Miracle-dz/UniFAR-FTUs huggingface dataset:
A Petri-net-based Method For Detecting Bugs in Multiple Threads

A multi-thread program easily suffers from concurrent bugs such as livelock, deadlock and data inconsistency. The Petri-net-based methods are widely used to detect these errors. The existing models are good at modeling the logical structures such concurrency and synchronization locks, but short of modeling the changes of variables. However, some bugs are closely related with the changes of variables. In order to solve this problem, we put forward a new Petri net model called multi-thread net. This model can characterize overwriting, shared reading and synchronization locks. Moreover, we present a method to construct a reachability graph for every multi-thread net. Based on this graph, we develop an algorithm to detect concurrent bugs including deadlock, data race, order violation, and atomic violation. Furthermore, a Petri net tool is developed to model current programs and detect their concurrent bugs.
Spatio-Temporal Scheduling of Heterogeneous Delay-Constrained Tasks in Geo-Distributed Green Clouds

The skyrocketing growth in types and number of heterogeneous applications dramatically increases the amount of energy consumed by distributed green data centers (DGDCs). The spatial and temporal variations in prices of power grid and availability of renewable energy make it highly challenging to minimize the energy cost by intelligently scheduling arriving tasks of heterogeneous applications among green data centers while meeting their expected delay bound constraints. Unlike existing studies, this work proposes a Spatio-Temporal Task Scheduling (STTS) algorithm to minimize the energy cost by cost-effectively scheduling all arriving tasks to meet their delay bound constraints. It well uses spatial and temporal variations to achieve DGDC cost reduction and throughput improvement. In each time slot, the energy cost minimization problem for DGDC providers is formulated as a nonlinear constrained optimization one, and addressed with the proposed Genetic Simulated-annealing-based Particle swarm optimization. Trace-driven experiments show that STTS achieves larger throughput and lower energy cost than several typical task scheduling approaches while strictly meeting all tasks’ delay bound constraints.
Patient Stress Estimation for Using Deep Learning with RRI Data Sensed by WBAN

Patient’s stress or positiveness during reha-bilitation play an important role in determining the outcome of rehabilitation. Estimate of patient’s stress must be useful to motivate training of patients without feeling stress and to improve effect of the rehabilitation. It is known that stress is correlated with vital signal such as electrocardiogram(ECG), electro-encephalogram(EEG), heartbeat and respiration. Wireless Body Area Network(WBAN) can measure patient’s vital signal in real time during rehabilitation. In this research, we estimate patient’s stress with neural network(NN) from their R-R interval(RRI) data of ECG measured by WBAN. We perform preprocessing based on medical knowledge and transfer learning method to improve stress estimation and reduce calculation complexity. Preprocessing extract part of features of stress before training of neural network. Through these methods, feature extraction in the NN became efficient, which not only reduced calculation complexity but also improved training speed and accuracy.
Detecting trapped humans through chest wall movements with Non-contact Doppler sensor

This paper aims to examine a noncontact pulmonary detection method in a laboratory setting, based on the use of frequencies similar to the current smartphone-liked devices. The paper utilises existing Wi-Fi 2.4 GHz spectrum wavelength, and a Doppler effect radar that detects and measures movements of the human chest during in- and exhalation under conditions that simulate entrapment caused by natural or man-made disasters. Based on the simulated scenarios, 100% accuracy was achieved for detecting a human subject when compared to traditional contact or invasive respiration measurements. This approach could be used as an alternative to the commercially designed devices currently used in rescue relief events.
Paper-Based, Hand-Painted Strain Sensor Based on ITO Nanoparticle Channels for Human Motion Monitoring

Paper-based strain sensors have attracted attention as a cost-effective and environmentally friendly approach to wearable electronic devices. This paper proposes a paper-based strain sensor created solely using hand-painting, which is a simple, equipment-free process utilized in green electronics. Commercial printing paper is coated with polydimethylsiloxane diluted with heptane, which provides moisture resistance and mechanical robustness while increasing the hydrophobicity of the surface. The electrodes and conduction channels are fabricated using a pencil for graphite deposition and a brush for the deposition of tin-doped indium oxide nanoparticles, respectively. With a gauge factor of 41.98 in tensile strain tests and 21.36 in compressive strain tests, our strain sensor exhibits a good sensitivity that is similar to that of previously reported paper-based sensors. We also demonstrate that our proposed strain sensor can be used as a weak motion detection device for humans and robots.
Vision-based Detection of Road Users and Infrastructure Elements for Automated Driving

The topic of this paper is the implementation and analysis of vision algorithms for the detection of static and dynamic objects in videos. These algorithms are typically components of the visual perception module of modern driver assistance systems or even autonomous cars. Whereas the vast majority of todays’ papers from the vision community uses convolutional deep neural networks (CNNs), this paper explores the more traditional approaches, namely HOG (Histogram of Oriented Gradients) as well as SVM (Support Vector Machine). The static and dynamic objects which are to be recognized are traffic signs and motorcycles, respectively. These two object classes have been chosen, since traffic signs are on one hand relatively easy to detect and on the other hand, motorcycles state a much more complex task. Thus, this paper tackles differently difficult tasks with a single set of algorithms.
Environmental Impact for Labor Stations Learning through Emotional Domotics Analysis and Workstation Simulation

Ergonomic, Emotional and House Automation (Domotic) analysis, are essential for process planning, job design, the design of tools/machine/equipment and work station set up. Human safety and comfort have become a key factor in maximizes the labor productivity and life quality. With the evolution of digital technologies, the analysis and simulation of the human factor in work processes are acquiring a major role. The use of advanced approaches in innovative technologies, such as ergonomic analysis tools in the future digital factory and physical environment, contributes to achieving continuous improvement in the management of quality while searching for reserves in managing human factor reliability. This paper presents a teaching methodology for an integrated project related with industrial and systems engineering and mechatronic engineering; which the main objective is to set the stage when the work systems design begins. The paper comprises theoretical starting points, results of laboratory verification and results in outputs.
Design and Development of Semi-Autonomous Wall Climbing Robot Mechanism

The purpose of this research paper is to present a wall climbing robot mechanism which can be easily scaled up and applied in various fields like, search and rescue, surveillance etc. The objective is to make a four wheeled robot which can climb and manoeuver surfaces at any inclination be it perpendicular, acute or obtuse. This robot will be made by adding two upward and downward thrusters to a four wheeled robot. On approaching an inclination, the sensor mounted in the center of the robot will calculate the angle of inclination autonomously. The amount of upward thrust required to lift the front wheels of the robot is calculated and the ESCs ensures that the required thrust is generated by the propeller in the necessary angle. Thus, the front wheels have a greater area of contact on the wall and therefore can help it climb up. The motors will be connected to a beam which is connected to two servo motors on either side, thus allowing us to control the angle at which the motor is fixed, the angle is autonomously calculated by the robot, thus enabling the mechanism to manoeuver terrains with inclinations of any magnitude. This mechanism is semi-autonomous in the sense that the operator has to manually control the robots movement but the propeller set up autonomously aligns itself based on the inclination of the wall.
Modeling of Attacks in Sigma Delta ADC for Hardware Security

This paper discusses the designing and simulation of 1 bit Sigma Delta Modulator ADC ,Two attack models has been proposed in order to detect the Hardware Trojan present in the circuit. First attack model describes the controllable Hardware Trojan circuit for output signal modulation and the second attack model describes the capacitive triggered Hardware Trojan circuit. total power consumed in simulation has been calculated keeping in mind the decreasing power requirement in growing CMOS technology The state of art designs adopted is critically reviewed and improvements are suggested.
Inverse Dynamics of an Industrial Robot Using Motion Constraints

In multibody dynamics holonomic constraint equations exist because of mechanical joints, or specified motion trajectories. In order to solve the dynamic equations of motion of the constrained multibody system, one can use a coordinate partitioning approach to eliminate dependent coordinates. An alternative approach is to use the Lagrange multiplier technique. A generalized constraint force vector associated with the system generalized coordinates has to be added in the equations of motion, which is computed by a multiplication of the transposed constraint Jacobian times a Lagrange multiplier. At this point, it should be noted that the constraint force vector is in general not the vector of actual reaction forces in the joints. For example, in robotics it is of particular interest to prescribe the motion of the tool center point and determine the drive torques in the joints. This leads to an optimal control problem, which is in general very expensive to solve. However, an alternative method is to apply a motion as a rheonomic constraint equation on the tool center point of the robot. We show that the resulting generalized constraint force vector, which is acting on the body of the tool center point, can be converted into equivalent drive torques in the joints by the use of the principle of virtual work. The advantage of the proposed method is that the solution can be obtained after one single forward simulation of the multibody system, since the conversion into the drive torques is just a post-processing step.
Readout Circuitry Moving Average for Sensing Redox Reaction in 3-electrode Cells

This paper presents the effect of moving average algorithm incorporated in a potentiostat circuit that acts as a readout interfacing circuit for a 3-electrode cell system. The potentiostat senses redox reaction with an internal Differential Pulse Voltammetry (DPV) electroanalytical technique provided by an ARM Cortex-M microcontroller. The moving average calculation is embedded in the applied microcontroller. Two moving average techniques are compared: Simple Moving Average, (SMA) and Adjacent Averaging, (AA). The objective is to reduce noise during redox reaction current sensing when the potentiostat hardware implementation is constructed using a single microcontroller with open shelf electronics. DPV current properties of 3-electrode cell redox reaction in 10mM of Ferricyanide solution was measured by the designed potentiostat and subjected to moving average to smoothen the data. It is found that even though the real-time moving average techniques are able to reduce noise by 99%, both approaches reduce peak current vs time measured at the output of the potentiostat. The findings provide important information for interpreting sensor behavior accurately based on in-situ measured values.
Using the method of pairwise comparisons for the multifactor selection of infographics design alternatives

On the basis of theoretical research, we worked out a number of literary sources, which indicate the importance of infographics and the correctness of the information submission. To systematize the received information and solve the problem of recommendations for the creation of qualitative, understandable infographic design, it was proposed to use the theory of graphs and the method of pairwise comparisons. Using this technique, we carried out the study of the factors influencing the compositional design of infographics and established the importance of the technological process for the design of high-quality infographics. We developed the hierarchical model of the factors priorities of infographic composite design. In particular, we carried out the evaluation and optimization of the developed model, by using the pair comparison method and the relative importance scale at Saaty. The method of the hierarchy analysis provided an opportunity to evaluate elements of higher levels, based on the interconnection of different levels of the hierarchy, and not with the direct dependence of the elements on these levels.Three different design projects of infographics were explored. From the results obtained, the most effective in terms of perception and memorizing the information was a project that focused on textual design using graphic elements to enhance visual perception of information.These studies have found that the use of pairwise comparisons makes it possible to compare factors that cannot be qualified and quantified. The received calculations allow us to confirm the reliability of obtaining a high or low consistency of the results of expert surveys.
An autonomous and self-locating handling device for reverse engineering systems

High accuracy digitalisation of geometric models, related to big size objects, usually is performed by means of multiple acquisitions from different scanning locations. It needs to correctly place the acquired point clouds in 3D digital environment. For this purpose, it is very important identifying the exact scanning location in order to correctly realign point clouds and automatically generate an accurate 3D CAD model. The present paper focuses on design and prototype of a mobile handling device for reverse engineering scanning systems, named Dedalo. It is able to locate itself using a sensor fusion method based on a Kalman Filter. The sensor equipment is composed by wheel encoders and an ultrasonic sensor for measuring the distance from a known reference. Although Dedalo is equipped with low-cost hardware, results have showed a location accuracy by 0,1% error/meter, better than each sensor accuracy.
Rice Blast Disease Detection and Classification Using Machine Learning Algorithm

Rice blast disease is the major problem in all over the world of agriculture sector. The early detection of this disease will prevent the huge economic loss for the farmer. This paper proposes a machine learning algorithm to find the symptoms of the disease in the rice plant. Automatic detection of plant disease is carried out using machine learning algorithm. Images of healthy and blast disease affected leaves are taken for the proposed system. The features are extracted for the healthy and disease affected parts of the rice leaf. The total data set consists of 300 images and divided for training and testing purposes. These images are processed with the proposed method and the leaf is categorized as either infected or healthy. The simulation results provide an accuracy of 99% for the blast infected images and 100% for the normal images during the training phase. The testing phase accuracy is found to be 90% and 86% for the infected and healthy images respectively.
The Video Authentication and Camera Identification Database: A New Database for Video Forensics

Modern technologies have made the capture and sharing of digital video commonplace; the combination of modern smartphones, cloud storage, and social media platforms have enabled video to become a primary source of information for many people and institutions. As a result, it is important to be able to verify the authenticity and source of this information, including identifying the source camera model that captured it. While a variety of forensic techniques have been developed for digital images, less research has been conducted toward the forensic analysis of videos. In part, this is due to a lack of standard digital video databases, which are necessary to develop and evaluate state-of-the-art video forensic algorithms. In this paper, to address this need, we present the video authentication and camera identification (video-ACID) database, a large collection of videos specifically collected for the development of camera model identification algorithms. The video-ACID database contains over 12 000 videos from 46 physical devices representing 36 unique camera models. Videos in this database are hand collected in a diversity of real-world scenarios are unedited and have known and trusted provenance. In this paper, we describe the qualities, structure, and collection procedure of video-ACID, which includes clearly marked videos for evaluating camera model identification algorithms. Finally, we provide baseline camera model identification results on these evaluation videos using the state-of-the-art deep-learning techniques. The Video-ACID database is publicly available at misl.ece. drexel.edu/video-acid.
Effective Mathematical Model of Synchronous Machine with Turn-to-turn Short-circuit of Rotor Winding for Adaptive Methods of Identification

A number of requirements are shown for perspective discrete mathematical models: fine discretisation pitch, the guaranteed asymptotic stability, obvious type of numerical methods, program and algorithmic optimization of calculations. In this paper the application of bilinear transformation for the creation of discrete mathematical synchronous machine model with rotor inter-winding fault is considered. This approach allows solving a number of challenges which arise using standard optimization methods, vide licet non stationarity of dynamic systems, influence of integration fault on accuracy. In this paper the transition from the system of differential equations describing operation physics of the synchronous generator to differential is considered. In terms of the received differential equations, a schematic structure allowing to implement a mathematical model without the application of additional controller mathematical functions is made. By means of experimental installation the approbations are made with validity check, as a result, a discrete mathematical model of the synchronous generator with rotor inter-winding fault is obtained. The offered approach to the creation of a discrete mathematical model of the synchronous machine could be used for the identification problems solution for the turn-to-turn short circuit where the adequate, high-speed and steady model is necessary.
Multiplicative Filter Design Using S-Transform

The S-transform (ST) is a hybrid combination of the short time Fourier transform (STFT) and wavelet transform (WT), which gives absolutely referenced phase and frequency information. In this paper, the multiplicative filter design based on the convolution theorem is explored and also commutative property, associative property and distributive properties of convolution theorem is proposed in the ST domain.
An Energy-Efficient Static Multi-Hop (ESM) Routing Protocol for Wireless Sensor Network in Agriculture

Energy efficient routing is a crucial area of research for wireless sensor network(WSN). The communication between the wireless sensor nodes is handled by the routing protocols. The nature of the link, low power and limited recourse makes it a difficult task to design an energy and performance efficient routing protocol for WSN. This paper proposes an energy-efficient route selection protocol for a multi-hop network with a static link for deployment in the field of agriculture. An energy model is also given in this paper to evaluate the energy consumption of the network. The proposed energy-efficient static multi-hop(ESM) routing protocol is evaluated based on the performance parameters for energy efficiency, network lifetime and pack loss and compared with an existing scheme. The simulation results represent that the proposed protocol increases the network throughput and the lifetime.
Real-Time Drone (UAV) Trajectory Generation and Tracking by Optical Flow

Vision systems in drones, specifically Unmanned Aerial Vehicles (UAV) are currently used for retrieving real-time video of the area to fly over, nevertheless commercially there are no identification nor control applications for this type of aircrafts. Some existing technologies for trajectory generation are based on cubic polynomials and Bezier curves [5] but this paper presents the implementation of an optical flow algorithm that assures to track and control the trajectory in a quadrotor drone in real time, determining the distance between the drone and a certain objective as well as the speed calculation and its displacement direction by the means of the implementation of the Lucas-Kanade (LK) algorithm. The core purpose of this paper is to achieve a low-cost and faster (due to the UAV's dynamics) solution besides the implementation of a Global Positioning System (GPS) using an Inertial Navigation System (INS) to generate an aerial trajectory, therefore it can be generated in real time converting the UAV into a semi-autonomous aircraft. The aerial robot's dynamic model was obtained by the Euler-Lagrange method, the image processing is based on the pin-hole model of a camera and optical flow. The main advantages and results obtained through the experimentation was first to correctly detect the line, and by LK implementation on OpenCV the line was correctly tracked by the drone achieving this with a low computational cost and with the possibility of indoor use covering the applications where the GPS is not able to work.
Experimental Study on Multiple Face Detection with Depth and Skin Color

Human face considers as an important biometric trait for person identification or video surveillance due to the digital camera technology that available on our daily life gadget. Since the digital signage easily found in the public and uncontrolled environment, the common situation could be single or multiple audiences that viewing at the digital signage display. The digital camera acts as a non-invasive detector for non-obtrusive digital advertising to collect the surrounding people’s face. The accuracy rate of face detection becomes the priority to detect the audience’s face. Besides that, the processing time also become a concern for time-constrained applications. This paper develops a framework for non-obtrusive digital advertising that applied the depth camera to detect multiple audiences for the audience location simulation and gather the depth information restrict the region of interests (ROI). Viola-Jones algorithm detects the audience frontal face who is facing towards the digital signage in the ROI. Subsequently, skin color analysis verifies the skin face and exclude the non-skin face to improve the face detection true detection rate. The depth information is combined with the face XY-position to map the audience actual location in the real-world environment on the aerial map. The experiment result shown that post-processing approach for Viola-Jones algorithm with skin color analysis increases the face true detection rate with the short processing time. Meanwhile, the simulation of multiple audience locations in the ROI can be shown on the aerial map which corresponds to the actual location in the real-world environment.