Miracle-dz/UniFAR-FTUs huggingface dataset:
Forward collision warning based on kernelized correlation filters

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.
Impact of Device Parameters on the Threshold Voltage of Double-Gate, Tri-Gate and Gate-All-Around MOSFETs

Multi-gate (MG)metal-oxide semiconductor field-effect transistors (MOSFETs)are the preferred choice to overcome the limitations of scaling. In this paper, we have investigated the dependency of threshold voltage of Double-gate (DG), Tri-gate (TG) and Gate-All-Around (GAA) MOSFETs on various device parameters. Threshold voltage model of DG MOSFET has been extended to other gate architectures using geometrical transformations. The effort is to compare MG devices using simple and less time consuming mathematical calculations which reproduce the simulated/fabricated results with deviations within 5%.
Forensic Detection Using Bit-Planes Slicing of Median Filtering Image

For the detection of median filtering (MF) forensics, this paper proposes the feature vector extracted from the bit-planes slicing of the forged image. The assembled feature vector is trained in a support vector machine (SVM) classifier for the MF detection (MFD) of the forged images. The performance of the proposed MFD scheme is measured with several types of forged images: unaltered, Gaussian filtering (<inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>), averaging filtering (<inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>), downscaling (0.9), upscaling (1.1), and post-frame-up, respectively, in a block size <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> pixels. Subsequently, in experimental items, a classification ratio, Area Under the Curve (AUC), <inline-formula> <tex-math notation="LaTeX">${P} _{\mathrm {TP}}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${P} _{\mathrm {FP}} =0.01$ </tex-math></inline-formula>, and <italic>Pe</italic> (a minimum average decision error) are estimated. The result in terms of AUC shows that the estimation of the proposed MFD scheme is graded as ‘ <italic>Excellent</italic> (<inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>)’.
A Novel Fully Reconfigurable Non Foster Capacitance Using Distributed Negative Group Delay Networks

A novel reconfigurable non-Foster capacitance (NFC) using negative group delay (NGD) circuit is presented in this paper. The NGD circuit is based on a multi-order distributed amplifier which is used to achieve both NGD performance and gain compensation. By introducing shunt and series varactors next to trans-conductance transistors in the amplifier, the characteristic impedance, and electric length of transmission lines in the distributed amplifier can be flexibly tuned. As a result, the gain, phase shift, and negative group delay value of the proposed NGD circuit can be easily controlled as desired, and therefore, the scattering parameters $S_{21}$ can be adjusted to match with an ideal NFC, resulting in reconfigurable non-Foster capacitance. The working frequency ranges, equivalent negative capacitance value, and connection modes of the NFC be fully reconfigured by appropriately controlling the voltages applied to the varactors. Both simulation and measurement results were given, which agree well with each other. The experimental results show that the NFC value can be tuned from −1.1 to −2.5pF with 1 GHz center frequency and the working frequency can be tuned from 0.7 to 0.8 GHz with −10 pF shunt NFC.
Confined PCM-based Analog Synaptic Devices offering Low Resistance-drift and 1000 Programmable States for Deep Learning

We have demonstrated, for the first time, a combination of outstanding linearity of analog programming with matched PCM pairs, small analog programming noise, an extremely low resistance drift (R-drift) coefficient (0.005, median) and high endurance for a CVD-based confined phase change memory (PCM) with a thin metallic liner. In-depth analysis of linear analog programming is also presented. MNIST simulations using a pair of these confined PCM devices as a synaptic element yield a high test accuracy of 95%.
The Stubbed Waveguide Cavity

A novel structure, namely the stubbed waveguide cavity (SWC), is introduced in this paper. The SWC consists of a main TE201 mode cavity which is loaded with a pair of E-plane waveguide stubs. The stubs behave as two embedded half-mode cavities supporting a TM110 and TE011 mode, respectively. Three poles and two transmission zeros can be controlled with high design flexibility by properly dimensioning the various structural design parameters. The SWC is particularly suitable for the implementation of symmetric transmission zeros extremely close to the passband edges. The experimental results of a K-band prototype prove the feasibility of this new waveguide cavity.
A Study of Opinion Mining and Data Mining Techniques to Analyse the Cryptocurrency Market

The value of various Cryptocurrencies such as Bitcoin, Litecoin, Ethereum are always elusive. Hence, it would be a great value addition to investors if a model is able to predict what would be the nature of the crypto market for the next day. Through this paper, a time-series model using Long Short-Term Memory Networks is built to determine the value of cryptocurrency in the future. As a study, three cryptocurrencies – Bitcoin, Litecoin and Ethereum has been taken into consideration. A comparison of the results by using opinion mining to interpret the mood of the market on the current day for different currencies has been done. The sentiment scores got from natural language processing of textual data are used as features to the model used for predictions. The time-series charts are plotted using Plotly – python library for graphing plots. The Mean Absolute Error calculated between the actual and predicted values is used as the uncertainty quantification method. These uncertainty quantification methods are compared to analyze the present-day scenario of the market using opinion mining.
Performance Comparison of Patch and Loop Antennas for the Wireless Power Transfer and Transcutaneous Telemetry in the 860–960 MHz Frequency Band

The growth of implantable medical devices demands to establish safety and reliable wireless communication systems. Radio-frequency Identification (RFID) is a key architecture as it avoids the use of battery in the implanted transponder. This paper aims to compare the performance of loop and slotted-patch antennas within a battery-less through-the-body telemetry system based on backscattering modulation in the UHF band. A complete analysis of the possible combinations between the two antennas is discussed accounting for the efficiency of power transfer, the safety regulations (Specific Absorption Rate - SAR) and the quality of the link (Bit Error Rate). The SAR constraint revealed to be the bottleneck of the communication, but it can be released by increasing the reader-skin distance. Loop-loop resulted the best antenna combination, in case of an optimal reader-skin distance of 2 mm. Experimental tests with a semisolid multi-layered human phantom corroborated the numerical results.
Web Scraping And Data Acquisition Using Google Scholar

In a world where technological advancement forms the forefront of our lives, the foundation of this advancement is characterized by an exponential growth in research output from the scientific community. Research papers and their authors, however, have to go through an exhaustive ordeal to keep up to pace with the ever-changing demands of the publication committees. Hence, there is significant need for an interface that would allow researchers to easily download, sort and maintain their papers and publications with minimum work from the user end. This paper attempts to set up an interface that would use web scraping techniques and Python modules to link a researcher’s list of publications present on Google Scholar to a MySQL database and Excel application, allowing them to access and manipulate their works in minimal steps.
Course Recommendations In Moocs : Techniques And Evaluation

In the recent years, there has been a massive growth in the amount of online technical information available to the users on internet, in the form of open-access publications, tutorials, and a variety of other open source material. The usage of Massive Open Online Courses (MOOCs) like Coursera, Udacity, etc. is increasing rapidly. These MOOCs offer a huge variety of courses containing a lot of information. This overload of information means that, MOOC users spend a lot of time surfing through the internet to find the course that suits them best. Recommendation technology, thus, plays an important role in assisting the users to find their favorite content from the bulk of content available. This paper is a comparative study of the different techniques used to build recommendation systems. The paper discusses various approaches for recommendation of courses to new and existing users on MOOC platforms. Different evaluation techniques are also discussed.
Application of Image Processing and Machine Learning for Smart Restaurants

The idea is to provide a smart package for restaurants using machine learning techniques. The techniques we have used are image processing, association rule generation and sentiment analysis . Main objective is to provide an interface for the customer where he can get complete information about the restaurant. Customer has to click and upload a picture of the restaurant. The system will perform image processing and as a response it will identify the restaurant (just by the picture). Along with identification of the restaurant it will also give the customer the previous reviews about the restaurant. These reviews will help the customer in order to decide whether the restaurant is of his choice or not. Our solution for smart restaurants also includes smart menu card for each restaurant. Here we are performing association rule mining on the previous year’s transaction of each restaurant and hence obtain a list of most frequently ordered dishes according to customer’s choice and then form combos of the same in order to attract the customers. In the end we have an automated mail generating system which sends an e-mail to all the customers in the restaurants database about the offers they can avail in the restaurants. This will help in getting more customers to the restaurants.
A Soft Linear Actuator with Gentle Manipulation Mechanism

This paper proposes a soft linear actuator imitating a foxtail movement and introduces two applications. The proposed biomimetic soft linear actuator mainly consists of three parts: a soft core containing a vibration motor with silicone spikes on its surface, a tubular passage to guide the movement of the soft core, and a switching mechanism to change the direction of the movement of the soft core when an excessive tensile force is applied. When vibration is generated, the soft core moves along the tubular passage toward one direction depended on the leaning direction of the spikes; namely, it can be utilized as a linear actuator to manipulate an object if the core is wired onto it. By inserting a spring at the end of the wire, the leaning direction of the spikes can be changed reversely if the tensile force between the core and the object connected by wire-spring exceeds either the kinetic force toward movement direction generated by the core or the frictional force between the spikes and the tubular passage. Consequently, this characteristic endows the proposed soft linear actuator with a gentle and safe manipulation to handle the object. We present the two applications of gentle manipulation: an elbow manipulator mimicking a human bicep to lift its lower arm and a five-finger robot hand grasping a fragile object. By testing these two applications, we could substantiate the feasibility of the proposed soft linear actuator as a core element to realize safe human-robot interaction or cooperation.
A 1GHz, 56.7dB Linear-in-dB CMOS Wideband Variable Gain Amplifier

A four-stage wideband Variable Gain Amplifier (VGA) with linear-in-dB gain control characteristic is presented and designed in 0. 18μm CMOS Technology. The modified Cherry-Hooper amplifier is employed for wide bandwidth and large gain tuning range. The input AC-coupling and the dual feedback DC-offset canceller are combined to suppress the undesired DC offsets. The pseudo-exponential function circuit is added to transform the linear-in-magnitude VGA into the linear-in-dB one. Simulation results show the presented VGA achieves 3-dB bandwidth of 1GHz, linear-in-dB gain tuning range of 56.7dB from -6.7dB to 50dB, and IP1-dB ranging from -60dB to -17dB. Besides, it consumes 7.7mW from a single supply voltage of 1.8V and occupies a silicon area of 100μm × 134μm.
Six-DoF Pose Estimation for a Tendon-Driven Continuum Mechanism Without a Deformation Model

In recent years, the application of continuum mechanisms increased as they provide high dexterity, consume a low amount of space, and handle unforeseen collisions with ease. Typically, a deformation model of the continuum is applied to compute task space poses from actuator variables. However, simple models usually lack accuracy, whereas accurate models are computationally demanding. This letter deals with the pose estimation problem for a continuum mechanism based on length sensors without using a deformation model. A subset of the sensors belongs to the actuating tendons with a fixed routing, whereas the rest of the length sensors can be placed freely. Two major issues are discussed. At first, a sensor placement strategy based on the coupling matrix is derived and discussed, which improves the observability for static pose estimation. Second, the feasibility of a sensor fusion between an inertial measurement unit and the length sensors to enhance the pose estimation is presented. Both investigations are done experimentally on DAVID. By placing the additional sensors correctly, the maximum estimation error can be lowered by 50% in comparison to poorly placed sensors.
High-Efficiency Broadband PA Design Based on Continuous Class-F Mode with Compression

The paper presents a novel reformulation of the Continuous Class-F (CCF) mode by including an I-V knee scaling equation in the drain current waveform. The device’s knee region scales down the waveform and distorts the ideal flat performances especially when the device is in compression. The output power and drain efficiency are no longer constant across α variation in the CCF mode. Instead, symmetrical performances across α space are simulated and measured showing drain efficiency is at peak when α is at its extreme: +1 or −1. 10W GaN HEMT broadband PA (1.7 GHz to 2.7 GHz) is designed by restricting phase rotation of 2nd harmonic impedances within α=−1 ranges across bandwidth. The manufactured PA achieved 11.3W-18.4W Pout and 65.7%-83.4% DE. When operated with 10MHz LTE signal with 7.6dB PAPR at 2.7 GHz, the PA achieved ACPR levels of −53.6/−54.6 dBc after DPD.
Low-Resolution ADCs for Wireless Communication: A Comprehensive Survey

With the rapid growth of wireless data traffic and antennas configuration, higher spectrum efficiency and lower power consumption processing have evoked remarkable attention from the research and industry community for the deployment of future wireless communication. It has become a heated topic quickly in recent years and gives rise to the widespread interest around the world. As a core technology of the fifth-generation (5G) mobile communication, massive multi-input multi-output (MIMO) technology can fully exploit the space resources and greatly improve the spectral and energy efficiency. However, massive MIMO systems are faced with the problems of mass data processing, high hardware cost, and huge total power consumption. To cope with these problems, a useful solution is that the receiver equips with finite resolution analog-to-digital (ADC) converters. A large number of research results show that the low-resolution quantization technology brings significant performance within the allowable loss of capacity. This promising technique has attracted many scholars to do tremendous endeavor on it. As a motivation, we make a comprehensive survey about low-resolution ADCs for wireless communication. This paper summarizes the latest developments in the design of low-resolution communication systems, focusing on system performance analysis, some key technologies of the receiver, and typical application scenarios for the low-resolution ADCs. In view of the adverse effects caused by coarse quantization, some potential implementations are presented to alleviate this dilemma. Future research directions are also given and suggested in this paper. This overview contributes significantly to providing an informative and tutorial reference for the key technologies of low-resolution ADCs as well as its applications in practical systems.
Exploration of Automatic Mixed-Precision Search for Deep Neural Networks

Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited computation and storage resources, the weight quantization technique has been widely adopted. In practice, 8-bit or 16-bit quantization is mostly likely to be selected in order to maintain the accuracy at the same level as the models in 32-bit floating-point precision. Binary quantization, on the contrary, aims to obtain the highest compression at the cost of much bigger accuracy drop. Applying different precision in different layers/structures can potentially produce the most efficient model. Seeking for the best precision configuration, however, is difficult. In this work, we proposed an automatic search algorithm to address the challenge. By relaxing the search space of quantization bitwidth from discrete to continuous domain, our algorithm can generate a mixed-precision quantization scheme which achieves the compression rate close to the one from the binary-weighted model while maintaining the testing accuracy similar to the original full-precision model.
Current Trends on Visible Light Positioning Techniques

This paper presents a survey on visible light positioning techniques. The paper briefly reviews conventional positioning methods based on Rx signal strength (RSS), time difference of arrival (TDOA)and angle of arrival (AOA). Then it focus on the current research trends, relying on the machine learning techniques, sensor fusion and communication requirements.
CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot

Robots must be able to recognize human emotions to improve the human–robot interaction (HRI). This study proposes an emotion recognition system for a humanoid robot. The robot is equipped with a camera to capture users’ facial images, and it uses this system to recognize users’ emotions and responds appropriately. The emotion recognition system, based on a deep neural network, learns six basic emotions: happiness, anger, disgust, fear, sadness, and surprise. First, a convolutional neural network (CNN) is used to extract visual features by learning on a large number of static images. Second, a long short-term memory (LSTM) recurrent neural network is used to determine the relationship between the transformation of facial expressions in image sequences and the six basic emotions. Third, CNN and LSTM are combined to exploit their advantages in the proposed model. Finally, the performance of the emotion recognition system is improved by using transfer learning, that is, by transferring knowledge of related but different problems. The performance of the proposed system is verified through leave-one-out cross-validation and compared with that of other models. The system is applied to a humanoid robot to demonstrate its practicability for improving the HRI.
Miniaturized Reconfigurable Dual-Band Bandstop Filter with Independent Stopband Control using Folded Ridged Quarter-Mode Substrate Integrated Waveguide

This paper presents a reconfigurable dual-band bandstop filter utilizing miniaturized folded ridged quarter-mode substrate integrated cavity. By loading a quarter-mode substrate integrated cavity with a capacitive ridge structure and folding the dominant field around said structure, significant miniaturization is achieved. Folded ridged quarter-mode substrate integrated cavity is shown to reduce the area of standard quarter-mode substrate integrated cavity by 92%, while also increasing the spurious-free region above the fundamental mode. A reconfigurable dual-band bandstop filter using miniaturized folded ridged quarter-mode technology is designed, fabricated, and measured. In order to generate independent tuning of each stopband, two resonators are each loaded with a tunable capacitance, and then individually coupled to the same nonresonating node, producing a frequency agile filtering section. Two filtering sections are then coupled together through an impedance inverter to produce a dual-band, two-pole bandstop filter. To the best of the authors’ knowledge, this paper presents the first dual-band bandstop filter using quarter-mode substrate integrated cavity technology in the present literature.
Detection of Face Spoofing using Multiple Texture Descriptors

Automatic face recognition systems are being increasingly developed and deployed in various applications in both civilian and defense sectors. But the emergence of techniques for producing fake face images presents a new challenge and demonstrates high security risks on such systems thus necessitating imposter detection system. Among the numerous imposter detection methods reported in literature, LBP is often used as one of the best local pattern descriptor, as local descriptors are frequently used for imposter detection but the traditional LBP descriptor has the limitation of having small spatial support area and being sensitive to illumination changes. To handle such issues of LBP based descriptors, the proposed work explored a new approach to estimate rotation invariant uniform LBP pattern features from histograms of noninvariant LBP patterns. The proposed approach makes use of both structural information and the magnitude information of 3×3 neighborhood of each pixel with center gray level value to attain more discriminative power, which is more effective for detection of face spoofing. The proposed technique has achieved efficacy 98.97% recognition rate on NUAA dataset