bluuebunny/crossref_metadata_2025 huggingface dataset:
Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography
Authors: Lohvithee Manasavee , Sun Wenjuan , Chretien Stephane , Soleimani Manuchehr
Published: January 2021

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.
Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results
Authors: Buzzelli Marco , Segantin Luca
Published: January 2021

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.
Interband Cascade Photonic Integrated Circuits on Native III-V Chip
Authors: Meyer Jerry R. , Kim Chul Soo , Kim Mijin , Canedy Chadwick L. , Merritt Charles D. , Bewley William W. , Vurgaftman Igor
Published: January 2021

We describe how a midwave infrared photonic integrated circuit (PIC) that combines lasers, detectors, passive waveguides, and other optical elements may be constructed on the native GaSb substrate of an interband cascade laser (ICL) structure. The active and passive building blocks may be used, for example, to fabricate an on-chip chemical detection system with a passive sensing waveguide that evanescently couples to an ambient sample gas. A variety of highly compact architectures are described, some of which incorporate both the sensing waveguide and detector into a laser cavity defined by two high-reflectivity cleaved facets. We also describe an edge-emitting laser configuration that optimizes stability by minimizing parasitic feedback from external optical elements, and which can potentially operate with lower drive power than any mid-IR laser now available. While ICL-based PICs processed on GaSb serve to illustrate the various configurations, many of the proposed concepts apply equally to quantum-cascade-laser (QCL)-based PICs processed on InP, and PICs that integrate III-V lasers and detectors on silicon. With mature processing, it should become possible to mass-produce hundreds of individual PICs on the same chip which, when singulated, will realize chemical sensing by an extremely compact and inexpensive package.
Biosensors Based on Isothermal DNA Amplification for Bacterial Detection in Food Safety and Environmental Monitoring
Authors: Leonardo Sandra , Toldrà Anna , Campàs Mònica
Published: January 2021

The easy and rapid spread of bacterial contamination and the risk it poses to human health makes evident the need for analytical methods alternative to conventional time-consuming laboratory-based techniques for bacterial detection. To tackle this demand, biosensors based on isothermal DNA amplification methods have emerged, which avoid the need for thermal cycling, thus facilitating their integration into small and low-cost devices for in situ monitoring. This review focuses on the breakthroughs made on biosensors based on isothermal amplification methods for the detection of bacteria in the field of food safety and environmental monitoring. Optical and electrochemical biosensors based on loop mediated isothermal amplification (LAMP), rolling circle amplification (RCA), recombinase polymerase amplification (RPA), helicase dependent amplification (HDA), strand displacement amplification (SDA), and isothermal strand displacement polymerisation (ISDPR) are described, and an overview of their current advantages and limitations is provided. Although further efforts are required to harness the potential of these emerging analytical techniques, the coalescence of the different isothermal amplification techniques with the wide variety of biosensing detection strategies provides multiple possibilities for the efficient detection of bacteria far beyond the laboratory bench.
Deep Temporal Convolution Network for Time Series Classification
Authors: Koh Bee Hock David , Lim Chin Leng Peter , Rahimi Hasnae , Woo Wai Lok , Gao Bin
Published: January 2021

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.
Structural Damage Identification Based on Integrated Utilization of Inverse Finite Element Method and Pseudo-Excitation Approach
Authors: Li Tengteng , Cao Maosen , Li Jianle , Yang Lei , Xu Hao , Wu Zhanjun
Published: January 2021

The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give rise to a novel structural health monitoring (SHM) framework showing various advantages, particularly in aspects of enhanced adaptability and robustness. As the key component of the method, the inverse finite element method (iFEM) enables precise reconstruction of vibration displacements based on measured dynamic strains, which, as compared to displacement measurement, is much more adaptable to existing on-board SHM systems in engineering practice. The PE, on the other hand, is applied subsequently, relying on the reconstructed displacements for the identification of structural damage. Delamination zones in a carbon fibre reinforced plastic (CFRP) laminate are identified using the developed method. As demonstrated by the damage detection results, the iFEM-PE method possesses apparently improved accuracy and significantly enhanced noise immunity compared to the original PE approach depending on displacement measurement. Extensive parametric study is conducted to discuss the influence of a variety of factors on the effectiveness and accuracy of damage identification, including the influence of damage size and position, measurement density, sensor layout, vibration frequency and noise level. It is found that different factors are highly correlated and thus should be considered comprehensively to achieve optimal detection results. The application of the iFEM-PE method is extended to better adapt to the structural operational state, where multiple groups of vibration responses within a wide frequency band are used. Hybrid data fusion is applied to process the damage index (DI) constructed based on the multiple responses, leading to detection results capable of indicating delamination positions precisely.
Evaluation of the Size-of-Source Effect in Thermal Imaging Cameras
Authors: Pušnik Igor , Geršak Gregor
Published: January 2021

In numerous applications, including current body temperature monitoring in viral pandemic management, thermal imaging cameras are used for quantitative measurements. These require determination of the measurement accuracy (error) and its traceability (measurement uncertainty). Within error estimation, the size-of-source effect (SSE) is an important error source. The SSE is the relation between the physical size of a target and the instrument’s nominal target size. This study presents a direct evaluation of the error due to the SSE. A stable and uniform temperature, generated by blackbodies, was measured by a high-quality thermal imager. To limit the generated radiation, custom-made blocking tiles with different apertures were used. Effects of aperture shapes and positions, camera-target distances and temperature levels on the error were investigated. The study findings suggest that due to the SSE the measured temperatures are too low, especially at longer camera-target distances. The SSE error depends on the number of pixels available and included into the region of interest, for which the accurate measurement is about to be performed. For an accurate temperature measurement, an array of at least 10 × 10 pixels should be exposed to the observed target radiation, while 3 × 3 central pixel area should be included in the temperature calculation.
The Radial Electric Field Excited Circular Disk Piezoceramic Acoustic Resonator and Its Properties
Authors: Teplykh Andrey , Zaitsev Boris , Semyonov Alexander , Borodina Irina
Published: January 2021

A new type of piezoceramic acoustic resonator in the form of a circular disk with a radial exciting electric field is presented. The advantage of this type of resonator is the localization of the electrodes at one end of the disk, which leaves the second end free for the contact of the piezoelectric material with the surrounding medium. This makes it possible to use such a resonator as a sensor base for analyzing the properties of this medium. The problem of exciting such a resonator by an electric field of a given frequency is solved using a two-dimensional finite element method. The method for solving the inverse problem for determining the characteristics of a piezomaterial from the broadband frequency dependence of the electrical impedance of a single resonator is proposed. The acoustic and electric field inside the resonator is calculated, and it is shown that this location of electrodes makes it possible to excite radial, flexural, and thickness extensional modes of disk oscillations. The dependences of the frequencies of parallel and series resonances, the quality factor, and the electromechanical coupling coefficient on the size of the electrodes and the gap between them are calculated.
Noncontact Damage Topography Reconstruction by Wavenumber Domain Analysis Based on Air-Coupled Ultrasound and Full-Field Laser Vibrometer
Authors: Zhang Hui , Liang Dongmei , Rui Xiaobo , Wang Zhuochen
Published: January 2021

Noncontact ultrasonic detection technology is an effective method to detect damage in time. This paper proposes a noncontact damage detection system based on air-coupled ultrasound and full-field laser vibrometer, which realizes the excitation of relatively single-mode guided waves and the wavefield automatic detection. The system performance is verified through experiments, and the experimental wavenumber is consistent with the theoretical dispersion characteristics of the Lamb wave in the A0 mode. Based on this system, the topography reconstruction algorithms, including the Wavenumber Filtering Algorithm and Spatial Wavenumber Algorithm, were tested and compared with the aluminum alloy plate and the carbon fiber reinforced polymer plate. The results show that, based on the air-coupled ultrasound and full-field laser vibrometer detection system, the Spatial Wavenumber Algorithm has better imaging error and contrast, and the damage edge detection is smoother.
Marine Icing Sensor with Phase Discrimination
Authors: Elzaidi Abdulrazak , Masek Vlastimil , Bruneau Stephen
Published: January 2021

In this paper, a novel approach is presented to the measurement of marine icing phenomena under the presence of a two-phase condition. We have developed a sensor consisting of an electrostatic array and a signal processing based on a decision tree method. A three-element electrostatic array is employed to derive signals having linearly decoupled characteristics from which two key parameters, ice and water accretion layer dimension, can be determined for the purpose of environmental monitoring. The quantified characteristics revealed a correlation with the ice layer thickness in spite of the strong influence from the top water phase layer. The decision tree model established a relationship between the signal characteristics and the two accretion thickness parameters of water and ice layer. Through experimental verification, it has been observed that our sensor array in combination with the decision tree model based signal processing provides a simple practical solution to the challenging field of a two phase composition measurement such as in the marine icing considered in this study.
A Self-Powered Hybrid SSHI Circuit with a Wide Operation Range for Piezoelectric Energy Harvesting
Authors: Wu Liao , Zhu Peidong , Xie Minghua
Published: January 2021

This paper presents a piezoelectric (PE) energy harvesting circuit, which integrates a Synchronized Switch Harvesting on Inductor (SSHI) circuit and a diode bridge rectifier. A typical SSHI circuit cannot transfer the power from a PE cantilever into the load when the rectified voltage is higher than a certain voltage. The proposed circuit addresses this problem. It uses the two resonant loops for flipping the capacitor voltage and energy transfer in each half cycle. One resonant loop is typically used for the parallel SSHI scheme, and the other for the series SSHI scheme. The hybrid SSHI circuit using the two resonant loops enables the proposed circuit’s output voltage to no longer be limited. The circuit is self-powered and has the capability of starting without the help of an external battery. Eleven simple discrete components prototyped the circuit. The experimental results show that, compared with the full-bridge (FB) circuit, the amount of power harvested from a PE cantilever and the Voltage Range of Interest (VRI) of the proposed circuit is increased by 2.9 times and by 4.4 times, respectively. A power conversion efficiency of 83.2% is achieved.
CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning
Authors: Saeed Umer , Lee Young-Doo , Jan Sana Ullah , Koo Insoo
Published: January 2021

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.
Contribution to the Understanding of the Interaction between a Polydopamine Molecular Imprint and a Protein Model: Ionic Strength and pH Effect Investigation
Authors: Tlili Amal , Attia Ghada , Khaoulani Sohayb , Mazouz Zouhour , Zerrouki Chouki , Yaakoubi Nourdin , Othmane Ali , Fourati Najla
Published: January 2021

Several studies were devoted to the design of molecularly imprinted polymer (MIP)-based sensors for the detection of a given protein. Here, we bring elements that could contribute to the understanding of the interaction mechanism involved in the recognition of a protein by an imprint. For this purpose, a polydopamine (PDA)-MIP was designed for bovine serum albumin (BSA) recognition. Prior to BSA grafting, the gold surfaces were functionalized with mixed self-assembled monolayers of (MUDA)/(MHOH) (1/9, v/v). The MIP was then elaborated by dopamine electropolymerization and further extraction of BSA templates by incubating the electrode in proteinase K solution. Three complementary techniques, electrochemistry, zetametry, and Fourier-transform infrared spectrometry, were used to investigate pH and ionic strength effects on a MIP’s design and the further recognition process of the analytes by the imprints. Several MIPs were thus designed in acidic, neutral, and basic media and at various ionic strength values. Results indicate that the most appropriate conditions, to achieve a successful MIPs, were an ionic strength of 167 mM and a pH of 7.4. Sensitivity and dissociation constant of the designed sensor were of order of (3.36 ± 0.13) µA·cm−2·mg−1·mL and (8.56 ± 6.09) × 10−11 mg/mL, respectively.
Effect Analysis of GNSS/INS Processing Strategy for Sufficient Utilization of Urban Environment Observations
Authors: Shi Bo , Wang Mengke , Wang Yunpeng , Bai Yuntian , Lin Kang , Yang Fanlin
Published: January 2021

The occlusion of buildings in urban environments leads to the intermittent reception of satellite signals, which limits the utilization of observations. This subsequently results in a decline of the positioning and attitude accuracy of Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated system (GNSS/INS). This study implements a smooth post-processing strategy based on a tightly coupled differential GNSS/INS. Specifically, this strategy used the INS-estimated position to reinitialize integer ambiguity. The GNSS raw observations were input into the Kalman filter to update the measurement. The Rauch–Tung–Striebel smoothing (RTSS) algorithm was used to process the observations of the entire period. This study analyzed the performance of loosely coupled and tightly coupled systems in an urban environment and the improvement of the RTSS algorithm on the navigation solution from the perspective of fully mining the observations. The experimental results of the simulation data and real data show that, compared with the traditional tightly coupled processing strategy which does not use INS-aided integer ambiguity resolution and RTSS algorithm, the strategy in this study sufficiently utilized INS observations and GNSS observations to effectively improve the accuracy of positioning and attitude and ensure the continuity of navigation results in an obstructed environment.
Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning
Authors: Morshedi Maghsoud , Noll Josef
Published: January 2021

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.
Back-Gate GaN Nanowire-Based FET Device for Enhancing Gas Selectivity at Room Temperature
Authors: Khan Md Ashfaque Hossain , Debnath Ratan , Motayed Abhishek , Rao Mulpuri V.
Published: January 2021

In this work, a TiO2-coated GaN nanowire-based back-gate field-effect transistor (FET) device was designed and implemented to address the well-known cross-sensitive nature of metal oxides. Even though a two-terminal TiO2/GaN chemiresistor is highly sensitive to NO2, it suffers from lack of selectivity toward NO2 and SO2. Here, a Si back gate with C-AlGaN as the gate dielectric was demonstrated as a tunable parameter, which enhances discrimination of these cross-sensitive gases at room temperature (20 °C). Compared to no bias, a back-gate bias resulted in a significant 60% increase in NO2 response, whereas the increase was an insignificant 10% in SO2 response. The differential change in gas response was explained with the help of a band diagram, derived from the energetics of molecular models based on density functional theory (DFT). The device geometries in this work are not optimized and are intended only for proving the concept.
Smart Artificial Markers for Accurate Visual Mapping and Localization
Authors: Ortiz-Fernandez Luis E. , Cabrera-Avila Elizabeth V. , Silva Bruno M. F. da , Gonçalves Luiz M. G.
Published: January 2021

Artificial marker mapping is a useful tool for fast camera localization estimation with a certain degree of accuracy in large indoor and outdoor environments. Nonetheless, the level of accuracy can still be enhanced to allow the creation of applications such as the new Visual Odometry and SLAM datasets, low-cost systems for robot detection and tracking, and pose estimation. In this work, we propose to improve the accuracy of map construction using artificial markers (mapping method) and camera localization within this map (localization method) by introducing a new type of artificial marker that we call the smart marker. A smart marker consists of a square fiducial planar marker and a pose measurement system (PMS) unit. With a set of smart markers distributed throughout the environment, the proposed mapping method estimates the markers’ poses from a set of calibrated images and orientation/distance measurements gathered from the PMS unit. After this, the proposed localization method can localize a monocular camera with the correct scale, directly benefiting from the improved accuracy of the mapping method. We conducted several experiments to evaluate the accuracy of the proposed methods. The results show that our approach decreases the Relative Positioning Error (RPE) by 85% in the mapping stage and Absolute Trajectory Error (ATE) by 50% for the camera localization stage in comparison with the state-of-the-art methods present in the literature.
Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver
Authors: De Landro Martina , Espíritu García-Molina Ignacio , Barberio Manuel , Felli Eric , Agnus Vincent , Pizzicannella Margherita , Diana Michele , Zappa Emanuele , Saccomandi Paola
Published: January 2021

This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used to assess the effect of the procedure. These indicators rely on the spectral variation of temperature-dependent tissue chromophores, i.e., oxyhemoglobin, deoxyhemoglobin, methemoglobin, and water. Laser treatment was performed at specific temperature thresholds (from 60 to 110 °C) on in-vivo animal liver and was assessed with a hyperspectral camera (500–995 nm) during and after the treatment. The indicators were extracted from the hyperspectral images after the following processing steps: the breathing motion compensation and the spectral and spatial filtering, the selection of spectral bands corresponding to specific tissue chromophores, and the analysis of the areas under the curves for each spectral band. Results show that properly combining spectral information related to deoxyhemoglobin, methemoglobin, lipids, and water allows for the segmenting of different zones of the laser-induced thermal damage. This preliminary investigation provides indicators for describing the thermal state of the liver, which can be employed in the future as clinical endpoints of the procedure outcome.
Implementation of Neuro-Memristive Synapse for Long-and Short-Term Bio-Synaptic Plasticity
Authors: Mannan Zubaer I. , Kim Hyongsuk , Chua Leon
Published: January 2021

In this paper, we propose a complex neuro-memristive synapse that exhibits the physiological acts of synaptic potentiation and depression of the human-brain. Specifically, the proposed neuromorphic synapse efficiently imitates the synaptic plasticity, especially long-term potentiation (LTP) and depression (LTD), and short-term facilitation (STF) and depression (STD), phenomena of a biological synapse. Similar to biological synapse, the short- or long-term potentiation (STF and LTP) or depression (STD or LTD) of the memristive synapse are distinguished on the basis of time or repetition of input cycles. The proposed synapse is also designed to exhibit the effect of reuptake and neurotransmitters diffusion processes of a bio-synapse. In addition, it exhibits the distinct bio-realistic attributes, i.e., strong stimulation, exponentially decaying conductance trace of synapse, and voltage dependent synaptic responses, of a neuron. The neuro-memristive synapse is designed in SPICE and its bio-realistic functionalities are demonstrated via various simulations.
Special Issue: ECG Monitoring System
Authors: Baty Florent
Published: January 2021

This editorial of the Special Issue “ECG Monitoring System” provides a short overview of the 13 contributed articles published in this issue [...]
Highly Efficient Lossless Coding for High Dynamic Range Red, Clear, Clear, Clear Image Sensors
Authors: Pawłowski Paweł , Piniarski Karol , Dąbrowski Adam
Published: January 2021

In this paper we present a highly efficient coding procedure, specially designed and dedicated to operate with high dynamic range (HDR) RCCC (red, clear, clear, clear) image sensors used mainly in advanced driver-assistance systems (ADAS) and autonomous driving systems (ADS). The coding procedure can be used for a lossless reduction of data volume under developing and testing of video processing algorithms, e.g., in software in-the-loop (SiL) or hardware in-the-loop (HiL) conditions. Therefore, it was designed to achieve both: the state-of-the-art compression ratios and real-time compression feasibility. In tests we utilized FFV1 lossless codec and proved efficiency of up to 81 fps (frames per second) for compression and 87 fps for decompression performed on a single Intel i7 CPU.
Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network
Authors: Adelmann Benedikt , Schleier Max , Hellmann Ralf
Published: January 2021

In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets of varying thicknesses with different laser parameter combinations and classifies them into cuts and cut interruptions. After a short learning period of five epochs on a certain sheet thickness, the images are classified with a low error rate of 0.05%. The use of color images reveals slight advantages with lower error rates over greyscale images, since, during cut interruptions, the image color changes towards blue. A training set on all sheet thicknesses in one network results in tests error rates below 0.1%. This low error rate and the short calculation time of 120 µs on a standard CPU makes the system industrially applicable.
An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets
Authors: Larriva-Novo Xavier , Villagrá Víctor A. , Vega-Barbas Mario , Rivera Diego , Sanz Rodrigo Mario
Published: January 2021

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.
Resonance Energy Transfer-Based Biosensors for Point-of-Need Diagnosis—Progress and Perspectives
Authors: Weihs Felix , Anderson Alisha , Trowell Stephen , Caron Karine
Published: January 2021

The demand for point-of-need (PON) diagnostics for clinical and other applications is continuing to grow. Much of this demand is currently serviced by biosensors, which combine a bioanalytical sensing element with a transducing device that reports results to the user. Ideally, such devices are easy to use and do not require special skills of the end user. Application-dependent, PON devices may need to be capable of measuring low levels of analytes very rapidly, and it is often helpful if they are also portable. To date, only two transduction modalities, colorimetric lateral flow immunoassays (LFIs) and electrochemical assays, fully meet these requirements and have been widely adopted at the point-of-need. These modalities are either non-quantitative (LFIs) or highly analyte-specific (electrochemical glucose meters), therefore requiring considerable modification if they are to be co-opted for measuring other biomarkers. Förster Resonance Energy Transfer (RET)-based biosensors incorporate a quantitative and highly versatile transduction modality that has been extensively used in biomedical research laboratories. RET-biosensors have not yet been applied at the point-of-need despite its advantages over other established techniques. In this review, we explore and discuss recent developments in the translation of RET-biosensors for PON diagnoses, including their potential benefits and drawbacks.
Development of a Real-Time Human-Robot Collaborative System Based on 1 kHz Visual Feedback Control and Its Application to a Peg-in-Hole Task
Authors: Yamakawa Yuji , Matsui Yutaro , Ishikawa Masatoshi
Published: January 2021

In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system. We proposed an algorithm for visual sensing and robot hand control to perform collaborative motion, and we analyzed the stability of the collaborative system and a so-called collaborative error caused by image processing and latency. We achieved collaborative motion using this developed system and evaluated the collaborative error on the basis of the analysis results. Moreover, we aimed to realize a collaborative peg-in-hole task that required a system with high speed and high accuracy. To achieve this goal, we analyzed the conditions required for performing the collaborative peg-in-hole task from the viewpoints of geometric, force and posture conditions. Finally, in this work, we show the experimental results and data of the collaborative peg-in-hole task, and we examine the effectiveness of our collaborative system.
Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras
Authors: Ma Zhihong , Sun Dawei , Xu Haixia , Zhu Yueming , He Yong , Cen Haiyan
Published: January 2021

Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, we propose a method to improve the quality of 3D plant data using the time-of-flight (TOF) camera Kinect V2. A K-dimension (k-d) tree was applied to spatial topological relationships for searching points. Background noise points were then removed with a minimum oriented bounding box (MOBB) with a pass-through filter, while outliers and flying pixel points were removed based on viewpoints and surface normals. After being smoothed with the bilateral filter, the 3D plant data were registered and meshed. We adjusted the mesh patches to eliminate layered points. The results showed that the patches were closer. The average distance between the patches was 1.88 × 10−3 m, and the average angle was 17.64°, which were 54.97% and 48.33% of those values before optimization. The proposed method performed better in reducing noise and the local layered-points phenomenon, and it could help to more accurately determine 3D structure parameters from point clouds and mesh models.
A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems
Authors: Blanc Sara , Bayo-Montón José-Luis , Palanca-Barrio Senén , Arreaga-Alvarado Néstor X.
Published: January 2021

This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request–response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption.
The Event Detection System in the NEXT-White Detector
Authors: Esteve Bosch Raúl , Toledo Alarcón José F. , Herrero Bosch Vicente , Simón Estévez Ander , Monrabal Capilla Francesc , Álvarez Puerta Vicente , Rodríguez Samaniego Javier , Querol Segura Marc , Ballester Merelo Francisco
Published: January 2021

This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterráneo de Canfranc (LSC), Spain. The detector is based on a plane of photomultipliers (PMTs) for energy measurements and a silicon photomultiplier (SiPM) tracking plane for offline topological event filtering. The event detection system, based on the SRS-ATCA data acquisition system developed in the framework of the CERN RD51 collaboration, has been designed to detect multiple events based on online PMT signal energy measurements and a coincidence-detection algorithm. Implemented on FPGA, the system has been successfully running and evolving during NEXT-White operation. The event detection system brings some relevant and new functionalities in the field. A distributed double event processor has been implemented to detect simultaneously two different types of events thus allowing simultaneous calibration and physics runs. This special feature provides constant monitoring of the detector conditions, being especially relevant to the lifetime and geometrical map computations which are needed to correct high-energy physics events. Other features, like primary scintillation event rejection, or a double buffer associated with the type of event being searched, help reduce the unnecessary data throughput thus minimizing dead time and improving trigger efficiency.
The Effect of Recombinant Human Alpha-1,2-Fucosyltransferase and Alpha-Galactosidase A on the Reduction of Alpha-Gal Expression in the Liver of Transgenic Pigs
Authors: Wiater Jerzy , Karasiński Janusz , Słomski Ryszard , Smorąg Zdzisław , Wartalski Kamil , Gajda Barbara , Jura Jacek , Romek Marek
Published: January 2021

Genetically modified pigs lacking Galα1 → 3Gal and other immunogenic carbohydrates are considered as the most promising, alternative source of various tissues and organs for human transplantation. Here, we tested the hypothesis that combining the expression of human α1,2-fucosyltransferase
(hFUT2) and α-galactosidase A (hGLA) genes would allow for the removal of this specific carbohydrate in porcine transgenic livers. We investigated the expression profile of human α1,2-fucosyltransferase and α-galactosidase A proteins and the amount of Galα1
→ 3Gal antigen in the liver of single transgenic hFUT2 (n=5), hGLA (n=5), and double transgenic hFUT2 × hGLA (n=5) pigs. Both human proteins, α1,2-fucosyltransferase and α-galactosidase A, were abundantly expressed in the liver tissue in respective
transgenic lines as was revealed by confocal microscopy and Western blotting. The level of Galα1 → 3Gal epitope evaluated by lectin histochemistry and lectin blotting was significantly lower (p<0.05) in all genetically modified livers than that in the control non-transgenic porcine
livers. Importantly, the double transgenic line expressed a significantly lower (p<0.05), but still detectable level of this antigen, compared to both single transgenic pigs, as shown by lectin blotting. Histological evaluation of the liver samples stained with haematoxylin and eosin showed
no morphological evidence of hepatic abnormalities in all transgenic pigs. Our study indicates that the simultaneous expression of two protective transgenes hFUT2 × hGLA indeed improves the removal of the Galα1 → 3Gal epitope in porcine liver. However, this modification
alone is not sufficient enough for complete elimination of this antigen from porcine liver tissue.
Simulating distributed and global consciousness under spatial grasp paradigm
Authors:   Institute of Mathematical Machines and Systems Problems of the Ukraine National Academy of Science, Sapaty P.S.
Published: January 2021

The interest to what is often called awareness, consciousness, also self-consciousness, has grown enormously in recent years, with many theories and publications trying to explain what this can actually mean and where can be located. The paper is investigating the possibility of using developed and tested in different countries Spatial Grasp model and technology (SGT) for simulation of different ideas and concepts related to global awareness and consciousness in distributed dynamic systems, with potential applications in intelligent system management, industrial development, space research, security, and defense. The main technology component, Spatial Grasp Language (SGL), allows us to obtain powerful and compact spatial solutions of different problems by directly expressing their top semantics while hiding traditional system organization and management routines inside networked implementation. The paper describes in SGL a traditional organization of two opposing swarms, called “chasers” and “targets”, randomly operating in expected area. It then enriches the chasers swarm with global awareness and a sort of migrating consciousness, further strengthened by external super-consciousness capability, which allows it to drastically improve performance and make important nonlocal decisions, while moving to superior position over opposing targets swarm. Despite simplicity of the shown practical example, it gives us hope for the use of SGT for simulation of much broader and complex areas linked with consciousness like, for example, brain's biomolecular processes and basic structure of the universe. The developed networking technology can be implemented even in traditional university environments, as was done in the past for its previous versions in different countries.
The method of secondary sources in electrical engineering science and ill-conditioned matrices.
Authors:   Institute of Mathematical Machines and Systems Problems of the Ukraine National Academy of Science, Voloboev V.P. , Klymenko V.P. ,   Institute of Mathematical Machines and Systems Problems of the Ukraine National Academy of Science
Published: January 2021

A new approach to solving the problem of instability of a system of linear algebraic equations (SLAE) with an ill-conditioned matrix describing a discrete model of the Fredholm integral equation of the sec-ond kind, which reduces the calculation by the method of secondary sources of three-dimensional static and quasi-stationary electromagnetic fields of any geometry in inhomogeneous and nonlinear media, is considered. The essence of the new approach is all about. There is a method for correctly compiling a description of an electrical circuit. In this method, for the first time, when describing an electrical cir-cuit, the parameters of a specific task are taken into account, but they are not taking into account in other methods. As a result, the solution to the problem is stable even in the case of a SLAE with an ill-conditioned matrix. The disadvantage of this method is the description of the electrical circuit in the form of a graph. The description of the discrete model of the integral equation is proposed to be trans-formed to a form of representation that satisfies the method of describing the electric circuit. To achieve this goal, the following tasks have been completed. The requirements of the method of correct compila-tion of the description, which the form of the description of the discrete model of the integral equation must satisfy, are formulated. The analysis of the linear discrete model of the integral equation is carried out, the graph of the discrete model is constructed, and the requirements for the method of transform-ing this graph to the graph that meets the requirements of the method are formulated. A technique for transforming a graph of a discrete model into a graph that meets the requirements of the method has been developed. Final result: a description of a discrete model of the Fredholm integral equation of the second kind, compiled by the method of secondary sources in the form of a graph, satisfying the re-quirements of the method is presented.