Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets

Context: Quality Control (QC) has been constantly an essential concern in many fields like food industry production, medical drugs, environmental protection, and so on. An odor or flavor, as a global fingerprint, can be implemented as a non-invasive mechanism for quality assurance. This computer-based approach can assure accurate detection and precise identification of the product quality or manufactured goods. Objective: This paper aims to achieve a systematic review about e-nose by introducing the achievements made by researchers in this area, to summarize their findings, to provide motivations and challenges to new researchers in the field of e-nose. Methods: The articles that were being utilized in the e-nose field were systematically achieved using three search engines: The online library of IEEE Explore, Web of Science and Science Direct for time span of 7 years (from 2013 to 2020). Both medical literature reviews and technical reviews were considered in the criteria of the research for wider understanding in the field of e-nose. The articles were categorized according to the objective of the research and projected into four classes. Upon completion of screening process 333 research papers using the exclusion and inclusion conditions, as the final set 54 articles were selected. Results: The taxonomy of this research was classified into four categories. The first one included the suggested methods that introduced the utilization of the e-nose for classification purposes (9/54 papers). The second category comprises the methods related to the development of e-nose (24/54 papers). The third one included the review studies about the e-nose (8/54 papers). The fourth group comprises comparative studies and evaluation (13/54 papers). Discussion: This systematic review contributes for a clearer understanding and a full insight in the e- nose research field by surveying and categorizing pertinent research efforts. Conclusion: This review paper will help to address the up-to-date research opportunities, challenges, problems, motivations and recommendations related to the utilization of e-nose in all fields of sciences and industries.
LiDAR-Based 3D SLAM for Indoor Mapping

Aiming to develop methods for real-time 3D scanning of building interiors, this work evaluates the performance of state-of-the-art LiDAR-based approaches for 3D simultaneous localisation and mapping (SLAM) in indoor environments. A simulation framework using ROS and Gazebo has been implemented to compare different methods based on LiDAR odometry and mapping (LOAM). The featureless environments typically found in interiors of commercial and industrial buildings pose significant challenges for LiDAR-based SLAM frameworks, resulting in drift or breakdown of the processes. The results from this paper provide performance criteria for indoor SLAM applications, comparing different room topologies and levels of clutter. The modular nature of the simulation environment provides a framework for future SLAM development and benchmarking specific to indoor environments.
Third Rail Wear Inspection Based on Point Cloud Registration

Third rail inspection is vital to ensure that the train daily operations are able to run smoothly. With the limited maintenance downtime during the wee hours for inspection and maintenance, it is imperative to improve the time required while ensuring the maintenance crew safety. In this paper, singular value decomposition – Iterative Closest Point is proposed for the inspection of surface wear on the third rail. Firstly, the profile of the third rail data is acquired by a line laser. After the data had been acquired, unwanted data is removed and the measured model is split into two parts for registration. The measured model is then registered to the reference model. Finally, the wear amount is analyzed using the Hausdorff distance.
Secretation: Toward a Decentralised Identity and Verifiable Credentials Based Scalable and Decentralised Secret Management Solution

Secrets such as passwords, encryption keys, and certificates are used to assist in protecting access to resources such as computing devices, customer data and other information. Unauthorised access to resources can cause significant disruption and/or disastrous consequences. Given the importance of protecting these secrets to the security and privacy of many software systems, many solutions have been proposed. These solutions take two main directions: either securely store the secret and implement an access control mechanism, or divide the secret into a set of shares and distribute them in different machines (such as the Shamir’s secret sharing approach or multi-party computation MPC). However, apart from the MPC approach, they all share the same limitation: once the consumer receives the secret, it can be leaked and be used by any malicious actor. We believe that the secret management should not be centralised and that the secret should never be sent to the receiver. Therefore, in this paper we propose, Secretation, a new approach for managing the secrets in a decentralised way by leveraging decentralised identity concepts such as verifiable credential technologies, password-authenticated key exchange protocols and multi-party computation. The result is a more scalable and secure solution that significantly reduces the risk of leaking the secrets.
A New Advanced Method for an Accurate Assessment of Harmonic and Supraharmonic Distortion in Power System Waveforms

Waveforms distortion is a pressing concern in Smart Grids where a massive presence of new technologies in distributed energy resources and in advanced smart metering systems is expected. In this context, the increasing diffusion of high switching frequencies static converters and the growing usage of Power Line Communication push for research dealing with the assessment of waveforms with spectral components up to 150 kHz. The analysis of such waveforms is a challenge for researchers due to the contemporaneous presence of a high number of spectral components in the range of low- (up to 2 kHz) and high- (up to 150 kHz) frequencies, with their opposite needs in term of time window length (and frequency resolution). The main idea of this paper is to improve the performances of existing methods by using a joint method of analysis based on a profitable strategy of divide and conquer; the method guarantees the best compromise between accuracy and computational efforts. A Discrete Wavelet Transform initially divides the original waveform to obtain two frequency bands: the wavelet suitability for conducting multi-resolution time-frequency analysis on waveforms in different frequency bands with different frequency resolution is effectively exploited. Then, the sliding-window modified ESPRIT method and the sliding-window Discrete Fourier Transform which uses a Nuttal window are used for the analysis of the low- and high-frequency bands, respectively; the positive characteristics of each method are exploited, minimizing the drawbacks and integrating their behavior so that the whole joint method allows an accurate estimation of each low- and high-frequency spectral component with the required acceptable computational efforts. The proposed method is tested on synthetic and measured waveforms in terms of accuracy and computational efforts. The analysis of the numerical application results clearly reveals that the proposed method improves the performances of existing methods of analysis in the examined cases.
An improved method for cloth pattern cutting based on Holistically-nested Edge Detection

Image edge detection is the basis for precise positioning and accurate cutting of cloth pattern contours. Compared with the commonly used traditional edge detection methods, the Holistically-nested Edge Detection has clearer and more continuous detection results including the reduction of the false detection rate. However, this method extracts a coarser thick outline. In order to extract a high-precision cloth pattern outline, clearly distinguish the main body of the pattern from the background, provide convenience for the follow-up cutting machine for accurate cutting, this paper proposes an improved method for edge detection of cloth pattern cutting based on the holistically-nested Edge Detection method. The edge refinement and smoothing process are added, where the edge detection, edge refinement, and edge smoothing of the clothes images are carried out in sequences, so that the extracted cloth pattern contour is continuous, smooth, and detailed, allowing the respect of the cutting requirements of the cutting machine and the requirements of the factory production.
Spectral Clustering Based on Relation-Invariable Persistent Formation

In this paper, a spectral clustering method based on relation-invariable persistent formation (RIPF) is proposed, which can character the similarity graph succinctly. RIPF is an optimal formation, which can remain the formation by using least edges in the comminution graph. After generating the RIPF, the algorithm builds a network graph weighted according to distance between neighborhoods, and then applies spectral clustering. Three methods to construct similarity graphs in standard spectral clustering are ε -neighborhood graph, k- nearest neighbor graph, or fully connected graph. However, ε -neighborhood graph or k -nearest neighbor graph is sensitive to the parameters ε or k, and fully connected similarity graph of which is redundant. The similarity graph is constructed by generating RIPF, which can model the concise local neighborhood relationships by using the least edges in similarity graph. In this paper, a new algorithm is proposed to weaken the parameter dependence while maintaining the solution quality. Experimental results show that out algorithm performs as well as or even better than the state-of-the-art standard spectral methods.
Adaptive neural network control of an uncertain robotic manipulator with input constraint and external disturbance

This paper investigates the control problem of an uncertain robotic manipulator under the effect of bounded external disturbance and input constraint. A novel Neural Network (NN) controller is proposed to achieve asymptotic tracking of desired trajectory. The model uncertainty is lumped together with external disturbance and compensated by the NN term of the proposed adaptive controller, while the boundedness of input is ensured via an auxiliary system and a projection operator. The ISS property of the closed-loop system and the boundedness of input are rigorous proved. Finally, we show the effectiveness of the proposed controller in the numerical study.
Gender, Age and Number of Participants Effects on Identification Ability of EEG-based Shallow Classifiers

Biometric systems have numerous advantages over traditional authentication methods and should be taken into account: uniqueness, fraud-resistance, portability, convenience of use, scalability, possibility of reliable attendance recording. Use of electroencephalography (EEG) data as biometric factor can keep all these benefits and add new ones. This paper provides insight how EEG based biometrics identification ability can be affected by the number of participants, their gender and age. In order to study aforementioned factor's influence Leipzig Study for Mind-Body-Emotion Interactions (LEMON) resting state (both eyes-open state and eyes-closed state) EEG dataset were used. After few preprocessing adjustments and applying discrete wavelet transform as time-frequency domain feature extraction method resulting dataset were used for classification task. The testing was conducted in 5-fold cross-validation mode using the shallow model, Linear Support Vector Classifier. From experiment results following conclusions were made: increased number of participants decreased classifier accuracy, with age increase accuracy steadily improved, gender effects on accuracy are inconclusive.
Implementation of a Mobile Application for Teaching and Analyzing RLC Circuits

RLC circuits are fundamental topics for engineering undergraduate students especially in electrical and electronics majors, and they have numerous applications to design electrical circuits. Medical devices, mobile phones, computers, FM radios, and TVs are just a few examples that use different forms of RLC circuits. Although the circuits are very useful for an intended application, step-by-step calculations must be done to analyze the behavior of each component in the circuitry. In this study, a mobile application was developed to catch the difference between two types of RLC circuits, series and parallel, and understand the effects of each components’ value change by checking simulation results.
Practical Applications of a Vision-based Robot for Security and Safety of Tailings Tunnels Infrastructure in the Mining Industry

In the mining industry, inspection of water recovery tunnels from tailings ponds is a critical activity in order to prevent disasters that could harm personnel, the environment and the infrastructure. Tunnel inspections are considered of high risk due to hazardous conditions such as working near water sources, inside confined spaces and the presence of toxic gases. We propose the use of a vision-based robotic vehicle for water recovery tunnels inspection, improving the security and safety of this high risk but required task. This paper describesthe experience of performing a remote inspection of a tailing pond water recovery tunnel using a robot. The identification of challenges, the establishment of a inspection plan, the remote inspection methodology and the safety regulations required in the mining industry are detailed. After evaluating the performance of the inspection robot in field, the need for a 3D reconstruction functionality in order to improve the tunnel diagnosis was identified. The design, preliminary test and results of the performance of a 3D reconstruction module for the inspection robot are presented and discussed.
Conceptual Design of a Cargo UAV That Can Take-off and Land Vertically

Fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV)s are designed to combine the advantages of multi-rotor UAVs and fixed-wing UAVs in different flight phases in a single UAV. Fixed-wing VTOL UAVs can achieve the mobility of multi-rotor UAVs in hovering, vertical take-off and landing flight phases. Since fixed-wing UAVs are known for their efficiency in power consumption, fixed-wing VTOL UAVs were created to efficiently perform the cruise phase of the flight for a similar reason. In this paper, we made the conceptual design of a fixed-wing VTOL UAV which is called “DOGA”. The mission of DOGA is to carry 0.5 kg. payload. Firstly, maximum take-off weight was calculated, and then the airfoil profile, wings geometry, and the propulsion systems were determined. Also, design and performance parameters of DOGA were calculated. As a result, conceptual design of DOGA was completed and all the needed parameters for the detail design of the fixed-wing VTOL UAV were derived.
Stable Capturing Strategy in the Dual-arm Time-Delay Capturing System with Hardware Closed Chain

When a multi-manipulator system is employed to carry out a capturing task, the hardware closed chain is formed. The capturing process with hardware closed chain may fail when the robot control system has non-negligible time delay. In this paper, the mathematical model of the capturing system is built and simplified for a feasible stability analysis. Three-dimensional root-locus is applied to explore the system stable condition and promote the stable capturing strategy. Simulations are carried out to validate the proposed theory and the results show that the system controlled by the strategy shows a considerable convergence.
Collision-Free Deep Reinforcement Learning for Mobile Robots using Crash-Prevention Policy

In this paper, we propose a crash-prevention policy for an autonomous collision-free mobile robot based on deep reinforcement learning. The objective is to reach a random location in a limited workspace safely. We go beyond the well-treated navigation paradigm by introducing a crash-prevention policy derived from action-sensor-space characteristics to achieve collision-free learning. This approach enables efficient and safe exploration by guaranteeing continuous collision-free actions, especially for agents learning in physical systems. We use Deep Deterministic Policy Gradient as a base method to evaluate the proposed crash-prevention policy on a mobile robot environment. Experiments show that using our approach maintains or even slightly improves training results while collisions are entirely avoided.
Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time Systems

In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. Theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback.
Unknown System Dynamics Estimator based Control for Bilateral Teleoperation Systems

In this paper, we propose a novel control scheme for bilateral teleoperation system subject to the unknown system dynamics. To realize this purpose, the low-pass filter operation is introduced to construct a simple yet robustness unknown system dynamics estimator (USDE). With the help of the proposed USDE, not only the effect of unknown system dynamics can be eliminated but also the acceleration signal can be avoided. Moreover, one salient feature of the proposed USDE is that it only has one tuning parameter, which is more straightforward compared with traditional estimators. The convergence of the estimation error and the stability of the closed-loop control system are rigorously proved via the Lyapunov theorem. Finally, simulation is carried out to demonstrate the superior performance concerning the movement synchronization and transparency.
Manufacturing Big Data Modeling Based on KNN-LR Algorithm and Its Application in Product Design Business Domain

In product life cycle, it is very important to use the manufacturing big data to build prediction model and apply it to predict whether the design task of the product can be completed within the specified time. Most of the existing prediction models in manufacturing industry are built by a single algorithm or its improved version, and neglect the limitation of using a single forecasting algorithm, which may lead to poor forecasting accuracy. This paper aims to integrate the K-nearest neighbor classification algorithm and the logistic regression algorithm linearly in parallel to obtain the combined model which is called K-nearest neighbor-logistic regression (KNN-LR) in this paper, and use the combined model to predict whether the design task of the product can be completed within the specified time. Experimental results show that compared with the model built by a single algorithm, the combined model has better performance on model evaluation indicators such as accuracy, precision, F1 value. recall and classification error rate.
Key-Value Database Access Optimization For PMIx Standard Implementation

This paper outlines our results in the field of distributed computing systems software stacks based on Process Management Interface eXascale (PMIx). PMIx standard represents the runtime environment information as a Key-Value Database (KVDb) thatis typical lylocatedin the shared memory of the compute nodes. An important specific of PMIx is the parallel and intensive read accesses to the KVDb with relatiwly infrequent updates. Locking schemes for synchronizing access to shared memory database with a predominance of reading operations are considered. In this paper, we propose two modifications of the reviewed locking scheme aimed at optimizing the throughput in the concurrent read-write access mode. The reduction in overhead is achieved by improving the signaling method, which reduces the number of atomic operations required to access the KVDb. A micro-benchmark has been developed that allows evaluating the performance of locking schemes. The results of the experiments showed an increase in the throughput of the synchronization scheme for both read and write operations.
Robust State Estimation for Power Systems Using Quasi-Newton Method

This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.
LightWeight Deep Convolutional Neural Networks for Vehicle Re-Identification using Diffusion-based Image Masking

Re-Identification of Vehicles (Vehicle Re-ID) in surveillance videos is an important and emerging topic in computer vision and intelligent systems. In Vehicle Re-ID, a specific vehicle is searched through non-overlapping camera viewpoints, which is a difficult task due to varying image conditions (e.g. scale, variations of appearance, illumination). For vehicle Re-ID, low level features such as shape and color can be utilized. Recently, deep learning-based vehicle Re-ID solutions became popular. Many of these deep learning solutions use pretrained large convolutional neural networks (CNNs) to perform vehicle Re-ID. Most of the pre-trained networks for object recognition and retrieval work with a large input image size (e.g. $224\times 224\times 3)$ and many hidden layers/parameters. In this work, we propose a lightweight deep CNN model together with diffusion-based image masking. The lightweight network trains input images of size $32\times 32\times 3$, it also has a smaller number of parameters to train in comparison to large pre-trained networks. We use three lightweight CNN networks, the first network trains original colour vehicle images, the second network trains vehicle images after masking with fully diffused shape information image, and the third network trains vehicle images after masking with partly diffused shape information image. Feature vectors extracted from these three networks are used to generate three similarity scores. Then, these three similarity scores are fused to produce an overall similarity score for retrieval. We demonstrate that the proposed work performs better than many large pretrained networks on VeRi-776 dataset.