Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Prediction of Pedestrian Crossing Intention Using Roadside LiDAR Data

Pedestrians are vulnerable road users that need proactive protection. While both autonomous and connected vehicle technologies aim to deliver greater safety benefits, current designs heavily rely on vehicle-based or on-board sensors and lack strategic real-time interactions with pedestrians who do not have any communication means. As pedestrians are passively protected by the system, they might be put into hazardous situations when vehicle-mounted sensors fail to detect their presence. This paper is part of ongoing research that uses roadside light detection and ranging (LiDAR) sensors to develop a human-in-the-loop system that brings pedestrians into the connected environment. To proactively protect pedestrians, accurate prediction of their intention for crossings at locations, such as unsignalized intersections and street mid-blocks is critical, and this paper presents a modified Naïve Bayes approach for this purpose. It features a probabilistic approach to overcoming the common deficiencies in deterministic methods and provides valuable comparisons between feature-based data processing methods, such as artificial neural network (ANN) and model-based Naïve Bayes approach. A case study was conducted by using a low-cost 16-line LiDAR sensor installed at the roadside. Pedestrians’ crossing intention was predicted at a range of 0.5–3 s before actual crossings. The results satisfactorily demonstrated the properties of the modified Naïve Bayes model, as well as its higher flexibility, compared with the ANN approaches in practice.
Genome wide in silico analysis of AP2/ERF transcription factor in Arachis duranensis

Transcription factors are the group of proteins considered as key player for regulation of gene expression. There are different families of transcription factor reported in the literatures. Among them, AP2/ERF superfamily is found to have major role in regulation of various stress responses in plants. The family consists of AP2/ERF domain involved in DNA binding comprises of about 60 to 70 amino acids. Till date, functional prediction of ERF proteins in Arachis duranensis genomes are not reported. In the present study a comprehensive genome-wide analysis of 17 AP2/ ERF proteins in Arachis (Ardu ERF) using several computational techniques were presented. The physical and biochemical properties analysis, phylogenetic analysis, conserved motif analysis, multiple sequence alignment, homology modeling of ERF proteins. The result of phylogenetic analysis showed these ERF members were classified into 03 major groups and various subgroups and they exhibited close relationship among the groups on the basis of protein motifs taken into account. Based on above analysis it may be concluded that AP2/ERF transcriptional factors are involved in responses to several stresses including both abiotic and biotic.
A High-Speed Pipelined-SAR ADC with Resistor-based Self-biasing RAMP

This paper proposes a two-stage Pipelined-SAR ADC for high-speed, high-resolution and low-power applications. The sub-ADCs adopt the lower plate sampling and upper plate sampling asynchronous SAR logic respectively. The inter-stage amplifier adopts ring amplifier. The self-biasing RAMP is based on a variable resistor, which decreases the settling time of ring amplifier while maintaining low power consumption and sufficient phase margin. In addition, the first stage sub-ADC uses two parallel routes to sample and transfer residue voltage alternately, which further enhance the sample rate. A prototype ADC is designed and simulated in TSMC40nm CMOS technology with a standard 1.1 V supply voltage. The SNDR and ENOB is 55.34 dB and 8.9bit respectively from the simulation results, with a Nyquist frequency input sampled at 200 MS/s, and figure of merit of 35.4fJ/conversion-step.
Influence of eddy current losses in the optimization of linear coreless BLDC motors with PCB windings

This paper presents a series of numerical models aimed at estimating the main characteristics of linear coreless BLDC motors for the optimal sizing of these latter. The paper concentrates on windings printed on PCB. This kind of winding offers opportunities to significantly improve motors' performance, although it is more subject to eddy current losses than wire windings. Therefore, considering the harmonics contained in the airgap magnetic field, the work especially focus on the development of a 2D FEM model able to estimate these eddy currents in the copper tracks. The objective is to evaluate the eddy current harmonics and to adapt the winding shape and the motor dimensions, accordingly. The final aim of this work is therefore to propose full motor optimizations for various operating points and to observe the evolution of its optimal configuration.
Patch Based Wiener filter for Image Denoising

In this paper, we propose an image denoising method based on performance limits analysis for denoising of images. Peak performing real-time denoising algorithm is derived from the earlier analysis. A wiener filter is proposed based on patches which utilize redundancy in patches for denoising of images. Different parameters of the filter are estimated using the geometrical and photometrical similar patches. We illustrate that from the input noisy image we can estimate these parameters. Our denoising algorithm, intended for near-ideal performance has credible statistical base that is deliberated in detail. The efficacy of algorithm is experimentally demonstrated on different images & noise models. The results demonstrated here imply that our proposed method is unique.
Efficient Uncertainty Quantification of FDTD based Microwave Circuit Models with Multiple Design Parameters

The polynomial chaos expansion (PCE) method has emerged as a promising uncertainty quantification technique compared to the commonly used yet computationally inefficient Monte Carlo method. However, PCE-based methods generally suffer from a "curse of dimensionality", where the computational cost increases rapidly with the number of random variables included in the analysis. This paper applies an orthogonal matching pursuit algorithm to mitigate the computational cost of PCE and facilitate the uncertainty analysis in finite-difference time-domain (FDTD) models of microwave circuits. The performance is demonstrated by modeling a cascaded stub filter with 13 geometric and material parameters, where a considerable computational advantage is achieved.
A New Technique to Extract Maximum Power from Wind Turbine

To extract maximum power from the wind, this paper proposes a novel energy conversion technique. In this manuscript, a novel Energy Conversion Device-1(ECD-1) is proposed to convert the kinetic energy from wind to mechanical energy. The obtained mechanical energy is transmitted as a liquid through the pipes. The transmitted mechanical energy is used to rotate the self-excited induction generator shaft through Energy Conversion Device-2(ECD-2). The proposed technique gives better performance and results as compared to the conventional approaches and it overcomes the major hurdle of generator burden on the tower by placing the generator at ground level or near the load. The proposed technique allows high altitude towers to get the maximum swept area so that the power extracted from the wind can be increased. The obtained waveforms at different stages of the proposed technique are compared with the conventional method for better understanding and validation. The dynamic model of the proposed system is developed and simulated in MATLAB/SimMechanics.
Online Brain Computer Interface Based Five Classes EEG To Control Humanoid Robotic Hand

The proposed system had three stages in general, first stage was feature extraction, second stage was training a machine learning algorithm and third stage was online feature extraction and classification of ME/MI to control HRH. Variation for two kinds of feature extraction methods were proposed, Autoregressive (AR) coefficients and Common Spatial Pattern (CSP). Principal Component analysis (PCA) was used to reduce the dimensionality of AR feature. The output of the two methods were concatenated and normalized to train Support Vector Machine (SVM) algorithm. During online stage, EEG signal was acquired using EMOTIV EPOC EEG headset and same processing steps were applied as in training phase. The trained SVM module was used to predict the class of motion from the acquired EEG signal with 97.5% of online accuracy with the aid of majority voting. The predicted class was used as online signal to move the HRH to its corresponding hand gesture.
Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning

Android platform due to open source characteristic and Google backing has the largest global market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of malicious applications. This paper proposes an effectual machine-learning based approach for Android Malware Detection making use of evolutionary Genetic algorithm for discriminatory feature selection. Selected features from Genetic algorithm are used to train machine learning classifiers and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that Genetic algorithm gives most optimized feature subset helping in reduction of feature dimension to less than half of the original feature-set. Classification accuracy of more than 94% is maintained post feature selection for the machine learning based classifiers, while working on much reduced feature dimension, thereby, having a positive impact on computational complexity of learning classifiers.
Estimation of Component Failure Rates Based on Survival Data for Assemblies

Component failure rates are typically estimated from results of life testing on these components with subsequent statistical analysis, or from data of field returns of assemblies that contain these components. However, analysis of the root causes of assembly failures and subsequent classification of failures, i.e., identification of the component that has caused failure of the assembly, is expensive for assembly manufacturers or system integrators. Furthermore, components are integrated into more and more complex systems, which makes it difficult to perform detailed failure analyses. Finally, low component failure rates require analysis of a large amount of data to get reliable results.
High-Modulus Injection-Locked Frequency Divider Using Multi-Resonance Tank

This letter designs a wide locking range high modulus ILFD designed in a foundry 0.18 μm CMOS process. The proposed LC ILFD uses two stacked LC ILFDs sharing the same dc current. The top ILFD is used as a ÷2 ILFD and the bottom ILFD is used as a ÷4 ILFD; the latter ILFD is used as the input device and the former ILFD is used as the output device. The ÷4 ILFD uses a multi-resonance resonator as shown experimentally by non-overlapped locking ranges. At the incident power of 0 dBm, the locking range is 4 GHz (38.835 %), from the incident frequency 8.3 GHz to 12.3 GHz. The ILFD core power consumption is 13.98 mW and the die size is 1.2 × 1.2 mm2.
Approximate Message Passing for Downlink Sparse Channel Estimation in FDD Massive MIMO

An important challenge in massive multiple-input multiple-output (MIMO) systems operating with frequency-division duplexing (FDD) is to accurately estimate the channel response with low pilot signal overhead. Relying on the sparse structure of the channel vector, we investigate and apply three recently proposed algorithms based on Approximate Message Passing (AMP) to the channel estimation problem. As these algorithms are defined for real-valued problems, we propose a means to appropriately transform the complex-valued problem. In simulation experiments, the estimation error with reduced number of pilot signals and its dependence on signal-to-noise ratio (SNR) are evaluated. The effects of pilot matrix design and prior probability distribution modeling are also considered.
A Review Paper of Malware Detection Using API Call Sequences

Traditional signature-based malware detection techniques have been used for many years owing to their high detection rates and low false positive rates. However, signature-based techniques are ineffective as they do not detect new, polymorphic or transient malware. To beat weaknesses in signature-detection techniques, researchers have turned to behaviour-based ones, which create a malware behaviour profile by capturing malicious API calls throughout execution. In this context, API matching techniques use API calls to calculate similarities between malware. However, API concatenation techniques need significant API call process resources, which makes these methods slow owing to process quality, and thus cannot scale to large API call sequences outside the lab. In this paper, we review present malware detection strategies supported by API call sequences.
Transmitter Design and Synchronization Concepts for DaViD Display Camera Communication

Visible Light Communication is a promising technology for data transmission, which has become more and more relevant in recent years. Other than the common approach of modulated LED lighting, display-camera communication has become an attractive novel take on the concept. As outlined in earlier papers, the DaViD (Data Transmission Using Video Displays) system provides a modulation concept allowing for a data overlay onto video content which is nearly invisible to the viewer, i.e. causing hardly any perceptible degradation in video quality. This is achieved by differentially modulating the chrominance information of the underlying video frames with low amplitudes. The resulting color differences within the recorded images can be interpreted by the receiver, allowing for reconstruction of the transmitted data. In this paper, we introduce further system details and improvements to the DaViD concept. In addition to traditional transmission systems, on the display-camera link, the receiver has to be not only temporally but also spatially synchronized to the transmitter. We address the spatial synchronization problem by utilizing localization patterns for detecting the modulation area and a separate optimization on columns and rows for data resampling. Using a slight temporal oversampling, clean data frames can be reconstructed on the receiver side. Based on our modulation and coding concept, we achieve a data rate up to 34.56 Mbit/s in our current demonstration setup with hardly any visible degradation in video quality.
Scatter Factor Analysis for Sample Fatigue Life Based on a Three Parameter Birnbaum-Saunders Distribution

The Birnbaum-Saunders (B-S) distribution was derived as a lifetime model for specimens subjected to cyclic patterns of stresses and strains. A new model of three-parameter Birnbaum-Saunders distribution was derived by proposing a dependence assumption on account of the assumption of independence of crack growth from cycle to cycle being unrealistic. A method for reliable fatigue life analysis and assessment based on three-parameter Birnbaum-Saunders distribution was studied, and fatigue life scatter factor formulae of three-parameter Birnbaum-Saunders distribution based on median value was derived, and based on the order statistic theory, the scatter factor formulae corresponding to maximum and minimum test life were presented. The results of calculating examples show that life scatter factors of three-parameter Birnbaum-Saunders distribution are usually better than that of the lognormal distributions as well as that of the original Birnbaum-Saunders distribution.
Alternative PNT based on Iridium Next LEO Satellites Doppler/INS Integrated Navigation System

This paper addresses an original problem of integrated navigation system based on iridium Next low Earth orbit Iridium Next satellites. In uncovered Radar areas such as oceanic regions, in the northern, southern poles, or in the desert regions, it is important that any aircraft can navigate even in denied GNSS environment. In such conditions, how to maintain tracking information of airlines especially during distress and emergency situations? to achieve that, a new design Inertial/Doppler integration design is developed and proposed. Position and speed of the aircraft are estimated based on multiple Doppler information fusion from Low Earth Orbit (LEO) satellites downlink signals. In this paper, Iridium Next LEO constellation is considered as an emerging technology, and privileged for search and rescue and flight safety applications. Simulations based on experimental data collected by USRP E310 demonstrated very good performances. The new navigation system represents a good alternative to GNSS Positioning, Navigation and Timing Solution (PNT). To achieve high performances, derivative free distributed nonlinear filtering algorithms based on multi variant Quadrature Kalman filters are considered and implemented. A distributed design centralized at the Iridium gateway carried out very good results to be considered as an extended solution to Radar information used to track airlines and manage airspace by its integration into the Air Traffic Management System (ATMS) in all countries.
A 250mW 5.4G-Novel-Pixel/s Photorealistic Refocusing Processor for Full-HD Five-Camera Applications

In this paper, we present an integrated circuit which supports Full-Hd photorealistic refocusing. In contrast to the conventional single-image blurring, it provides physically-correct bokeh effect by rendering and then averaging hundreds of novel views from five images taken in different perspectives. To address the huge requirement of DRAM bandwidth and computing power, we adopt a block-based multi-rate framework and further propose two techniques: four-direction view generation and highly-parallel view rendering. The former provides a compact system architecture to save 32% of SRAM area and 92% of DRAM bandwidth without noticeable quality degradation. The latter efficiently generates 5.4G novel pixels per second to provide high-quality refocusing. This chip is fabricated in 40nm CMOS process, and the core area is 3.61 mm2. It consumes 250mW when operating at 200MHz and 0.9V to support Full-HD photorealistic refocusing up to 40 fps.
Do Intermediate Gaits Matter When Rapidly Accelerating?

Transient locomotion is still poorly understood in terms of planning and implementation on robotic platforms, with most research concentrated on steady-state motion. In this letter, we investigate optimal rapid acceleration (positive and negative) maneuvers of a planar numerical quadruped and biped robot. The question we ask is whether legged robots should transition through discrete, intermediate gaits (walking to trot to bound) or plan a direct transition to the top-speed gait. We present numerical evidence supporting the energetic optimality of transitioning to a desired gait without intermediate gait transitions. Trajectories were generated from rest to steady state and vice versa. Two cost functions (cost of transport and a heat-based cost function) were analyzed and compared to observations made in nature. A full 30-m trajectory was generated and compared to the acceleration and deceleration results, which further supported transitioning directly to the desired gait. All the trajectories were observed to follow a sliding mass template model which, in future, can be used as a heuristic to plan these transient maneuvers.
Development of Wireless Sensor Network Congestion Detection Classifier Using Support Vector Machine

The interconnection of sensor nodes is deployed to monitor physical phenomenon known as wireless sensor network (WSN) . When an event occurs the congestion occurs in network near the nodes at sink. Support vector machine (SVM) is useful classification algorithm of machine learning . In this paper LibSVM(open source library for SVM) and SMO (sequential minimal optimization) algorithms are applied to build classifier for congestion detection into three levels low, medium and high using wireless sensor network traffic parameter data. The kernel function is used to map the non linearly separable data points into higher dimensions. The kernel used is radial basis function (RBF) as it has less computational complexity and it produces simple model. While choosing the model with RBF as kernel function, the parameters gamma (γ) and cost(c) plays important role. The aim of this paper is to find the good pair of (γ,c)values for sensor network data set. The grid search is performed with exponentially growing sequence of γ and c. The optimized model with gamma 23 and cost is 215 has been preferred for the sensor network traffic dataset based on percentage of accuracy and false positive rate.
Experimental study of variation local search mechanism for bee algorithm feature selection

The Bees Algorithm (BA) has been applied for finding the best possible subset features of a dataset. However, the main issue of the BA for feature selection is that it requires long computational time. This is due to the nature of BA combination search approach that exploits neighborhoods with random explorative. This situation creates unwanted suboptimum solution(s) leading to the lack of accuracy and longer processing time. A set of different local neighborhood search extension and their combination approaches have been proposed, including Simple-swap, 2-Opt, 3-Opt, and 4-Opt. The performance of the proposed mechanism was compared and analyzed using benchmark dataset. The results from experimental work confirmed that the proposed approach provides better accuracy with suitable time.
Power Detector Based On Voltage Squaring

This paper presents a novel power detector using a voltage squaring circuit as power sensing element. The proposed solution features low power capabilities, extendable input range and low distortion. The circuit is designed for 1.8V supply voltage and 50A current consumption. The maximum input voltage range is designed to be 250mV and non-linearity is less than 1% for 14dB dynamic range. The circuit is tested using 65nm CMOS triple well process and simulation results verify the theoretical analysis.
Real-Time Optimization for Disaster Response: A Mathematical Programming Approach

Disasters are sudden and calamitous events that can cause severe and pervasive negative impacts on society and huge human losses. Governments and humanitarian organizations have been putting tremendous efforts to avoid and reduce the negative consequences due to disasters. In recent years, information technology and big data have played an important role in disaster management. While there has been much work on disaster information extraction and dissemination, real-time optimization for decision support for disaster response is rarely addressed in big data research. With big data as an enabler, optimization of disaster response decisions from a systems perspective would facilitate the coordination among governments and humanitarian organizations to transport emergency supplies to affected communities in a more effective and efficient way when a disaster strikes. In this paper, we propose a mathematical programming approach, with real-time disaster-related information, to optimize the post-disaster decisions for emergency supplies delivery. Since timeliness is key in a disaster relief setting, we propose a rounding-down heuristic to obtain near-optimal solutions for the provision of rapid and effective response. We also conduct two computational studies. The first one is a case study of Iran that aims to examine the characteristics of the solutions provided by our solution methodology. The second one is to evaluate the computational performance, in terms of effectiveness and efficiency, of the proposed rounding-down heuristic. Computational results show that our proposed approach can obtain near-optimal solutions in a short period of time for large and practical problem sizes. This is an extended work of Kuo et al., 2015, which has been published in the Proceedings of IEEE International Congress on Big Data (Big Data Congress) 2015.