Miracle-dz/UniFAR-FTUs huggingface dataset:
A Redundancy-Based Background Calibration for Comparator Offset/Threshold and DAC Gain in a Ping-Pong SAR ADC

A background calibration algorithm is proposed for comparator offsets and thresholds, and for DAC gains. It leverages redundancy in a 2-step ADC to detect errors without the need for additional analog blocks. The algorithm considers an error to have occurred when the fine phase outputs a result within its redundant range. The detected errors are classified into possible erroneous comparisons, and the source of the error is estimated by analyzing the error occurrence pattern. It has been implemented off-chip for a $4\times $ time-interleaved Ping-Pong SAR ADC, which demonstrates the tolerance for ±8% VDD variation and convergence within 180k samples after a sudden 5% VDD drop.
Combined Two-Point and Three-Point Interpolated DFT for Frequency Estimation

In this paper, a novel methodology for frequency estimation is proposed. It is based on a root-mean-squared combination of the two-point and three-point Interpolated Discrete Fourier Transform (IpDFT) with a rectangular window. The advantages of the proposed modified IpDFT are its short window length and high accuracy in frequency estimation as compared to the conventional two-point IpDFT and three-point IpDFT methods. The effects of harmonics and noise in the measurement signal (the proposed technique is used for frequency estimation of this signal) on the proposed technique are also studied. The proposed method is tested on computer-generated test signals. The numeric results obtained prove the efficacy and accuracy of the proposed technique.
Fault Diagnosis of Bearings under Multiple Operating Conditions Based on Modular Neural Network

Fault diagnosis for rolling bearings are important since they are critical components in rotating machinery used in intelligent production process. Although deep learning is efficient for fault diagnosis of rolling bearing, once the equipment operates in multiple conditions it is difficult to diagnosis all faults accurately due to its bad generalization ability. In order to overcome the above problems, this paper designs a modular neural network with dynamic routing technology as the module fusion mechanism (DR-MNN). We first divide the data collected in multiple conditions into different modules according to different working conditions, and perform preliminary feature extraction according to each working condition. Then, reorganize the extracted features of different working conditions. The innovation of our work is that we use vector neuron ideas to further divide the reorganized features into modules. Dynamic routing technology is used to adaptively assign weights to different fault modules to achieve module fusion. Finally, to multi- condition fault diagnosis on the original bearing data. Moreover, the efficiency of this method is verified by using the bearing dataset of Case Western Reserve University.
Robust Fisher Linear Discriminant Analysis with Generalized Correntropic Loss Function

Fisher linear discriminant analysis (LDA) is widely used to solve classification problems. The classical LDA is developed based on the L2-norm, which is very sensitive to outliers. In this paper, we propose a new LDA with generalized correntropic loss function (GCLF), termed as LDA-GCLF, to improve the robustness of LDA. LDA-GCLF is realized by the alternating direction method of multipliers (ADMM) and the gradient descent method. Experiments results on synthetic and real-world datasets demonstrate the effectiveness of LDA-GCLF.
Circuit Techniques for High Efficiency Fully-Integrated Switched-Capacitor Converters

This brief presents a brief tutorial on recent circuit design techniques for high efficiency fully-integrated switched-capacitor (SC) converters. Design challenges for fully-integrated SC converters are highlighted, followed by consideration of tradeoffs among topology generation, parasitic loss reduction, clock generation and distribution, and close-loop regulation. Circuit techniques and design guidelines are suggested.
Global Maximum Power Point Tracking of PV Arrays Under Partial Shading Conditions Using Improved PSO and PID Algorithm

Under the condition of partial shading, the output characteristic curve of the PV system will show a global maximum power peak(GMPP) and multiple local maximum power peaks(LMPP). Since the traditional method can only track GMPP, the traditional method will not be suitable for partial occlusion. Under the dynamic partial shadowing conditions (PSCs), using traditional methods to track dynamic GMPP will become more difficult. This paper proposes a compound control algorithm that combines improved particle swarm optimization (PSO) algorithm with PID algorithm. Based on the traditional PSO, this paper uses adaptive inertia weight to make the algorithm jump out of the LMPP and search for the GMPP quickly and accurately. Meanwhile, PID controller is used to reduce system overshoot and make the system more stable. Compared with the traditional PSO algorithm, the proposed method has better tracking effect under static and dynamic PSCs.
Low Power Scalable Ternary Hybrid Full Adder Realization

Multi-level electronic systems offer speed and area simplicity, reducing the complexity of implementation and power dissipation. In this paper, a Hybrid ternary Full Adder (FA) is proposed using Conventional Complementary Metal Oxide Semiconductor (CCMOS), Double Pass-transistor Logic (DPL), and Pass Transistors (PT). The proposed FA is extended up to 64-bits to test scalability. To validate the proposed full adder and calculate its performance analysis, the Cadence Virtuoso toolset is used at technology 130nm with supply voltage 0.9V. An extra transistor is added to overcome the sneak path problem that was detected during the simulation. The ternary values 0, 1, and 2 are represented with 0V, 0.45V and 0.9V respectively. According to the simulation results, the proposed work shows a superior performance, which could be considered a promising alternative for low power applications.
ACCNet: Attention-based Contextual Convolutional Network for Crowd Counting

In recent years, crowd counting has been widely used in computer vision. But on many issues like large scale variations, perspective distortions and complex backgrounds, it remains a challenging task. Previous methods usually adopted dilation convolution to enlarge the receptive field, these methods can generate poor density maps. To solve this problem, in this paper, we proposed a novel structure called Attention-based Contextual Convolution Network for crowd counting. ACCNet contains two components. The Contextual Convolution Network first gets the relative influence of different scale-aware feature and attention module to further enlarge the range of scales while getting the final crowd density map in the network. The key section of our network is the dense dilated module, this method can make the front and back dilation layers tightly connected to preserve information from changing scales. Other than that, attention mechanism also plays an important role in learning density maps from input images. To verify the feasibility of our method, we compare it against the state-of-the-art structure on four crowd counting datasets (ShanghaiTech Part_A, ShanghaiTech Part_B, WorldEXPO’10 and UCF_CC_50), which demonstrates that ACCNet is effective and robust for complex scenes.
Hierarchical HMMs on Eyes for Driver Drowsiness Detection

Driving with fatigue is one of the main factors of traffic accident, so that timely driver drowsiness detection is very crucial. This paper proposes a novel drowsiness detection method by constructing Hierarchical Hidden Markov Models(HHMMs) on eyes sequences. The method includes two key parts: (1) take Faster-RCNN as backbone to train a robust and efficient eyes extractor by imposing additional constraints. (2) build a two-layer HHMMs to fully exploit temporal context information on different levels. We can calculate the likelihood of drowsy and nondrowsy separately for eye state sequences under two trained HHMMs to determine whether the driver is sleepy or not. In general, our approach achieves 92.98% accuracy and 25 fps speed on the evaluation set of the public NTHU-DDD Dataset. The experimental results show that the proposed method outperforms the existing driver drowsiness detection algorithms.
QN-S3VM Method for Evaluation of Liver Functional Reserve

When doctors assess the liver reserve function of patients with liver cancer before hepatectomy, if the assessment is not appropriate, patients are prone to postoperative liver failure(POLF). Child-Pugh is the most commonly used method for clinical evaluation, but this method only relies on biochemical indicators, which may lead to poor prediction. This paper aims to evaluate the liver reserve function of patients with imaging omics information using an effective machine learning scheme. The performance of models including support vector machine (SVM), COP-Kmeans, MLP, SGD-S3VM, QN-S3VM are predicted by comparing accuracy and F1 Score. In particular, the QN-S3VM model with the best performance can achieve an accuracy of 0.91, which is higher than the Child-Pugh method. As such, the proposed intelligent diagnostic algorithm can provide routine diagnostic assistance and consultation for doctors to achieve the purpose of reducing the workload.
Multilayer Perceptron Analog Hardware Implementation Using Low Power Operational Transconductance Amplifier

This paper presents analog hardware implementation of multilayer perceptron (MLP) using operational transconductance amplifier (OTA) that is implemented and simulated in CMOS 180nm technology with 1.8V supply. The implemented circuits using the OTA perform addition, multiplication and activation which are the needed operations for any MLP. The components count in each circuit is small which allows implementing larger circuits. These circuits are current-mode (CM) circuits which makes the addition operation very straightforward and needs no power consumption using KCL. The power consumption and bandwidth of the OTA are 6. 75μW and 32 KHz, respectively.
Amorphous boron coatings produced with vacuum arc deposition technology

In principle, boron (B) as a material has many excellent surface properties, including corrosion resistance, very high hardness, refractory properties, and a strong tendency to bond with most substrates. The potential technological benefits of the material have not been realized, because it is difficult to deposit it as coatings. B is difficult to evaporate, does not sputter well, and cannot be thermally sprayed. In this article, first successful deposition results from a robust system, based on the vacuum (cathodic) arc technology, are reported. Adherent coatings have been produced on 1100 Al, CP–Ti, Ti–6Al–4V, 316 SS, hard chrome plate, and 52 100 steel. Composition and thickness analyses have been performed by Rutherford backscattering spectroscopy. Hardness (H) and modules (E) have been evaluated by nanoindentation. The coatings are very pure and have properties characteristic of B suboxides. A microhardness of up to 27 GPa has been measured on a 400-nm-thick film deposited on 52 100 steel, with a cor.
Frequency Control Strategy of Isolated Grid System Using Sliding Mode Algorithm

Frequency stability is a primary condition for secure operation of isolated micro-grid. However, the load frequency control (LFC) is complicated with parameter uncertainties and external disturbances. This letter concentrates on addressing the critical problem of frequency deviations by a novel sliding mode control (SMC). Firstly, a model of the island micro-grid LFC is investigated. Secondly, Secondly, a SMC based on disturbance observer is designed for isolated systems, where a neural network observer is presented to estimate disturbances. Finally, several simulation examples are carried out to verify the feasibility of the SMC, which can eliminate the frequency deviations.
Prevention and Control Strategy for Multi-group Epidemics Based on Delay and Isolation Control

Aiming at the prevention and control of multi-group epidemics, the dynamic model of disease transmission is established, and an optimal delay and isolation strategy is proposed. The control variables in the model represent the strength of various measures taken to block and isolate contact between groups. The optimal control problem is solved by Pontryagin maximum principle and the corresponding numerical iterative algorithm is given. The results show that the delay and isolation control strategy can effectively control the epidemic of infectious diseases while minimizing the cost of infectious diseases. The outbreak of the COVID-19 in 2020 is a serious threat to people's lives and health. It is because of the timely adoption of delay and isolation control strategy of "early detection and early isolation" and "joint-prevention and jointcontrol", China took the lead in the fight against the epidemic situation and made an example for the whole world.
Alteration of chaotic advection in blood flow around partial blockage zone: Role of hematocrit concentration

Spatial distributions of particles carried by blood exhibit complex filamentary pattern under the combined effects of geometrical irregularities of the blood vessels and pulsating pumping by the heart. This signifies the existence of so called chaotic advection. In the present article, we argue that the understanding of such pathologically triggered chaotic advection is incomplete without giving due consideration to a major constituent of blood: abundant presence of red blood cells quantified by the hematocrit (HCT) concentration. We show that the hematocrit concentration in blood cells can alter the filamentary structures of the spatial distribution of advected particles in an intriguing manner. Our results reveal that there primarily are two major impacts of HCT concentrations towards dictating the chaotic dynamics of blood flow: changing the zone of influence of chaotic mixing and determining the enhancement of residence time of the advected particles away from the wall. This, in turn, may alter the extent of activation of platelets or other reactive biological entities, bearing immense consequence towards dictating the biophysical mechanisms behind possible life-threatening diseases originating in the circulatory system.
A Low-Power 0.4–2.3GHz NB-IoT UE Receiver with −15dBm OOB-Tolerant RF Front End

This paper presents the system and circuit level design of a NB-IoT receiver (RX) based on 3GPP Technical Specification (TS) 36.101. This design targets serving many NB-IoT operation bands, so no off-chip filter is used. This dictates large linearity specification on the RF front end to avoid desensitization by −15dBm out-of-band blockers (OOBs). Large linearity specification leads to large power consumption. A solution is proposed to reduce power consumption of the RF front end, while achieving high gain and high linearity. The proposed RF front end provides 29.5dB gain and −10.9dBm IIP3 while consuming 2mW from a single 1.1V power supply. The solution includes adding gain programmability to conventional resistive feedback LNA to relax the trade-off between linearity and power consumption. The RF front end is designed using a 40nm CMOS technology and occupies an area of 0.2mm2.
Multi-source data fusion for recognition of cervical precancerous lesions

Cervical cancer is a major threat to women’s health. Fusion of patients’ multi-source data is helpful to improve the diagnostic accuracy of cervical precancerous lesions. In this end, we propose a novel convolutional neural network framework that fuses the features of the two-source images (acetic acid and iodine test images) to identify cervical precancerous lesions. In our framework, we first leverage two ResNet to extract spatial features to obtain feature maps. Then, We have developed a feature selection module based on attention mechanism to integrate the related information in the feature maps. To obtain the global feature description, we leverage factorized bilinear pooling technique to fuse the feature maps of different data sources. We make use of a cervical dataset of 2,800 clinical data to train and evaluate our proposed method. The result shows that the fusion of information from multi-source data is effective. Also, the proposed method has better performance than the related approaches.
Email Organization Through Deep Learning Algorithms

Email overload has been an issue for all the email users over the world. In this research we aim to address the issue by devising an intelligent classifier that categories emails into various user defined group using deep learning approaches. We have experimented MLP (Multi-Layer Perceptron) based deep learning approaches for email classification on three datasets of varying size and intent. The experimental results have been tested by varying various parameters like no. of epochs, drop out and hidden dimensions. The tested system has also analyzed the use of various advanced techniques like weight initialization, dropout ratio, use of optimization tools, batch normalization, wherever applicable, to get optimized results. The classifier has various application areas including both domains of industry and academia.
New Proposed Methodology for Radiation Hardening By Design of MOS Circuits

One of the radiation effects on ICs is the Total Ionizing Dose (TID) effects. TID effects are accumulative effects that build up during the exposure time and may cause a functionality failure for the exposed ICs. In this paper, we propose a new systematic methodology for developing a predictive TID-aware models for bulk FETs. TID-aware models developed using our methodology enable circuit designers to predict the expected worst case performance degradation of their circuits if exposed to high radiation doses.
An intra-day rolling dynamic dispatch for isolated microgrid with MPC method

Common microgrid dispatches are mainly based on static model and ignore the dynamic characteristic of the micro-turbine system when dispatching on a short time scale. In this paper, based on an isolated microgrid, an intra-day rolling dynamic dispatch problem considering the dynamic characteristic of micro-turbine on a short dispatch time scale is formulated under the framework of model predictive control (MPC), and then solved by the mixed integer linear programming (MILP) algorithm. In addition, the actual output of the micro-turbine is obtained and compared with its setpoint given by the dispatch results. The simulation results show that the dynamic dispatch strategy has less output power deviation and can make full use of the micro-turbine to formulate a more reasonable dispatch plan compared with the static one.
Detection of Islanding for Microgrid Based on Discrimination Factor of Islanding

The present work proposes an islanding identification scheme based on the discriminating factor of islanding (DFI) extracted by the superimposed angle of impedance. The suggested methodology effectively identifies islanding within half of the cycle, discriminates islanding events (IEs) from non-islanding events (NIEs) at 0% power mismatch (PM), and unnecessary tripping due to different forms of NIEs. A significant Figure of IEs and NIEs have been tested across a wide assortment of PM on 7 bus microgrid test systems. Within this work, three separate cases (I, II, and III) are addressed. In the last, the results that were obtained from the proposed methodology is compared.
A fast flame recognition algorithm based on tracking and marking

Fire will cause great loss to life, property and ecological environment. The key to reduce the loss of fire is to send out fire warning quickly. Based on the requirement of real-time flame recognition of embedded video monitoring equipment, this paper proposes a fast flame recognition algorithm based on tracking and marking. First, the improved color model of flame is used to complete the segmentation of candidate regions. Then neural network is used to give the final classification results. The core idea of the proposed algorithm is to replace the process of classification with the process of tracking and marking, for the reason that the time consuming of classification through neural network is much longer than that of tracking and marking and the number of calls of neural network can reduce in the proposed algorithm. Comparing the proposed algorithm with other flame recognition algorithms in recent years, it can not only keep the high accuracy of classification, but also shorten the execution time of the algorithm.
American License Plate Recognition Algorithm Based on Deep Multi-Model Fusion

In order to realize the accurate recognition of American license plate, an algorithm based on multi-model fusion is proposed. The algorithm divides the task of license plate recognition into two steps: first, the license plate number and the state name are located, and then the regions are recognized. On the basis of YOLO v3, the feature extraction network is redesigned. Combined with the depthwise separable convolution and attention mechanism, a lightweight object detection network is proposed to locate license plate number and state name. For the license plate number, the Convolutional Recurrent Neural Network (CRNN) is used to recognize the license plate number, and transfer learning is introduced to solve the problem of insufficient data in license plate number training set. For the state names, ResNet-50 is used for classification model. Finally, the feasibility of the algorithm is verified by the experiment.
A Novel Speech Recognition Model with Bayesian Optimization

In order to solve the problem of the speech recognition, this paper proposes a novel framework to process speech signals by using the deep fully convolutional neural network (DFCNN) as the acoustic model and the Transformer as the linguistic model. DFCNN can learn more about speech signals through a deep network structure, while Transformer's self-attention mechanism makes it an efficient linguistic model. In order to better extract the characteristic information of speech signal, we transform the speech signal into a two-dimensional language spectrum by frame division and Fourier transform. In this way, the language spectrum can be processed by using convolutional neural network, which has promising ability to process two-dimensional data. The output of the acoustic model is passed into the linguistic model, and a complete sentence is output. At the same time, considering that there are many hyper parameters in the model, in order to adjust these hyper parameters efficiently, we employ the Bayesian optimization algorithm to optimize the hyper parameters of the acoustic model and the linguistic model, so as to further improve the accuracy of speech recognition. Experimental results on speech recognition library THCHS30 show that compared with other models, the optimized model has higher recognition rate for audio and stronger generalization ability.
The Performance of Brain Tumor Diagnosis Based on Machine Learning Techniques Evaluation - A Systematic Review

This research aims to investigate the performance of brain tumor diagnosis and treatment using machine learning algorithms. This study provides systematic review of papers on the improvement of human life. The papers reviewed are taken from relevant articles published between October 2012 and December 2019. The investigation is done against the algorithm type, dataset, the proposed model, and the performance in each of the papers. The accuracy result among the papers papers studied is ranged between 79% - 97.7%. The algorithms they used are CNN, KNN, C-means, RF, respectively, ordered from the highest frequency of use to the lowest. In the papers studied, it was shown that various methods had been used with good results. However, the confidence in the research results in term of accuracy for the detection of brain tumors still needs to be increased. Furthermore, building a software applications can be very useful to solve real cases.