Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive-Tunable-Based Hybrid RBF Network for EGTM Prediction

Aero-engine exhaust gas temperature margin (EGTM) is one of the main indexes of engine replacement; however, the application of existing methods in EGTM forecasting is restricted because of the limited prediction accuracy and many non-linearities. In this study, an adaptive-tunable-based hybrid radial basis function (RBF) network is proposed to improve the prediction accuracy of aero-engine EGTM. Firstly, a hybrid RBF network consisting of a RBF network and a linear regression model is built as a fundamental EGTM predictive algorithm. Secondly, to increase the network’s adaptation capabilities, the structural parameters of the proposed network are adaptively modulated by Brownian motion modeling and particle filter without physics-based models. Finally, multiple sets of EGTM data from a certain type aero-engines in an airline company is selected for engine removal time prediction. Experiment results demonstrate that the proposed adaptive-tunable-based hybrid RBF network with a high prediction accuracy, and can reflect the characteristics of EGTM well and truly, which can capture the dynamic nature of EGTM in time during the forecasting process.
Real-time evaluation of error for electronic voltage transformers based on autocorrelation and large-dimensional random matrices

Electronic voltage transformer is an electricity parameter measuring device widely used in intelligent substations, and its measurement accuracy is related to fairness and justice of power energy trade settlement and normal operation of measurement, control and protection devices, so affecting the safety, stability and economic operation of power systems. Existing online evaluation methods are difficult to reliably evaluate the error of voltage transformers under actual non-stationary output. Therefore, a evaluation method based on ACFLDRM(autocorrelation function and large-dimensional random matrices) for electronic voltage transformers is proposed. For the non-stationary measurement data of electronic voltage transformers, the autocorrelation function and differential method are used to extract stationary parameter. By single-ring law, evaluation limit and real-time indexes are extracted. Simulation experiment and analysis of spot data verify the effectiveness of the method.
Image Based Automatic Traffic Surveillance System Through Number-Plate identification And Accident Detection

The unpredictability and blockage of current transportation frameworks frequently produce traffic circumstances that endanger the security of the individuals in question. A simple invention can make easy to control the traffic system. This paper presents a programmed traffic observation framework to gauge significant traffic boundaries from video arrangements utilizing just captures from cameras. A traffic control kit is developed to detect over speeding cars on highways, number plates in Bengali, and initiating emergency call to 999 on detecting accidents. A GPS enabled traffic surveillance camera can detect the location of accident and send message to the traffic control room with location information of accident. A Python program is used to detect over speed which provides accurate speed of a vehicle very fast. OCR Tesseract is used to detect number plate which has very high performance in detecting noisy texts. To identify a case of accident, a simple Python code with Dens-net Architecture is used. A GSM module of the experimental kit initiate the call and message after analyzing the data through a code of C language. Machine Learning (ML) is used to train the program in identifying number plates. It is done by Anaconda.
Dense Sub-Circuit Reduction in RC Circuits

Circuit simulation is a crucial but time-consuming step. Dense circuits and sub-circuits are significantly more expensive to simulate than sparse ones. In this paper, a novel dense sub-circuit reduction system is introduced. The proposed methodology uses dense sub-graph mining techniques on top of the model order reduction techniques to reduce the dense sub-circuits in the designs. This paper focuses on comparing four different dense sub-graph mining algorithms in the context of circuit reduction. The conducted experiments showed that the greedy approximation algorithm for edge density maximization is the most suitable for our circuit reduction problem since it provides the best results in terms of scalability on large graphs as well as the density and the size of the detected dense sub-graphs.
Research on the Correlation between Information Technology Capability and Enterprise’s Sustainable Competitive Advantage Based on Game Theory

The 21st century is the era of information economy and knowledge economy. Market competition has transformed into speed competition, information competition and knowledge competition, and the scope of competition has also expanded from regions to the world. The world economy is developing in the direction of informatization, and information technology (IT) and its industries have become a new growth point for the world economy. This thesis builds a theoretical model of the correlation between information technology capabilities and sustained competitive advantage based on resourcebased theory, and further analyzes the model based on resource-based theory and organizational learning theories. At the same time, the dynamic game model is used to analyze the impact of the three factors of enterprise information efficiency, capital utilization efficiency and original product sales price on the level of enterprise information technology capabilities. The relationship between information technology capability level advantage and enterprise competitive advantage is studied. The research results show that only by investing in information technology to form unique information technology capabilities that are superior to other competitors can companies better use the power of information technology to gain and maintain their competitive advantage.
Infrared Small Target Detection and Tracking Method Suitable for Different Scenes

An infrared small target detection and tracking method suitable for different scenes is proposed. For a given infrared image sequence, 1) firstly, the complexity of the scene is judged according to the number of Harris corners; 2) for the image sequence containing simple scenes, the method of local adaptive threshold segmentation and track association is used to complete the detection and tracking of multi-target; 3) for the image sequence containing complex scenes, SSD detector is used to detect the initial target, after the target is detected, SiamRPN++ tracker is started to track the target. The tracking result is judged in the tracking process. When the target is lost or wrong, the SSD detector is restarted immediately to capture the correct target position and fed it into the tracker to complete the reinitialization and correction. In the tracking process, different targeted strategies are adopted to deal with different situations, such as the target entering and leaving the field of view, the sudden change of the moving state, false alarm interference and so on, ensuring that the detection and tracking of the whole image sequence is completed efficiently and stably. The proposed method which provides solutions for different scenes can successfully realize the continuous and accurate positioning of multiple infrared targets in different scenes, improving the ability of the infrared detection system to solve practical problems. Especially for the complex scene, the neural network based intelligent detector and tracker complement each other to improve the detection and tracking efficiency of infrared dim small targets, and have strong robustness and generalization performance.
Laser transmission model for a cross-medium uplink channel

In order to study the laser channel model in the uplink transmission process and the optical pulse distribution in the receiver. Based on the existing Monte Carlo method for underwater channel analysis, the random wave theory and the linear superposition method are used to model the wave, so a complete channel model is established. It is assumed that the transmitter emits a blue-green laser with a wavelength of 532nm underwater in the Coastal ocean. By tracking the photon transmission process, statistical results are obtained to analyze the influence of underwater transmission distance and sea surface wind speed on the photon distribution at the receiving end. The simulation results show that the wave height and variance of the wave spectrum increase with the increase of wind speed. When the sea surface wind speed is constant, the distribution of photons at the receiving end is more divergent with the depth of the transmitter under the water. When the underwater transmission distance is fixed, the number of photons received decreases with the increase of wind speed, and the distribution of photons is more divergent. Therefore, the sea surface wind speed and the depth of underwater transmitter have great influence on the transmission, and the research results will be of great reference value for the establishment of an accurate laser uplink channel transmission model.
Dual-Band Concurrent LNA with Low Gain Deviation and Low Noise Figure

This paper presents 1.713.5 GHz dual-band concurrent LNA with small gain deviation and low NF. Proposed LNA incorporates mutual induction matching circuit and notch filter matching circuit on input and output side to improve gain deviation. Also, a part of the primary inductor of the input matching circuit adopts an external inductor to improve NF. The measured Sii, S22, S21 and NF were -6.44,-15.7,10.9, and 2.67 dB at 1.7 GHz and -7.65, -6.05,10.2 and 2.02 (4.0 GHz) dB at 3.5 GHz, respectively. The power consumption is 14.0 mW from a 1.8 V supply voltage. The proposed LNA is designed and fabricated by using the TSMC-180 nm CMOS process. The concurrent LNA in this study gives the lowest NF in our knowledge.
Internet Forum Texual Information Filtering Mechanism for Emergency based on Bi-LSTM Neural Network

The internet forums are an important way of emergency information dissemination. However, with the development of internet and the vast amounts of users, there is plenty of useless information in internet forum data, especially during emergency. This not only increases the load for the subsequent data processing, but also influences the efficiency and accuracy of public opinion analysis. As a result, the useless data is harmful for the internet public opinion guidance for emergencies. Therefore, in view of internet forum text data of emergency events, in this work, we propose an information filtering mechanism of internet forum based on bilayer long short-term memory (Bi-LSTM) neural network. First, we propose a text information classification model based on LSTM neural network and related mechanism. Then, we evaluate the proposed mechanism by using the practical internet forums. The experimental results and analysis reveal that the proposed algorithm significantly improves the textual information quality as well as the accuracy.
Analysis and Optimization of Packaged Floating-Ground RF Power GaN-HEMTs

This paper presents the work of analyzing and optimizing a discrete packaged floating-ground RF power GaN-HEMT targeting medium-power reverse-type envelope tracking applications like base stations for 5G or power amplifier systems for space communications. Investigations on fabricated samples demonstrate the impact of the internal layout on the achievable instantaneous modulation bandwidth (IBW) and operating frequency. It is found that in a standard RF package the IBW is determined by the RF bypass capacitor and the inductances of the connecting bond wires to the same extent. Depending on the package dimensions, the size and technology of the RF bypass capacitor and the bond wire inductances, it is expected that the IBW can be extended to beyond 500 MHz.
Simultaneous Computation of Arithmetic Logic Operation Based on Polarized Optical Shadow Casting Method

The volume of data is increasing day by day. For data transmission photon has already replaced the electron, but still at transmit and receive ends, devices lose energy while converting electrons into photon and back. So, the concept of optical gates might be a potential resource in the future. In this paper, we theoretically design a two input Arithmetic Logic Unit (ALU) that computes addition, subtraction, AND, OR, XOR operations separately and simultaneously. For design and implementation of optical logic gates Polarized-Encoded Optical Shadow Casting (POSC) algorithm is utilized. Three planes are considered here: source plane, input plane, and output plane. In source plane incoherent Light-Emitting Diode (LED) array works as the light source. We designed the scheme for input pixel, then overlapped those two pixel patterns at the input plane and finally illuminated the overlapped pixel pattern on output screen by light emitting diodes of source plane. By altering the minterm group, various input coding patterns are being calculated. Several equations are obtained from truth table for required operations.
Efficient final output feature map processing method supporting real-time object detection and recognition

Recent advanced in CNN acceleration systems using hardware and software co-design for real-time object detection and recognition, the speedup of post processing can reduce the burden of designing the acceleration logic to meet the real-time goal. In this paper, we propose an optimization method that eliminates the operation of acquiring redundant bounding boxes without degrading performance. It is implemented on a Xilinx Zynq Ultrascale+ MPSoC ZCU106 and shows the performance improvement about 20% that of the conventional method.
Performance Comparison of Different Convolutional Neural Network Architectures for Plant Seedling Classification

Agriculture is the backbone of many nations. Many developing and under-developed countries’ economies still depend on agriculture. The main target is to increase production while reducing cost, supply food to the increased population worldwide. In the agricultural system, weeds are a major concern for farmers. They compete vigorously with the crops for nutrition and water and cause huge loss. The use of chemical herbicides eliminate weeds but do a lot of damage to the environment also increases cost. Regarding the environment and cost an automated machine vision system is required that can identify crops and weeds in a safe and cost-efficient manner. In this work, we have implemented several Convolutional Neural Network architectures to find the best performing architecture that classifies plants and weeds accurately. For this reason, we have used a database that contains a total of 5539 images of 12 classes (3 crops and 9 weeds) at their several early stages. We have calculated the accuracy, precision, recall, and f1-score of each architecture. From this study, we have found that ResNet-50 is the best performing architecture with an overall test set accuracy of 96.21% that is higher than other architectures used in this task. This model can be a useful tool for farmers in identifying weeds at their early stages.
Design and Implementation of Test System for Power 5G Communication Module Base on Cloud Computing Architecture

5G is the most fundamental communication infrastructure in the “New Construction”. network communication module base on 5G is widely used in power grid and other IoT network. To ensure the module can meet the strict requirement of stability and compatibility in power grid usage, an efficient and trustable automation test system is needed. At the same time, to fully utilize the network resource and the needed for storage and management of the test result, a cloud computing based test system is designed, and a message queue-RabbitMQ is creatively introduced to connect cloud based test system management service and local test execution engine, which simplified the cross platform and cross program language system design, also guaranteed the efficiency and reliability of the test task distributing, executing and result upload. This paper is also give the other detail architecture of the test system.
Exploitation of Triangular Lattice Arrays for Improved Spectral Efficiency in Massive MIMO 5G Systems

A thorough analysis of the performance of planar arrays with a regular periodic lattice is carried out and applied to massive multiple-input-multiple-output (MIMO) systems operating within 5G NR n257 and n258 frequency band. It is shown that, among different arrangements with uniform spacing, a triangular lattice guarantees the reduction of the Average Side Lobe Level (ASLL), a better angular scan resolution of the main beam within a predefined angular sector and a lower mutual coupling level among elements. Moreover, single beam and multibeam application scenarios are considered for the performance comparison and both cases assess the remarkable features offered by a triangular arrangement. Particular attention is paid to illustrate, for different propagation channel scenarios, the effects of the array lattice on overall system performance including average gain as well as Signal-to-Interference plus Noise Ratio (SINR) and Sum Spectral Efficiency (SSE). The obtained results prove that a regular and periodic triangular lattice is appealing for arrays to be adopted in massive MIMO 5G systems.
A 122 GHz On-Chip 3-Element Patch Antenna Array with 10 GHz Bandwidth

This paper presents the design and measurements of a 122 GHz on-chip patch antenna array in a 130 nm SiGe BiCMOS technology. The array is formed by three rectangular patches in the top metallization layer with the ground plane in the lowest layer. Slots in the patches create a dual-resonance, which helps achieving a simulated bandwidth of 10 GHz and 28% radiation efficiency. The effects of fill structures between the patches and the ground plane are taken into consideration by introducing an equivalent dielectric constant. This makes the array fully compliant with metal density design rules of modern semiconductor processes, while maintaining good performance over a high bandwidth, which makes it attractive for numerous wideband applications, e.g. high-resolution short range radar or high data rate communications.
A Fast Full-System Simulation Environment for Memory System Evaluation

In this paper, we propose a fast full-system simulation environment that complements the shortcomings of each simulator to evaluate the memory system. We used a full-system simulator as a memory trace generator, except for the unnecessary details of it. Generated traces become inputs of trace-driven memory simulator. We also fine-tuned the memory simulator to get higher accuracy. The results show that the simulation can run at a speed of 1/7158x of the real system on average and the accuracy of memory system simulation is achieved by averaging 92.5% for bandwidth and 85.7% for latency.
3D Dilated Dense Network for Automatic Brain Tumor Segmentation

Due to the irregular size and shape of brain tumors, the location is uncertain, and the boundary is blurred, robust and accurate automatic brain tumor segmentation from magnetic resonance imaging (MRI) is a challenging task. In this paper, we develop a novel 3D Dilated Dense Network (DDNet) based on encoder-decoder architecture for brain tumor segmentation. Our network architecture provides a dense connection and dilated convolutions between layers. It aims to improve the information flow in the network and multi-scale feature representation. Eventually, we extensively evaluate our proposed DDNet on BraTS 2018 dataset against several existing approaches using different segmentation metrics. Quantitative results and visual analysis show that the segmentation performance of DDNet proposed in this paper is better than the baseline, and it has the same segmentation performance as the typical brain tumor segmentation method.
Power-Efficient Deep Convolutional Neural Network Design Through Zero-Gating PEs and Partial-Sum Reuse Centric Dataflow

Convolution neural networks (CNNs) have shown great success in many areas such as object detection and pattern recognition at the cost of extreme high computation complexity and significant external memory access, which makes state-of-the-art deep CNNs difficult to be implemented on resource-constrained portable/wearable devices with limited capacity of battery. To address this design challenge, a power-efficient CNN design through zero-gating processing elements (PEs) and partial-sum reuse centric dataflow is proposed in this paper. Unlike the existing works which either only consider the zeros in activation maps or use off-chip training process for on-chip computation reduction, a zero-gating PE design is proposed to avoid unnecessary on-chip computation by taking advantages of the large number of zeros in both the filter’s weights of pre-trained models and the activation maps. Furthermore, a partial-sum reuse centric dataflow is also proposed for off-chip DRAM access reduction. The evaluation results show that the overall power consumption of PE arrays with our proposal can be reduced by 37% and 14% at the cost of 8% and 1% area overhead when compared to the baseline PE design and the existing only-activation-gated design (i.e. that in Eyeriss), respectively. Moreover, the proposed method can achieve 35% and 47% DRAM access reduction with the corresponding 14% and 49% energy savings for AlexNet and VGG-16 when compared to that in Eyeriss.
A Distributed Stream Data Processing Platform Design and Implementation in Smart Cities

The existence of bounded data and unbounded data gives a great challenge for data processing in smart cities. The wide application of the internet of things (IoT) makes the data amount rapidly increase. This leads to further raise the requirement for data processing in smart cities, especially the demand for low latency and abundant data in real-time video services. To solve this problem, a Flink based framework with smart city adaption is proposed. A mathematical model for data processing in smart cities is formulated. Through this model's solution, the path with the minimum resource occupancy ratio (ROR) is obtained. The superiority and feasibility of our work are validated via numerical simulation and prototype implementation, respectively.
Voice Activity Detection with >83% Accuracy under SNR down to −3dB at $1.19\mu \mathrm{W}$ and 0.07mm2 in 40nm

This work presents a voice activity detector for keyword spotting in self-powered speech interfaces with sub-syllable latency. A simple decision stump classifier and time averaging are introduced to provide >83% accuracy in noisy environments with SNR down to −3dB for reliable operation under a wide range of usage contexts (8–15dB lower than prior art). $1.19-\mu\mathrm{W}$ power and 0.07mm2 area are shown in 40nm.
Co-optimization targeting future interconnection

The future interconnection techniques should satisfy greater bandwidth, less latency, and higher energy efficiency for both out-of-chip and inside-chip scenarios; while the engineering constraints and cost-performance trade-off on channel characteristics, modulation/coding technique, and analog-digital transceiver scheme, must restrict the fulfilment of interconnection design target. In order to attain the destination mentioned above, co-optimization is employed toward channel, modulation/coding, and transceiver scheme. In this paper, the requirement for future interconnection, channel situation, and some implementation trends are presented.