bluuebunny/crossref_metadata_2025 huggingface dataset:
A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images
Authors: Ullah Naeem , Khan Mohammad Sohail , Khan Javed Ali , Choi Ahyoung , Anwar Muhammad Shahid
Published: October 2022

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.
Hybrid Carbon Nanotubes/Gold Nanoparticles Composites for Trace Nitric Oxide Detection over a Wide Range of Humidity
Authors: Hannon Ami , Seames Wayne , Li Jing
Published: October 2022

Composites of functionalized single walled carbon nanotubes (SWCNTs) and gold nanoparticles (Au NPs) of ≈15 nm diameter were drop-cast on a printed circuit board (PCB) substrate equipped with interdigitated electrodes to make a hybrid thin film. Addition of Au NPs decorated the surface of SWCNTs networked films and acted as catalysts which resulted into an enhanced sensitivity and low ppb concentration detection limit. The compositions of the film were characterized by scanning electron microscope (SEM). SWCNTs clusters were loaded with various amount of Au NPs ranging from 1–10% (by weight) and their effect on Nitric oxide (NO) sensitivity was studied and optimized. Further, the optimized composite films were tested in both air and nitrogen environments and as well as over a wide relative humidity range (0–97%). Sensors were also tested for the selectivity by exposing to various gases such as nitrous oxide, ammonia, carbon monoxide, sulfur dioxide and acetone. Sensitivity to NO was found much higher than the other tested gases. The advantage of this sensor is that it is sensitive to NO at low ppb level (10 ppb) with estimated response time within 10 s and recovery time around 1 min, and has excellent reproducibility from sensor to sensor and works within the wide range of relative humidity (0–97%).
A Primal–Dual-Based Power Control Approach for Capacitated Edge Servers
Authors: Zhang Qinghui , Li Weidong , Su Qian , Zhang Xuejie
Published: October 2022

The intensity of radio waves decays rapidly with increasing propagation distance, and an edge server’s antenna needs more power to form a larger signal coverage area. Therefore, the power of the edge server should be controlled to reduce energy consumption. In addition, edge servers with capacitated resources provide services for only a limited number of users to ensure the quality of service (QoS). We set the signal transmission power for the antenna of each edge server and formed a signal disk, ensuring that all users were covered by the edge server signal and minimizing the total power of the system. This scenario is a typical geometric set covering problem, and even simple cases without capacity limits are NP-hard problems. In this paper, we propose a primal–dual-based algorithm and obtain an m-approximation result. We compare our algorithm with two other algorithms through simulation experiments. The results show that our algorithm obtains a result close to the optimal value in polynomial time.
Soft Wearable Robots: Development Status and Technical Challenges
Authors: Shi Yongjun , Dong Wei , Lin Weiqi , Gao Yongzhuo
Published: October 2022

In recent years, more and more research has begun to focus on the flexible and lightweight design of wearable robots. During this process, many novel concepts and achievements have been continuously made and shown to the public, while new problems have emerged at the same time, which need to be solved. In this paper, we give an overview of the development status of soft wearable robots for human movement assistance. On the basis of a clear definition, we perform a system classification according to the target assisted joint and attempt to describe the overall prototype design level in related fields. Additionally, it is necessary to sort out the latest research progress of key technologies such as structure, actuation, control and evaluation, thereby analyzing the design ideas and basic characteristics of them. Finally, we discuss the possible application fields, and propose the main challenges of this valuable research direction.
A Novel High-Sensitivity MEMS Pressure Sensor for Rock Mass Stress Sensing
Authors: Wang Honghui , Zou Dingkang , Peng Peng , Yao Guangle , Ren Jizhou
Published: October 2022

This paper proposes a novel high-sensitivity micro-electromechanical system (MEMS) piezoresistive pressure sensor that can be used for rock mass stress monitoring. The entire sensor consists of a cross, dual-cavity, and all-silicon bulk-type (CCSB) structure. Firstly, the theoretical analysis is carried out, and the relationship between the structural parameters of the sensor and the stress is analyzed by finite element simulation and curve-fitting prediction, and then the optimal structural parameters are also analyzed. The simulation results indicate that the sensor with the CCSB structure proposed in this article obtained a high sensitivity of 87.74 μV/V/MPA and a low nonlinearity error of 0.28% full-scale span (FSS) within the pressure range of 0–200 MPa. Compared with All-Si Bulk, grooved All-Si Bulk, Si-Glass Bulk, silicon diaphragm, resistance strain gauge, and Fiber Bragg grating structure pressure sensors, the designed sensor has a significant improvement in sensitivity and nonlinearity error. It can be used as a new sensor for rock disaster (such as collapse) monitoring and forecasting.
SealID: Saimaa Ringed Seal Re-Identification Dataset
Authors: Nepovinnykh Ekaterina , Eerola Tuomas , Biard Vincent , Mutka Piia , Niemi Marja , Kunnasranta Mervi , Kälviäinen Heikki
Published: October 2022

Wildlife camera traps and crowd-sourced image material provide novel possibilities to monitor endangered animal species. The massive data volumes call for automatic methods to solve various tasks related to population monitoring, such as the re-identification of individual animals. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in Lake Saimaa, Finland, and is one of the few existing freshwater seal species. Ringed seals have permanent pelage patterns that are unique to each individual and that can be used for the identification of individuals. A large variation in poses, further exacerbated by the deformable nature of seals, together with varying appearance and low contrast between the ring pattern and the rest of the pelage makes the Saimaa ringed seal re-identification task very challenging, providing a good benchmark by which to evaluate state-of-the-art re-identification methods. Therefore, we make our Saimaa ringed seal image (SealID) dataset (N = 57) publicly available for research purposes. In this paper, the dataset is described, the evaluation protocol for re-identification methods is proposed, and the results for two baseline methods—HotSpotter and NORPPA—are provided. The SealID dataset has been made publicly available.
Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions
Authors: Franco Tiago , Sestrem Leonardo , Henriques Pedro Rangel , Alves Paulo , Varanda Pereira Maria João , Brandão Diego , Leitão Paulo , Silva Alfredo
Published: October 2022

The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O(1) were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.
Study on In-Service Inspection of Nuclear Fuel Assembly Failure Using Ultrasonic Plate Wave
Authors: Xiao Xiang , Zhou Guo Zheng , Wang Ke Qing , Xi Feng , Zeng Kun
Published: October 2022

As protection for nuclear power plants is quite necessary, the nuclear fuel is sealed in zirconium alloy thin wall cladding. During service, fuel rods might be damaged caused by wall-thickness thinning, cladding corrosion and cracking, etc. This will cause the coolant to enter into the fuel rod, which may lead to the failure of the fuel assembly. However, current diagnostic methods have limitations due to the special structure of the fuel assembly and the underwater and radioactive environment. In this paper, a novel inspection method is proposed to recognize the failure of a fuel rod. The fuel rod failure can be detected based on the presence or absence of coolant inside the fuel rod by using an ultrasonic plate wave. The inspection model and process algorithm are proposed for in-service inspection. The relationship between signal and scanning position is established and analyzed. Both ultrasound field simulation and experiment have been carried out for validation. The corresponding results illustrate that the failed nuclear fuel rod of the whole fuel assembly (including the internal rods) can be effectively detected without the influence of the near-field region by using the proposed method.
An Optimal Decision Support System Based on Crop Dynamic Model for N-Fertilizer Treatment
Authors: Singh Abhaya Pal , Yerudkar Amol , Liuzza Davide , Liu Yang , Glielmo Luigi
Published: October 2022

The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive NO3− accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a 28% to 53% increase in biomass growth under certain scenarios was attained.
Flexible Antennas for a Sub-6 GHz 5G Band: A Comprehensive Review
Authors: John Deepthi Mariam , Vincent Shweta , Pathan Sameena , Kumar Pradeep , Ali Tanweer
Published: October 2022

The ever-increasing demand and need for high-speed communication have generated intensive research in the field of fifth-generation (5G) technology. Sub-6 GHz 5G mid-band spectrum is the focus of the researchers due to its meritorious ease of deployment in the current scenario with the already existing infrastructure of the 4G-LTE system. The 5G technology finds applications in enormous fields that require high data rates, low latency, and stable radiation patterns. One of the major sectors that benefit from the outbreak of 5G is the field of flexible electronics. Devices that are compact need an antenna to be flexible, lightweight, conformal, and still have excellent performance characteristics. Flexible antennas used in wireless body area networks (WBANs) need to be highly conformal to be bent according to the different curvatures of the human body at different body parts. The specific absorption rate (SAR) must be at a permissible level for such an antenna to be suited for WBAN applications. This paper gives a comprehensive review of the current state of the art flexible antennas in a sub-6 GHz 5G band. Furthermore, this paper gives a key insight into the materials for a flexible antenna, the parameters considered for the design of a flexible antenna for 5G, the challenges for the design, and the implementation of a flexible antenna for 5G.
Monitoring of Hidden Corrosion Growth in Aircraft Structures Based on D-Sight Inspections and Image Processing
Authors: Katunin Andrzej , Nagode Marko , Oman Simon , Cholewa Adam , Dragan Krzysztof
Published: October 2022

Hidden corrosion in aircraft structures, not detected on time, can have a significant influence on aircraft structural integrity and lead to catastrophic consequences. According to the widely accepted damage tolerance philosophy, non-destructive inspections are performed to assess structural safety and reliability. One of the inspection techniques used for such an inspection is the optical D-Sight technique. Since D-Sight is used primarily as a qualitative method, it is difficult to assess the evolution of a structural condition simply by comparing the inspection results. In the following study, the method to monitor hidden corrosion growth is proposed on the basis of historical data from D-Sight inspections. The method is based on geometric transforms and segmentation techniques to remove the influence of measurement conditions, such as the angle of observation or illumination, and to compare corroded regions for a sequence of D-Sight images acquired during historical inspections. The analysis of the proposed method was performed on the sequences of D-Sight images acquired from inspections of Polish military aircraft in the period from 2002 to 2017. The proposed method represents an effective tool for monitoring hidden corrosion growth in metallic aircraft structures based on a sequence of D-Sight images.
Low Power Environmental Image Sensors for Remote Photogrammetry
Authors: Balde Alpha Yaya , Bergeret Emmanuel , Cajal Denis , Toumazet Jean-Pierre
Published: October 2022

This paper aims to prove the feasibility of a 4D monitoring solution (3D modeling and temporal monitoring) for the sandbar and to characterize the species’ role in the landscape. The developed solution allows studying the interaction between the river dynamics and vegetation using a network of low resolution and low power sensors. The issues addressed concern the feasibility of implementing a photogrammetry solution using low-resolution sensors as well as the choice of the appropriate sensor and its testing according to different configurations (image capture and storage on the sensor and/or image transmission to a centralization node) and also the detailed analysis of the different phases of the process (camera initialization, image capture, network transmission and selection of the most appropriate standby mode). We reveal that the tiny, low-cost board (ESP32-Cam) can perform a 3D reconstruction and propose using the camera’s UXGA (1600, 1200) resolution because of the quality rendering and energy consumption. A multi-node scenario based on a combined Wi-Fi and GSM relay is proposed in the study showing several years of autonomy for the system. Finally, to illustrate the energy cost of the module, we have defined a study process, where we have identified and quantified one by one the different phases of operation of the card for better energy optimization (setup, camera configuration, shooting, saving on SD card, or sending by Wi-Fi). The device is now operational for deployment on the Allier River (France).
Revisiting the Modeling of the Conversion Gain of CMOS Image Sensors with a New Stochastic Approach
Authors: Cherniak Gil , Nemirovsky Amikam , Nemirovsky Yael
Published: October 2022

A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS) with at least four transistors is presented. This model, based on the fundamental principles of electronic noise, may provide a reliable calibration of the gain conversion, which is one of the most important parameters of CMOS Image Sensor pixels. The new model revisits the “gold standard” ratio method of the measured variance of the shot noise to the mean value. The model assumes that shot noise is the dominant noise source of the pixel. The microscopic random time-dependent voltage of any shot noise electron charging the junction capacitance C of the sensing node may have either an exponential form or a step form. In the former case, a factor of 1/2 appears in the variance to the mean value, namely, q/2C is obtained. In the latter case, the well-established ratio q/C remains, where q is the electron charge. This correction factor affects the parameters that are based on the conversion gain, such as quantum efficiency and noise. The model has been successfully tested for advanced image sensors with six transistors fabricated in a commercial FAB, applying a CMOS 180 nm technology node with four metals. The stochastic modeling is corroborated by measurements of the quantum efficiency and simulations with advanced software (Lumerical).
Human-to-Human Position Estimation System Using RSSI in Outdoor Environment
Authors: Yamamoto Takashi , Yamaguchi Tomoyuki
Published: October 2022

Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the “position” with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area.
Online Detection System for Wheat Machine Harvesting Impurity Rate Based on DeepLabV3+
Authors: Chen Man , Jin Chengqian , Ni Youliang , Xu Jinshan , Yang Tengxiang
Published: October 2022

Wheat, one of the most important food crops in the world, is usually harvested mechanically by combine harvesters. The impurity rate is one of the most important indicators of the quality of wheat obtained by mechanized harvesting. To realize the online detection of the impurity rate in the mechanized harvesting process of wheat, a vision system based on the DeepLabV3+ model of deep learning for identifying and segmenting wheat grains and impurities was designed in this study. The DeepLabV3+ model construction considered the four backbones of MobileNetV2, Xception-65, ResNet-50, and ResNet-101 for training. The optimal DeepLabV3+ model was determined through the accuracy rate, comprehensive evaluation index, and average intersection ratio. On this basis, an online detection method of measuring the wheat impurity rate in mechanized harvesting based on image information was constructed. The model realized the online detection of the wheat impurity rate. The test results showed that ResNet-50 had the best recognition and segmentation performance; the accuracy rate of grain identification was 86.86%; the comprehensive evaluation index was 83.63%; the intersection ratio was 0.7186; the accuracy rate of impurity identification was 89.91%; the comprehensive evaluation index was 87.18%; the intersection ratio was 0.7717; and the average intersection ratio was 0.7457. In terms of speed, ResNet-50 had a fast segmentation speed of 256 ms per image. Therefore, in this study, ResNet-50 was selected as the backbone network for DeepLabV3+ to carry out the identification and segmentation of mechanically harvested wheat grains and impurity components. Based on the manual inspection results, the maximum absolute error of the device impurity rate detection in the bench test was 0.2%, and the largest relative error was 17.34%; the maximum absolute error of the device impurity rate detection in the field test was 0.06%; and the largest relative error was 13.78%. This study provides a real-time method for impurity rate measurement in wheat mechanized harvesting.
Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering
Authors: Cheng Cheng , Luan Xiaoli
Published: October 2022

As the core link of the “Internet + Recycling” process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method.
Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs
Authors: Foster Marc , Wu Tianfu , Roberts David L. , Bozkurt Alper
Published: October 2022

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.
Method and Device of All-in-Focus Imaging with Overexposure Suppression in an Irregular Pipe
Authors: Wang Shuangjie , Xing Qiang , Xu Haili , Lu Guyue , Wang Jiajia
Published: October 2022

To avoid depth-of-field mismatches caused by the changes in pipe structure and image overexposures caused by highly reflective surfaces while radial imaging irregular pipes, this paper proposes a novel all-in-focus, adaptable, and low scene-coupling method that suppresses overexposures in support of fault detection. Firstly, the pipeline’s radial depth distribution data are obtained by sensors, and an optimal all-in-focus imaging scheme is established by combining camera parameters. Secondly, using digital imaging technology, the high reflection effect produced by disparate light sources is comprehensively evaluated for overexposure suppression. Thirdly, a device is designed for imaging non-Lambertian free-form surface scenes under low illumination, providing the sequence images needed for the next step. Lastly, specific digital fusions are made to the sequential images to obtain an all-in-focus final image without overexposure. An image-quality analysis method is then used to measure the efficacy of the system in obtaining the characteristic information of the inner surfaces of an irregular pipe. Results of the experiment show that the method and device used are able to distinguish small 0.5 mm wide lines ranging from 40–878 mm depth and are capable of providing efficient image support for defect inspection of irregular pipes and free-form surfaces amongst other irregular surfaces.
Sensor Acquisition and Allocation for Real-Time Monitoring of Articulated Construction Equipment in Digital Twins
Authors: Talmaki Sanat A. , Kamat Vineet R.
Published: October 2022

The visibility available to an equipment operator on a dynamic construction site can often be blocked by various obstacles such as materials, temporary or permanent facilities, other equipment, and workers. Equipment monitoring in real-time digital twins can thus play a crucial role in accident prevention. This paper develops a scalable technical approach and presents a prototype application framework for transmitting real world sensor data to update 3D equipment models inside a graphical digital twin for concurrent visualization of a monitored construction operation. The developed framework and workflow can be extended to visualize any construction operation, as it occurs, inside a dynamic 3D world simply by outfitting the real equipment with appropriate sensors and connecting them to their virtual counterparts. The implemented proof-of-concept interface is described in the context of a real-time 3D digital twin for assisting excavator operators prevent unintended strikes with underground utilities. Experiments to validate the proposed technical approach by simulating the real-time motion of a backhoe loader’s articulated arm using orientation sensors installed on its boom, stick, and bucket are described. The experimental results characterize the scope and potential reasons for spatio-temporal discrepancies that can occur between a monitored real operation and its replicated digital twin. The effect of an operator warning mechanism based on preset safety thresholds is also investigated and described.
Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review
Authors: Dolson Conor M. , Harlow Ethan R. , Phelan Dermot M. , Gabbett Tim J. , Gaal Benjamin , McMellen Christopher , Geletka Benjamin J. , Calcei Jacob G. , Voos James E. , Seshadri Dhruv R.
Published: October 2022

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.
Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios
Authors: Caba Julián , Barba Jesús , Rincón Fernando , de la Torre José Antonio , Escolar Soledad , López Juan Carlos
Published: October 2022

Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.
Deep Learning for Encrypted Traffic Classification and Unknown Data Detection
Authors: Pathmaperuma Madushi H. , Rahulamathavan Yogachandran , Dogan Safak , Kondoz Ahmet M.
Published: October 2022

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed.
Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure
Authors: Han Songtong , Zhang Bo , Wen Zhu , Zhang Chunwei , He Yong
Published: October 2022

After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east–west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method’s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification.
Robotic Non-Destructive Testing
Authors: Mineo Carmelo , Javadi Yashar
Published: October 2022

Non-destructive testing (NDT) and evaluation (NDE) are commonly referred to as the vast group of analysis techniques used in civil, medical, and industrial sectors to evaluate the properties of materials, tissues, components, or structures without causing any damage [...]
Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies
Authors: Cai Wanhan , Ji Lixia , Guo Chenglin , Mei Ke , Zeng Hao
Published: October 2022

This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples.
Functionalization of Internal Resonance in Magnetically Coupled Resonators for Highly Efficient and Wideband Hybrid Vibration Energy Harvesting
Authors: Aouali Kaouthar , Kacem Najib , Bouhaddi Noureddine
Published: October 2022

The functionalization of internal resonance (IR) is theoretically and experimentally demonstrated on a nonlinear hybrid vibration energy harvester (HVEH) based on piezoelectric (PE) and electromagnetic (EM) transductions. This nonlinear phenomenon is tuned by adjusting the gaps between the moving magnets of the structure, enabling 1:1 and 2:1 IR. The experimental results prove that the activation of 2:1 IR with a realistic excitation amplitude allows the improvement of both the frequency bandwidth (BW) and the harvested power (HP) by 300% and 100%, respectively compared to the case away from IR. These remarkable results open the way towards a very large scale integration of coupled resonators with simultaneous internal resonances.
Electrotactile Communication via Matrix Electrode Placed on the Torso Using Fast Calibration, and Static vs. Dynamic Encoding
Authors: Malešević Jovana , Kostić Miloš , Jure Fabricio A. , Spaich Erika G. , Došen Strahinja , Ilić Vojin , Bijelić Goran , Štrbac Matija
Published: October 2022

Electrotactile stimulation is a technology that reproducibly elicits tactile sensations and can be used as an alternative channel to communicate information to the user. The presented work is a part of an effort to develop this technology into an unobtrusive communication tool for first responders. In this study, the aim was to compare the success rate (SR) between discriminating stimulation at six spatial locations (static encoding) and recognizing six spatio-temporal patterns where pads are activated sequentially in a predetermined order (dynamic encoding). Additionally, a procedure for a fast amplitude calibration, that includes a semi-automated initialization and an optional manual adjustment, was employed and evaluated. Twenty subjects, including twelve first responders, participated in the study. The electrode comprising the 3 × 2 matrix of pads was placed on the lateral torso. The results showed that high SRs could be achieved for both types of message encoding after a short learning phase; however, the dynamic approach led to a statistically significant improvement in messages recognition (SR of 93.3%), compared to static stimulation (SR of 83.3%). The proposed calibration procedure was also effective since in 83.8% of the cases the subjects did not need to adjust the stimulation amplitude manually.
Robust Flow Estimation Algorithm of Multichannel Ultrasonic Flowmeter Based on Random Sampling Least Squares
Authors: Xu Zhijia , Li Minghai , Han Yuqiang , Li Xin , Shi Guangmei
Published: October 2022

The multi-path ultrasonic flowmeter is widely used in engineering practice, and the flow algorithm is important for its accuracy. The least-squares estimation method is simple and efficient and has good engineering application value. In practical applications, noises are inevitably introduced to the measurement process because of the flowmeter itself or flow-field interference. The results of classical least squares will deviate from reality because it lacks robustness. In this regard, two flow algorithms of multi-path ultrasonic flowmeter are proposed based on least-squares and random-sampling consensus algorithms, which are widely used in the image field. The two algorithms can resist gross errors effectively by avoiding the interference of external points in the sampling points. To verify the effectiveness of the proposed algorithms, we take the double-bend flow field, which is a typical damaged flow field in engineering, as the research object, and then we compare the four algorithms. It can be seen that the two flow algorithms have higher accuracy and better robustness in the presence of interference noise.
High Performance of Metallic Thin Films for Resistance Temperature Devices with Antimicrobial Properties
Authors: Souza Arthur L. R. , Correa Marcio A. , Bohn Felipe , Castro Helder , Fernandes Margarida M. , Vaz Filipe , Ferreira Armando
Published: October 2022

Titanium-copper alloy films with stoichiometry given by Ti1−xCux were produced by magnetron co-sputtering technique and analyzed in order to explore the suitability of the films to be applied as resistive temperature sensors with antimicrobial properties. For that, the copper (Cu) amount in the films was varied by applying different DC currents to the source during the deposition in order to change the Cu concentration. As a result, the samples showed excellent thermoresistivity linearity and stability for temperatures in the range between room temperature to 110 °C. The sample concentration of Ti0.70Cu0.30 has better characteristics to act as RTD, especially the αTCR of 1990 ×10−6°C−1. The antimicrobial properties of the Ti1−xCux films were analyzed by exposing the films to the bacterias S. aureus and E. coli, and comparing them with bare Ti and Cu films that underwent the same protocol. The Ti1−xCux thin films showed bactericidal effects, by log10 reduction for both bacteria, irrespective of the Cu concentrations. As a test of concept, the selected sample was subjected to 160 h reacting to variations in ambient temperature, presenting results similar to a commercial temperature sensor. Therefore, these Ti1−xCux thin films become excellent antimicrobial candidates to act as temperature sensors in advanced coating systems.
Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
Authors: Wang Xinyi , Li Weiping , Wang Mingxu , Bian Chengzhen , Wei Yi , Zhou Wen
Published: October 2022

The millimeter-wave frequency band provides abundant frequency resources for the development of beyond 5th generation mobile network (B5G) mobile communication, and its relative bandwidth of 1% can provide a gigabit-level communication bandwidth. In particular, the D-band (110–170 GHz) has received much attention, due to its large available bandwidth. However, certain bands in the D-band are easily blocked by obstacles and lack penetration. In this paper, D-band millimeter-wave penetration losses of typical materials, such as vegetation, planks, glass, and slate, are investigated theoretically and experimentally. The comparative analysis between our experimental results and theoretical predictions shows that D-band waves find it difficult to penetrate thick materials, making it difficult for 5G millimeter waves to cover indoors from outdoor macro stations. The future B5G mobile communication also requires significant measurement work on different frequencies and different scenarios.
COVID-19 Contagion Risk Estimation Model for Indoor Environments
Authors: Costanzo Sandra , Flores Alexandra
Published: October 2022

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells–Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.
A Reconfigurable Visual–Inertial Odometry Accelerated Core with High Area and Energy Efficiency for Autonomous Mobile Robots
Authors: Tan Yonghao , Sun Mengying , Deng Huanshihong , Wu Haihan , Zhou Minghao , Chen Yifei , Yu Zhuo , Zeng Qinghan , Li Ping , Chen Lei , An Fengwei
Published: October 2022

With the wide application of autonomous mobile robots (AMRs), the visual inertial odometer (VIO) system that realizes the positioning function through the integration of a camera and inertial measurement unit (IMU) has developed rapidly, but it is still limited by the high complexity of the algorithm, the long development cycle of the dedicated accelerator, and the low power supply capacity of AMRs. This work designs a reconfigurable accelerated core that supports different VIO algorithms and has high area and energy efficiency, precision, and speed processing characteristics. Experimental results show that the loss of accuracy of the proposed accelerator is negligible on the most authoritative dataset. The on-chip memory usage of 70 KB is at least 10× smaller than the state-of-the-art works. Thus, the FPGA implementation’s hardware-resource consumption, power dissipation, and synthesis in the 28 nm CMOS outperform the previous works with the same platform.