Miracle-dz/UniFAR-FTUs huggingface dataset:
[The search for a predictor of deterioration of the nonspecific stress index K6 among urban residents: The KOBE study].

Objective To explore health-related and socioeconomic factors that can predict future deterioration in Psychological Distress Scale (K6) scores.Method We conducted a baseline (2010, 2011) self-administered questionnaire survey of the Kobe Orthopedic and Biomedical Epidemiological study (KOBE study) involving 1,117 participants who had no previous history of cancer or cardiovascular diseases and were not under treatment for hypertension, diabetes, or dyslipidemia. We used the Japanese K6 as an indicator of stress and defined K6≧5 points as a high-stress group and K6<5 points as a low-stress group. A four-year follow-up survey (2014, 2015) was conducted on 1,004 people (follow-up rate of 90%). We excluded 39 participants with missing values, and 185 people whose K6 score was higher than 5 points at baseline. A final total of 780 participants' scores were examined for: gender, age, living situation, physical activity level, average sleep time, as well as the K6 score, the Japanese Hearing Handicap Inventory for Elderly (HHIE-S), and the Japanese Oswestry Disability Index (ODI). We conducted a logistic regression analysis using K6≧5 points at the four-year follow-up survey as the dependent variable and each factor at the baseline survey as the independent variable.Results Of the 780 subjects analyzed, 132 (16.9%) were highly stressed (K6≧5 points) at the four-year follow-up point. A logistic regression analysis found age (40s/70s) (odds ratio 3.38, 95% confidence interval 1.45-7.86), living situation (single/living together) (odds ratio 1.98, 95% confidence interval 1.07-3.68), and ODI scores (every 1%) (odds ratio 1.05, 95% confidence interval 1.01-1.09), to all be significantly associated with high stress.Conclusion This study suggests that age, living situation, and ODI scores are related to future stress.
Lightweight Anomaly Detection Framework for IoT

Internet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.
A Deep Learning based Accident Detection System

In this fast-paced world, the number of deaths due to accident is growing at an expeditious rate. Major reasons for these accidents are rash driving, drowsiness, drunken driving, carelessness, etc. An indicator of survival rates after detecting accidents is the time between the occurrence of accidents and the advent of medical care to the victim. The rapid growth of technology has made everything more facile and this advancement in technology additionally increased accidents. Due to this delayed medical attention, the accident victims might die as well. As a solution to these problems, we introduce a system that detects road accidents and will provide an alert message to the most proximate control room immediately. The camera module of the system is deployed in accident-prone areas. Whenever an accident occurs, it will detect the accident and immediately report about it to the nearby control room. The working of the system is based on deep learning techniques that use convolutional neural networks. By utilizing this system, many people can be saved from death.
Evolution of Escherichia coli Expression System in Producing Antibody Recombinant Fragments

Antibodies and antibody-derived molecules are continuously developed as both therapeutic agents and key reagents for advanced diagnostic investigations. Their application in these fields has indeed greatly expanded the demand of these molecules and the need for their production in high yield and purity. While full-length antibodies require mammalian expression systems due to the occurrence of functionally and structurally important glycosylations, most antibody fragments and antibody-like molecules are non-glycosylated and can be more conveniently prepared in E. coli-based expression platforms. We propose here an updated survey of the most effective and appropriate methods of preparation of antibody fragments that exploit E. coli as an expression background and review the pros and cons of the different platforms available today. Around 250 references accompany and complete the review together with some lists of the most important new antibody-like molecules that are on the market or are being developed as new biotherapeutics or diagnostic agents.
Speech Source Separation Using Variational Autoencoder and Bandpass Filter

Speech source separation is essential for speech-related applications because this process enhances the input speech signal for the main processing model. Most of the current approaches for this task focus on separating the speech of commonly high-frequency noises or a particular background sound. They cannot clear the signals which intersect with the human speech in its frequency range. To deal with this problem, we propose a hybrid approach combining a variational autoencoder (VAE) and a bandpass filter (BPF). This method can extract and enhance the speech signal in the mixture of many elements such as speech signal, the high-frequency noises, and many kinds of different background sounds which interfere with the speech sound. Experimental results showed that our model can extract effectively the speech signal with 15.02 dB in Signal to Interference Ratio (SIR) and 12.99 dB in Signal to Distortion Ratio (SDR). On the other hand, we can adjust the passband to identify the range of frequency at the output signal to apply for a particular application like gender classification.
Impact of roe deer Capreolus capreolus browsing on understorey vegetation in small farm woodlands

The impact of around nine roe deer Capreolus capreolus! km2 on ground and shrub vegetation was assessed in a sample of six small woodlands on a largely arable estate in Dorset, southern England. In January 1996, 30 exclosures of 2 × 2 × 1.5 m and 30 paired controls were set up. Measurements of vegetation density at six height categories using a cover board were taken in late winter and mid-summer in each of the four years 1996–1999. Mean cover values were calculated for each woodland, and they indicated that the density of vegetative cover was reduced by deer browsing in winter and in summer. The effect of the browsing increased significantly within the four-year study period, and plant species composition had changed by the end of the study period. Our results suggest that roe deer may be having a substantial and potentially widespread effect on vegetative structure and composition in small farm woodlands in arable ecosystems in central southern England. The implications of this, for the characteristic wildlife and game species found in this common woodland habitat, are discussed.
Insulator Profile Detection of Transmission Line Based on Traditional Edge Detection Algorithm

The accurate detection of insulator profile of mine transmission line is the basis and premise of mine intelligent UAV (unmanned aerial vehicle) inspection, but it has not been effectively solved. In this paper, the mathematical model of traditional edge detection operator is first derivative operator: Robert operator, Prewitt operator, Sobel operator, and operators whose mathematical model is second derivative Laplacian operator, LOG operator, Canny operator and so on are studied comprehensively. These algorithms are applied to the insulator profile detection of transmission lines, and the experimental results are analyzed and compared.
Approximate Computing based on Latest-result Reuse for Image Edge Detection

This paper describes a memo-table-free approximate computing technique for an image edge detector to reuse the computed result only in the closest past. Decision of reuse is made based on tolerance-level controllable similarity checking for two consecutive 3x3 input pixels. We designed and implemented the proposed approximate edge detector in an FPGA and evaluated with 6 benchmark images. Results have demonstrated that by controlling the tolerance level the execution time is reduced by 15% and the energy dissipation is saved by 3-12% compared with the precise computation at acceptable quality in edge detected images.
A Comparative Analysis of Cardiac Data Classification using Support Vector Machine with Various Kernels

Accurate and early diagnosis of cardiac disease is necessary to prevent the death rate. Support vector machine (SVM) is one of the most powerful data classification technique which has been used by the researchers for classifying different types of data. The authors in this paper have compared the performance of SVM with four different types of kernels for classifying cardiac data. The data has been collected from the University of California Irvine (UCI) machine learning repository. From the result, it can be noticed that SVM with the polynomial kernel is performing better as compared to the other three. The proposed result is also compared with some earlier works.
Research on Thermal Capacity of a High-Torque-Density Direct Drive Permanent Magnet Synchronous Machine Based on a Temperature Cycling Module

It is difficult to meet the engineering requirements for a high-torque-density direct drive (HTDDD) permanent magnet synchronous machine (PMSM) using the thermal load design method for traditional motors. In this study, a waterway structure design method for a HTDDD PMSM depending on the motor thermal capacity is proposed based on a temperature cycling module. The design program of the motor waterway structure is based on a waterway thermal calculation and an equivalent thermal network method. The waterway structure can be rapidly designed by cyclic checking of the temperature at each node in the motor. A fluid-solid coupled temperature field simulation is used to analyze the influences of the thermal capacity, the waterway structure, and the water velocity on the temperature rise and the cooling effect of the motor. Finally, the algorithm is used to create a prototype, and the proposed method is experimentally validated.
Utilization of Opportunistic Networks for Transmitting Data of Medical Importance

Due to global warming, natural disasters are increasing in the world and this leads to mass casualities. In such scenarios, systems which are self reliant are required. In a disaster-prone area, network availability is very less due to freequent link breakage between the nodes. Opportunistic networks being category of Delay Tolerant Networks are suitable for such areas as they have the capability to transmit message even if there exists a link interruption. This paper has considered the three of the opportunistic network protocols (Epidemic, Direct delivery, First contact) to transmit data. As it is particular about a disaster-prone area, the basic medical data such as pulse rate, voice and electrocardiogram (ECG) are considered. The efficiency of all the three protocols are compared considering packet delivery ratio and latency.
Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Color correction and enhancement for underwater images is challenging due to attenuation and scattering. The underwater images often have low visibility and suffer from color bias. This paper presents a novel color correction method based on color filter array (CFA) and an enhancement method based on Retinex with dense pixels and adaptive linear histogram transformation for degraded color-biased underwater images. For any digital image in the RGB space, which is captured by digital camera with CFA, their RGB values are dependent and coupled because of the interpolation process. So we try to compensate red channel attenuation of underwater degraded images from the green channel and blue channel. Retinex model has been widely used to efficiently handle low brightness and blurred images. The McCann Retinex (MR) method selects a spiral path for pixel comparison to estimate illumination. However, the simple path selection doesn’t include global light dark relationship of the whole image. So we design a scheme to gain much well-distributed and denser pixels to obtain more precise intensity of illumination. Besides, we design a piecewise linear function for histogram transform, which is adaptive to the whole RGB value. Experiments on a large number of underwater degraded images show that, the processed images by our method have clearer details and uniform visual effect for all channels in RGB color space and our method can also obtain good performance metrics.
An improved binocular LSD_SLAM method for object localization

Vision sensors can simulate human eyes to process visual scenes. Vision-based object localization technology has been widely studied and applied in some fields such as autonomous driving. SLAM technology can estimate the surrounding environment and locate itself in real time without prior knowledge. The existing binocular vision positioning SLAM has problems such as insufficient positioning accuracy, high data requirements and high computational cost. This paper proposes an improved binocular LSD_SLAM method with census transform, which purpose is to reduce the requirement for the initial value and optimize the localization accuracy. Experiments in several office scenarios show that the proposed method is improved on Average Precision and Average Recall, and its computing cost still needs to be improved.
Processing of LiDAR for Traffic Scene Perception of Autonomous Vehicles

The future of automated driving system deals with the emergence of autonomous vehicles popularly referred as self-driving cars or driver-less cars. The ability of sensing its environment with or without human interaction is the redeeming feature of autonomous vehicles. The traditional segmentation algorithms mainly uses camera data,whose accuracy is degraded by the effect of shadows, bright sunlight or headlight of other cars. This paper focuses on Lidar sensors which provides 3-D geometry information of the vehicle surroundings with high accuracy. Apart from the previous works which deals only with segmentation using data from Lidar sensor, it deals with predicting the probabilities of detecting things such as human, vehicles, traffic signals, stationary objects etc. Moreover with the use of Ford campus vision and KITTI vision detection bench marks it achieves a high accuracy of about 90 percentage.
A 20.6 Gb/s Programmable Peaking Gain CTLE

In this paper, a continuous-time linear equalizer (CTLE) with programmable peaking gain for high speed wired data communication is presented. It provides a fixed DC gain of ~1dB and programmable 10.3GHz AC gain of ~3 to ~19dB in ~1.2dB steps. It is fabricated in 0.25um SiGe BiCMOS process as part of a linear redriver. Its core draws 9.5mA from a 1.8V supply and takes an area of 0.031mm2. Since there are not more than two stacked transistors in the employed architecture, it is suitable for low voltage supply applications. The equalizer performs a weighted addition of two signal paths by changing ratio of current in two branches, one of which has zero or low peaking gain and the other provides the maximum peaking gain. Its gain is selectable by adjusting the weights of the two gain paths via controlling the input DC bias of the core equalizer through input buffer.
Integrated Transfer Learning Based on Group Sparse Bayesian Linear Discriminant Analysis for Error-Related Potentials Detection

Brain-computer interface is a technology that is helpful for these people with dyspraxia or strokes to obtain the ability to communicate with others or control devices again. However, due to the brain signal collected by the system has terrible quilty, the error decision is often made by the BCI system, which hinders the development of the technology. Therefore, detecting the error from the BCI system holds a great significance by error-related potential (ErrP) generated when erroneous feedback from the system is found by the subject. In this paper, we propose an integrated transfer learning based on Group Sparse Bayesian Linear Discriminant Analysis (ITL_GSBLDA) to detect ErrPs. In this way, the Group Sparse Bayesian Linear Discriminant (GSBLDA) has better performance with the help of transfer learning. The experiment has been finished with the dataset of Kaggle competition. In the experiment, sensitivity, specificity, and Area Under Curve (AUC) are used to evaluate the performance of the decoder. Finality, the results are 71.49% sensitivity, 66.49% specificity, and 0.7624 AUC when using the signal features and meta-feature. And in this condition, our decoder surpasses the 5th place in the competition.
Classification of Electroencephalogram Signals for Detecting Predisposition to Alcoholism using Computer Vision and Transfer Learning

Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with addiction; this hampers early diagnosis and reduces the effectiveness of treatment. However, electroencephalogram (EEG) exams can contribute with more reliable data for this analysis. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism. It offers a method for examining the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach combines Transfer Learning and Con-volutional Neural Networks (CNN) to EEG signals analysis. The methodology to evaluate our proposal used 21 combinations of the classification traditional methods and 35 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.
Pose Tolerant Face Recognition: A Review

One of the main hazards found in the face recognition techniques lies in the difficulties of manipulating varying poses. The facial image variations caused due to the rotations are often more notable than the inter-person variations, used in differentiating identities. Pose-invariant face recognition (PIFR) has high potentials in several applications that deal with the uncooperative subjects, in which the full potential of facial recognition being a quiet biometric technique that can be implemented and utilized. Extensive attempts have been set into the research towards PIFR over recent years, and many eminent strategies have been introduced. However, there are some issues in face recognition across pose that remain unsolved, such as lack of knowledge about the subspaces of pose variant images. This paper comes up with a crucial survey of researches on image-based face recognition across varying poses. The current techniques are holistically reviewed and presented.
Distress Screaming vs Joyful Screaming: An Experimental Analysis on Both the High Pitch Acoustic Signals to Trace Differences and Similarities

A shout, scream or yell is a loud vocalization in which air is passed through the vocal folds with greater force than is used in regular or close-distance vocalization. This process can be performed by any creature possessing lungs, including humans. A scream is a natural action, with a strong emotional aspect, like fear, pain, surprise, joy, anger, and others. But there is a difference between the scream in fear or pain and joy or surprise. This research is based on the study of such parameters those separates a scream of distress from a hoot in the joy of a person specifically women, having objective to design and develop security devices based on women emotions to provide them security. The present study has used high-reliability audio recordings to capture the acoustic features of both high pitch signals of women. Results indicated that perceptually categorized both the signals have distinct acoustic features. Distress scream reflect a slightly higher level of intensity than joyful scream. Moreover, the duration of the signal plays a vital role.
Temporal Imaging for Ultrafast Spectral-Temporal Optical Signal Processing and Characterization

Temporal imaging is one of the most promising all-optical signal processing techniques that enable single-shot ultrafast signal characterization in both spectral and temporal domains. However, conventional temporal imaging systems have inherent constraints such as limited record length, narrow spectral observation range and complex system configuration, which prevent their potential to be fully unleashed. In this article, we review the recent efforts in developing advanced temporal imaging systems with much enhanced performance. Their powerful applications are demonstrated by real-time single-shot characterization of Kerr soliton generation in microresonator, noise-like pulses in mode-locked fiber lasers, broadband frequency combs and the dynamic waveform in ultrafast optical tomography. In addition, potential future developments of this technique in several important directions are discussed.
Deep Learning in Object Detection: a Review

Object detection continues to play a significant part in computer vision theory, study and practical application. Conventional object detection algorithms were primarily derived from machine learning. This involved the design of features for describing the object's characteristics followed by an integration with classifiers. In recent years, the application of deep learning (DL), and more specifically Convolutional Neural Networks (CNN) have elicited a great advancement and promising progress, and has therefore, received much attention on the global stage of research about computer vision. This paper conducts a review about some of the most important and recent developments and contributions that have been made towards research in the use of deep learning in object detection. Moreover, as evidently demonstrated, the findings of numerous studies suggest that the application of deep learning in object detection much surpasses conventional approaches focused on handcrafted and learned features.
A Novel Approach to Tennis Game Analysis and Emulation using Computer Vision

Continuous practice, analysis and game experience are essential to tennis players hoping to improve their game. While practice for tennis players is being made available through thousands of academies around the world, game analysis and game experience are lacking. In this paper, we propose a method of obtaining a set of statistics quantifying a tennis player’s game from a video using an optimized version of a state-of-the-art computer vision algorithm to detect the position of the ball in a frame in real-time and infer the state of the game through a series of such detections. We also propose a system that is capable of virtually simulating a player’s game in real-time using a graphics processing unit. This virtual simulation can be extended to real life with the use of three ball shooting machines, a camera, and a processing unit. We are able to obtain the statistics with an accuracy of 70% on a test set of two videos consisting of tennis rallies.