Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Neural Synaptic Plasticity-Based Stochastic Deep Neural Network Computing

With the increasing popularity of deep neural networks (DNNs), a large amount of research effort has been devoted to the hardware acceleration of DNNs to achieve efficient processing. Nevertheless, few works have explored the similarities between the biological essence of DNNs and arithmetic circuits. Moreover, stochastic computing (SC), which implements complex arithmetic operations with simple logic gates, has been applied to the acceleration of DNNs. However, traditional SC suffers from high latency and large hardware cost of pseudo-random number generators (PRNGs). Inspired by neural synaptic plasticity and SC, in this work, we present the reconfigurable neural synaptic plasticity-based computing (RNSP) to mimic the biological neuron behaviors and exploit the parallelism of SC to the full extent while maintaining a small hardware footprint compared to fixed-point counterparts. RNSP converts fixed-point numbers to parallel bits without logic resources, which are then synthesized by bit-wise multiplications and some full adders. In addition, we propose the arithmetic unit based on RNSP and use re-training to mitigate the accuracy degradation. Finally, a convolution engine (CE) built on RNSP with high memory bandwidth efficiency is designed. According to the implementation results on FPGA, the proposed RNSP-based CE outperforms the fixed-point counterpart in terms of power consumption and area.
Asynchronous Focusing Time Reversal Wireless Power Transfer for Multi-users with Equal Received Power Assignment

Time reversal wireless power transfer (TR-WPT) provides an efficient radiated method for wirelessly transferring power to multiple users at a middle distance in complicated multipath environments. However, TR-WPT based on the direct time reversal scheme cannot ensure each user to have the equal received power although the maximum transfer efficiency can be achieved. In order to realize the same received power assignment for each user as well as high transfer efficiency, an asynchronous focusing time reversal (AFTR) scheme is proposed in this paper. Different from the TR-WPT based on the direct time reversal technique, the radiated power does not focus on multi-users at the same time. Instead, the AFTR-WPT system operates in an asynchronous focusing mode. For the continuous-wave wireless power transfer, asynchronous focusing means that the phases of the focused power signals received by multi-users are different. By optimizing the phase of the focused signal received by each user, the AFTR-WPT system can not only transfer the equal power to each user but also achieve the optimal transfer efficiency. To demonstrate the proposed scheme, a porotype of multi-user AFTR-WPT system of 16 transmit antennas and 9 receivers is developed with the continuous-wave power transfer in an indoor laboratory environment. Utilizing the proposed asynchronous focusing scheme, we successfully demonstrate the equal power assignment for multiple users and achieve the optimal transfer efficiency, approaching the maximum efficiency of the TR-WPT based on direct time reversal scheme for all studied cases.
Research on Unstructured Mega Data Analysis Algorithm of Communication Network Based on Feature Fusion

In communication network mega data, unstructured data is characterized by large scale, diversity and timeliness. Traditional unstructured processing methods have been difficult to meet the data processing needs. Complex data sets and large data orders in modern mega data require professional analysis tools to realize analysis. Information fusion is a multi-source information processing technology, which can optimize and synthesize redundant information from multiple sensors in space and time, and obtain more accurate and complete values than single information source, and obtain the consistent description of the measured object. In order to effectively solve the problem of unstructured data model of communication network mega data, this paper proposes an algorithm for unstructured data analysis of communication network based on feature fusion, and analyzes the key problems in the process of unstructured data feature modeling, such as the storage of original data and feature data, the selection of feature space, information query and data visualization.
An Improved Sliding Window BATS Code

Batched sparse (BATS) codes, a class of random linear network coding, are proposed to transmit a collection of packets through erasure networks. BATS codes generalize the fountain codes and preserve properties such as ratelessness and low encoding/decoding complexity. In BATS codes, the destination node starts decoding information packets only after receiving a sufficient number of coded packets. This induces a latency. The larger the size of information block is, the longer the latency. In this paper, we apply a sliding window framework over BATS codes, that divides the information block into smaller sub-blocks, thus helping to reduce the latency. In the proposed sliding window BATS coding scheme, multiple degree distributions are designed based on the overlapping pattern of input packets in a window, and coded packets are generated accordingly. Based on asymptotic performance analysis, we formulate a heuristic optimization problem to jointly optimize the degree distribution and the window selection probability for each sub-window. Simulation results show that the proposed sliding window BATS code outperforms the standard BATS codes and existing sliding window BATS codes in terms of overhead and latency.
30MHz high-frequency contrast-enhanced ultrasound and super-resolution imaging using long-lasting microbubbles

Brain blood flow imaging has been a hot topic in ultrasound imaging in recent years. Previous studies of functional ultrasound (fUS) and super-resolution ultrasound (SRUS) mainly focus on the center frequency of 15MHz or below. The pursuit of higher temporal and spatial resolution has always been an important direction in this field. Increasing the center frequency is an important and basic method to improve the spatial resolution of ultrasound imaging. This study uses a 30MHz center frequency combined with long-lasting microbubbles to perform whole-brain blood flow imaging in baby rats and proves that ultrasound imaging can cover the whole brain depth under 30MHz.
Ten basic factors to identify suitable subcontractors for construction projects

With unpredictable workloads and a need for a multitude of specialized skills, many main contractors rely heavily on subcontracting to reduce their risks (Bresnen et al., 1985; Beardsworth et al., 1988). This is especially the case In Hong Kong, where the average direct labour content accounts for only around 1% of the total contract sum (Lai, 1987). Extensive usage of subcontracting is also reported in many other countries, including the UK (Gray and Flanagan, 1989) and Japan (Bennett et al., 1987). In addition, and depending upon the scale and complexity of works, it is not uncommon for subcontractors to further sublet their works to lower tier(s) subcontractors. Richter and Mitchell (1982) argued that main contractors can obtain a higher profit margin by reducing their performance costs by subcontracting work to those who have the necessary resources to perform the work more efficiently and economically. Subcontracting is also used strategically to allow firms to employ a minimum work force under fluctuating demand (Usdiken and Sozen, 1985). Through subcontracting, the risks of main contractors are also reduced, as errors in estimating or additional costs caused by delays or extra labour requirements can be absorbed by the subcontractors involved (Woon and Ofori, 2000). Despite these benefits, the quality of work can suffer when incapable or inexperienced subcontractors are employed. Additional problems also exist in the form of bid shopping, unclear accountability, and high fragmentation (Palaneeswaran et al., 2002). A recent CIB TG 23 International Conference, October 2003, Hong Kong report produced by the Hong Kong Construction Industry Review Committee (CIRC) points to development of a framework to help distinguish between capable and incapable subcontractors (Tang, 2001). This paper describes research aims at identifying and prioritising criteria for use in such a framework.
A High Power Ku- to Ka-Band Single-Pole-Double-Throw Switch with Capacitive Loading for Isolation Improvement

In this paper, we report a design of single-pole-double-throw (SPDT) passive high-electron-mobility transistor (HEMT) switch using stacked-FET configuration with capacitive loading for isolation improvement. Based on the analysis subject to the insertion loss and isolation, devices with proper gate peripheries were selected for the realization of the SPDT switch. Implemented in commercial 0.15-μm GaAs pHEMT technology, the proposed design showed an insertion loss of less than 3 dB with an isolation greater than 30 dB from Ku-band up to Ka-band frequencies. Moreover, the SPDT switch achieved a nice power handling capability of better than 31.5 dBm across 26–40 GHz.
Research on Mobile Reading Weak Communication Promotion System of University Library Based on Mobile Big Data Decision-making Analysis

With the rapid development of information and communication technologies such as mobile Internet, cloud computing, Internet of Things, edge technology, and data mining, the library industry has entered the era of mobile big data. This paper studies the weak dissemination promotion system of mobile readers in university libraries based on mobile big data decision analysis, points out the main problems existing in the mobile reading promotion of libraries in the mobile big data era, and puts forward the mobile big data decision analysis library mobile Read the specific strategies for promoting the problem.
Silicon carbide power electronics for high temperature applications

This paper describes the characterization and modeling of the performance and reliability of SiC devices. A fundamental study of material defects and process techniques in SiC is needed for significant material purification. It will also result in a comprehensive physics-based defect model, a high-voltage diode behavioral circuit model, and improved edge-termination and passivation techniques. This paper shows initial results of experimental determination and modeling of the role of material defects and surface passivation on 100-V diode breakdown and electrical performance. The devices are subjected to dynamic stresses typical of switching applications to determine the dynamic SOA and avalanche rating of the devices.
Neural Network based Ramp Raise Optimized Algorithm for Prediction of Seizure Pre-ictal Stage

People with epilepsy will benefit from the adoption of the Brain Computer Interface (BCI) for seizure prediction. Epilepsy is a frequent neurological condition of the brain's Central Nervous System that has negative consequences for patients. The human brain has millions of neurons, each of which is responsible for a distinct motor action in the human body. The neurons send out aberrant electrical signals that go throughout the body, causing involuntary movements and seizures. During the seizure, the patient collapses and experiences jerky movements, tongue biting, nausea, and other symptoms. Recurrent seizures are a hallmark of epilepsy patients. EEG (Electroencephalogram) is one of the common procedures to observe the signals produced by brain and helps to examine seizure occurrence in the patients. Seizure has four different stages ictal (during seizure), pre-ictal (before seizure), inter ictal (time interval between two successive seizures) and post ictal (after seizure) (after seizure). The patient will be kept in a safer state if the pre-ictal stage of the patient can be predicted. With the introduction of cutting-edge Neural Network techniques, their various activation functions, and Gradient Boosting, the patient's pre-ictal state can be predicted and diagnosed, allowing proper seizure medication to be administered. The suggested model uses the Neural Network Based Ramp Raise Optimized Algorithm (NNBRROA) with hyper parameter tuning to predict pre-ictal patients with 98 percent accuracy.
A Review on High Frequency Communication

Several major technical advances have taken place in telecommunications and other industries since the Second World War. Microwave communication is signal transmission through radio waves using a series of radio wavelengths measured in small centimeters. A microwave radio system is an equipment used for transmission and broadcasting. The microwave network incorporates high-range radios on top of microwave towers, which support the communication of microwave radio by sightline. Microwave is also used for point-to-point communication because their short wavelength makes it possible for antennas of convenient size to be guided directly through narrow beams pointing at the antenna receiver. Although, the high frequency path loss limitations and health hazards need to be ensured. Although, apart from frequency the power density has a great role to play in deciding the hazardous effects of RF waves. The article mainly focuses on the effects of high frequency communication along with their hazards.
Rolling Bearing Fault Diagnosis of Train Running Gear Based on Optimized Deep Residual Network

With the development of the railway in China, fault diagnosis of rolling bearings is important in high-speed and heavy-load trains. Aiming at the shortcomings of traditional fault diagnosis methods, such as low recognition rate, poor robustness, and relying on manual extraction of features, this paper proposes a method based on the optimized deep residual network SE-RESNET-26 for the rolling bearing fault diagnosis method of train running parts. The deep residual network is composed of stacking residual blocks, which can effectively improve the gradient disappearance and network performance degradation of the traditional deep learning models. In this paper, the channel attention mechanism, Squeeze-and-Excitation block (SE), is embedded into the residual block, and the model structure is adjusted according to the characteristics of fault data to further improve the recognition accuracy. Experimental results show that the proposed method can adaptively extract fault features of bearings under complex working conditions and realize high-precision intelligent fault diagnosis, which is superior to the comparison method.
Development and characterization of a sparse ellipsoidal 256 element array for volumetric ultrasound imaging

Sparse 2D matrix arrays represent an alternative to fully populated arrays for a more efficient use of available electronic channels. While individually driving the elements of fully populated matrix arrays requires electronics with thousands of elements, the requirements to the multichannel electronics can be dramatically reduced with sparse arrays. In our work, we developed and characterized a sparse 2D array with a spiral element pattern. The array consists of 256 elements in a 513 µm pitched matrix. An elliptical geometry was chosen in view of a later use in volumetric transperineal imaging. The array was processed on a conductive backing. An average centre frequency of 4,2 MHz (1,4 MHz −6 dB bandwidth) was measured. A wire phantom was used for assessment of the imaging performance. When excited with a single plane wave (0°), the FWHM ranged between 750 µm and 1100 µm (in x- and y- direction respectively).
Critical Thinking in Software Engineering and Virtual Classrooms: What are we doing?

Critical thinking consists of analyzing and evaluating the coherence of reasoning. This ability is considered crucial for the development of new software engineering professionals, given their continued need for problem-solving. The problem arises from the diversity of proposals and the lack of rigor of existing experiments, which makes it difficult to find specific recommendations to assessing critical thinking, especially in online contexts. This article analyzes the results obtained from the execution of a systematic mapping of the literature. We seek to raise awareness of what we are doing as an educational community and where we should be going.
Fault Organized Decoders Using Analog VLSI Implementations- A Survey

VLSI technology had a key role in the design of most of today’s high-tech electronic circuits. Though smaller, less expensive, requiring less power, highly dependable, and full of functionality, VLSI design takes an extended amount of time to manufacture a high-risk product. The complexity of computing of such default coding systems however stands in the way of its application. Many researchers have shown that fault checking decoders can be construed on the Probability Spread Network as a type of factor graph as a Sum-Product Algorithm operation. The most advanced communication systems are based on fault structured codes. The most famous codes of this kind include turbo codes, low-density parity tests and versions for performing the closest to the theoretical Shannon limit. These are decoded by the ’Generic Summary Model Probability’ or by the versions thereof. In this document, many of the most often used fault organizing codes are fully analyzed and investigated.
Personalized Social Image Tag Recommendation Algorithm Based on Tensor Decomposition

This paper proposes a three-dimensional tensor model, which uses the three dimensions of the three-dimensional tensor to describe three types of entities in the social tag recommendation system, and builds a recommendation model for three different data sets. First introduced the traditional label recommendation algorithm and its shortcomings, and then introduced the personalized social speech image recommendation algorithm based on tensor decomposition. The user performs similarity calculation and classifies users with similar characteristics to ensure that the data has initial aggregation and optimized the weight. Experiments show that this algorithm reduces the data sparsity problem in the tag recommendation system and improves the accuracy of the recommendation results.
High Frequency, Low Loss and Low TCF Acoustic Devices on LiTaO3-on-SiC Substrate

In this work, a novel heterogeneous substrate of lithium tantalate thin film on silicon carbide (LiTaO3-on-SiC, LTOSiC) was proposed and prepared. Since the SiC features an extremely-high phase velocity, a low acoustic loss and a small thermal expansion coefficient (CTE), surface acoustic wave devices with high frequency, low loss and low TCF were achieved on the LTOSiC substrate. The fabricated shear horizontal surface acoustic wave (SH-SAW) resonator shows a maximum Bode-Q (Bode-Qmax) of 2235 and a low TCF of -9.4 ppm/K at 1.90 GHz, while the longitudinal leaky surface acoustic wave (LLSAW) resonator shows a high Bode-Qmax of 1180 and an improved TCF of -24.7 ppm/K at 4.40 GHz. The ladder-type filter composed of SH-SAW resonators was also demonstrated, showing a center frequency (fc) of 3.03 GHz, an insertion loss (IL) of 0.9 dB and a 3-dB bandwidth of 101 MHz. The TCFs at - 3 dB attenuation of the lower and higher frequency side of passband are -13.8 and -15.8 ppm/K, respectively. The acoustic devices on LTOSiC substrate possess the prospect of commercial applications in 5G era.
The Uncertainty Quantification for Parameters Optimization in SERF Atomic Magnetomer

The magnetometer is a multi-parameter system, parameters optimization is always the problem to improve the sensitivity of the atomic magnetometer. Multiple parameters hardly decide the optimized conditions in the experiments because of the influence of several parameters and parameters may interact with each other. Besides, the influence of parameters on the signal-to-noise ratio can hardly be analyzed theoretically due to the complex physical configuration. We used a generalized polynomial chaos expansion to construct an agent model for the SERF atomic magnetometer to replace its complex physical model, and used a variance-based sobol method to perform a global sensitivity analysis of the parameters in this paper. The mathematical method of uncertainty quantification is used to comprehensively analyze six parameters’ effect on SERF atomic magnetometer performance. The results show that the probe laser power has the greatest impact on the atomic magnetometer, and it is the parameter with the highest degree of linearity with the atomic magnetometer. In accordance with the results of the uncertainty quantification analysis, the parameters of the probe laser power of the SERF atomic magnetometer were experimentally optimized, and the performance of the magnetometer was significantly improved.
Mixed Priority Queue Scheduling Based on Spectral Clustering in Spatial Crowdsourcing

With the ubiquity of GPS-enabled smart devices equipped with high-fidelity sensors and increased availability of the wireless network, spatial crowdsourcing has been recently proposed as a general framework to employ smart device carriers as workers to provide services and perform location-sensitive tasks. In this paper, we focus on the task assignment in spatial crowdsourcing, which aims to find the optimal strategy to assign each task to a proper worker such that the total number of completed tasks is maximized and the traveling time cost is minimized, while the workers can return to their initial locations before deadlines after performing the assigned tasks. Finding the optimal global assignment turns out to be intractable since it does not simply imply optimality for an individual worker, as a typical nearest-neighbor heuristic does not render a satisfactory result in general. In spatial crowdsourcing, we model the task assignment problem as a multiple objective joint optimization problem, which focuses on maximizing accomplished task rate and minimizing travel time cost rate simultaneously, and solves it with a mixed priority queue scheduling algorithm. We also introduce a spectral clustering algorithm in spatial crowdsourcing for the first time to divide the task network into different subdomains, considering the task clustering phenomena in real scenarios. Experiments on synthetic and real-world networks demonstrate the efficiency and effectiveness of our method in the task assignment of spatial crowdsourcing and provide insights into its application in practice.
Non-invasive Intravascular Pressure Gradient Estimation using Synthetic Aperture Ultrasound

This study presents a new method for noninvasive pressure gradient estimations. It combines a new method for estimating the spatio-temporal acceleration of the flowing blood with the Navier-Stokes equation. The method is based on a double cross-correlation approach, which is hypothesized to generate more precise pressure gradients than previous methods. The acceleration is determined by cross-correlating the correlation functions attained from the velocity estimation. Data are acquired using a 5.2 MHz linear transducer in combination with a Verasonics research scanner. A permuted, pulse inverted, interleaved sequence with four virtual sources evenly distributed on the aperture is used. The performance of the method is tested on experimental data, measured on a bifurcation phantom of the common carotid artery. The pressure drop across nine flow cycles varied from -20 Pa to 40 Pa, with a standard deviation of 15.7%.
Symbol Message Passing Decoding of LDPC Codes for Orthogonal Modulations

A simple decoder for q-ary low-density parity-check codes is studied, termed symbol message passing. The decoder passes hard decisions from a q-ary alphabet. For orthogonal modulations over the additive white Gaussian channel for which the modulation order and the field order q are equal, it is shown that the extrinsic messages can be modelled as observations of a q-ary symmetric channel, allowing to work out density evolution equations. A stability analysis is provided which emphasizes the influence of degree-3 variable nodes. Simulation results show performance gains for increasing q w.r. t. binary low-density parity-check codes with bit-interleaved coded modulation, and potential savings in decoding complexity.