Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved traffic flow prediction model: Spatial-Temporal network based on Wavelet and LSTM

Short term traffic prediction is a common Spatial-Temporal sequence training and learning prediction. This paper presents a new prediction model STWLSTM. Firstly, the model is decomposed and reconstructed by wavelet analysis, and then LSTM is trained. The LSTM prediction results are decomposed by wavelet, and the wavelet high-frequency coefficients of historical time data and first-order neighborhood spatial data are fused before reconstruction to obtain the final results. The experimental results show that the prediction results of STWLSTM model conform to the inherent attributes of traffic prediction: periodic stability and mutation.
MEMS Microphone Array for Airborne Ultrasonic 3D Tracking

This contribution illustrates how to design a newly developed modular hardware platform to enable hand tracking. The proposed system features Infineon's innovative MEMS transceivers combining best-in-class audio performance and ultrasonic pulse-echo measurement capabilities. Specifically, the MEMS device can be excited with ultrasonic bursts between 20000 Hz and 90000 Hz to function as ultrasonic speaker. The devices have a miniaturized package (3.6× 2.5×1 mm3), consume very little power (<2 mW), achieve an SNR of 70 dB(A), and have standard microphone interfaces. In this work we demonstrate how these devices can be effectively integrated, meeting the more stringent requirements of battery-powered miniaturized systems, such as True-Wireless Stereo (TWS) earbuds.
Multi-patch localization Spiking Neural Network for Object Detection

As the third generation of neural network after Artificial Neural Network, Spiking Neural Network (SNN) has the advantages of low energy consumption and high computing efficiency. However, due to the complex neurons and non-differentiable characteristics of SNN, the current results mostly stay in the software simulation stage, focusing on tasks with relatively simple processes such as image classification, and less achievements in Object Detection (OD) tasks with more application value. At present, the OD based on SNN mostly uses frequency coding, but the regression problem requires high precision and requires a large number of timesteps, resulting in a long time-consuming inference of a picture and a large overall power consumption. In this regard, based on Two-stage OD network, this paper decouples its detection process classification and localization, and proposes a multi-patch classification with localization network, which is completed by using a classification SNN. And take the drone shot by the infrared camera as the dataset, the OD as the application scene. The OD network is deployed on the neuromorphic chip PAICORE1.0 of Peking University, which proves the feasibility of applying our method to real-world neuromorphic chip and facilitates the development of SNN OD and its neuromorphic chip deployment.
Stochastic Scheduling of a Wind-Photovoltaic-Hydro Complementary System Using Stochastic Dual Dynamic Programming Method

This work presents a multistage stochastic programming formulation of a wind-photovoltaic-hydro complementary system considering the uncertainties in the system. It aims to meet the scheduling requirements of the power grid and maximize utilization level of renewable energy. To solve the problem using FAST toolbox, the stochastic scheduling optimization model is linearized, which is resolved based on stochastic dual dynamic programming. Finally, the effectiveness and superiority of the applied method are verified by a case study.
SAPIVe: Simple AVX to PIM Vectorizer

Larger vector extensions are one of the commonly used techniques to meet the growing demands from computational systems. These extensions, capable of operating over multiple data elements with a single instruction, exert a lot of pressure on the memory hierarchy, increasing the impact of growing problems such as Memory-Wall and von Neumann bottleneck. An alternative to work around these problems would be adding processing elements close to the memory, known as Processing-In-Memory (PIM). As with processor vector extensions, the most efficient PIM techniques use in-memory vector processing units. There are several ways to convert a code into in-memory vector processing, such as binary hardware translation, which may not depend on programmers or adapted software and can be carried out transparently to its users. However, in the context of in-memory processing, this conversion technique presents some challenges related to the PIM instructions format and the structure of the loops present in each application. Thus, this article proposes and evaluates Simple AVX to PIM Vectorizer (SAPIVe), a hardware binary translation mechanism from processor vector instructions into in-memory vector instructions, which, in addition to processing more data, also performs loads, operations, and stores at once. Our results show that our mechanism can accelerate kernels up to 5 times with possible performance losses prevented using loop predictors.
Underwater Exploration and Mapping

This paper analyzes the open challenges of exploring and mapping in the underwater realm with the goal of identifying research opportunities that will enable an Autonomous Underwater Vehicle (AUV) to robustly explore different environments. A taxonomy of environments based on their 3D structure is presented together with an analysis on how that influences the camera placement. The difference between exploration and coverage is presented and how they dictate different motion strategies. Loop closure, while critical for the accuracy of the resulting map, proves to be particularly challenging due to the limited field of view and the sensitivity to viewing direction. Experimental results of enforcing loop closures in underwater caves demonstrate a novel navigation strategy. Dense 3D mapping, both online and offline, as well as other sensor configurations are discussed following the presented taxonomy. Experimental results from field trials illustrate the above analysis.
A Proposed Fault Detection Using Continues Wavelet Transform and Transfer Learning via AlexNet

Medium Voltage Distribution systems are susceptible to high impedance faults (HIFs) and incipient faults (IFs). The HIFs are permanent, not detectable by Conventional relays, and caused by an energized conductor that in contact with ground surfaces. The IFs occur to underground cables, which are self-cleared, and may develop to a permanent fault if they kept unchecked. This paper proposes an automated detection and classification technique based on the Morlet Wavelet and the transfer learning technique via AlexNet Convolutional Neural Network (CNN). At first, these faults, as well as load switching and capacitor energization, were created on the IEEE-13 bus system. These events were detected using the proposed technique via the Morlet Wavelet. Then, the detected events are converted to scalogram RGB images with Continuous Wavelet Transform (CWT). These images were used as a set of training data for the transferred AlexNet CNN. After that, to have our testing images for the transferred AlexNet, a new set of events are created on the IEEE-34 bus system where they are detected using the same proposed technique and converted to RGB images. The discoveries were very promising where the proposed technique had a 100% detection accuracy, and the classification process had a 100% classification accuracy for HIFs, IFs and load switching. The classification accuracy for the capacitor energization were 94% which can be improved dramatically if the proposed technique was linked to a simple logic algorithm with the timing schedule of capacitor energization.
Research on Syllable-Based Language Model in Malay Speech Recognition

Malay language has a relatively standardized syllable structure. This paper converts graphemes into syllable symbols. According to some rules, the syllable structure of a word can be obtained with high accuracy. With the vowels, compound vowels, single consonants and double consonants of Malay, this paper formulates the rules and algorithms for automatic syllabification of Malay words, which can be applied to actual speech recognition systems. And then a syllable-based language model can be obtained through the syllabification of words. Using SRILM, a language model perplexity (PPL) calculation tool, this paper calculated the perplexities of different n-gram language models with different units (characters, phonemes, syllables, words) from a public Malay Twitter corpus. The experimental results show that the syllable-based language model is better than the word-based language model, and the character- or phoneme-based language models are better than the syllable-based language model. However, in most of speech recognition systems, a language model needs to cooperate with an acoustic model and the output result is words. And the speech recognition system with syllable-based can perform as well as the system based on deep learning with character-based language model, while the cost of syllable-based speech recognition system is lower.
A High PSR and Fast Transient Response Output-Capacitorless LDO using Gm-Boosting and Capacitive Bulk-Driven Feed-Forward Technique in 22nm CMOS

This paper presents an output-capacitorless low-dropout regulator (OCL-LDO) using capacitive bulk-driven feed-forward (CBDFF) technique and an adaptive-biasing error amplifier with gm-boosting to enhance the power supply rejection (PSR) and the transient response. The proposed OCL-LDO has been implemented in a 22nm CMOS technology. It consumes a quiescent current of 49 µA from a power supply of 1.05-1.25 V and has a dropout voltage of 200 mV. The OCL-LDO achieves -84 dB PSR at low frequency and -69 dB PSR at 1 MHz for the load current of 20 mA. It achieves a line regulation of 0.18 mV/V, a load regulation of 0.77 µV/mA, and a settling time of 135 ns.
HMLET: Hunt Malware Using Wavelet Transform on Cross-Platform

As the importance of cyberspace grows, malicious software (malware) is threatening not only individuals but also countries. In addition, numerous malware is still circulating in cyberspace, and as technology advances, new or advanced malware are emerging. In the real world, files from cross-platforms are distributed via e-mail, network-attached storage (NAS), shared drives, Etc. However, most malware detection models target only single-platform. Therefore, malware detection of cross-platforms has a significant and essential role. We propose HMLET, a cross-platform malware detection model. Our proposed HMLET uses content-based information that binary files all have in common, rather than file structure-based information that is not common to each platform, to detect malware of various file types in the cross-platform. We create file content-based information features for malware detection using wavelet transform. However, fixing the input data length when performing wavelet transform was necessary because the amount of information extracted according to the input data length was not constant. We fix the input data length through the Joint Probability Distribution (JPD) matrix to solve the input data length problem. After learning in the machine learning model, malware detection performance was evaluated through extracted features. Malware detection performance on the Windows platform showed 97% accuracy on HMLET and 99% on the Linux platform. In addition, on the Windows & Linux platform, HMLET showed 97% accuracy. According to experiment results, HMLET shows high-performance malware detection in the cross-platform, making it suitable to be used as a malware detection model in the cross-platform.
Sectorial forms and unbounded subnormals

Abstract We use the theory of sectorial sesquilinear forms to characterize the closure of the Creation Operator of Quantum Mechanics in the classical set-up. Further, we bring that theory to bear upon the class of unbounded cyclic subnormal operators that admit analytic models; in particular, we provide a sufficient condition for the existence of complete sets of eigenvectors for certain sectorial operators related to unbounded subnormals. The relevant theory is illustrated in the context of a class of analytic models of which the classical Segal–Bargmann space is a prototype. The framework of sectorial sesquilinear forms is also shown to be specially useful for treating questions related to the existence, uniqueness and stability of certain parabolic evolution equations naturally associated with such analytic models.
Estimation of Lightning Overvoltages and Critical Currents Causing Flashover to a 150 kV Overhead Transmission Line: Effects of Recorded Lightning Current Waveforms

This work evaluates the influence of typical first return-stroke current waveforms of negative downward lightning flashes, as recorded in literature, on the computed fast-front overvoltages on a typical 150 kV overhead transmission line. The critical flashover currents are also assessed. The investigation is performed through ATP-EMTP simulations considering lightning strikes to towers (backflashover) and phase conductors (shielding failure). Results for recorded lightning current waveforms are compared with those obtained for the widely used CIGRE waveform; the front time, maximum steepness, and time to half value of the latter are varied according to their statistical distributions. A comparison between CIGRE and double-peak waveforms is also performed. It is shown that the use of actual recorded return-stroke current waveforms may affect considerably the computed overvoltages and critical currents. The leader development models employed for flashover prediction are discussed in terms of the criterion used to terminate leader propagation for withstand cases.
A Reconfigurable SRAM Computing-in-Memory Macro Supporting Ping-Pong Operation and CIM pipeline for Multi-mode MAC operations

In this work, we present a reconfigurable SRAM computing-in-memory (CIM) macro supporting ping-pong operation and pipeline operation for multi-mode multiply-and-accumulate (MAC) operations. The macro can be reconfigured to execute AND or XNOR, offering great flexibilities to cover binary neural network (BNN), ternary neural network (TNN), and multi-bit operation through serially 1-bit AND operations. The main contributions include: (1) A reconfigurable scheme to map inputs and weight of 8T1C bit-cell, supporting three MAC operations; (2) An architecture integrated ping-pong operation and two-level CIM pipeline. Simulated in a standard 28-nm process, the proposed design shows good computing linearity and variations. The average energy efficiency of 1b-AND, BNN, and TNN MAC operation are 1533.7, 1522.9, and 1713.2 TOPS/W, respectively.
Circuit and Training Techniques Compensating for Non-Ideal Effects in Memristor Neural Networks

Edge-intelligence hardware such as Internet of Things (IoT) sensors should be very energy-efficient, because most of them are powered by batteries with very limited energy. In-memory computing circuits can be suitable to the edge-intelligence hardware, due to the energy efficiency better than the Von Neumann computing circuits. But, non-volatile devices for in-memory computing suffer non-ideal effects such as parasitic resistance, stuck-at-fault defects, etc. To compensate for the non-ideal effects, in this paper, several circuit and training techniques are reviewed and discussed. First, the parasitic resistance compensation circuit is explained. Second, the neuron deactivation scheme is discussed, in which significantly-defective columns are deactivated so that they do not interfere with neural network operation. Third, the training scheme that takes into account both synapses and neurons is examined in order to reduce the defect map overhead required during defect-tolerant training.
Effect of Thickness on Breakdown and Mechanical Properties of XLPE in Mesoscale

The conservative insulation thickness design increases the production cost of the cable, while the increase in thickness leads to a reduction in the electrical properties of the insulation material. Therefore, the study of thickness effect of insulation breakdown field strength of high-voltage DC cables has practical engineering significance. In this paper, we selected Nordic Chemical 220 kV high-voltage AC XLPE insulation materials and studied the effect of thickness on the electrical and mechanical properties of XLPE insulation materials by conducting AC breakdown experiments, conductivity tests and mechanical property tests on different thicknesses of the styles. The results show that: the AC breakdown field strength of XLPE decreases with increasing thickness, and the breakdown field strength of XLPE at 30 microns increases by 189.6% compared with that of XLPE at 300 microns; the DC conductivity of XLPE increases with increasing thickness of the samples, and the conductivity of XLPE samples also increases with increasing temperature. The thickness has an effect on the mechanical tensile properties of XLPE. The elongation at break of XLPE samples increases with the increase of thickness, and the XLPE breaking strength also increases with the increase of thickness. The experimental results indicate that both electrical and mechanical properties of XLPE insulation are affected by material thickness, which provides experimental support for the thickness design of XLPE insulated cables.
Compensating the Thermally Derated Torque for Six-Phase Induction Machine Based Electric Drive System Using Linear Parameter Varying Control

Multiphase induction machine posses the several advantages compared to three-phase induction machine when used in the Electric Drive System (EDS) of Electric Vehicles (EV). The performance of an EDS in extreme working conditions is extremely compromised. An electric drive suffers from torque derating as its parameters change due to the rise in operating and ambient temperatures. The performance of commonly used Field-oriented Control (FOC) in the EDS of EV is deteriorated under the vast variations in the rotor and stator resistances due to the change in the operating conditions. In this work, a robust Linear Parameters Varying (LPV) based FOC technique is proposed to estimate the thermally derated torque and flux to improve the performance of EDS when it operated in harsh environment. The performance of the proposed control technique is verified and investigated for the electric drive of a passenger bus (class-4) used in urban transportation. The Federal Urban Driving Schedule (FUDS) is adopted for the simulation experiment and validation of the proposed approach. The nonlinear simulation results show the capability of the proposed technique to accurately estimate and track the derated torque and flux demands in a six-phase Induction Machine (IM) based EDS.
Speed of Sound Estimation at Multiple Angles from Common Midpoint Gathers of Non-Beamformed Data

Sound speed estimation is a promising quantitative parameter in ultrasound for both for it's potential as a biomarker, where sound speed is known to be correlated with biological changes, and for aberration correction, where knowledge of the sound speed allows for correction of distortions in the image. Previous methods for sound speed estimation rely on the use of an a-priori sound speed for beamforming, from which sound-speed estimates are then calculated. However, this assumption introduces a bias in the estimation technique, because errors in the a-priori sound speed lead to distortions in the image. Here we present a method for sound speed estimation that avoids this bias by estimating the sound speed without beamforming.
Optical Flow Assisted Super-Resolution Ultrasound Localization Microscopy using Deep Learning

Ultrasound localization microscopy provides resolution enhanced ultrasound images and demonstrates clinical potential in myocardial infarction and diabetes. The conventional model-driven methods localize the microbubble by tracing the peak of the point spread function. Such numerical schemes demonstrate weakness in identifying superimposed microbubbles, indicating the limitations for super-resolution (SR) images. Recently, learning-based approaches have been studied for precise localization of densely distributed microbubbles. However, prior arts reconstruct the SR images from static B-mode images, which results in inconsistent localization of microbubbles across sequential frames. In this paper, we propose a temporal relational ultrasound microscopy network (TRUM-Net). The TRUM-Net adopts optical flow estimation of consecutive frames and a feedback loop for detailed super-resolution imaging. The proposed scheme enhances the accuracy of microbubble localization by 25.8% and the structural similarity up to 54.9%.
Multi-Objective-Optimized Parameter Design Method for High-frequency Resonant Converter

In recent years, the high-frequency resonant converter has become a research hotspot for soft switching and high-power density. However, the increasing number of resonant components adds difficulties to the resonant parameter design. For this reason, this paper proposes an optimized parameter design method for high-frequency resonant converters, which utilizes the back propagation neural network (BPNN) prediction model combined with a multi-objective genetic algorithm (MOGA). This method comprehensively considers the main parameters that can optimize efficiency. A prototype with 18V input, 5V/10W output and 10MHz switching frequency was built in the laboratory. Experimental results verify the effectiveness of the proposed parameter optimization method.
FPGA Logic Cell Improvements for Popcount Computation in BNN

Binarized Neural Network (BNN) is an ultra-reduced-size version of Convolutional Neural Network (CNN), with weights and activations reduced to 1-bit, which largely decreases parameters of a CNN. The convolutional operation in CNN can be transformed into XNOR-popcount operation in BNN. FPGA is an appealing platform to implement BNN. In order to increase popcount computation density on FPGA, FPGA logic cell needs to modify. Compare to previous modifications, we propose two improvements. First, we propose area-saving full adder reuse technique, the full adder can propagate SUM or CARRY. Second, modified logic cell can efficiently implement 6:3 compressor which decreases number of full adders in popcount computation. compared to previous modifications, our proposed improvements decrease FPGA logic cell area by 1.65X—2.48X, and critical path doesn’t deteriorate. Improvements don’t add any port to original logic cell which doesn’t affect routing. Compared with original logic cell to implement 6:3 compressor, we reduce 3X logic cells.
Scaling-up the Ultrasound-Enhanced Electrospinning Device

Ultrasound-enhanced electrospinning (USES) is an electrospinning method that utilizes focused ultrasound to produce nanofibers. The focused ultrasound creates an acoustic fountain on a polymer solution surface. With a high-voltage electric field, electrospinning is initiated from the fountain. Until now USES has been limited by its production rate due to the use of a single ultrasound transducer. Here we present a multi-transducer USES device, with a similar footprint as our old device. Increase in throughput was studied using three of the transducers. Simultaneous, stable spinning, with three transducers was not achieved, however we double the amount of produced fiber with two transducers. Compared to the previous USES device, the results indicate that USES can potentially be scaled up.