Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study of various Cloud Algorithms using Edge Cloud SIM

Cloud computing is the on-demand availability of computer resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. It has various challenges, to solve the challenges, new technology is introduced by transferring the cloud into an edge server. For making massive changes such as reduced latency into the current system of cloud computing by introducing the latest automation. And this is time that we know which will be the most effective algorithm as there are quite a few of them are currently being studied. So as to find an algorithm that works the best for offloading different tasks. There are already many simulators that can simulate network activity at various levels comminute. Starting with current simulators, a significant programming effort is required to create a simulation tool that meets all requirements. Designing a new edge computing tool, on the other hand, will provide several technological challenges. A new simulator tool called EdgeCloudSim is particularly intended for edge computing frameworks to help overcome these hurdles. It satisfies the demand for edge computing, which is dependent on both computational and networking factors. There are numerous aspects to estimate. Edge computing using EdgeCloudSim. In this paper will be observing different algorithms already present to find the best approach for future use in the study of Edge Cloud Computing and for practical uses
Optimization of Energy management using a particle swarm optimization for hybrid renewable energy sources

This study investigated a stand-alone hybrid renewable energy system’s optimum energy management system. The proposed optimization method is called Particle swarm optimization and it is an evolutionary computing methodology (PSO). The proposed technique (PSO) for managing the energy system is to calculate the amount of power that the battery must generate and that is through adjusting the values PI controller gains while taking account the amount of power generated by the significant resources (in our case PV and wind).
Non-intrusive Monitoring Framework for NoC-based Many-Cores

Many-core Systems on Chip (MCSoCs) require resource management to achieve scalability at the computation and communication levels. The monitoring infrastructure feeds management tasks with raw data, enabling these tasks to detect behaviors corresponding to constraint violations or a trend that signalizes a future violation. Several works available in the literature use monitoring to apply their management techniques but do not specify how to implement the monitoring framework. We propose a monitoring framework for MCSoCs, with the following features: (i) generic: the infrastructure can carry data related to different monitored features; (ii) monitored data does not disturb NoC flows; and (iii) reduced overhead compared to other monitoring methods. The monitoring framework is loosely coupled to the MCSoC by using a dedicated NoC to carry monitoring and management messages, decoupling data traffic from management traffic. Results adopt the Observe-Decide-Act management method, comparing the proposed monitoring framework to a standard monitoring approach. Results show a reduction in the data NoC traffic (12%), faster management responsiveness to act on deadline violations (up to 77%), and reduced applications execution time (on average 8%).
The use of ALSTM-FCN for tobacco planting extraction from time-series Sentinel-1A Sar images

Spatial information on tobacco planting is crucial to many agricultural applications regarding tobacco production and management. This paper presents a deep learning model, i.e., Attention Long Short-Term Memory Fully Convolutional Network (ALSTM-FCN), to extract tobacco planting areas using time-series Sentinel-1A (S1A) SAR images. Using the ALSTM-FCN model, high-level temporal and spatial image features are fused to characterize the growth of tobacco planting. We applied the ALSTM-FCN to extract tobacco in the Fujian area using time-series S1A SAR data acquired in 2020. We compared the proposed method with a conventional LSTM and a machine learning method (e.g., Light GBM). Our results show that the extracted results by the ALSTM-FCN model have a higher extraction accuracy of 0.93 than that of the LSTM of 0.92 and the Light GBM of 0.91. We conclude that the proposed ALSTM-FCN method can be used as a promising solution for extracting tobacco using time-series SAR data in cloudy and rainy areas.
Harmonic Analysis of Input DC Current in Six- and Nine-Phase Voltage Source Inverters

Characterization of the input dc current ripple is imperative for dc-bus capacitor sizing in voltage source inverters (VSIs). While multiphase VSIs are known for their reduced dc current stress, the underlying reason for this reduction is not explored in literature. Furthermore, the effect of winding configuration, namely symmetric vs. asymmetric, was not investigated. This paper presents a detailed harmonic analysis of the input dc current in multiphase inverters, namely six-phase and nine-phase two-level VSIs for the different load configurations. Additionally, benchmarking against three-phase VSIs is conducted to quantify the reduction factor of the current stress in multiphase inverters. It was found that the current stress on the dc-capacitor when supply symmetric six- and nine-phase loads is lower by around 4% when compared to asymmetric loads. The present analysis is validated by simulations and experiments on a six-phase VSI.
Using machine learning to understand Twitter users' urban green space activities during COVID-19 pandemic period

Volunteered Geographic Information (VGI) provides effective information for evaluating the usage of urban green space (UGS). Geo-referenced Tweets become very popular in the assessment of UGS use because of data availability and large data volume compared with traditional surveying methods, which are time-consuming and inefficient. However, previous studies lack efficient methods to extract and interpret Twitter data for UGS activities evaluation. Therefore, this paper aims to present a framework that enables high-efficient extraction of public UGS activities from Twitter. Greater London was selected as a case study to describe the framework development. First, Twitter data within Greater London over a certain COVID-19 lockdown period are collected, cleaned and pre-processed. Second, word vector representations were generated using Word2vec model, and then document vector representations were obtained by using Doc2vec model. Next, all the Tweets were clustered by using K-means algorithm to reveal the UGS activities during lockdown period. The framework can be used as a tool for UGS planners and managers to enable a holistic understanding of public activities engagement in UGS and increase the degree of public participation in UGS management.
Comparative Investigation of Corona Pulse Characteristics under DC and AC Voltages

This paper presents a comparative investigation of AC, + DC, and - DC corona discharge pulse characteristics using machine learning (ML) algorithms. The corona discharges under different types of excitation are generated via a rod-plane elec-trode system with a constant gap spacing. The corona discharge pulses are recorded using a shunt resistor via an oscilloscope. After noise elimination from the discharge pulses, nine features extracted from the noise-free signals are inputted to several ML models to identify the corona discharges with respect to the voltage types. To increase the performance of a single model, ensemble learning, which is the combination of ML algorithms, is employed. It is observed that the corona discharge types are effectively identified with these features using ensemble learning with a high accuracy rate.
A 1000 fps Spiking Neural Network Tracking Algorithm for On-Chip Processing of Dynamic Vision Sensor Data

Dynamic vision sensor (DVS), an event-based camera, has attracted significant attention due to its unique characteristics. Unlike frame-based cameras, the data format of DVS makes it difficult for traditional algorithms to process it directly. On the other hand, as a new type of brain-like neural network, the spiking neural network is specially used to process spiking data, and it is well suited for this type of event-based camera. In addition, because of the rapid development of neuromorphic hardware in recent years, it is possible to deploy SNN applications on edge-side system-on-chip. Therefore, based on the characteristics of dynamic vision sensors, this paper designs a spike encoding module and an SNN for processing sensor information. We use selective search to accomplish object tracking by classifying targets and backgrounds. The SNN can achieve 98.66% classification accuracy on our synthetic test dataset, and the tracking algorithm can achieve over 1000 fps after quantizing and compiling the network to the hardware simulator.
Learnable Regularization via Padé Activation Units for Flexible Model-Based Beamforming

Aperture Domain Model Image REconstruction (ADMIRE) is a beamformer that suppresses sources of acoustic clutter by performing model-based fits of channel data. Each fit requires solving an ill-posed, inverse problem using regularization. Initially, these fits were performed with the elastic-net constraint, but we recently proposed using a deep neural network (DNN) for model fitting based on the learned iterative shrinkage thresholding algorithm (LISTA). This approach used a sigmoidal soft-thresholding activation function with a trainable threshold to allow more flexible regularization, but the range of learnable proximal operators of regularization priors is still limited. Therefore, in this work, we propose using fully trainable activation functions to allow for a wider range of operators to be learned. Moreover, using simulated data, we demonstrate the ability of this approach to provide further improvements in ultrasound image quality.
A New Adaptive Imaging Technique Using Generalized Delay Multiply and Sum Factor

Although conventional delay-and-sum (DAS) beam-forming is the most common beamforming method to reconstruct ultrasound images, alternative approaches might provide better image quality. As such, delay multiply and sum (DMAS) beam-forming has recently been proposed. In this paper, we investigate the use of a more efficient approach for the DMAS method together with high frame rate (HFR) imaging and coherent compounding. The proposed beamformer is evaluated on datasets acquired by computer simulation and experimentally in vivo. Imaging results demonstrate that it provides better clutter and noise rejection, and enhances the image quality in terms of resolution and contrast as compared with DAS and DMAS methods, without compromising the computational complexity.
A Single-input Dual-output Three-level Buck Converter for SoC Applications

This paper proposes a single-input dual-output (SIDO) three-level buck converter to meet the requirements of multi-voltage domain and high voltage stress for SoC applications. The topology is based on three-level buck with standard 1.8V devices, where only an additional power switch is added. By using this structure, the second output can be powered by a fly capacitor, and $V$CF calibration is achieved by the control loop for reliability issues. Hence the efficiency and the power density are enhanced with standard devices and reducing the number of power switches. Moreover, the control loop with error processor and driver module is demonstrated based on the topology analysis. Ultimately, the proposed converter is designed and fabricated with 0. 18μm CMOS process, which handles the input range of 3.3-2.8V and dual-output of 1.8V and 1.2V with 96.9% peak efficiency. The power density is 2.557W/mm2, and the active area is only 0.49 mm2.
Condition Monitoring of Direct Torque Controlled Permanent Magnet Synchronous Machines

The hysteresis comparators in direct torque control pose challenges in fault detection and separation for permanent magnet synchronous machines. Variations in the switching frequency, determined by the hysteresis band, may result in unobservable fault effects in direct torque control drives. Consequently, the faulty operation may lead to unstable control of permanent magnet synchronous machines. Considering the behaviour mentioned above, this paper proposes a non-invasive fault indication method for direct torque control driven permanent magnet synchronous machines using the shift in commanded $MT$ voltages. Three faults are considered: inter-turn short circuit, high resistance connection, and partial demagnetization. Numerical and experimental results demonstrate the effectiveness of utilizing the proposed voltage shift in detecting and classifying faults.
An Open-Source Radon-Transform Shear Wave Speed Estimator with Masking Functionality to Isolate Different Shear-Wave Modes

The Radon Transform (RT) approach is a common method used to estimate shear wave trajectory and speed in ultrasound shear wave elasticity imaging (SWEI). The RT calculates the sum of 2D spatiotemporal data amplitude under each potential linear trajectory to determine the trajectory with the greatest value. We divide the RT of data by the RT of a ones matrix to normalize by trajectory path length, enabling the use of arbitrary data masks. We demonstrate that masking can isolate the two simultaneous SH and SV shear wave modes observed in in vivo skeletal muscle data. 38 rotational SWEI acquisitions were collected in vastus lateralis muscle, for a total of 2736 space-time plots, and shear waves were identified by manually drawing masks. Using these labeled data, we trained a deep neural network to generate masks from a space-time plot to reduce the need to hand-draw masks in the future. On a held-out test case, 91% of predicted trajectories corresponded to a labeled shear wave, and estimated speeds had a mean absolute error of 7.6%. Despite frequent use of the RT method in the literature, no openly available code exists. We have released our code at https://github.com/fqjin/radon-transform.
An FPGA-Based 15-level Arbitrary Waveform Generator System with Phase/Frequency Modulation for Ultrasound Coded Excitation Using Pulse-Amplitude Modulation (PAM)

Several research communities have presented innovative approaches to the challenge of generating complex waveforms required to evaluate new ultrasonic transmission techniques. Dedicated high-speed digital-to-analog converters combined with high-voltage (HV) power amplifiers are employed, although their design is relatively complicated. This paper presents a reconfigurable multi-level arbitrary waveform generator (AWG) for ultrasound-coded excitation research using Pulse-Amplitude Modulation (PAM). The proposed hardware architecture consists of a Terasic DE2-115 board coupled to the developed AWG daughterboard. The transmission board was implemented using state-of-the-art devices providing complete digital control, in which the main component is the MD2134 high-speed ultrasound source driver. The design includes an MD0100 HV T/R switch, a voltage-controlled gain wideband amplifier VCA810, and a 12-bit 80 MSPS ADC for pulse-echo applications. Testing results show that the AWG system can generate HV pulses (up to 200 Vpp) from 2 MHz to 10 MHz (central frequency). The conventional sinusoidal excitation pulses can reach a −6 dB relative bandwidth of 3.5% to 103.5%, and linear frequency modulated (LFM) chirp can be generated using different windowing functions with programmable duration (step of 0.1 μs) and taper ratio (0 to 100% with a step of 5%). The proposed PAM AWG system can be used to evaluate different phase/frequency modulated excitation methods and applications, such as pulse inversion and dynamic transmit focusing.
Motion Planning for Serial Mechanisms Based on Precurved Flexible Beams

The field of Robot-Assisted Surgery is of great interest. Such robots can improve procedures leading to quicker recovery, smaller scars, less blood loss, and less pain. In this study, we present the motion planning scheme of a flexible mechanism whose motion depends on the insertion of precurved hyper elastic beams into an external multilumen tube that bends according to its geometric and elastic properties. We first define the robot’s shape according to the inserted beam’s known properties. We present the physical model of this system, explore how contact with obstacles affects the robot’s final shape and calculate the forces that it exerts on the tissues on which it leans. We plan the beams insertion order that allows reaching the goals without colliding with obstacles. Finally, we verify our calculations and assumptions on a real-world system. This technology can be used in many robotic fields such as Minimally Invasive Surgeries and Natural Orifice Transluminal Endoscopic Surgeries.
Delay-Multiply-and-Sum Beamforming with Transmit Minimum-Variance Estimation in Multi-Angle Plane-Wave Imaging

For multi-angle plane-wave (PW) imaging, Delay-Multiply-and-Sum (DMAS) beamforming has been used to improve the image resolution and contrast by introducing signal coherence into the image display. Signal coherence can be extracted in the dimension of either PW transmit angle (Tx) or receive channel (Rx). Nonetheless, excessive signal coherence may compromise the speckle quality and thus degrade the contrast-to-noise ratio (CNR). In this study, minimum-variance (MV) estimation is combined with DMAS beamforming to increase the image resolution without excessive signal coherence while the background speckle quality is better maintained. In the proposed Tx-MV-DMAS beamforming, low-resolution images are firstly constructed for all available PW transmit angles to generate the transmit vector. After magnitude-scaling the transmit vector by p-th root while maintaining the phase, the MV weighting is adaptively estimated for each image pixel and then the high-resolution image is produced by weighted summation of the transmit vector. Finally, the magnitude of the high-resolution image is restored by p-th power to introduce the signal coherence in DMAS beamforming. PICMUS simulation and vivo data are used to compare the performance of Tx-MV-DMAS with the conventional Tx-DMAS and CPWC.B-mode images show that the Tx-MV-DMAS offer significant improvements over Tx-DMAS in terms of image resolution and contrast while the background speckle remains visually comparable.
Gradient-Based Predictive Pulse Pattern Control with Active Neutral Point Balancing for Three-Level Inverter Medium-Voltage Drives

This paper presents a control method for three-level neutral point clamped (NPC) inverter medium-voltage (MV) drives that addresses the stator current control problem and balancing of the neutral point (NP) potential in a single control loop. To do so, a model predictive control (MPC) algorithm, designed as a multiple-input multiple-output (MIMO) controller, manipulates optimized pulse patterns (OPPs) in real time. As a result, minimal current harmonic distortions are produced, while the NP potential is kept balanced both during steady state and transients. The presented results demonstrate the effectiveness of the proposed control strategy for three-level NPC inverter MV variable speed drive systems.
Expected Pinball Loss For Inverse CDF Estimation

We analyze and improve a recent strategy to train a quantile regression model by minimizing an expected pinball loss over all quantiles. We give an asymptotic convergence rate that shows that minimizing the expected pinball loss can be more eﬃcient at estimating single quantiles than training with the standard pinball loss for that quantile, an insight that generalizes the known deﬁciencies of the sample quantile in the unconditioned setting. Then, to guarantee a legitimate inverse CDF, we propose using ﬂexible deep lattice networks with a monotonicity constraint on the quantile input to guarantee non-crossing quantiles, and show lattice models can be regularized to the same location-scale family. Our analysis and experiments on simulated and real datasets show that the proposed method produces state-of-the-art legitimate inverse CDF estimates that are likely to be as good or better for speciﬁc target quantiles.
Machine Learning Bill Prediction for IoT-based Utility Management System

Electricity consumption has become a forefront issue of global energy demand management, and one of the biggest contributors to Malaysia’s high electricity demand is the residential sector. Hence, user monitoring of energy consumption is critical for global energy efficiency. The aim of this project is to develop a smart energy metering and appliance control using a microcontroller ESP32 and Arduino IDE, provide prediction for bills to allow decision-making based on energy conservation measures using artificial neural network (ANN) model on MATLAB, provide a dashboard to monitor energy consumption, display accumulated bill and control of appliances on Adafruit IO platform, and provide notifications through email when bill exceeds limit using If This Then That (IFTTT) software platform. At about 94% accuracy of bill prediction, the developed system is believed to be able to contribute significantly to an efficient household utility management system.
Design of computing granularity configurable processor based on RISC-V extended instruction

Aiming at the problems of long operation time and poor flexibility of embedded processor, this paper proposes a structure of configurable execution unit based on RISC-V extended instruction set for computing granularity. The structure sets up basic parallel computing units and configuration registers, which can realize a large number of parallel operations such as multiplication, addition, or higher abstractions similar to convolutional neural networks by software configuration. This paper completed the design of this structure and integrated it into a four-stage pipelined RISC-V processor. The white blood cell images collected by lensless system are used to test various image algorithms. The experimental results show that the processor speed designed in this paper is 5.95 times to 22.21 times higher than that of the ordinary pipeline processor, and the number of instructions required is reduced by 1.16 times to 7.49 times. Therefore, the processor has a certain improvement in processing speed and flexibility, which is of great significance for the implementation of various algorithms in embedded systems.