Miracle-dz/UniFAR-FTUs huggingface dataset:
A Gate Drive Circuit and Dynamic Voltage Balancing Control Method Suitable for Series-Connected SiC mosfets

Compared with silicon insulated-gate bipolar transistor, silicon carbide (SiC) metal–oxide–semiconductor field-effect transistor (mosfet) presents good features that it clearly shows on its switching and thermal performance. However, the fast switching speed of SiC mosfet makes it much more difficult for solving the dynamic voltage imbalance problem caused by the parameter fluctuations of the SiC mosfets and drive circuits when they are connected in series. This article proposes an effective gate drive circuit and its dynamic voltage balancing control method to tackle this issue. The proposed method not only balances the dynamic voltage during high-speed switching but also adapts to the variation of the dc-bus voltage by utilization of overdrive control method (ODCM). Furthermore, to widen the control range of the ODCM, a switched-capacitor compensation method by switching and combining different drive capacitors into different values is proposed. The operation principles and the parameter design method are discussed in detail. The performance of the proposed series-connected SiC mosfets gate drive circuit and its dynamic voltage balancing control method is validated by experimental results. The dynamic voltage can be well balanced within the design range.
Improved Interfacial and Electrical Properties of MoS2 Transistor With High/Low-Temperature Grown Hf0.5Al0.5O as Top-Gate Dielectric

Uniform Hf<sub>0.5</sub>Al<sub>0.5</sub>O top-gate (TG) dielectric can be grown directly on the MoS<sub>2</sub> surface using a high/low-temperature deposition method, and excellent performances for few-layer (FL) MoS<sub>2</sub> transistors with Hf<sub>0.5</sub>Al<sub>0.5</sub>O as TG dielectric can be achieved: high mobility of 90 cm<sup>2</sup>/Vs, small sub-threshold swing of 77 mV/dec and low interface-state density of <inline-formula> <tex-math notation="LaTeX">$1.08\times {10}^{{12}}$ </tex-math></inline-formula> eV<sup>−1</sup>cm<sup>−2</sup>. The involved mechanisms lie in the facts that <xref rid="deqn1" ref-type="disp-formula">(1)</xref> the TG dielectric can prevent the MoS<sub>2</sub> surface from absorbing the oxygen and moisture in the air; <xref rid="deqn2" ref-type="disp-formula">(2)</xref> growing the Hf<sub>0.5</sub>Al<sub>0.5</sub>O dielectric at low temperature can well provide nucleation sites for its subsequent high-temperature growth; and (3) doping Al into HfO<sub>2</sub> can reduce the trap charges in the resulting HfAlO dielectric to weaken the Coulomb scattering and also produce densified and uniform dielectric film. Therefore, the Hf<sub>0.5</sub>Al<sub>0.5</sub>O TG-dielectric structure and its growth method described in this work have a high potential to fabricate high-performance FL MoS<sub>2</sub> field-effect transistors for practical electron device applications.
Effects of Interface Traps and Self-Heating on the Performance of GAA GaN Vertical Nanowire MOSFET

In the past couple of years, GaN-based vertical FETs have been explored to complement their potential logic applicability along with its well-known advantages in high-power and RF applications. In this article, the performances of short-channel gate-all-around (GAA) GaN vertical nanowire MOSFETs, fabricated for a possible low-voltage logic application, have been investigated via simulation, assuming the multilevel trapping effects at the gate interface and the self-heating effects. The simulation results reveal that shallow traps at the interface increase the OFF-state current, the subthreshold swing, and the drain-induced barrier lowering, while deep traps at the interface lower the ON-state current and cause the threshold voltage instability. When the gate voltage is higher than the flat-band voltage of the nanowire channel in the saturation region of operation, the mobility degradation, related to the self-heating, becomes significant due to the increased incorporation of the optical phonon scattering.
Land Cover Classification of Full Polarimetric PALSAR Images using Decision Tree based on Intensity and Texture Statistical Features

Although there are numerous land cover classification methods, still some restraints presents while labelling distinct classes to which it actually belongs to, without any past available information For an SAR image backscattering coefficient and its texture are significant characteristic to portray an image. In this paper, a classification technique for PALSAR image using decision tree based on intensity and its texture statistical features has been developed. The statistic texture features like homogeneity, mean, entropy, variance, contrast, correlation, dissimilarity, and second moment is analyzed and their capability to classify SAR image into diverse land cover classes has been evaluated. The Seperability index idea is used to analyze the prominence of texture features in classifying each land cover class from remaining classes. The proposed classification method is applied on ALOS PALSAR HV polarized image. The decision tree based classifier uses these data to classify individual pixel into one of the four categories: water, bare soil, urban and vegetation. The quantitative results shown by the proposed method gives overall classification accuracy of about 95.88% and kappa coefficient of 0.9490.
Speech Recognition Model for Assamese Language Using Deep Neural Network

The work presents a speech recognition model for the Assamese language of the state of Assam of India. We experimented the model on the digits of Assamese language. The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks is the primary layer of our neural network model. We also use Mel Frequency Cepstral Coefficients for choosing the speech features. Finally, the accuracy of the model is evaluated based on the recognition rate.
Evaluation of Churn Rate Using Modified Random Forest Technique in Telecom Industry

In recent years, the competition between telecom industries has widely enhanced. Therefore, to remain strong in this fierce market of telecom industries, the understanding of subscriber's need is become an essential factor, especially those subscribers which are trying to switch their telecom partner. Thus, to retain those customers, the customer churn estimation is become a mandatory requirement which can be achieved using Big Data Analytics and Data Mining techniques. Therefore, we have introduced a modified random forest algorithm to estimate customer churn rate in telecom industry using data mining techniques. This technique provides an extra layer to enhance accuracy using random variables and avoid over fitting. This technique helps to avoid regression problems often occurs while predicting churn rate. The performance of our proposed random forest technique is tested on Orange dataset. Experimental results verifies superiority of our proposed modified random forest algorithm in contrast to conventional churn estimation algorithms in terms of accuracy, error rates and area under curve (AVe). So that it can predict churn rate in a much better way which helps telecom industries to retain their customers.
Secure Secret Key and Private Key Generation in Source-Type Model With a Trusted Helper

Encrypting information with pre-agreed key is an effective method to ensure information security. With the increasing requirement for key rate, this paper investigates the problem of simultaneously generating a secret key (SK) and a private key (PK) over three legitimate terminals with a trusted helper via public discussion that a passive eavesdropper completely access to. The legitimate terminals and the helper observe the correlated source sequences from the outputs of discrete memoryless source (DMS). The legitimate terminals wish to generate an SK that is concealed from the eavesdropper, and two designated legitimate terminals want to generate a PK that needs to be additionally keep secret from the third legitimate terminal. Under this model, the SK-PK capacity region is fully characterized, and the source coding scheme is further provided to show that the region is achievable. The security performance of the generated keys is analyzed in terms of key leakage rate.
Novel Antenna-in-Package Design for Automotive Surround-View Radar Systems

Ultra-short range radar sensing and 360° surround view is emerging. To meet the sensing application, a novel antenna-in-package design with enhanced isolation for automotive surround-view radar systems is proposed. The design is composed of arrays of folded dipole elements and two additional stubs, which can enhance measured isolation peak to be 43 dB. Details of the design and measured results are presented.
A Prototype of a Motor-Learning Support System Using a Motion-Capture Device

In this study, we develop and review an easy, low-cost movement learning support system for elementary and junior high school students using Kinect. This system presents three-dimensional images of the movements of learners during exercise. This makes it possible for learners to understand their own movements, and one can expect that this will have the effect of encouraging a subjective attitude toward motor learning.
Modified Slow Fading Channel Estimation Technique And Fast Fading Channel Estimation Technique For OFDM Systems

Orthogonal Frequency Division Multiplexing (OFDM) is a recent trend which encodes digital data on multiple carrier frequencies. It is widely used in wireless communication systems because of its high data rate transmission power with high bandwidth. Though the channel state changes with time, a transmitted signal will be faded while it will travel through channel. If there is no dominant scatterer present in the medium, the channel response is known as Rayleigh channel else it is known as Rician channel. So, the most crucial part of OFDM system is channel estimation. Our task is to estimate the channel with time and correct the received signal accordingly, so that a receiver can receive the information properly. In this paper, we have implemented two different OFDM channel estimation methods for single carrier systems in which one estimation method is based on fast fading channel or Rician channel and another method is based on the slow fading channel or Rayleigh channel. The Preamble based channel estimation is for slow fading channel in which we have introduced Zadoff Chu sequence as special preamble sequence for estimation. The second method is for fast fading channel which uses pilots for channel estimation. We have introduced a special arrangement of pilots and data to make a OFDM symbol which is able to estimate the channel properly. For both the cases we get a very high signal to noise ratio(SNR) and finally, we made a comparison table between them and made a comparison of our proposed algorithm with the existing algorithm.
Deep Learning Based Speed Profiling for Mobile Users in 5G Cellular Networks

Future mobile networks promise to be more intelligent and to guarantee a better service for users. This intelligence can be highly accentuated by cognition of mobile usersâ€™ behavior and conditions. The user speed is an important element of user profile. We are interested in real time speed profiling by detecting speed range of an active user. We refer to this as Mobile Speed Profiling (MSP) of users. Indeed, performing MSP can notably improve the self-adaptation and self-optimization capabilities of these networks. It can help mobile network in resource management and handover management. This paper introduces a Deep Learning based solution to automatically construct the MSP of a mobile user. We empirically evaluate the effectiveness of our approach using real-time and highly representative radio data that best captures the real daily movements of users. This data includes ground truth information and the whole data set has been gathered massively from many diversified mobility situations. Results show that the profiling of UEâ€™s speed with fine granularity or on multiple ranges can be achieved with high accuracy on real data measured in heterogeneous deployments for 5G networks
FPGA based Optimized LMS Adaptive Filter using Distributed Arithmetic

In this paper an efficient VLSI architecture for LMS adaptive filter using distributed arithmetic is presented. DA is serial bit computation. Here, two separate look up tables are used to store possible filter partial product of input sample and filter coefficients, this is followed by access and summation of entries, with the idea of the proposed design. Offset binary coding technique is used to reduce the area complexity. The multiplexed LUTs are used to access the contents to improve the performance. Using LMS algorithm all the content of memory locations of LUT are recalculated based on samples and the contents of LUTs are updated based on proposed strategy. The design is simulated in Xilinx ISE and synthesized with Spartan-6 FPGA, XC6SLX45-2CSG324.
Difference Between Expert's and Novice's Gaze Point in Playing Competitive Puzzle Game

In competitive computer games, players need to acquire various skills to become strong. If the game designer can control the task difficulty faced by the player, a learning support system for skills necessary to achieve the task in the game is available. However, such support is difficult because the level of difficulty in competitive games is mainly determined by the skill difference with other players. Therefore, although the expert players can expect to further improve their skills by playing, the novice players may continue to defeat unilaterally when they compete with the experts, and may not achieve skill acquisition. In this research, we investigated the difference between the gaze point of the experts and that of the novices during play the game. We especially focused on the puzzle game named as “PuyoPuyo”, one of the most famous puzzle games. An experimental result showed that the expert gazed at a wider range than the novice did.
ARM: An Accelerator for Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is an emerging paradigm which has drawn much attention from the academy and industry. Leveraging 5G technique, MEC provisions the ability to support the latency-sensitive and compute-intensive services by deploying computing and storage capacity at the network edge. As one of the critical problems in MEC, resource allocation problem needs to be solved within a very short time to satisfy the low latency requirement of services. In this paper, we propose an Accelerator for Resource allocation in MEC (ARM), which can directly solve the resource allocation problem based on deep neural network. With the aid of our scale-free representation scheme and feasible guarantee algorithm, ARM can solve the problem in milliseconds. Meanwhile, our algorithm achieves near 2-factor approximation to the optimal solution.
Random Space–Time Line Code With Proportional Fairness Scheduling

As the demand for and services of the Internet of Things (IoT) increase, the dense deployment of a massive number of devices is expected in future IoT networks. Accordingly, an appropriate scheduling method will be required to fairly support the overwhelmingly large number of devices or users operating in practical environments. In this paper, we consider a single-cell downlink network in which one transmitter, such as an access point and base station, supports the massive number of users by using a space–time line code (STLC) scheme. Here, to resolve a prohibitively large amount of channel state information (CSI) feedback from the users to the transmitter, we propose a new STLC scheme called random STLC that uses a random vector for the STLC encoding. Furthermore, a proportional fairness (PF) scheduler is modified with a priority factor that is proportional to the devised alternative signal-to-interference-plus-noise ratio. By a comparison with an optimal STLC system that uses full CSI with a PF scheduler, we justify the benefit of the proposed random STLC with a PF scheduler using partial CSI for massive-user networks. Numerical results obtained from a rigorous simulation confirm that the proposed scheme can provide a comparable to achievable rate and fairness with massive number of users together with affordable feedback overhead in massive networks.
HPV-associated Head and Neck Cancer: An Epidemiologic Challenge with Preventive Possibilities

Cancers of the Head and Neck (HNC) comprise approximately four percent of all solid tumors. American Cancer Society (ACS) statistics for 2014 has estimated 42,440 new cases of oral and oropharyngeal cancer, which are the most likely to harbor the Human Papillomavirus (HPV), [1]. Eighty to ninety percent are squamous cell histology with the remainder comprised of adenocarcinoma, muco-epidermoid, and adenoid cystic histologies; these latter types are seen mostly in salivary glands. Thyroid tumors are typically considered separately as an endocrine tumor and managed differently. Tumors of the brain and central nervous system and lymphomas of the head and neck are also considered separately. With those exclusions, the common anatomic sites of HNC include the oral cavity, nasal cavity, and pharyngeal cavity with sub-sites defined for nasopharynx, oropharynx, and hypopharynx, and the laryngeal-epigottic region (considered part of the hypo-pharynx).
Flexible PMOS Inverter and NOR Gate Using Inkjet-Printed Dual-Gate Organic Thin Film Transistors

We report the fabrication of low voltage dual-gate organic thin film transistors, and their possible applications for PMOS inverters and NOR logic gates, on $125~\mu \text{m}$ -thick flexible polyethylene naphthalate (PEN) substrates using solution-processed techniques. Source, drain, and gate electrodes are made by inkjet-printing, while active semiconductor is printed by a dispenser system over parylene dielectric. Bottom and top channel interfaces have been characterized by taking long drain current transients. Formation of stable top channel interface, with low trap density, is a reason for higher top channel current than bottom channel. Increase in drain current transients, and positive threshold voltage shift is attributed to the accumulation of extra positive charge carriers induced by slow polarization of the dipoles in the parylene dielectric.
Estimating Pose of Omnidirectional Camera by Convolutional Neural Network

Estimating camera pose is a basic task for computer/robot vision. In this paper we investigated the estimation of the pose of an omnidirectional camera by applying the PoseNet [1] to omnidirectional images. An omnidirectional image is represented by an equirectangular image, perspective mosaic image representation generated from a cubemap, and multi-channel perspective image representation. The comparative experiments are carried out for these three representation of omnidirectional images and the effect of the field of view of an omnidirectional image on the accuracy of camera pose estimation. The improvement of the pose accuracy with the increase of the field of view is confirmed.
Accurate motion plan of ultrasonic linear array transducer for non-destructive 3D endoscopy of iconic cultural heritage assets

In the present work an instrumentation that combines ultrasonic tomography with accurate motion control is presented. The acquisition of high-response tomographic images is achieved using transducers in a linear array through the efficient control of their phase characteristics both in transmit and receive modes. Moreover, an accurate mechanical adaptation is designed in order to move the ultrasonic probe with a constant velocity and a sequence of tomographic images is recorded. Then, the 3D endoscopic characteristics of the measured object are extracted via the reconstruction of the 3D volume. Finally, the functionality of the proposed device is validated in a test-case that simulates a realistic mosaic that is covered by mortar.
An Ultra wideband GaN Power Amplifier for T/R Module Application

This paper presents Power Amplifier (PA) design for wideband transmit chain which can be utilized to fulfill the multipurpose or shared aperture technique in wireless communication and Radars. This PA design utilizes Gallium Nitride (GaN) on Silicon Carbide (SiC) based T2G6003028-FS transistor manufactured by Qorvo to get better efficiency and thermal stability. Achieved gain of PA is 10dB at center frequency 3.5GHz. The substrate used for the PA simulation is 10mil thick Rogers RT/Duroid-5880. The dimensions of PA layout are 65mm x 25mm. Simulation results show that the proposed PA is capable of peak power of 47dBm and 48 dBm at 1 dB and 3 dB compressions respectively at 3.5GHz. All simulation results of PA are presented.
Improved Variance K Means Algorithm using Multi Objective Genetic Algorithm for Validate Cluster Generation

In past decade, several methods have been introduced to identify the solutions of multiple clustering. Arrangement of these strategies is chiefly in view of the examination of genuine information space, space nature transformation, and sub-space projections. In this paper, an improved k-means Multi Objective Genetic Algorithm(MOGA) is proposed for detecting a generic optimal separation of the given heterogeneous numeral and categorical data within a clearly identified number of clustersProposed method integrates the genetic algorithm within the k-means algorithm with improved cost function to manage the numeral data. For the effective evaluation of the proposed algorithm three original datasets are used from UCI largest dataset repository center. Experimental result shows the effectiveness of the proposed algorithm in regaining the unexpressed cluster designs from categorical dataset if such designs alive. Improved illustration for cluster center is used which can draw cluster behavior with effectiveness because it carries the distribution of all the unreserved values in Cluster. Comparative analysis showed the superiority of proposed algorithm over VK-means algorithm.
Analysis and Nudging of Personally Identifiable Information in Online Used Markets

In web or mobile app online used markets, users often leave their private contact information intentionally to sell their goods through quick communication. However, the Personally identifiable information (PII) uploaded by users is vulnerable to unwanted advertisement, exploits or even crime. In this work, we examine how PII elements are exposed at popular online used markets in Brazil, China and Korea. Regardless of country, platform, and PII protection system, PII are exposed. From 1-year data collection, we found that phone numbers and email addresses are frequently observed such that 69.43% of posts include phone numbers and 90.86% showed email addresses, and that 36.41% of PII lasted longer than 90 days with the slow PII decay rate of 0.03% per day in Korean used market. To protect the privacy of sellers exposed on the posts, we present a simple nudging web browser extension that detects and notifies PII elements.
Energy Efficiency Improvement with Hydraulic Electric Hybrid Vehicles

Hydraulic electric hybrid vehicle (HEHV) is designed, and its energy management control strategy is discussed in this paper. Matlab/Simulink is used to establish a battery electric vehicle (BEV) and HEHV models, and backward simulation is adopted for the simulation to get the variation of torque and battery state of charge (SOC) through New York City cycle of the US Environmental Protection Agency (NYCC). In this paper, the rule-based control strategy and the genetic algorithm are applied for the energy distribution management strategy. The simulation results show that the electricity economic performance of the designed hydraulic hybrid vehicle is improved by 43.65% than that of battery electric vehicle.