Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-label Classification for Metal Defects from SEM Images using Deep Learning

Significant concerns have focused on how to discern different categories of metal pipes failure during routine industrial production inspection. Traditional failure analysis techniques based on scanning electron microscopy (SEM) are inefficient and rely on the expertise of experts. In order to reduce the high labor cost and accuracy associated with manual judgment, we developed a classification system using two deep learning methods, Vision Transformer (ViT) and Convolutional neural network (CNN), to examine metal pipe defects. In this study, the multi-label classification dataset containing 2,075 SEM images of four subcategories was first created. To identify the speed and effect, eight models containing three resolution were trained, validated, and tested to obtain the optimal. Finally,inference speed of these models were tested to match the real-time criteria on x86 and arm architectures computing platforms. Results showed that models based on EfficinetNet and Vision Transformer can accurately identify metal flaws on SEM images in real-time, which are similar as the results from manual judgement. In particular, EfficientNetB4 had higher accuracy of 0.969 in cracking, 0.933 in fatigue, and 0.938 in inclusion at 600x450 resolution, while ViT performed better in hole images (0.974 accuracy). EfficientNetB0 had the fastest detection speed at the same resolution, which can be used on devices with low computing performance.
Automated Classification of Pathological Speech Signals

In this paper, an automated technique for the classification of pathological speech is presented. The proposed technique has following major stages: Preprocessing, Feature extraction, Feature selection and Classification. Saarbrucken Voice Database (SVD) dataset is taken for our study, comprising both the pathological and healthy speech signals of vowel /a/ at normal pitch. The different features of pathological speech and healthy speech signals are extracted and most relevant features are selected by comparing the feature score. We found that the bark spectrum is the most important feature for the classification of pathological and healthy speech signals. The selected features are given as input to the classifiers with 5-fold cross-validation. K nearest neighbour (KNN), support vector machine (SVM), ensemble model, and neural network (NN) model are discussed in this paper which classifies the pathological and healthy speech with 94.45%, 90.00%, 95.60%, 93.35% accuracy respectively.
Multi-layered Radar Absorbing Structure based on Lossy FSS for Broadband Applications

Stealth technology is widely implemented in modern military aircraft in order to reduce radar cross section (RCS) which in turn avoid detection by enemy radars. Multilayered radar absorbing structures (RAS) are used in this respect to get wide absorption bandwidth with minimal thickness. In this regard, a lossy frequency selective surface (FSS) based multi-layered RAS model, having a total thickness of 5.84mm, is proposed in the present paper. The full wave simulation results as well as the equivalent circuit model (ECM) of the proposed design show that the reflection coefficient is below -10dB in the frequency range from 2GHz to 24GHz for wide range of incident angles for both perpendicular as well as parallel polarizations. This in turn implies that the percentage of power absorbed is greater than 90%. Further, RCS analysis of the planar RAS model presents an average RCS reduction (RCSR) of around 12dB.
Neural Network based Realtime Walking Speed Estimation and Gait Phase Detection using Smart Insoles

Three machine learning based gait analysis models have been developed for real-time walking speed estimation, offline gait phase detection and real-time prediction of incoming gait cycle profile. All these have been developed by performing rigorous trials on multiple subjects to make these models deployable for any new subject with features within the extremum considered in model development. These utilize a single sensor based lucid and economical hardware for effective gait analysis in both clinical and outdoor environment. While the offline gait analysis provide insight into characteristics gait patterns of individuals, the real-time estimations and predictions are effective in designing control schemes for wearable assistive devices.
Validation of ORB-SLAM3 and VINS-Mono with Low-Cost Sensor Setup in Outdoor Environment

In this paper, a comprehensive introduction of ORB-SLAM3 and VINS-Mono is presented. Both visual SLAM (vSLAM) solutions have been tested in real-time under real conditions. For their validation publicly available datasets collected by using high-quality synchronized sensors and a dataset created by the authors have been considered. The latter one contains high-quality reference data captured by an accurate IMU-RTKGNSS system as well as lower quality test data (acquired by our low-cost camera-IMU system) on which the above methods have been tested. The results of the performed evaluation well reflect both the advantages and disadvantages of both methods. This survey highlights also the problems, which arise due to the use of low-cost sensors. We also verified that the ORB-SLAM3 Mono-Inertial method cannot be used with unsynchronized sensors while VINS-Mono can handle the timeshifts between the sensor messages. According to our results, both methods provide the same level of accuracy in case of considering the above mentioned datasets.
Peer to Peer Energy Transaction Market Prediction in Smart Grids using Blockchain and LSTM

Predicting future demand for distributed energy sources is difficult because of the energy generations is unpredictability. We proposed an auto executable blockchain-based peer-to-peer energy transaction market with Long ShortTerm Memory (LSTM) neural network for energy trading and predictions. Our proposed energy transaction market and trading strategy are built via smart contracts inside the blockchain. The energy trading process in the market is auto executable and does not need a long waiting time as in the conventional bidding strategy. LSTM is integrated into the market for predicting future energy usage. Experimental results show that the proposed blockchain-based peer-to-peer energy transaction market can earn more profit than the traditional method.
Direct Demonstration-Based Imitation Learning and Control for Writing Task of Robot Manipulator

In this paper, we propose an imitation learning method based on a direct demonstration of robot manipulators. To track the desired position and force, we designed an impedance controller. As a result of imitation learning, the robot can be acted as intended even if the initial position is different, and be able to perform a writing task well even if a different contact force is applied to the changing environment. We propose Long Short-Term Memory (LSTM)-based imitation learning method through the demonstration data. Finally, the proposed method was verified by applying the writing task with the actual industrial robot manipulator that acts as the expert's intention for the direct demonstration.
Phonocardiogram Segmentation with Tiny Computing

The stethoscope is a daily used tool that allows medical doctors to diagnose common cardiovascular diseases by listening to heart sounds. However, dedicated medical training is required to operate it. Numerous machine learning techniques have been used in attempts to automate this process and have yielded highly accurate results. However, creating a low power, portable, economical, and accurate machine learning stethoscope calls for tiny processing of phonocardiograms i.e., heart sound digital processing to run within an embedded device. To address the need to deploy the solution within a constrained tiny device, we propose an 8-bit deep learning model with low embedded FLASH and RAM utilization of 126 KiB and 45 KiB respectively, which is optimized for inference on an off-the-shelf STM32H7 microcontroller with an inference time of 12 ms, in 126KiB FLASH and 45 KiB RAM being 91.65% accurate.
Range inclusion for normal derivations

For a (bounded, linear) operator A in a (complex, infinite-dimensional, separable) Hilbert space ℋ, the inner derivation DA as an operator on ℬ(ℋ), is defined by DAX = AX – XA. Johnson and Williams [4] showed that, when A is a normal operator, range inclusion DBℬ(ℋ)⊆DA(ℋ)⊆ is equivalent to the condition that B = f(A), where f is a Lipschitz function on σ(A) such that t(z, w)(f(z)–f(w))/(z–w) is a trace class kernel on L2(μ) whenever t(z, w) is such a kernel. (Here μ is the dominating scalar valued spectral measure of A constructed in multiplicity theory). This result is deep and its proof is difficult. In the present paper, we establish the following analogous result which is easier to prove: for a normal operator A, range inclusion DB℘2(ℋ) holds if and only if B = f(A) for some Lipschitz function f on σ(A). Here ℘(ℋ) stands for the Hilbert-Schmidt class of operators on ℋ. As by-products of our argument, we generalize some results in [4], [8], [9] concerning the non-existence of a one-sided ideal contained in certain derivation ranges; for example, we show that if A is hyponormal and if the point spectrum σP(A*) of A* is empty, then DAℬ(ℋ) does not contain any nonzero right ideal.
Localization of the Sensor Nodes in Precision Agriculture using a Hybrid Model

Localization has become a major attraction for researchers in the last few decades in the domain of wireless sensor networks. It is necessary for a variety of surveillance applications like fire, and flood detection to locate the exact spot of the event. The purpose of this study was to address the problem of the localization of sensor nodes on the agricultural land of India. This study focuses on bumpy agricultural land, to find out the precise location of a sensor node to help farmers and society. The authors have proposed a hybrid model based on the relative signal strength (RSS) scheme to find out the precise location of sensor nodes in the flat as well as the bumpy field. Based on testbed deployment and simulation results our method is 7.3 % better than previous work that has been done to find out event spots.
Stability zone limit: a balance exercise intensity indicator for Kinect-based balance exergames

Balance exercise prescription is saddled with the lack of quantitative method for assigning balance exercise intensity. Virtual Reality based exergames that record three-dimensional (3D) position data has the potential to incorporate center of mass (COM) variables that can represent the difficulty level of the exercise task. The current study developed stability zone limits, a COM-based balance intensity variable, for older adults. The variable can be used to evaluate performance of the user and it can be used to design future iterations of the balance exergame. Preliminary result shows good agreement between perceived task difficulty and stability zone limits.
Survival prediction of COVID-19 patients using multi-modal dataset

COVID-19 has proved to be a global emergency that has fractured the healthcare systems to the extent that its impact is too challenging to encompass. Though many Computer-Aided Diagnoses (CAD) systems have been developed for automatic detection of COVID-19 from Chest X-rays and chest CT images, very few works have been done on detecting COVID-19 from a clinical dataset. Resources needed for obtaining Clinical data like blood pressure, liver disease, past traveling history, etc., are inexpensive compared to collecting Chest CT images for COVID-19 infected patients. We propose a novel multi-model dataset for the survival prediction of patients infected with COVID-19. The dataset proposed is collected and created at Mahatma Gandhi Memorial Medical College, Indore. The dataset contains clinical data and chest X-ray images obtained from the same patient infected with COVID-19. For proper prognosis of the COVID19 positive patients from the clinical dataset, we have proposed a Bi-Stream Gated Attention-based CNN (BSGA-CNN) model. The BSGA-CNN model achieved an accuracy of 96.90% (± 3.05%). A CNN based on pre-trained VGG-Net is used to classify the corresponding Chest X-Ray images. It gave an accuracy of 87.76% (± 8.78%)%.
Decentralized Energy Trading Model with Smart Grids and Blockchain

Due to the renewable and natural energy resources, there exist many opportunities to use smart grids and smart meters that enhance energy efficiency. With the increasing supply and requirements of energy, many smart grid models have been proposed in the literature. One of the most popular models is to use the centralized energy sharing model. However, the centralized energy sharing model is vulnerable to reliability issues such as a single point of failure. Due to the centralized energy sharing model, maintaining trust and reliability becomes a challenging issue. In this article, we propose a blockchain-based energy trading model to solve the issues energy sharing model, namely, a single point of failure, load balancing, and price governance. The proposed model improves the trust and reliability of the energy-sharing system. The integration of blockchain with the smart grid opens up numerous possibilities in the area of energy sharing and enhances privacy and security.
A Central Limit Theorem for Reversible Processes with Nonlinear Growth of Variance

Kipnis and Varadhan (1986) showed that, for an additive functional, S n say, of a reversible Markov chain, the condition E[S n 2] / n → κ ∈ (0, ∞) implies the convergence of the conditional distribution of S n / √E[S n 2], given the starting point, to the standard normal distribution. We revisit this question under the weaker condition, E[S n 2] = n l(n), where l is a slowly varying function. It is shown by example that the conditional distributions of S n / √E[S n 2] need not converge to the standard normal distribution in this case; and sufficient conditions for convergence to a (possibly nonstandard) normal distribution are developed.
Acceptability and Trustworthiness of Virtual Agents by Effects of Theory of Mind and Social Skills Training

We constructed a social skills training system using virtual agents and developed a new training module for four basic tasks: declining, requesting, praising, and listening. Previous work demonstrated that a virtual agent's theory of mind influences the building of trust between agents and users. The purpose of this study is to explore the effect of trustworthiness, acceptability, familiarity, and likeability on the agents' theory of mind and the social skills training contents. In our experiment, 29 participants rated the trustworthiness and acceptability of the virtual agent after watching a video that featured levels of theory of mind and social skills training. Their ratings were obtained using self-evaluation measures at each stage. We confirmed that our users' trust and acceptability of the virtual agent were significantly changed depending on the level of the virtual agent's theory of mind. We also confirmed that the users' trust and acceptability in the trainer tended to improve after the social skills training.
An Automated System for Sleep Staging using EEG Brain Signals Based on A Machine Learning Approach

Correctly classifying the sleep stages is essential for analyzing sleep quality and diagnosing sleep disorders. This research article aims to explore the classification performance of state-of-the-art machine learning (ML) models for classifying the sleep stages of different health-conditioned subjects and to identify whether the model performed well consistently for all kinds of sleep-disordered data. However, it has been noticed that the existing sleep staging methods have the following limitations:(1) Manual inspection of sleep behavior requires experts’ knowledge and which requires more workforce and a time-consuming process (2) Due to similar characteristics of the sleep stages features, it is essential to discriminate the changes aspects of the individual sleep stages with proper selection of parts (3) Acquisition of different medical-conditioned data has high demands on equipment. To address these challenges, we proposed an ML-based automated sleep staging system to analyze and classify sleep patterns. We use three different subgroups, namely SG-I, SG-II, and SG-III, to validate the proposed model’s effectiveness. First, our proposed model considered three different categories of sleep data with varying recordings of sessions and then extracted various features to understand the sleep behavior and applied feature selection algorithms to validate the feature concerning different subgroups’ sleep dates. Finally, apply the support vector machine (SVM) algorithm to classify them as Wake vs. Sleep on different sub-groups data ISRUC-Sleep dataset. The experimental results of the proposed method on the public repository ISRUC-Sleep show that it is improved. The results signify that the proposed method performs better than a state-of-the-art method.
Exploration of driverless electric vehicle interaction interface design based on human-computer system

Through the anthropomorphic design of the human-computer interaction interface outside the driverless electric vehicle and the use of multi-channel information expression inside the vehicle, the user can obtain the vehicle driving information more accurately and efficiently, better communicate with the driverless vehicle and effectively enhance the trust of the driverless vehicle. Using literature research as a means to summarise the main points of the design of the human-computer interaction interface outside the car, the design points are presented in an anthropomorphic way. The design elements are presented in an anthropomorphic way in the design of the interaction interface, and then the design elements are verified to be reasonable by means of a design test of the human-machine interaction interface outside the vehicle. The reasons for low trust in unmanned driving are analysed in the vehicle, and different multi-channel combinations are briefly described for different driving scenarios, and audio-visual and tactile multi-channels are selected as test objects to verify the effectiveness of the multi-channel combination in improving trust in driving. The combination of multiple channels in the vehicle can be used to express a reasonable signal strength for a specific driving scenario, which can effectively improve the trust level of unmanned driving.
Beyond the potential of 5G

The lower latency and bandwidth of 5G will cause an increase in the diversity and volume of data, Internet of Things (IoT) devices, and innovation, which can create several challenges. As such, it results in the urge to understand better the potential and risks of 5G networks. This paper’s aim extends beyond the examination of 5G network potential. At first, it gives a categorization and description of the 5G ecosystem elements. Then, in line with the main focus of our work, the potential of cybersecurity assurance in 5G networks is examined. We suggest that organizations embrace security by design and security in process mindset and build trust throughout the ecosystem. Additionally, we shed light on the interdisciplinary scheme for better network equipment security following standard guidelines–NESAS (Network Equipment Security Assurance Scheme). The implementation of 5G networks should ensure security across policy, technology, and standards. Vendors should be positioned to address and lead critical priorities and actions that will guide countries through this transition.
Effect of Dilated Convolution on Performance and Parameters of Devanagari Script-based P300 Speller

P300 speller is a well-known Brain-Computer Interface (BCI) application that allows users to spell words using cognitive ability and establishes a pathway between the human mind and a computer. P300 detection is the most crucial stage in the design of the P300 character speller. However, present Convolutional Neural Network (CNN) architectures hinder the use of CNNs in portable BCIs as they restrict future accuracy improvements of P300 detection and require significant complexity to attain competitive accuracy. Furthermore, the multi-trial approach adopted in most of the recent works is a major bottleneck in the real-time implementation of such a speller. To deal with both issues, the authors propose a single trial P300 detection using compact CNN architecture with dilated convolution (D-EEGNet). The proposed model with 1066 parameters achieves a classification accuracy of 80.86 % for a Devanagari Script-based P300 speller. Apart from lessening the trainable parameters, D-EEGNet also reduces computational complexity. Moreover, the proposed model demonstrates the ability to deal with high variance often encountered in single-trial detection.
Machine Learning Assisted Temporal Continuous Clustering

Unsupervised learning of temporal sequences is an essential data mining technique to explore and find groups of similar time series objects. The application appears in numerous fields of science like in finance, engineering, healthcare, etc. Traditionally feature-based clustering required domain knowledge to extract high quality features. Now deep autoencoder models can achieve dimension reduction and learn good representations in an unsupervised fashion at the same time. These models with a well-chosen architecture can be applicable for many different data types, eliminating the strong reliance on domain specific knowledge for feature extraction that later used for clustering purpose. This paper presents the evaluation of the Deep Continuous Clustering method for processing continuous data series. As visible from the results, it has significantly better accuracy than the widely used DBSCAN method.
A PCBA Solder Joint Defects Inspection System Based on Deep Learning Technology

This paper proposes a deep learning-based Printed Circuit Board Assembly (PCBA) solder joint defects inspection system. The proposed system is comprised of a camera module, an AI edge computing module, a conveyor, and a PCBA quality management platform. Therefore, the proposed system can use the camera module to capture the PCBA snapshots of the inspection area on the conveyor. Then, the PCBA snapshots are forwarded to the AI edge computing module to inspect whether the solder joint defect occurs via deep learning object detection technology. The experimental results show that the proposed system can inspect three common solder joint defects with average precision (AP), recall, and precision scores ranging from 72.3% to 100.0%, 0.98 to 1.00, and 0.98 to 0.99, respectively, and provide an adequate solder joint defects detection within 1.2 seconds, improving production yield.
Implementation and research on indoor mobile robot mapping and navigation based on RTAB-Map

The positioning, mapping and navigation of mobile robots has always been a hot topic which attracts widespread attention around the world. In this paper, mapping and navigating in a complex indoor environment of 100 square meters is done with the RTAB-Map algorithm for multi-sensor information fusion using an RGB-D camera, a single-line lidar, a gyroscope, an odometers and other sensors. Navigation tasks will be published after the map construction tasks is done, the experimental results are scored based on expressions of different approach. This study compares the scene data collected with the data in the real environment, analyzes the accuracy and error of the map with different methods. This study also compares the visual-laser combined mapping and navigation scheme with the pure vision schemes or pure laser schemes. Result shows that the task completion is 100% with the visual and lidar combined, compared with the scheme that uses lidar only (45%) or the RGB-D camera only (75%).