bluuebunny/crossref_metadata_2025 huggingface dataset:
Link Budget Analysis for GNSS-R Sea Surface Return in Arbitrary Acquisition Geometries Using BA-PTSM
Authors: Di Martino Gerardo , Di Simone Alessio , Iodice Antonio , Riccio Daniele
Published: January 2022

In this article, we present a link budget analysis for Global Navigation Satellite System (GNSS) signals scattered off the sea surface in arbitrary acquisition geometries. The aim of our study is to investigate the reliability of the Geometrical Optics (GO) scattering model, which accurately describes sea surface scattering at and near the specular reflection direction, in properly modeling the sea surface return in far-from-specular acquisition geometries, which are of interest for maritime surveillance purposes and where GO is expected to fail. To this end, we adopted the recent Bistatic Anisotropic Polarimetric Two-Scale Model (BA-PTSM), which revealed good agreement with advanced scattering models, such as the second-order Small Slope Approximation (SSA2), regardless of the acquisition geometry, with the advantage of a reduced computational complexity. Numerical results have been derived for both circular polarization channels and for both spaceborne and airborne GNSS-Reflectometry (GNSS-R). It has been shown that, as long as conventional GNSS-R processing is assumed, GO can be safely adopted for simulation and analysis of spaceborne GNSS-R data regardless of the acquisition geometry and sea state, whereas more accurate scattering models, e.g., BA-PTSM, should be used for airborne receivers in far-from-specular configurations.
Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network
Authors: Zheng Xin , Chen Feng , Lou Liming , Cheng Pengle , Huang Ying
Published: January 2022

To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms.
Wi-Fi Access Point Design Concept Targeting Indoor Positioning for Smartphones and IoT
Authors: El-Gendy Mohamed S. , Ashraf Imran , El-Hennawey Samy
Published: January 2022

Indoor positioning systems (IPS) have been regarded as essential for many applications, particularly for smartphones, during the past decade. With the internet of things (IoT), and especially device-to-device (D2D) cases, the client is supposed to have a very simple structure and low cost. It is also desirable that the client contains minimal software modules specifically for IPS purposes. This study proposes a new IPS technique that satisfies these conditions. The evaluation of the technique was previously executed based on a manual procedure. This technique utilizes Wi-Fi technology in addition to a new design of two orthogonal phased antenna arrays. This paper provides a complete design of a Wi-Fi access point (AP), considered as the proof of concept of a commercial AP. For the system to be fully automatic, the proposed architecture is based on a Raspberry Pi, external Wi-Fi modules, a powered universal serial bus (USB) hub, and two orthogonal phased antenna arrays. The phases of each antenna array are governed by extra-phase circuits as well as a radio frequency (RF) switch. Extensive design parameters have been chosen through parametric sweeps that satisfy the design conditions. Software testing results for the antenna arrays are included in this paper to show the feasibility and suitability of the proposed antenna array for IPS.
Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
Authors: Torres Luis M. , Cañete Francisco J. , Díez Luis
Published: January 2022

The use of digital signal processors (DSP) to equalize coherent optical communication systems based on spatial division multiplexing (SDM) techniques is widespread in current optical receivers. However, most of DSP implementation approaches found in the literature assume a negligible mode-dependent loss (MDL). This paper is focused on the linear multiple-input multiple-output (MIMO) receiver designed to optimize the minimum mean square error (MMSE) for a coherent SDM optical communication system, without previous assumptions on receiver oversampling or analog front-end realizations. The influence of the roll-off factor of a generic pulse-amplitude modulation (PAM) transmitter on system performance is studied as well. As a main result of the proposed approach, the ability of a simple match filter (MF) based MIMO receiver to completely eliminate inter-symbol interference (ISI) and crosstalk for SDM systems under the assumption of negligible MDL is demonstrated. The performance of the linear MIMO fractionally-spaced equalizer (FSE) receiver for an SDM system with a MDL-impaired channel is then evaluated by numerical simulations using novel system performance indicators, in the form of signal to noise and distortion ratio (SNDR) loss, with respect to the case without MDL. System performance improvements by increasing the transmitter roll-off factor are also quantified.
Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine
Authors: Afza Farhat , Sharif Muhammad , Khan Muhammad Attique , Tariq Usman , Yong Hwan-Seung , Cha Jaehyuk
Published: January 2022

The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient.
CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition
Authors: Ding Ing-Jr , Zheng Nai-Wei
Published: January 2022

Pixel-based images captured by a charge-coupled device (CCD) with infrared (IR) LEDs around the image sensor are the well-known CCD Red–Green–Blue IR (the so-called CCD RGB-IR) data. The CCD RGB-IR data are generally acquired for video surveillance applications. Currently, CCD RGB-IR information has been further used to perform human gesture recognition on surveillance. Gesture recognition, including hand gesture intention recognition, is attracting great attention in the field of deep neural network (DNN) calculations. For further enhancing conventional CCD RGB-IR gesture recognition by DNN, this work proposes a deep learning framework for gesture recognition where a convolution neural network (CNN) incorporated with wavelet image fusion of CCD RGB-IR and additional depth-based depth-grayscale images (captured from depth sensors of the famous Microsoft Kinect device) is constructed for gesture intention recognition. In the proposed CNN with wavelet image fusion, a five-level discrete wavelet transformation (DWT) with three different wavelet decomposition merge strategies, namely, max-min, min-max and mean-mean, is employed; the visual geometry group (VGG)-16 CNN is used for deep learning and recognition of the wavelet fused gesture images. Experiments on the classifications of ten hand gesture intention actions (specified in a scenario of laboratory interactions) show that by additionally incorporating depth-grayscale data into CCD RGB-IR gesture recognition one will be able to further increase the averaged recognition accuracy to 83.88% for the VGG-16 CNN with min-max wavelet image fusion of the CCD RGB-IR and depth-grayscale data, which is obviously superior to the 75.33% of VGG-16 CNN with only CCD RGB-IR.
Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis
Authors: Hsiu Hsin , Lin Shun-Ku , Weng Wan-Ling , Hung Chaw-Mew , Chang Che-Kai , Lee Chia-Chien , Chen Chao-Tsung
Published: January 2022

Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer’s-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation (R2 = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia.
Architecting Intelligent Smart Serious Games for Healthcare Applications: A Technical Perspective
Authors: Ahmad Shabir , Mehmood Faisal , Khan Faheem , Whangbo Taeg Keun
Published: January 2022

The immune system of human beings plays a pivotal role in guarding against different types of diseases. During the COVID-19 pandemic, people with weak immune systems were more likely to die. Regular physical activities and healthy food intake can significantly improve the immune system; however, people with a sedentary lifestyle and a busy job schedule find it challenging and tedious to maintain regularity. Different approaches have been used over the years to engage people in various physical activities and improve their mental and physical health. The concept of employing serious games (games whose primary purpose is not fun or entertainment, but a serious goal) to effectuate better results has become one of the popular choices among healthcare professionals and research communities. Internet of things (IoT) has enabled digital transformation with smart cities, smart infrastructure, and the fourth industrial revolution. There have been some relevant studies on the encouragement of serious games in healthcare in the past few years. However, few research studies encourage IoT-enabled serious games played with IoT devices (sensors and actuators) by making the game experience more ubiquitous and pervasive. Consequently, the adaptation of the IoT in serious games for healthcare applications is a massive gap despite its growing need in an era significantly affected by COVID-19. This paper discusses the possibilities of integrating serious games with IoT and discusses the standard architecture, core technologies, and possible challenges. Finally, we present a prototype architecture and its various components and a qualitative analysis with recent studies.
Artificial Intelligence-Based Assistance System for Visual Inspection of X-ray Scatter Grids
Authors: Selmaier Andreas , Kunz David , Kisskalt Dominik , Benaziz Mohamed , Fürst Jens , Franke Jörg
Published: January 2022

Convolutional neural network (CNN)-based approaches have recently led to major performance steps in visual recognition tasks. However, only a few industrial applications are described in the literature. In this paper, an object detection application for visual quality evaluation of X-ray scatter grids is described and evaluated. To detect the small defects on the 4K input images, a sliding window approach is chosen. A special characteristic of the selected approach is the aggregation of overlapping prediction results by applying a 2D scalar field. The final system is able to detect 90% of the relevant defects, taking a precision score of 25% into account. A practical examination of the effectiveness elaborates the potential of the approach, improving the detection results of the inspection process by over 13%.
Distributed Strain Monitoring Using Nanocomposite Paint Sensing Meshes
Authors: Li Sijia , Shu Yening , Lin Yun-An , Zhao Yingjun , Yeh Yi-Jui , Chiang Wei-Hung , Loh Kenneth J.
Published: January 2022

Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, prior to their deployment. There is an opportunity to design smart and multifunctional paints that can be directly pre-applied onto structural surfaces to serve as a sensing layer among their other layers of functional paints. Therefore, the objective of this study was to design a strain-sensitive paint that can be used for structural monitoring. Carbon nanotubes (CNT) were dispersed in paint by high-speed shear mixing, while paint thinner was employed for adjusting the formulation’s viscosity and nanomaterial concentration. The study started with the design and fabrication of the CNT-based paint. Then, the nanocomposite paint’s electromechanical properties and its sensitivity to applied strains were characterized. Third, the nanocomposite paint was spray-coated onto patterned substrates to form “Sensing Meshes” for distributed strain monitoring. An electrical resistance tomography (ERT) measurement strategy and algorithm were utilized for reconstructing the conductivity distribution of the Sensing Meshes, where the magnitude of conductivity (or resistivity) corresponded to the magnitude of strain, while strain directionality was determined based on the strut direction in the mesh.
Inpainted Image Reconstruction Using an Extended Hopfield Neural Network Based Machine Learning System
Authors: Citko Wieslaw , Sienko Wieslaw
Published: January 2022

This paper considers the use of a machine learning system for the reconstruction and recognition of distorted or damaged patterns, in particular, images of faces partially covered with masks. The most up-to-date image reconstruction structures are based on constrained optimization algorithms and suitable regularizers. In contrast with the above-mentioned image processing methods, the machine learning system presented in this paper employs the superposition of system vectors setting up asymptotic centers of attraction. The structure of the system is implemented using Hopfield-type neural network-based biorthogonal transformations. The reconstruction property gives rise to a superposition processor and reversible computations. Moreover, this paper’s distorted image reconstruction sets up associative memories where images stored in memory are retrieved by distorted/inpainted key images.
Kinematic Effect on the Navicular Bone with the Use of Rearfoot Varus Wedge
Authors: Gómez Carrión Álvaro , de los Ángeles Atín Arratibel Maria , Morales Lozano Maria Rosario , Martínez Sebastián Carlos , de la Cruz Torres Blanca , Sánchez-Gómez Rubén
Published: January 2022

Background: The rearfoot varus wedge (RVW) is a common treatment for foot pain and valgus deformity. There is research on its effects in the calcaneus, but there is little research on the navicular. More research is needed with the use of RVW due to the relationship that exists between the position of the navicular and the risk of suffering an injury. Objectives: this study sought to understand how RVW can influence the kinematics of the navicular bone, measuring their movement with the 6 SpaceFastrak system. Methods: a total of 60 subjects participated in the study. Two sensors were used to measure the movement of the calcaneus and navicular using RVWs as compared in the barefoot position in a static way. Results: there were statistically significant differences, the use of RVWs caused changes in the navicular bone, with subjects reaching the maximum varus movement with the use of RVW 7 mm of 1.35 ± 2.41° (p < 0.001), the maximum plantar movement flexion with the use of RVW 10 mm of 3.93 ± 4.44° (p < 0.001). Conclusions: when RVWs were placed under the calcaneus bone, the navicular bone response was in varus movement too; thus, the use of rearfoot varus wedge can influence the movement of the navicular bone.
Surface Acoustic Wave (SAW) Sensors: Physics, Materials, and Applications
Authors: Mandal Debdyuti , Banerjee Sourav
Published: January 2022

Surface acoustic waves (SAWs) are the guided waves that propagate along the top surface of a material with wave vectors orthogonal to the normal direction to the surface. Based on these waves, SAW sensors are conceptualized by employing piezoelectric crystals where the guided elastodynamic waves are generated through an electromechanical coupling. Electromechanical coupling in both active and passive modes is achieved by integrating interdigitated electrode transducers (IDT) with the piezoelectric crystals. Innovative meta-designs of the periodic IDTs define the functionality and application of SAW sensors. This review article presents the physics of guided surface acoustic waves and the piezoelectric materials used for designing SAW sensors. Then, how the piezoelectric materials and cuts could alter the functionality of the sensors is explained. The article summarizes a few key configurations of the electrodes and respective guidelines for generating different guided wave patterns such that new applications can be foreseen. Finally, the article explores the applications of SAW sensors and their progress in the fields of biomedical, microfluidics, chemical, and mechano-biological applications along with their crucial roles and potential plans for improvements in the long-term future in the field of science and technology.
Real-Time Hyperspectral Video Acquisition with Coded Slits
Authors: Tang Guoliang , Wang Zi , Liu Shijie , Li Chunlai , Wang Jianyu
Published: January 2022

We propose a real-time hyperspectral video acquisition system that uses coded slits. Conventional imaging spectrometers usually have scanning mechanisms that reduce the temporal resolution or sacrifice the spatial resolution to acquire spectral information instantly. Recently, computational spectral imaging has been applied to realize high-speed or high-performance spectral imaging. However, the most current computational spectral imaging systems take a long time to reconstruct spectral data cubes from limited measurements, which limits real-time hyperspectral video acquisition. In this work, we propose a new computational spectral imaging system. We substitute the slit in a conventional scanning-based imaging spectrometer with coded slits, which can achieve the parallel acquisition of spectral data and thus an imaging speed that is several times higher. We also apply an electronically controlled translation stage to use different codes at each exposure level. The larger amount of data allows for fast reconstruction through matrix inversion. To solve the problem of a trade-off between imaging speed and image quality in high-speed spectral imaging, we analyze the noise in the system. The severe readout noise in our system is suppressed with S-matrix coding. Finally, we build a practical prototype that can acquire hyperspectral video with a high spatial resolution and a high signal-to-noise ratio at 5 Hz in real time.
Computation of Gait Parameters in Post Stroke and Parkinson’s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems
Authors: Cimolin Veronica , Vismara Luca , Ferraris Claudia , Amprimo Gianluca , Pettiti Giuseppe , Lopez Roberto , Galli Manuela , Cremascoli Riccardo , Sinagra Serena , Mauro Alessandro , Priano Lorenzo
Published: January 2022

The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.
Predicting Deep Body Temperature (Tb) from Forehead Skin Temperature: Tb or Not Tb?
Authors: Fisher Jason T. , Ciuha Urša , Tipton Michael J. , Ioannou Leonidas G. , Mekjavic Igor B.
Published: January 2022

There is a need to rapidly screen individuals for heat strain and fever using skin temperature (Tsk) as an index of deep body temperature (Tb). This study’s aim was to assess whether Tsk could serve as an accurate and valid index of Tb during a simulated heatwave. Seven participants maintained a continuous schedule over 9-days, in 3-day parts; pre-/post-HW (25.4 °C), simulated-HW (35.4 °C). Contact thermistors measured Tsk (Tforehead, Tfinger); radio pills measured gastrointestinal temperature (Tgi). Proximal-distal temperature gradients (ΔTforehead–finger) were also measured. Measurements were grouped into ambient conditions: 22, 25, and 35 °C. Tgi and Tforehead only displayed a significant relationship in 22 °C (r: 0.591; p < 0.001) and 25 °C (r: 0.408; p < 0.001) conditions. A linear regression of all conditions identified Tforehead and ΔTforehead–finger as significant predictors of Tgi (r2: 0.588; F: 125.771; p < 0.001), producing a root mean square error of 0.26 °C. Additional residual analysis identified Tforehead to be responsible for a plateau in Tgi prediction above 37 °C. Contact Tforehead was shown to be a statistically suitable indicator of Tgi in non-HW conditions; however, an error of ~1 °C makes this physiologically redundant. The measurement of multiple sites may improve Tb prediction, though it is still physiologically unsuitable, especially at higher ambient temperatures.
Stretching Method-Based Damage Detection Using Neural Networks
Authors: Daskalakis Emmanouil , Panagiotopoulos Christos G. , Tsogka Chrysoula
Published: January 2022

We present in this paper a framework for damage detection and localization using neural networks. The data we use to train the network are m×d pixel images consisting of measurements of the relative variations of m natural frequencies of the structure under monitoring over a period of d-days. To measure the relative variations of the natural frequencies, we use the stretching method, which allows us to obtain reliable measurements amidst fluctuations induced by environmental factors such as temperature variations. We show that even by monitoring a single natural frequency over a few days, accurate damage detection can be achieved. The accuracy for damage detection significantly improves when a small number of natural frequencies is monitored instead of a single one. More importantly, monitoring multiple natural frequencies allows for damage localization provided that the network can be trained for both healthy and damaged scenarios. This is feasible under the assumption that damage occurs at a finite number of damage-prone locations. Several results obtained with numerically simulated data illustrate the effectiveness of the proposed approach.
ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning
Authors: Chowdhury Deepraj , Das Anik , Dey Ajoy , Sarkar Shreya , Dwivedi Ashutosh Dhar , Rao Mukkamala Raghava , Murmu Lakhindar
Published: January 2022

Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.
Development of an Efficient Voltammetric Sensor for the Monitoring of 4-Aminophenol Based on Flexible Laser Induced Graphene Electrodes Modified with MWCNT-PANI
Authors: Nasraoui Salem , Ameur Sami , Al-Hamry Ammar , Ben Ali Mounir , Kanoun Olfa
Published: January 2022

Sensitive electrodes are of a great importance for the realization of highly performant electrochemical sensors for field application. In the present work, a laser-induced carbon (LIC) electrode is proposed for 4-Aminophenol (4-AP) electrochemical sensors. The electrode is patterned on a commercial low-cost polyimide (Kapton) sheet and functionalized with a multi-walled carbon nanotubes polyaniline (MWCNT-PANI) composite, realized by an in-situ-polymerization in an acidic medium. The LIC electrode modified with MWCNT-PAPNI nanocomposite was investigated by SEM, AFM, and electrochemically in the presence of ferri-ferrocyanide [Fe(CN)6]3−/4− by cyclic voltammetry and impedance spectroscopy. The results show a significant improvement of the electron transfer rate after the electrode functionalization in the presence of the redox mediators [Fe(CN)6]3−/4−, related directly to the active surface, which itself increased by about 18.13% compared with the bare LIG. The novel electrode shows a good reproducibility and a stability for 20 cycles and more. It has a significantly enhanced electro-catalytic activity towards electrooxidation reaction of 4-AP inferring positive synergistic effects between carbon nanotubes and polyaniline PANI. The presented electrode combination LIC/MWCNT-PANI exhibits a detection limit of 0.006 μM for the determination of 4-AP at concentrations ranging from 0.1 μM to 55 μM and was successfully applied for the monitoring in real samples with good recoveries.
Research on a Silicon Gyroscope Interface Circuit Based on Closed-Loop Controlled Drive Loop
Authors: Li Qiang , Ding Lifeng , Liu Xiaowei , Zhang Qiang
Published: January 2022

The existing analysis methods for the silicon gyroscope drive loop, such as the perturbation method and period average method, cannot analyze the dynamic characteristics of the system. In this work, a linearized amplitude control model of the silicon gyroscope drive loop was established to analyze the stability and set-up time of the drive loop, and the vibration conditions of the silicon gyro were obtained. According to the above results, a new silicon gyroscope interface circuit was designed, using a 0.35 μm Bipolar-CMOS-DMOS (BCD) process, and the chip area was 4.5 mm × 4.0 mm. The application-specific integrated circuit (ASIC) of the silicon gyroscope was tested in combination with the sensitive structure with a zero stability of 1.14°/hr (Allen). The test results for the ASIC and the whole machine prove the correctness of the theoretical model, which reflects the effectiveness of the stability optimization of the closed-loop controlled drive loop of the silicon gyroscope circuit.
Multi-Gas Detection System Based on Non-Dispersive Infrared (NDIR) Spectral Technology
Authors: Xu Manlin , Peng Bo , Zhu Xiangyi , Guo Yongcai
Published: January 2022

Automobile exhaust gases, such as carbon dioxide (CO2), carbon monoxide (CO), and propane (C3H8), cause the greenhouse effect, photochemical smog, and haze, threatening the urban atmosphere and human health. In this study, a non-dispersive infrared (NDIR) multi-gas detection system consisting of a single broadband light source, gas cell, and four-channel pyroelectric detector was developed. The system can be used to economically detect gas concentration in the range of 0–5000 ppm for C3H8, 0–14% for CO, and 0–20% for CO2. According to the experimental data, the concentration inversion model was established using the least squares between the voltage ratio and the concentration. Additionally, the interference coefficient between different gases was tested. Therefore, the interference models between the three gases were established by the least square method. The concentration inversion model was experimentally verified, and it was observed that the full-scale error of the sensor changed less than 3.5%, the detection repeatability error was lower than 4.5%, and the detection stability was less than 2.7%. Therefore, the detection system is economical and energy efficient and it is a promising method for the analysis of automobile exhaust gases.
Quantitative GC–TCD Measurements of Major Flatus Components: A Preliminary Analysis of the Diet Effect
Authors: Freire Rafael , Mego Marianela , Oliveira Luciana Fontes , Mas Silvia , Azpiroz Fernando , Marco Santiago , Pardo Antonio
Published: January 2022

The impact of diet and digestive disorders in flatus composition remains largely unexplored. This is partially due to the lack of standardized sampling collection methods, and the easy atmospheric contamination. This paper describes a method to quantitatively determine the major gases in flatus and their application in a nutritional intervention. We describe how to direct sample flatus into Tedlar bags, and simultaneous analysis by gas chromatography–thermal conductivity detection (GC–TCD). Results are analyzed by univariate hypothesis testing and by multilevel principal component analysis. The reported methodology allows simultaneous determination of the five major gases with root mean measurement errors of 0.8% for oxygen (O2), 0.9% for nitrogen (N2), 0.14% for carbon dioxide (CO2), 0.11% for methane (CH4), and 0.26% for hydrogen (H2). The atmospheric contamination was limited to 0.86 (95% CI: [0.7–1.0])% for oxygen and 3.4 (95% CI: [1.4–5.3])% for nitrogen. As an illustration, the method has been successfully applied to measure the response to a nutritional intervention in a reduced crossover study in healthy subjects.
Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System
Authors: Tassetti Anna Nora , Galdelli Alessandro , Pulcinella Jacopo , Mancini Adriano , Bolognini Luca
Published: January 2022

During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats—for which space and power onboard are often limited—as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management.
Fast and Sensitive Determination of Bioflavonoids Using a New Analytical System Based on Label-Free Silver Triangular Nanoplates
Authors: Furletov Aleksei , Apyari Vladimir , Garshev Alexey , Dmitrienko Stanislava , Zolotov Yury
Published: January 2022

Optical sensors based on silver triangular nanoplates (AgTNPs) are insufficiently studied as probes for the spectrophotometric determination of biologically active compounds. In the present article, an interaction between label-free AgTNPs and bioflavonoids in the presence of silver(I) ions was assessed to outline the possibilities of AgTNPs as a colorimetric probe for the fast and sensitive determination of bioflavonoids. It is shown that the interaction was accompanied by a bathochromic shift of the local surface plasmon resonance band of nanoparticles and an increase in its intensity. Seven bioflavonoids differing in their structure were tested. The influence of the structure of analytes and the main external factors on the analytical signal is discussed in detail. It was found that the detection limits of bioflavonoids in the selected optimal conditions increased in the series morin < rutin = quercetin < taxifolin and came to 0.9, 1.2, 1.2, and 2.0 μmol L−1, respectively. Chrysin, naringenin, and naringin were found not to affect the spectral characteristics of AgTNPs. The suggested approach was applied for the spectrophotometric determination of flavonoids in pharmaceuticals and onion peel.
Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions
Authors: Dai Huatong , Chen Pengzhan , Yang Hui
Published: January 2022

Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based methods focus only on improving the performance of skid steering vehicles, while actuator faults that may lead to unsafe conditions or catastrophic events are frequently omitted in existing control schemes. This study proposes a meta-RL-based fault-tolerant control (FTC) method to improve the tracking performance of vehicles in the case of actuator faults. Based on meta deep deterministic policy gradient (meta-DDPG), the proposed FTC method has a representative gradient-based metalearning algorithm workflow, which includes an offline stage and an online stage. In the offline stage, an experience replay buffer with various actuator faults is constructed to provide data for training the metatraining model; then, the metatrained model is used to develop an online meta-RL update method to quickly adapt its control policy to actuator fault conditions. Simulations of four scenarios demonstrate that the proposed FTC method can achieve a high performance and adapt to actuator fault conditions stably.
Technology-Based Social Innovation: Smart City Inclusive System for Hearing Impairment and Visual Disability Citizens
Authors: Chang Ignacio , Castillo Juan , Montes Hector
Published: January 2022

The multilayer technology integration of hardware and software will reduce the social inclusion gap and increase the support in case of an emergency for people with special needs at hearing and visual levels. This research shows a development based on Internet of Things to support people with visual disabilities (PwVD) for indoor and outdoor activities. The decision-making process is made at the operational, tactical, and strategic level, providing a safe place so people with visual and hearing special needs can make decisions, their families can make decisions, and the government authorities can make decisions in case of an emergency or even on a day-by-day basis. In the case of the authorities, the smart visualization of the data according to the information provided facilitates Comprehensive Disaster Risk Management (CDRM) and Disaster Risk Reduction (DRR). The main findings are based on the need to develop mobile applications, dashboard and web applications that are responsive to people with visual or hearing disabilities, and the need to develop an infrastructure of communication systems assisted by batteries and clean energy, and independent of the current telecommunications system, to allow greater reliability.
Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness
Authors: Ohradzansky Michael T. , Humbert J. Sean
Published: January 2022

Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves.
Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs through the Discrete-Continuous Vortex Search Algorithm
Authors: Cortés-Caicedo Brandon , Molina-Martin Federico , Grisales-Noreña Luis Fernando , Montoya Oscar Danilo , Hernández Jesus C.
Published: January 2022

This paper discusses the minimization of the total annual operative cost for a planning period of 20 years composed by the annualized costs of the energy purchasing at the substation bus summed with the annualized investment costs in photovoltaic (PV) sources, including their maintenance costs in distribution networks based on their optimal siting and sizing. This problem is presented using a mixed-integer nonlinear programming model, which is resolved by applying a master–slave methodology. The master stage, consisting of a discrete-continuous version of the Vortex Search Algorithm (DCVSA), is responsible for providing the optimal locations and sizes for the PV sources—whereas the slave stage employs the Matricial Backward/Forward Power Flow Method, which is used to determine the fitness function value for each individual provided by the master stage. Numerical results in the IEEE 33- and 69-node systems with AC and DC topologies illustrate the efficiency of the proposed approach when compared to the discrete-continuous version of the Chu and Beasley genetic algorithm with the optimal location of three PV sources. All the numerical validations were carried out in the MATLAB programming environment.
Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering
Authors: Kern Jonathan , Dupraz Elsa , Aïssa-El-Bey Abdeldjalil , Varshney Lav R. , Leduc-Primeau François
Published: January 2022

This paper presents a quantized Kalman filter implemented using unreliable memories. We consider that both the quantization and the unreliable memories introduce errors in the computations, and we develop an error propagation model that takes into account these two sources of errors. In addition to providing updated Kalman filter equations, the proposed error model accurately predicts the covariance of the estimation error and gives a relation between the performance of the filter and its energy consumption, depending on the noise level in the memories. Then, since memories are responsible for a large part of the energy consumption of embedded systems, optimization methods are introduced to minimize the memory energy consumption under the desired estimation performance of the filter. The first method computes the optimal energy levels allocated to each memory bank individually, and the second one optimizes the energy allocation per groups of memory banks. Simulations show a close match between the theoretical analysis and experimental results. Furthermore, they demonstrate an important reduction in energy consumption of more than 50%.
A Fault Diagnosis Scheme Using Hurst Exponent for Metal Particle Faults in GIL/GIS
Authors: Duan Dawei , Ma Hongzhong , Yan Yan , Yang Qifan
Published: January 2022

A diagnosis scheme using the Hurst exponent for metal particle faults in GIL/GIS is proposed to improve the accuracy of classification and identification. First, the diagnosis source signal is the vibration signal generated by the collision of metal particles in the electric field. Then, the signal is processed via variational mode decomposition (VMD) based on particle swarm optimization with adaptive parameter adjustment (APA-PSO). In the end, fault types are classified and identified by an SVM model, whose feature vector is composed of the Hurst exponents of each intrinsic mode function (IMF-H). Extensive experimental data verify the effect of this new scheme. The results exhibit that the classification performance of SVM is significantly improved by the new feature vector. Furthermore, the VMD based on APA-PSO with adaptive parameter adjustment can effectively enhance the decomposition quality.
Validated Capillary Zone Electrophoresis Method for Impurity Profiling and Determination of NiII(3-OMe-Salophene)
Authors: El Deeb Sami , Ibrahim Adel Ehab , Al-Harrasi Ahmed , Wolber Gerhard , Gust Ronald
Published: January 2022

A capillary zone electrophoresis method was developed for the determination of NiII(3-OMe-salophene), a substance with anticancer activity in vitro. A fused silica capillary (56 cm × 100 µm) was used for this purpose. The method was optimized in terms of parameters affecting the electrophoretic conditions in order to optimize separation efficiency and total time of migration. The analysis was best performed using an operating buffer of 50 mM borate, adjusted to pH 9.3, mixed with acetonitrile (50%, v/v) as organic modifier. Injections were performed hydrodynamically by applying a pressure of 50 mbar for 8 s, and a 30 kV separation voltage was selected at 25 °C. Detection was carried out at 250 nm using diode array detector (DAD). The method allowed the separation of NiII(3-OMe-salophene) from four other structurally related impurities in a total migration time (tm) of 8 min. Peak identification was achieved using the standard reference of individual impurities. The purity of the migrated NiII(3-OMe-salophene) was confirmed by Ultra-violet (UV) scan overlay depending on DAD. The linear ranges for the determination of NiII(3-OMe-salophene) was 400–20,000 ng mL−1 with limit of detection (LOD) of 120 ng mL−1. Acceptable intra-day and inter-day precisions were achieved (%relative standard deviation (RSD) results were less than 0.76% and 0.30%, respectively). The proposed method was assessed for greenness and compared to reported methodologies to prove superiority.