibrium is established by minimax principle, and the coupling Hamilton-Jacobi-Isaacs (HJI) equation is further solved by adaptive dynamic programming (ADP) method. Besides, a FTC learning law of the weight is proposed, which only uses a critic network. Finally, an example is given to illustrate the efficiency and applicability of the obtained theoretical results.
Finite-Element Collocation Based Successive Convexification for Powered Landing Guidance of Reusable Rockets

The problem of powered landing guidance of reusable rockets is formulated in the presence of aerodynamic drag forces. This problem is challenging for onboard guidance applications due to non-convex thrust constraints and nonlinearities introduced by aerodynamic drag, mass-depletion dynamics, and free final time. Convex programming, which is characterized by deterministic convergence, is becoming increasingly attractive to address such problems. Lossless convexification is used to deal with the non-convex thrust constraints to make this guidance problem tractable. In addition, other non-convexities are eliminated by finite-element collocation based successive convexification, which relies on finite-element collocation discretization, linearization, trust region, and relaxation. Through successive convexification, a sequence of second-order cone programming problems is solved in each iteration by a state-of-the-art interior-point algorithm. The proposed approach is verified and compared with a direct method associated with an efficient nonlinear programming algorithm by numerical simulation.
Optimal Charge Scheduling for Energy-Constrained Wireless-Powered Network

As a candidate power supply solution for the Internet of Things, radio frequency (RF) energy harvesting has attracted great attention recently. In this paper, we consider an RF wireless-powered network, which consists of a dedicated power beacon (PB) and multiple user nodes. The PB is assumed to have limited energy and transfers its power to user nodes wirelessly. The user nodes work only based on energy harvested from the PB, and are assumed to be in either energy harvesting mode, energy consumption mode, or idle mode. To maximize the total harvested energy, we coordinate the behaviors of the PB and user nodes, and propose a general charge scheduling scheme that can achieve the system’s maximum harvested energy. We then compare the results of our proposed scheme with the benchmark schemes, and simulation results demonstrate the effectiveness of our proposed scheduling.
Log Likelihood Monitoring for Multimode Process Using Variational Bayesian Mixture Factor Analysis Model

When a traditional mixture factor analysis (MFA) model is used for multimode process monitoring, the determination of parameter is complex, and the construction of monitoring statistics only considers the expectation in probability distributions of factor space and residual space. In this paper, a novel fault detection method based on a variational Bayesian MFA model for multimode process is introduced. The parameters of the MFA model structure, namely the number of local factor analyzer and the reduced dimensionality inside each factor analyzer, can be easily obtained through the birth-and-death Markov chain Monte Carlo algorithm and the variational inference technique. After parameter estimation for the Bayesian MFA model is done, a new monitoring index called negative variational log likelihood is developed by utilizing the whole information in probability distribution functions of all parameters. At last, two case studies, including a numerical example and the Tennessee Eastman (TE) process, verify the effectiveness and feasibility of the proposed monitoring scheme.
Crowd Counting Method Based on Convolutional Neural Network With Global Density Feature

Crowd counting is an important research topic in the field of computer vision. The multi-column convolution neural network (MCNN) has been used in this field and achieved competitive performance. However, when the crowd distribution is uneven, the accuracy of crowd counting based on the MCNN still needs to be improved. In order to adapt to uneven crowd distributions, crowd global density feature is taken into account in this paper. The global density features are extracted and added to the MCNN through the cascaded learning method. Because some detailed features during the down-sampling process will be lost in the MCNN and it will affect the accuracy of the density map, an improved MCNN structure is proposed. In this paper, the max pooling is replaced by max-ave pooling to keep more detailed features and the deconvolutional layers are added to restore the lost details in the down-sampling process. The experimental results in the UCF_CC_50 dataset and the ShanghaiTech dataset show that the proposed method has higher accuracy and stability.