Miracle-dz/UniFAR-FTUs huggingface dataset:
Road Defect Detection Based on Semantic Transformed Disparity Image Segmentation

Road defects can severely affect the safety of road users and vehicle conditions. Over the past decade, due to the limited amount of labeled training data, machine vision-based road defect detection approaches have been mainly used, while machine/deep learning-based methods were merely discussed. With the recent development of artificial intelligence, convolutional neural network (CNN)-based road defect detection systems for automated road condition assessment have become an active sphere of study. In this regard, this paper presents a comprehensive road defect detection system based on computer stereo vision, non-linear regression, and CNN. A dense disparity image is first estimated from a pair of stereo road images using an efficient stereo matching algorithm. The estimated disparity image is then transformed to better identify road defects by minimizing a global energy function w.r. t. road disparity projection model coefficients and stereo rig roll angle, using the non-linear regression approach. Finally, three popular semantic segmentation CNNs are trained using the transformed disparity images. Extensive experiments are conducted to demonstrate the performance of our proposed road defect detection approach. The achieved pixel-level accuracy and intersection over union (IoU) are 98.37% and 67.65%, respectively.
Design and analysis of cable-driven robotic arm with variable stiffness modular joint

This paper describes a robotic arm that allows for safe human-robot interaction. The robotic arm is essentially a cable-driven robotic arm with passive variable stiffness features; the cable-driven section of the arm maintains high stiffness and strength by utilizing a unique lightweight tension amplification system. The stiffness of the joints, which is intimately related to the performance of motion control, is quadratically amplified. We propose a 1-DOF and 3-DOF joint mechanism using the tension amplification method, which we combine to construct the robotic arm's elbow and wrist. Modular joints with variable stiffness are used in the 3-DOF shoulder. The passive compliant parts of the variable stiffness modular joint with progressive stiffness could make the actuator inherently soft at low contact torque levels and significantly stiffer at higher interaction torque levels, resolving common design trade-offs in linear series elastic actuators. To assess the workspace of the robotic arm, a kinematic model of the 7-DOF robotic arm is built. Finally, a cable-driven robotic arm prototype with variable stiffness joint module is developed.
Real-Time Voice Enabled Drivers' Safety Monitoring System Using Image Processing

Accidents and road crashes occurring due to dissociation of driver from his consciousness is one of the hot topics researchers have been contemplating to curtail, from the past few years. This dissociation wanes the driver's ability to concentrate on the road while driving and promotes occurrence of mishaps. There has been a challenge to non-intrusively detect drowsiness and fatigue-level of drivers in real-time environments and concomitantly make it computationally faster and less complex. The paper proposes a faster real-time driver inactiveness detection system capable of alerting drivers of their drowsiness by inspecting and scrutinizing the state of eyes and mouth and follows a non-obtrusive approach. The system utilizes eyes to monitor the drooping nature of eyes and examines the mouth's current state to detect any yawning activity performed. Initially, it uses Haar transform to extract facial features and strong classifiers for detecting faces by incorporating Adaboost algorithm. Next, shape predictors are used to locate the key points for obtaining position of mouth and eyes within the face area. States of the mouth and eyes are continuously monitored with the help of the metrics viz. Eye Aspect Ratio (EAR) and Mouth Yawn Width respectively and are used to alert the driver through voice-enabled commands followed by a warning alarm sound to trigger them thereby aiding in regaining their consciousness back and focusing on their driving. The algorithm used in this system is tested under different lighting conditions and against different possible situations and accurate results in terms of successful detection rates are obtained.
An Efficient Probe Selection Method for 5G Base Station OTA Testing with MPAC Setup

Over-the-Air (OTA) testing, due to its capacity of reproducing the desired radio channels in lab environments, is considered as the promising testing solution for 5G multi-input-multi-output (MIMO) devices, especially for 5G massive MIMO base station (BS). A sectored multi-probe anechoic chamber (MPAC) OTA system for 5G massive MIMO BS testing has been proposed and discussed in many literatures. The probe selection is essential for 5G BS OTA testing, since the MPAC system cost is determined by the ports of channel emulator, i.e., the number of OTA probes. However, the classical convex-optimization-based probe selection method becomes complexity-prohibitive compared with the 2D MPAC system, due to the large amount of candidate probes. This paper proposes a novel probe selection for 5G BS OTA testing with MPAC setup based on the compressed sensing theory. The simulation results indicate that the proposed method can achieve high performance over single-cluster and multi-cluster channel models, in terms of high emulation accuracy and low computational complexity.
Transformer Aging Diagnosis Method Based on Raman Spectroscopy Wavelet Packet-SPCA Feature Extraction

Ensuring the safety and reliable operation of a power transformer is a top priority in grid work. There is an urgent need to study methods for measuring and evaluating the aging state of oil–paper insulation in older transformers. Raman spectroscopy is widely used in material detection; the spectra can represent the inner components of transformer oil and help diagnose the aging states of the transformer efficiently. In this article, thermal accelerated aging experiments were conducted, and oil–paper insulation samples with different aging states were obtained. The aging states of oil–paper insulation were tagged by the degree of polymerization (DP) value of the paper. Four wavelet packet decompositions were used for the Raman spectra, and sparse principal component analysis was used to extract the features in the wavelet packet coefficients. A 14-D feature was built and trained with a multiclassification support vector machine (SVM), and a diagnosis model for aging states was established. The results show that the accuracy of the diagnosis model reaches 94.9%. Seven samples of transformers in operation also verify the method’s effectiveness. This method has practical significance for quickly detecting the aging states of operating transformers.
Sewage Pipeline Fault Detection using Image Processing

This research proposes a ground breaking method for examining the condition of sewage pipes. The underground sewage piping system in cities is a vital form of common infrastructure because it helps to ensure a safe atmosphere. One of the most commonly used sewer inspection process which uses CCTV systems, has a weak performance. A camera is installed on one side of the pipe or on some other unit, and video is recorded within the pipes and sent off-line to an engineer to classify possible faults. The machine-controlled detection and testing of the location of divergences within the internal structure is the subject of this project with the help of Open Source computer vision Library techniques. Many steps are used in the machine-controlled inspection technique, including normalize RGB, Background Subtraction, Canny edge detection, Arc Detect, contours High-light, time Convert, and circular Mask, which involves segmenting the image into Mathematical choices and the defected unit field. Recognizing and classifying defects found within the pipe of an area unit using computation perception techniques helped reconcile image processing. The method of detection is both fast and automatic.
Time-of-Flight Cameras in Space: Pose Estimation with Deep Learning Methodologies

Recently introduced 3D Time-of-Flight (ToF) cameras have shown a huge potential for mobile robotic applications, proposing a smart and fast technology that outputs 3D point clouds, lacking however in measurement precision and robustness. With the development of this low-cost sensing hardware, 3D perception gathers more and more importance in robotics as well as in many other fields, and object registration continues to gain momentum. Registration is a transformation estimation problem between a source and a target point clouds, seeking to find the transformation that best aligns them. This work aims at building a full pipeline, from data acquisition to transformation identification, to robustly detect known objects observed by a ToF camera within a short range, estimating their 6 degrees of freedom position. We focus this work to demonstrating the capability of detecting a part of a satellite floating in space, to support in-orbit servicing missions (e.g. for space debris removal). Experiments reveal that deep learning techniques can obtain higher accuracy and robustness w.r. t. classical methods, handling significant amount of noise while still keeping real-time performance and low complexity of the models themselves.
Multi-Class Brain Computer Interface Using Steady-State Somatosensory Evoked Potentials

Steady-state somatosensory evoked potential (SSSEP) is response of electroencephalogram to mechanical or electrical vibratory stimulation to human body such as a finger and sole, which is synchronized with the stimulus frequency in the range of 15-35 Hz. Brain computer interface based on SSSEP (SSSEP-BCI) has been proposed by exploiting the modulation of SSSEP associated with selective attention on one of two tactile stimuli. However, the number of options in the most of conventional SSSEP-BCIs has been limited to two for expressing user’s intentions. This study thus aimed at improving the performance of SSSEP-BCI by increasing the number of intentional options from two to four through the addition of new mental task “Shiritori” to the conventional selective attention task. The highest overall discrimination accuracy reached 78.3 % and 56.3 % for the proposed three-class and four-class SSSEP-BCIs, respectively. The highest discrimination accuracies for the proposed BCIs were obtained when the amplitude of alpha waves in the Shiritori task was combined with SSSEP as features for discrimination using the support vector machine, suggesting that the validity of the mental task for SSSEP-BCI. These results demonstrate the feasibility of a multi-class SSSEP-BCI which has more than three classes as intentional options.
A Home Energy Management System

Global warming and climate changes have become the world's serious issues, so researchers have focused on analyzing and reducing the energy consumption. Transitioning to renewable energy is one way to help slow down the effects of climate change. In this paper, an energy management system, particularly an energy monitoring system, in a residential house is presented. The system is part of a smart home automation system called qToggle. Energy monitoring systems are considered as next-generation energy saving solutions. The system is monitored and controlled remotely using an Android app.
Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area

In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.
A microscopic model of electron transport in quantum dot infrared photodetectors

A theoretical model describing the electron transport in vertical conductivity quantum dot infrared photodetectors is presented. The carrier wave functions and energy levels were evaluated using the strain dependent eight-band k∙p Hamiltonian and used to calculate all intra- and interperiod transition rates due to interaction with phonons and electromagnetic radiation. The interaction with longitudinal acoustic phonons and electromagnetic radiation was treated perturbatively within the framework of Fermi’s golden rule, while the interaction with longitudinal optical phonons was considered taking into account their strong coupling to electrons. A system of rate equations was then formed, from which the macroscopic device output parameters such as dark current and responsivity, as well as microscopic information about carrier distribution in quantum dots and continuum states, could be extracted. The model has been applied to simulate the dark current, as well as the midinfrared photoresponse in an experimen.
A Mismatch Error Calibration Method for 14-bit 500MS/s Time-interleaved Charge-Domain ADC

A mix-signal type high precision mismatch error calibration method for time-interleaved ADCs is proposed. The calibration method calibrates the clock skew mismatch error based on binary search algorithm. Based on the proposed calibration method, a 14-bit 500MS/s ADC is designed and realized in a 1P6M 0.18 $\mu$m CMOS process. Test results show the 14-bit 500MS/s ADC achieves the SNR of 69.8dBFS and the SFDR of 82.3dB, with 72.1MHz input at 500MS/s, while consumes the power consumption of 365mW.
The polarization of thermal radiation from supermassive accretion disks

Thermal radiation from an accretion disk is a promising explanation for the optical-ultraviolet bump noted in the spectra of active galactic nuclei. However, the expected polarization of radiation from a standard thin disk conflicts with observation. The polarization of light emitted by a thin disk with a rough surface and by a thick disk (radiation torus) is computed. Improved agreement with observations is found, particularly in the case of a thick disk. 20 refs.
An Improved and Efficient Technique for detecting Bengali Fake News using Machine Learning Algorithms

Fake news is appearing in the news a number of Business, Communities, Political and others reasons and being common in the air world. Humans can be infected it is easy with these false stories of the words which the product has the largest result in a non-Internet environment. Thus, interest in research in this area arose. Extensive research done for the discovery of false stories for English News but few in the Bangla language. We have done a job showing the feasibility study of Bangla's false news from the social media still needs lot of attention. We have used a variety of feature extraction methods and Machine Learning Algorithms for detecting Bangla News which are treated as Fake. We have implemented our proposed system by using the Python programming. Finally, we have gained the best result that is 98.08% of accuracy.
Reinforcement Learning for Computational Offloading in Fog-based IoT Systems: Applications, and Open Research Issues

The fog computing has been widely integrated in the IoT-based systems allowing the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to improve the systematic performance (i.e., reduced response delay, energy saving). However, designing the efficient offloading algorithms to achieve such the benefits is still challenging, mainly due to the complexity of fog computing environment and complicated requirements of computational tasks. Apart from many optimization, game theory, and heuristics based solutions, reinforcement learning (RL) is recently applied to provide intelligent offloading policies that can release the challenges efficiently. This paper presents an overview of RL applications to solve the computation offloading related problems in the fog computing environment. The open issues and challenges are explored and discussed for further study.
Multicast MMSE-based Precoded Satellite Systems: User Scheduling and Equivalent Channel Impact

Very High Throughput Satellite (VHTS) systems are characterized by a multi-beam footprint covering wide areas and providing service to large numbers of users. Multicasting comes naturally to exploit the multiuser diversity in VHTS systems, where the data of different users are multiplexed in a single PHY frame. Following the DVB-S2(X) standard, the resulting PHY frame is encoded using a single codeword. The latter brings some practical implementation challenges when precoding is considered, as the precoder can no longer be designed on a user-by-user basis. Avoiding overambitious and impractical precoding designs, our work focuses on the low-complexity MMSE-based precoding, which has been considered as the baseline for early satellite over-the-air precoding tests. While the multicast scheduling has been widely investigated in the literature, we will show in this work that its performance is significantly impacted by the equivalent multicast channel calculation. Therefore, in this paper, we analyze and report the impact of the user scheduling (i.e., selection of users to be multiplexed together in a single PHY frame) as well as the methodology employed for the equivalent multicast channel considered for the precoding computation.
A More Efficient Approach for Pedestrian Attribute Recognition

In video surveillance scene, pedestrian attributes recognition is a complex task. There are some challenges in pedestrian attribute recognition, such as background noise and data imbalance. The problem is still open. This paper combines several effective methods and proposes a simpler and more effective method. Firstly, we restrain the weight of labels with more samples and improve the weight of the labels with less samples to relieve the effect of data imbalance. Secondly, we apply attention mechanisms which are effective in extracting the discriminative features. Also, we use bag of tricks to achieve a strong baseline, such as cosine learning rate, warm-up, data augmentation, etc. Finally, we evaluate these effective tricks in Pedestrian Attribute Recognition task and our model achieves state-of-the-art results in several important metrics in datasets PETA, RAP and PA-100k.
Measurement and localization of interface wave reflections from a buried target

It is demonstrated that seismic interface waves on the surface of a natural beach can be used to identify the position of a buried object. For this experiment, the waves were created with a sediment-coupling transducer and received on a three-element horizontal line array of triaxial geophones. The source and its coupling to the medium provided a high degree of signal repeatability, which was useful in improving signal-to-noise ratio. Reception of all three directions of particle velocity made it possible to augment conventional beamforming techniques with polarization filters to enhance interface-wave components. Reverberation in the beach was found to be large, though, and coherent background subtraction was required to isolate the component of the sound field reflected by the target. Propagation loss measurements provided comparisons of reflected signal power with predictions made previously, and the two were found to agree closely.
An Integrated Framework of Autonomous Vehicles Based on Distributed Potential Field in BEV

This paper presents an integrated framework of decision-making and motion planning for autonomous vehicles (AV) based on real-time trajectory prediction and distributed potential field in Bird's-Eye View (BEV). Firstly, the distributed tracking system is applied for global trajectory tracking of traffic participants in BEV, which can avoid the physical limitations of onboard sensors. Then, the end-to-end network model BiLSTM-MDN is utilized for the trajectory prediction of traffic participants. To cope with the prediction error caused by other neural networks outputting only one prediction trajectory, the model BiLSTM-MDN outputs a probability density function (PDF) generated by Gaussian Mixture Model (GMM). At the same time, the PDF is adopted in the potential field model, which describes road constraints and traffic participants with diverse behaviors by different potential functions. In addition, the Frenet Coordinates substitute for the Cartesian Coordinates to simplify the calculation. Eventually, a constrained multi-objective optimization problem is proposed to integrate decision-making and motion planning. It is reduced to a P problem and solved by the Depth-First-Search (DFS) algorithm to obtain an optimal solution. The typical intersection scenario is taken to validate the performance of the presented approach. Simulation results show that the integrated system is feasible and effective because it can address various actions of other traffic participants adequately through appropriate decisions and robust planning.
Motion Estimation by Deep Learnings Using Ambient Sensor Network

In this paper, we collect motion data of 7 subjects from an inertial measurement unit and a smartphone simultaneously using our ambient sensor network and then estimate 7 types of human motions of standing up, standing, walking, turning, trotting, sitting down, and sitting by machine learnings. We first perform sensor calibration, coordinate transformation from the sensor coordinate system to the absolute coordinate system, and sensor fusion. Then we compare the accuracy of 4 deep learnings for 3 cases of (1) only accelerations, (2) accelerations and angular velocities, (3) accelerations, angular velocities, and transformed accelerations. As a result, we confirmed that deep convolution long short-term memory achieved 94% accuracy for accelerations, angular velocities, and transformed accelerations.
Systematic Analysis of Big Data Based Machine Learning Algorithms on Various Fields

Machine Learning is a basic innovation in foreseeing results in view of data. Four famous Big Data machine learning algorithms are tended to in this paper: Bayesian Decision Theory Classification, and Linear Regression. The underlying two are overseen learning algorithms, the third an independent learning algorithm, and the fourth a relationship algorithm. Advantages of machine learning integrate flexibility and adaptability differentiated and customary biostatistical methods, which makes it deployable for certain tasks, similar to bet partition, finding and gathering, and perseverance assumptions. One more benefit of machine learning algorithms is the capacity to examine assorted data types. Every methodology is broadly explored and examined. Likewise, the accentuation is placed on how the four techniques transaction with one another to rouse investigation of more strong and data-proficient algorithms. At long last, the review portrays the impediments, talks about research difficulties, and recommends future chances to propel the examination on data-proficiency in machine learning.