Miracle-dz/UniFAR-FTUs huggingface dataset:
1/ ${f}^{\gamma}$ Low Frequency Noise Model for Buried Channel MOSFET

The Low Frequency Noise (LFN) in MOSFETs is critical to Signal-to-Noise Ratio (SNR) demanding circuits. Buried Channel (BC) MOSFETs are commonly used as the source-follower transistors for CCDs and CMOS image sensors (CIS) for lower LFN. It is essential to understand the BC MOSFETs noise mechanism based on trap parameters with different transistor biasing conditions. In this paper, we have designed and fabricated deep BC MOSFETs in a CIS-compatible process with 5 V rating. The <inline-formula> <tex-math notation="LaTeX">${1}/{f^{\gamma }}$ </tex-math></inline-formula> LFN is found due to non-uniform space and energy distributed oxide traps. To comprehensively explain the BC MOSFETs noise spectrum, we developed a LFN model based on the Shockley–Read–Hall (SRH) theory with WKB tunneling approximation. This is the first time that the <inline-formula> <tex-math notation="LaTeX">${ 1}/{f^{\gamma }}$ </tex-math></inline-formula> LFN spectrum of BC MOSFET has been numerically analyzed and modeled. The Random Telegraph Signal (RTS) amplitudes of each oxide traps are extracted efficiently with an Impedance Field Method (IFM). Our new model counts the noise contribution from each discretized oxide trap in oxide mesh grids. Experiments verify that the new model matches well the noise power spectrum from 10 to 10k Hz with various gate biasing conditions from accumulation to weak inversion.
Forecasting BIST100 Index with Neural Network Ensembles

This paper aims to provide a neural network-based approach to forecast the direction of movement of BIST 100 stock price index and investigates the difficulties of such an implementation. It is observed that a neural network implementation is highly sensitive to selection of features and optimization parameters such as learning rate. A methodology to overcome the difficulties of neural network implementations to financial time series is proposed in the paper. Several feature selection methods are employed to obtain a subset of the features that can be used in the training of any classification algorithm. The difficulties and benefits of using an ensemble of neural networks instead of a single neural network are also studied. Results have shown that the use of neural network ensembles yields promising results. Keywords: Neural Networks, Ensemble, Bagging, Forecast.
Stock Market Forecasting using Machine Learning: Today and Tomorrow

Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. Investors in the stock market seek to maximize their profits for which they require tools to analyze the prices and trend of various stocks. Machine learning algorithms have been used to devise new techniques to build prediction models that can forecast the prices of stock and tell about the market trend with good accuracy. Many prediction models have been proposed to incorporate all the major factors affecting the price of stocks. This paper focuses on portraying distinct machine learning algorithms such as support vector machine, deep learning, random forest, boosted decision trees, ensemble methods and a few hybrid methods which have been used to build prediction model and predict the stock prices for different stock exchanges. This paper also covers the various challenges that are encountered while building prediction models.
Research Progress on Control of Bioinspired Flapping-wing Micro Air Vehicles

The Bioinspired Flapping-wing Micro Air Vehicle (FMAV) has been attracting many research institutions due to the advantages they present, such as greater maneuverability, greater efficiency, and lower energy consumption. Compared with the fixed and rotary airfoils, maneuvering characteristics of FMAV depends much upon efficient control of flight attitude. So, an effective model of flight control is of utmost importance. The developments of bioinspired FMAV in recent years is firstly highlighted in the paper, and we specifically discuss the different mechanisms for bioinspired FMAV flight control as well as model-based control and the model-free control. We conjointly define the challenges related to making associate degree integrated bioinspired FMAV.
An Effective Dynamic Programming Algorithm for Optimal Coalition Structure Generation

Coalition formation is one of the most studied topics in multi-agent systems. Central to this endeavor is the problem of partitioning the set of agents into exhaustive and disjoint coalitions so as to maximize social welfare. The coalition structure generation problem is challenging due to the fact that it needs to explore an exponential number of partitions. The fastest exact algorithm to solve this combinatorial optimization problem is ODP-IP [1], which is a hybrid version of two previously established algorithms, namely IDP (Improved Dynamic Programming [2] and IP [3]. Given this, it is desirable to come up with a new algorithm which could build on the same principles as IDP follows and which in turn, improves upon the state. In this paper, we propose a new algorithm EDP (Effective Dynamic Programming). This algorithm is a new design paradigm for this difficult problem. Both EDP and IDP have been implemented and tested on well-known data distribution. We prove that EDP is practically faster than IDP.
Experimental Investigation of Efficiency Map for an Inverter-Fed Surface-Mount Permanent Magnet Synchronous Motor

Losses in inverter-fed permanent magnet motors are underestimated by using analytical or numerical approach since additional losses due to extra harmonics of the frequency converter are normally skipped. Further, losses in switches and passive components of the converter and the effect of switching frequencies cannot be numerically taken into consideration. Loss-minimizing control and proper efficiency analysis of inverter-fed permanent magnet motors cannot be achieved if an efficiency map is built based on a numerical investigation of the motors alone. This works first reviews losses in a surface-mount permanent magnet synchronous motor (SPMSM) and frequency converters. The efficiency map of a surface-mount permanent magnet synchronous motor in both motoring and generating mode, voltage source inverter and entire drive considering the effect of the switching frequency and modulation, is experimentally investigated in different load conditions. Finally, an appropriate range of switching frequencies in order to maximize the drive efficiency is recommended through the investigation.
Performance Analysis for Faster-Than-Nyquist Signaling Under Multi-Path Channel

We propose in this letter the time-based receive transformation (TRT) to suppress the inter-block interference (IBI) in the single-input-single-output (SISO) system with faster-than-Nyquist (FTN) signaling under the multi-path channel. For the trailing-zeros based FTN signaling or the OFDM based FTN signaling, the achievable rate benefits from the increased symbol rate but is degraded by the increased number of symbols that multi-path inter-symbol interference (ISI) affects. We show that the TRT based FTN signaling can completely eliminate the effect of IBI so that its achievable rate benefits from both the increased symbol rate and the increased number of symbols that multi-path ISI affects. We also show that the achievable rate for the TRT based FTN signaling is larger than that for the trailing-zeros based FTN signaling and the OFDM based FTN signaling, and this advantage increases with the ratio of the symbol rate to the Nyquist rate.
Method for the Sea Clutter Characterization in HF Surface Wave Radars From the Fields Diffracted by the Sea Surface

A new method for the characterization of the sea clutter in high-frequency surface wave radars is presented. The method is applied for the bistatic and the monostatic case. This method is based on the discretization of the sea surface in multiple patches. The fields diffracted by each patch are determined. Subsequently, the voltage that the diffracted fields induce on the receiving antenna is calculated. The sum of the voltages induced by all the patches is the sea clutter to be characterized. The fields diffracted by the sea patches are characterized in the form of field charts on a Huygens surface surrounding each patch. Field charts are modeled by analytical expressions. The current induced on the receiving antenna by the diffracted fields is calculated by means of the Lorentz reciprocity theorem. The feasibility of the method of characterization of the sea clutter is verified by means of the characterization of the fields diffracted by a single patch. The calculation of the induced current is done using the diffracted fields that are reconstructed from the analytical expressions that model them. It is also verified that the transmitting and receiving antenna can be replaced by plane wave sources, in order to simplify the calculations of the radiated fields that take part in the Lorentz reciprocity theorem. As a validation of the proposed method, the Doppler spectrum of a moving sea surface is calculated for the monostatic case.
MIMO Adaptive Bspline-Based Wavelet NeuroFuzzy Control for Multi-Type FACTS

Motivated by the synergistic integration of soft computing paradigms this paper introduces a fully adaptive multiple-input-multiple-output NeuroFuzzy control for multi-type Flexible AC Transmission Systems (FACTS) to damp low frequency oscillations. The novel control strategy integrates the complementary features of locally controllable fuzzy Bspline membership functions and robust wavelet neural networks in NeuroFuzzy structure. The gradient decent based back-propagation mechanism used for parameters update has been optimized using online Adaptive Learning Rates (ALRs). The stability of the proposed algorithm has been ensured by deriving an upper bound on ALRs using Lyapunov stability criteria. The application of this controller to provide damping signals to various FACTS controllers like Static Synchronous Series Compensator (SSSC) and Static Synchronous Compensator (STATCOM) can effectively enhance the dynamic stability of the system. A benchmark multi-machine power system has been used for performance validation of the controller by applying various faults under different loading scenarios. Conventional Lead-Lag and NeuroFuzzy controls have been considered for comparative evaluation using nonlinear time and frequency domain techniques to reveal that the proposed control performs better in different operating regions. Furthermore, the graphical results obtained from time and frequency domain simulations have been quantified numerically using different performance indices and Energy Spectral Density (ESD), respectively. The temporal, spectral and numerical analysis confirms the superior performance of the proposed control scheme.
Future of Non-Volatile Memory -From Storage to Computing-

More than thirty years passed since the first NAND flash memory was presented at the IEDM. The NAND flash memory expanded its market and application by reducing the cost per bit ($/GB) and this advancement will continue in coming decades. Many innovations, such as double/quadruple patterning, multi-bit programming, wear leveling, etc. were introduced to keep the cost trend and improve the performance. Now 5G and AI are changing our society and "memory centric" computing system is required for such high level computing. Non-volatile memory is a key component to enable this paradigm shift. The challenges and opportunities of NAND flash and other emerging memories for next decades will be discussed.
English learning tool for elders- design of instant fruit identification system with TensorFlow

This study utilizes TensorFlow to train the picture recognition model with daily fruit data and provides corresponding English words for pictures. The main purpose of this system is to assist EFL elderly people to learn English without pressure. Although many scholars have used TensorFlow to train the image recognition model to identify many things (such as: daily necessities, food, etc.), the recognition accuracy of this model is still low. Other famous image recognition models, such as Google Image Recognizer has the same problem. Therefore, this study offers a method to improve the recognition degree for better accuracy of the picture recognition model.
User Recruitment Strategy for Maximizing Coverage in Mobile Crowdsensing

Over the last decade, mobile crowdsensing has been an effective way to collect data by utilizing users’ smart devices to collectively perform a large-scale sensing job. In this paper, we focus on the coverage problem of the user recruitment. In mobile crowdsensing, mobile users move on different paths among places; we focus on the problem that how users can be optimally selected in order to efficiently collect information from the sensing area. There are two goals in this paper: one is to select users at minimum cost when we can easily cover all points of interest (PoIs) in a designated area; the other is to maximize the coverage when cost is limited (recruiting the fixed number of users). We consider the scenarios with deterministic user mobility and formulate the user selection as a set cover problem with a submodular objective function. Obviously, the set cover problem is NP-hard and we propose the corresponding practical greedy heuristics and also infer their approximation ratios. Our simulation experiments with real datasets provide the performance of the greedy heuristics. The results satisfy the inferred approximation ratios to the optimal solution and achieve the better performances than the other compared solutions.
Low Area Overhead Custom Buffering for FFT

In this paper we propose a technique to minimise the area overhead of a double buffered implementation of Radix-4 Fast Fourier Transformation (FFT). Our proposal circumvents the need for double buffering by exploiting opportunities in the specific data reordering of the buffers that are needed when implementing a fully pipelined FFT. By using the same read and write pattern, a single buffer is sufficient to perform data reordering while maintaining data integrity without degrading performance. We demonstrate this approach in an FPGA implementation. As a result of our optimisation, the memory depth can be reduced by a factor of two with very small overhead in control logic complexity.
MALP: A More Effective Meta-Paths Based Link Prediction Method in Partially Aligned Heterogeneous Social Networks

In general, online social networks include different types of nodes and edges, which means that online social networks are a type of heterogeneous information network. Link prediction is a very important research problem in heterogeneous social networks. The solution to this problem is generally to predict the possibility of a link between two nodes by extracting the characteristics of the nodes in the network. However, the information provided by a single network may not be sufficient, so useful information can be passed from other networks to assist in link prediction in the target network. This is called a partially aligned heterogeneous social network link prediction problem. In this paper, a method, called Meta-path and AUC optimization based Link Predictor(MALP), is proposed to predict the social links in the partially aligned social networks at the same time with a semi-supervised AUC optimization technology. Experimental results on real social network data show that our approach exhibits better predictive performance than other state-of-the-art methods.
A Silicon Photonics Technology for 400 Gbit/s Applications

A Silicon photonics platform operating at 100 Gbit/s (53Gbaud-PAM4) per lane is demonstrated. Integration of 60 GHz High-Speed Photodiode and efficient High-Speed Phase Modulator into a 400G-DR4 3D test chip is shown. Extension towards 400G-FR4 is addressed by the introduction of a SiN layer allowing wideband fiber to the chip optical coupling and polarization management.
Sensorless Speed Control for Double-Sided Linear Induction Motor Applications

In this work, a flux and speed observer for double-sided linear induction motor applications is presented and experimentally validated. More in detail, from a Double-Sided Linear Induction Motor (DLIM) prototype, the complete modelling and the determination of the related parameters are here reported. Furthermore, the equations for a d-flux and q-flux observer are conceived and several simulation tests are performed. From the good agreements between the trends over time of the speed estimated by the observer and the simulated one, it can be stated that the observer is well designed. Moreover, in order to experimentally validate the proposed observer, a test bench is set-up for the DLIM/observer system. The experimental results carried out through means of a test bench have shown good agreement between measured and estimated speed, validating the proposed system.
Real-time Fault Detection System for Multiphase Plasma Etching Process using OES, Two-Step Division and Change Stage Alignment Method

For plasma etching process, developing efficient real-time fault detection systems is an active research area of semiconductor manufacturing, especially the systems using Optical Emission Spectroscopy(OES) data. Besides, as a typical multiphase process, it is promising to apply multiphase methods in the OES fault detection system. However, the process drifts existing in the system will lead to severe drifts in time direction and cause model mismatch issue when applying the multiphase methods. To cope with these problems, a real-time fault detection system for multiphase plasma etching using OES, two-step division and change stage alignment method is proposed in this paper. By capturing changes in process, a two-step division method divides the process into stationary phases and transition phases. To better overcome the drift problem, change stage in transition phase is further aligned before MPCA modeling and online monitoring. Case studies have shown that the proposed system is more reliable and sensitive comparing to the reference model and can better overcome the model mismatch problem.
Analyzing Factors Influencing Flight Delay Prediction

The growing aviation industry has resulted in air-traffic causing flight delays. Flight delays have huge economic impact on airlines and also have harmful environmental effects. Therefore, it is important to detect the major factors influencing flight delay. The factors influencing the flight delay range from natural factors like weather to factors like day, month, etc. This paper gives an insight into the vital factors influencing flight delay. On observing wide variety of flight data, a list of factors responsible for the delay was generated. There are other factors influencing the delay as well, but their scope is limited. This list primarily categorizes delay into two types, namely departure delay and arrival delay. Some of these features influence the flight delay drastically while others have a minor impact. In order to develop an efficient flight delay prediction system, these features along with their degree of impact on the delay must be taken into consideration. This paper attempts at providing a detailed analysis of factors influencing the flight delay.
Comparison of Student EQ with Completed and Dropout Students in a Special Graduate Educational Program

Hokkaido University provides the special graduate education program "Nitobe School" from 2015. The program adopts Student Emotional Quotient (SEQ) as a supportive system of the assessment of educational quality. We have reported the result of SEQ for the Japanese Nitobe School students who entered the program in academic year (AY) 2015 previously. This paper reports the results of SEQ for the Japanese students enrolled in AY 2016. We compared the score of AY 2016 students with AY 2015, and found the variation of score in AY 2016 is smaller than AY 2015. On the other hand, the SEQ score of students who dropped out Nitobe School showed a characteristic shape in AY 2016, compared with AY 2015. So, we classified the dropped out students into "withdrawals" and "ghost" and analyzed each group SEQ score to clarify the reason of dropout. The results of this analysis showed the "withdrawals" left the program because they could not stand up and overcome the initial phase of team-building, and the "ghost" acted a lot to advance teamworking but seem to be suffered from such effort. We found SEQ can be used to detect such hidden suffering students by analyzing the result of SEQ earlier.
Infrared Image Recognition Method of High Voltage Porcelain Insulator Based on Self-Constructed CNN

To achieve online automatic monitoring of high voltage porcelain insulator strings by infrared thermography, it is essential to obtain the temperature information of the iron cap and disc. In order to accurately extract the temperature, this paper proposes a self-constructed convolutional neural network (CNN) for automatic identification of the iron cap and disk area in an infrared image. The sample set of the algorithm consists of insulator images from multiple substations in various regions, without loss of generality. After training, the network finally outputs four classifiers of iron caps, discs, aluminum fittings and cables. Then we use these classifiers to identify the corrected insulator string region image. Finally, we use different colors to identify the target area and extract the temperature of the area with in-house code. By evaluating the relative temperature difference between an individual insulator and its adjacent insulators, we can discriminate whether there is deterioration in the insulator string. The experimental results show that the self-constructed CNN can achieve excellent recognition results for insulator string iron caps and disc surfaces of different voltage levels.
Line Source Scattering by Dielectric Objects inside a Circular Cylinder with Resistive Boundary – Part 1: Forward Scattering

In this two-part paper, direct and inverse scattering problems of cylindrical bodies of arbitrary cross section embedded in a circular cylinder with resistive boundary are presented. This work is an extension of the paper Yelkenci [1] to the case of a time-harmonic line source illumination. The numerical data needed for the implementation of the inverse problem are supplied artificially by the approximate numerical solution of the corresponding direct scattering problem. In Part–1, the forward problem is addressed. Part–2 will consider the inverse problem.
Automatic segmentation algorithm of femur and tibia based on Vnet-C network

Segmentations of the femur and tibia are very important in the development of an orthopedic surgery plan, but the current segmentation method for the femur and tibia requires a large amount of user interaction, which requires too much time and effort for the doctor. In order to make the segmentation of femur and tibia more efficient, we propose an automatic segmentation algorithm based on Vnet-C network. Firstly, the algorithm takes the three-dimensional volume data of the femur and tibia as input, and uses the three-dimensional convolution to extract the three-dimensional features of the femur and tibia; Then, we use residual learning to construct a deeper network to improve the accuracy of automatic segmentation. To verify the algorithm’s result, the segmentation experiment was performed on the lower extremity CT scan of 68 patients, and we evaluate the segmentation results through the following metrics: Precision(P), recall rate (R), F1 value, Dice score (DC) and Jaccard index (IOU). The results show that the Vnet-C network can achieve accurate automatic segmentation of the femur and tibia.