bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid SARS-CoV-2 Detection Using Electrochemical Immunosensor
Authors: Mojsoska Biljana , Larsen Sylvester , Olsen Dorte Aalund , Madsen Jonna Skov , Brandslund Ivan , Alatraktchi Fatima AlZahra’a
Published: January 2021

The outbreak of the coronavirus disease (COVID-19) pandemic caused by the novel coronavirus (SARS-CoV-2) has been declared an international public health crisis. It is essential to develop diagnostic tests that can quickly identify infected individuals to limit the spread of the virus and assign treatment options. Herein, we report a proof-of-concept label-free electrochemical immunoassay for the rapid detection of SARS-CoV-2 virus via the spike surface protein. The assay consists of a graphene working electrode functionalized with anti-spike antibodies. The concept of the immunosensor is to detect the signal perturbation obtained from ferri/ferrocyanide measurements after binding of the antigen during 45 min of incubation with a sample. The absolute change in the [Fe(CN)6]3−/4− current upon increasing antigen concentrations on the immunosensor surface was used to determine the detection range of the spike protein. The sensor was able to detect a specific signal above 260 nM (20 µg/mL) of subunit 1 of recombinant spike protein. Additionally, it was able to detect SARS-CoV-2 at a concentration of 5.5 × 105 PFU/mL, which is within the physiologically relevant concentration range. The novel immunosensor has a significantly faster analysis time than the standard qPCR and is operated by a portable device which can enable on-site diagnosis of infection.
High Resolution, High Contrast Beamformer Using Minimum Variance and Plane Wave Nonlinear Compounding with Low Complexity
Authors: Yan Xin , Qi Yanxing , Wang Yinmeng , Wang Yuanyuan
Published: January 2021

The plane wave compounding (PWC) is a promising modality to improve the imaging quality and maintain the high frame rate for ultrafast ultrasound imaging. In this paper, a novel beamforming method is proposed to achieve higher resolution and contrast with low complexity. A minimum variance (MV) weight calculated by the partial generalized sidelobe canceler is adopted to beamform the receiving array signals. The dimension reduction technique is introduced to project the data into lower dimensional space, which also contributes to a large subarray length. Estimation of multi-wave receiving covariance matrix is performed and then utilized to determine only one weight. Afterwards, a fast second-order reformulation of the delay multiply and sum (DMAS) is developed as nonlinear compounding to composite the beamforming output of multiple transmissions. Simulations, phantom, in vivo, and robustness experiments were carried out to evaluate the performance of the proposed method. Compared with the delay and sum (DAS) beamformer, the proposed method achieved 86.3% narrower main lobe width and 112% higher contrast ratio in simulations. The robustness to the channel noise of the proposed method is effectively enhanced at the same time. Furthermore, it maintains a linear computational complexity, which means that it has the potential to be implemented for real-time response.
2D Optimal Trajectory Planning Problem in Threat Environment for UUV with Non-Uniform Radiation Pattern
Authors: Galyaev Andrey A. , Lysenko Pavel V. , Yakhno Victor P.
Published: January 2021

Path planning is necessary in many applications using unmanned underwater vehicles (UUVs). The main class of tasks is the planning of safe routes with minimal energy costs and/or minimal levels of emitted physical and information signals. Since the action planner is on board the UUV, the main focus is on methods and algorithms that allow it to build reference trajectories while minimizing the number of calculations. The study is devoted to the problem of the optimal route planning for a UUV with a non-uniform radiation pattern. The problem is stated in the form of two point variational problem for which necessary and sufficient optimality conditions are proved. Particular attention is paid to cases where optimality conditions are not met. These cases are directly related to found specific forms of a radiation pattern. Sufficient optimality conditions are extended on the class of two-link and multi-link motion paths. Software tools have been developed and computer simulations have been performed for various types of radiation patterns.
On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
Authors: Mongay Batalla Jordi , Mavromoustakis Constandinos X. , Mastorakis George , Markakis Evangelos K. , Pallis Evangelos , Wichary Tomasz , Krawiec Piotr , Lekston Przemysław
Published: January 2021

This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.
A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation
Authors: Lu Sheng , Luo Zhaojie , Gao Feng , Liu Mingjie , Chang KyungHi , Piao Changhao
Published: January 2021

Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.
Tablet Technology for Writing and Drawing during Functional Magnetic Resonance Imaging: A Review
Authors: Lin Zhongmin , Tam Fred , Churchill Nathan W. , Schweizer Tom A. , Graham Simon J.
Published: January 2021

Functional magnetic resonance imaging (fMRI) is a powerful modality to study brain activity. To approximate naturalistic writing and drawing behaviours inside the scanner, many fMRI-compatible tablet technologies have been developed. The digitizing feature of the tablets also allows examination of behavioural kinematics with greater detail than using paper. With enhanced ecological validity, tablet devices have advanced the fields of neuropsychological tests, neurosurgery, and neurolinguistics. Specifically, tablet devices have been used to adopt many traditional paper-based writing and drawing neuropsychological tests for fMRI. In functional neurosurgery, tablet technologies have enabled intra-operative brain mapping during awake craniotomy in brain tumour patients, as well as quantitative tremor assessment for treatment outcome monitoring. Tablet devices also play an important role in identifying the neural correlates of writing in the healthy and diseased brain. The fMRI-compatible tablets provide an excellent platform to support naturalistic motor responses and examine detailed behavioural kinematics.
A UAV-Based Eddy Covariance System for Measurement of Mass and Energy Exchange of the Ecosystem: Preliminary Results
Authors: Sun Yibo , Ma Junyong , Sude Bilige , Lin Xingwen , Shang Haolu , Geng Bing , Diao Zhaoyan , Du Jiaqiang , Quan Zhanjun
Published: January 2021

Airborne eddy covariance (EC) measurement is one of the most effective methods to directly measure the surface mass and energy fluxes at the regional scale. It offers the possibility to bridge the scale gap between local- and global-scale measurements by ground-based sites and remote-sensing instrumentations, and to validate the surface fluxes estimated by satellite products or process-based models. In this study, we developed an unmanned aerial vehicle (UAV)-based EC system that can be operated to measure the turbulent fluxes in carbon dioxides, momentum, latent and sensible heat, as well as net radiation and photosynthetically active radiation. Flight tests of the developed UAV-based EC system over land were conducted in October 2020 in Inner Mongolia, China. The in-flight calibration was firstly conducted to correct the mounting error. Then, three flight comparison tests were performed, and we compared the measurement with those from a ground tower. The results, along with power spectral comparison and consideration of the differing measurement strategies indicate that the system can resolve the turbulent fluxes in the encountered measurement condition. Lastly, the challenges of the UAV-based EC method were discussed, and potential improvements with further development were explored. The results of this paper reveal the considerable potential of the UAV-based EC method for land surface process studies.
An Acoustic Sensor for Combined Sewer Overflow (CSO) Screen Condition Monitoring in a Drainage Infrastructure
Authors: See Chan H. , Horoshenkov Kirill V. , Ali M. Tareq Bin , Tait Simon J.
Published: January 2021

Combined sewer overflow structures (CSO) play an important role in sewer networks. When the local capacity of a sewer system is exceeded during intense rainfall events, they act as a “safety valve” and discharge excess rainfall run-off and wastewater directly to a natural receiving water body, thus preventing widespread urban flooding. There is a regulatory requirement that solids in CSO spills must be small and their amount strictly controlled. Therefore, a vast majority of CSOs in the UK contain screens. This paper presents the results of a feasibility study of using low-cost, low-energy acoustic sensors to remotely assess the condition of CSO screens to move to cost-effective reactive maintenance visits. In situ trials were carried out in several CSOs to evaluate the performance of the acoustic sensor under realistic screen and flow conditions. The results demonstrate that the system is robust within ±2.5% to work successfully in a live CSO environment. The observed changes in the screen condition resulted in 8–39% changes in the values of the coefficient in the proposed acoustic model. These changes are detectable and consistent with observed screen and hydraulic data. This study suggested that acoustic-based sensing can effectively monitor the CSO screen blockage conditions and hence reduce the risk of non-compliant CSO spills.
A Prosthetic Socket with Active Volume Compensation for Amputated Lower Limb
Authors: Seo Ji-Hyeon , Lee Hyuk-Jin , Seo Dong-Wook , Lee Dong-Kyu , Kwon Oh-Won , Kwak Moon-Kyu , Lee Kang-Ho
Published: January 2021

Typically, the actual volume of the residual limb changes over time. This causes the prosthesis to not fit, and then pain and skin disease. In this study, a prosthetic socket was developed to compensate for the volume change of the residual limb. Using an inflatable air bladder, the proposed socket monitors the pressure in the socket and keeps the pressure distribution uniform and constant while walking. The socket has three air bladders on anterior and posterior tibia areas, a latching type 3-way pneumatic valve and a portable control device. In the paper, the mechanical properties of the air bladder were investigated, and the electromagnetic analysis was performed to design the pneumatic valve. The controller is based on a hysteresis control algorithm with a closed loop, which keeps the pressure in the socket close to the initial set point over a long period of time. In experiments, the proposed prosthesis was tested through the gait simulator that can imitate a human’s gait cycle. The active volume compensation of the socket was successfully verified during repetitive gait cycle using the weight loads of 50, 70, and 90 kg and the residual limb model with a variety of volumes. It was confirmed that the pressure of the residual limb recovered to the initial state through the active control. The pressure inside the socket had a steady state error of less than 0.75% even if the volume of the residual limb was changed from −7% to +7%.
On-Glass Integrated SU-8 Waveguide and Amorphous Silicon Photosensor for On-Chip Detection of Biomolecules: Feasibility Study on Hemoglobin Sensing
Authors: Buzzin Alessio , Asquini Rita , Caputo Domenico , de Cesare Giampiero
Published: January 2021

An optoelectronic, integrated system-on-glass for on-chip detection of biomolecules is here presented. The system’s working principle is based on the interaction, detected by a hydrogenated amorphous silicon photosensor, between a monochromatic light travelling in a SU-8 polymer optical waveguide and the biological solution under analysis. Optical simulations of the waveguide coupling to the thin-film photodiode with a specific design were carried out. A prototype was fabricated and characterized showing waveguide optical losses of about 0.6 dB/cm, a photodiode shot noise current of about 2.5 fA/Hz and responsivity of 495 mA/W at 532 nm. An electro-optical coupling test was performed on the fabricated device to validate the system. As proof of concept, hemoglobin was studied as analyte for a demonstration scenario, involving optical simulations interpolated with experimental data. The calculated detection limit of the proposed system for hemoglobin concentration in aqueous solution is around 100 ppm, in line with colorimetric methods currently on the market. These results show the effectiveness of the proposed system in biological detection applications and encourage further developments in implementing these kinds of devices in the biomedical field.
A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion
Authors: Peng Cheng , Chen Yufeng , Chen Qing , Tang Zhaohui , Li Lingling , Gui Weihua
Published: January 2021

The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.
Modules and Techniques for Motion Planning: An Industrial Perspective
Authors: Quer Stefano , Garcia Luz
Published: January 2021

Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.
Photoacoustic Imaging of Human Vasculature Using LED versus Laser Illumination: A Comparison Study on Tissue Phantoms and In Vivo Humans
Authors: Agrawal Sumit , Kuniyil Ajith Singh Mithun , Johnstonbaugh Kerrick , C. Han David , R. Pameijer Colette , Kothapalli Sri-Rajasekhar
Published: January 2021

Vascular diseases are becoming an epidemic with an increasing aging population and increases in obesity and type II diabetes. Point-of-care (POC) diagnosis and monitoring of vascular diseases is an unmet medical need. Photoacoustic imaging (PAI) provides label-free multiparametric information of deep vasculature based on strong absorption of light photons by hemoglobin molecules. However, conventional PAI systems use bulky nanosecond lasers which hinders POC applications. Recently, light-emitting diodes (LEDs) have emerged as cost-effective and portable optical sources for the PAI of living subjects. However, state-of-art LED arrays carry significantly lower optical energy (<0.5 mJ/pulse) and high pulse repetition frequencies (PRFs) (4 KHz) compared to the high-power laser sources (100 mJ/pulse) with low PRFs of 10 Hz. Given these tradeoffs between portability, cost, optical energy and frame rate, this work systematically studies the deep tissue PAI performance of LED and laser illuminations to help select a suitable source for a given biomedical application. To draw a fair comparison, we developed a fiberoptic array that delivers laser illumination similar to the LED array and uses the same ultrasound transducer and data acquisition platform for PAI with these two illuminations. Several controlled studies on tissue phantoms demonstrated that portable LED arrays with high frame averaging show higher signal-to-noise ratios (SNRs) of up to 30 mm depth, and the high-energy laser source was found to be more effective for imaging depths greater than 30 mm at similar frame rates. Label-free in vivo imaging of human hand vasculature studies further confirmed that the vascular contrast from LED-PAI is similar to laser-PAI for up to 2 cm depths. Therefore, LED-PAI systems have strong potential to be a mobile health care technology for diagnosing vascular diseases such as peripheral arterial disease and stroke in POC and resource poor settings.
On the Optimal Lawful Intercept Access Points Placement Problem in Hybrid Software-Defined Networks
Authors: Xu Xiaosa , Jia Wen-Kang , Wu Yi , Wang Xufang
Published: January 2021

For the law enforcement agencies, lawful interception is still one of the main means to intercept a suspect or address most illegal actions. Due to its centralized management, however, it is easy to implement in traditional networks, but the cost is high. In view of this restriction, this paper aims to exploit software-defined network (SDN) technology to contribute to the next generation of intelligent lawful interception technology, i.e., to optimize the deployment of intercept access points (IAPs) in hybrid software-defined networks where both SDN nodes and non-SDN nodes exist simultaneously. In order to deploy IAPs, this paper puts forward an improved equal-cost multi-path shortest path algorithm and accordingly proposes three SDN interception models: T interception model, ECMP-T interception model and Fermat-point interception model. Considering the location relevance of all intercepted targets and the operation and maintenance cost of operators from the global perspective, by the way, we further propose a restrictive minimum vertex cover algorithm (RMVCA) in hybrid SDN. Implementing different SDN interception algorithms based RMVCA in real-world topologies, we can reasonably deploy the best intercept access point and intercept the whole hybrid SDN with the least SDN nodes, as well as significantly optimize the deployment efficiency of IAPs and improve the intercept link coverage in hybrid SDN, contributing to the implementation of lawful interception.
Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting
Authors: Retscher Guenther , Leb Alexander
Published: January 2021

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver.
Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges
Authors: Onozuka Yuya , Matsumi Ryosuke , Shino Motoki
Published: January 2021

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.
Particle Filters: A Hands-On Tutorial
Authors: Elfring Jos , Torta Elena , van de Molengraft René
Published: January 2021

The particle filter was popularized in the early 1990s and has been used for solving estimation problems ever since. The standard algorithm can be understood and implemented with limited effort due to the widespread availability of tutorial material and code examples. Extensive research has advanced the standard particle filter algorithm to improve its performance and applicability in various ways in the years after. As a result, selecting and implementing an advanced version of the particle filter that goes beyond the standard algorithm and fits a specific estimation problem requires either a thorough understanding or reviewing large amounts of the literature. The latter can be heavily time consuming especially for those with limited hands-on experience. Lack of implementation details in theory-oriented papers complicates this task even further. The goal of this tutorial is facilitating the reader to familiarize themselves with the key concepts of advanced particle filter algorithms and to select and implement the right particle filter for the estimation problem at hand. It acts as a single entry point that provides a theoretical overview of the filter, its assumptions and solutions for various challenges encountered when applying particle filters. Besides that, it includes a running example that demonstrates and implements many of the challenges and solutions.
Experimental Evaluation of 79 and 300 GHz Radar Performance in Fire Environments
Authors: Bystrov Aleksandr , Daniel Liam , Hoare Edward , Norouzian Fatemeh , Cherniakov Mikhail , Gashinova Marina
Published: January 2021

This paper presents an experimental study of the propagation of mm-wave/low-THz signals in the frequency ranges of 79 and 300 GHz through fire. Radar performance was investigated in various real scenarios, including fire with strong flame, dense smoke and water vapour. A stereo video camera and a LIDAR were used as a comparison with other common types of sensors. The ability of radars to enable the visibility of objects in fire environments was proven. In all scenarios, the radar signal attenuation was measured, and in the case of steam was compared with theoretical calculations. The analysis of the experimental results allows us to conclude that there are good prospects for millimetre wave and Low Terahertz radar in the field of firefighting imaging equipment.
Silver Enhances Hematite Nanoparticles Based Ethanol Sensor Response and Selectivity at Room Temperature
Authors: Garcia-Osorio Daniel , Hidalgo-Falla Pilar , Peres Henrique E. M. , Gonçalves Josue M. , Araki Koiti , Garcia-Segura Sergi , Picasso Gino
Published: January 2021

Gas sensors are fundamental for continuous online monitoring of volatile organic compounds. Gas sensors based on semiconductor materials have demonstrated to be highly competitive, but are generally made of expensive materials and operate at high temperatures, which are drawbacks of these technologies. Herein is described a novel ethanol sensor for room temperature (25 °C) measurements based on hematite (α‑Fe2O3)/silver nanoparticles. The AgNPs were shown to increase the oxide semiconductor charge carrier density, but especially to enhance the ethanol adsorption rate boosting the selectivity and sensitivity, thus allowing quantification of ethanol vapor in 2–35 mg L−1 range with an excellent linear relationship. In addition, the α-Fe2O3/Ag 3.0 wt% nanocomposite is cheap, and easy to make and process, imparting high perspectives for real applications in breath analyzers and/or sensors in food and beverage industries. This work contributes to the advance of gas sensing at ambient temperature as a competitive alternative for quantification of conventional volatile organic compounds.
Highly Sensitive Textile-Based Capacitive Pressure Sensors Using PVDF-HFP/Ionic Liquid Composite Films
Authors: Keum Kyobin , Heo Jae Sang , Eom Jimi , Lee Keon Woo , Park Sung Kyu , Kim Yong-Hoon
Published: January 2021

Textile-based pressure sensors have garnered considerable interest in electronic textiles due to their diverse applications, including human–machine interface and healthcare monitoring systems. We studied a textile-based capacitive pressure sensor array using a poly(vinylidene fluoride)-co-hexafluoropropylene (PVDF-HFP)/ionic liquid (IL) composite film. By constructing a capacitor structure with Ag-plated conductive fiber electrodes that are embedded in fabrics, a capacitive pressure sensor showing high sensitivity, good operation stability, and a wide sensing range could be created. By optimizing the PVDF-HFP:IL ratio (6.5:3.5), the fabricated textile pressure sensors showed sensitivity of 9.51 kPa−1 and 0.69 kPa−1 in the pressure ranges of 0–20 kPa and 20–100 kPa, respectively. The pressure-dependent capacitance variation in our device was explained based on the change in the contact-area formed between the multi-filament fiber electrodes and the PVDF-HFP/IL film. To demonstrate the applicability and scalability of the sensor device, a 3 × 3 pressure sensor array was fabricated. Due to its matrix-type array structure and capacitive sensing mechanism, multi-point detection was possible, and the different positions and the weights of the objects could be identified.
Silicone Oil Decreases Biofilm Formation in a Capacitance-Based Automatic Urine Measurement System
Authors: Slettengren Martin , Linnros Martin , van der Linden Jan
Published: January 2021

Capacitance-based automatic urine measurement is a validated technique already implemented in clinical practice. However, albuminuria and free hemoglobinuria cause progressive biofilm buildup on the capacitance sensors of the urinometers. The aim of this experimental study is to investigate the influence of albumin and free hemoglobin on the capacitance signal of an automatic urinometer with and without the addition of silicone oil. A solution of Ringer’s acetate mixed with either albumin or free hemoglobin was run through an automatic urinometer containing either a water-soluble capsule with silicone oil or not. In total, around 500 capacitance measurements were retrieved from the albumin and free hemoglobin group, respectively. The mean increase in capacitance in the albumin 3 g/L group was 257 ± 100 pF without and 105 ± 30 pF with silicone oil, respectively, during 24 h. After ten hours of recording, differences between the two albumin groups reached statistical significance. For the free hemoglobin groups (0.01 g/L), the mean increase in capacitance was 190 ± 170 pF with silicone oil, and 324 ± 80 pF without, with a significant difference between the groups after 20 h and onwards. Coating of the capacitance measurement membrane of the automatic urinometer by albumin or free hemoglobin was significantly decreased by silicone oil, prolonging the functionality of the device.
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
Authors: Churcher Andrew , Ullah Rehmat , Ahmad Jawad , ur Rehman Sadaqat , Masood Fawad , Gogate Mandar , Alqahtani Fehaid , Nour Boubakr , Buchanan William J.
Published: January 2021

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.
Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis
Authors: Li Xudong , Zheng Jianhua , Li Mingtao , Ma Wenzhen , Hu Yang
Published: January 2021

In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.
A Novel Tri-Axial Piezoelectric MEMS Accelerometer with Folded Beams
Authors: Liu Yan , Hu Bohao , Cai Yao , Liu Wenjuan , Tovstopyat Alexander , Sun Chengliang
Published: January 2021

Microelectromechanical (MEMS) piezoelectric accelerometers are diversely used in consumer electronics and handheld devices due to their low power consumption as well as simple reading circuit and good dynamic performance. In this paper, a tri-axial piezoelectric accelerometer with folded beams is presented. The four beam suspensions are located at two sides of the mass aligned with edges of the mass, and the thickness of the beams is the same as the thickness of the mass block. In order to realize the multi-axis detection, a total of 16 sensing elements are distributed at the end of the folded beams. The structural deformations, stress distribution, and output characteristics due to the acceleration in x-, y-, and z-axis directions are theoretically analyzed and simulated. The proposed accelerometer is fabricated by MEMS processes to form Mo/AlN/ScAlN/Mo piezoelectric stacks as the sensing layer. Experiments show that the charge sensitivity along the x-, y-, and z-axes could reach up to ~1.07 pC/g, ~0.66 pC/g, and ~3.35 pC/g. The new structure can provide inspiration for the design of tri-axial piezoelectric accelerometers with great sensitivity and linearity.
High-Linearity High-Resolution Time-of-Flight Linear-Array Digital Image Sensor Using Time-Domain Feedback
Authors: Kim Juyeong , Yasutomi Keita , Kagawa Keiichiro , Kawahito Shoji
Published: January 2021

This paper presents a high-linearity high-resolution time-of-flight (ToF) linear-array digital image sensor using a time-domain negative feedback technique. A coarse ToF measurement loop uses a 5-bit digital-to-time converter (DTC) and a delayed gating-pulse generator for time-domain feedback to find the zero of the difference between ToF and the digital estimate of the gating-pulse delay while maintaining a constant operating point of the analog readout circuits. A fine ToF measurement uses a delta-sigma modulation (DSM) loop using the time-domain feedback with a bit-stream signal form. Because of the self-contained property of the DSM for low distortion and noise exploited by the oversampling signal processing, the proposed technique provides high-linearity and high-range resolution in the fine ToF measurement. A prototype ToF sensor of 16.8 × 16.8 μm2 two-tap pixels and fabricated in a 0.11 μm (1P4M) CMOS image sensors (CIS) process achieves +0.9%/−0.47% maximum nonlinearity error and a resolution of 0.24 mm (median) for the measurement range of 0–1.05 m. The ToF sensor produces an 11-bit fully digital output with a ToF measurement time of 22.4 ms.
A Hierarchical Routing Graph for Supporting Mobile Devices in Industrial Wireless Sensor Networks
Authors: Kim Sangdae , Kim Cheonyong , Cho Hyunchong , Jung Kwansoo
Published: January 2021

As many industrial applications require real-time and reliability communication, a variety of routing graph construction schemes were proposed to satisfy the requirements in Industrial Wireless Sensor Networks (IWSNs). Each device transmits packet through a route which is designated based on the graph. However, as existing studies consider a network consists of static devices only, they cannot cope with the network changes by movement of mobile devices considered important in the recent industrial environment. Thus, the communication requirements cannot be guaranteed because the existing path is broken by the varying network topology. The communication failure could cause critical problems such as malfunctioning equipment. The problem is caused repeatedly by continuous movement of mobile devices, even if a new graph is reconstructed for responding the changed topology. To support mobile devices exploited in various industrial environments, we propose a Hierarchical Routing Graph Construction (HRGC). The HRGC is consisted of two phases for hierarchical graph construction: In first phase, a robust graph called skeleton graph consisting only of static devices is constructed. The skeleton graph is not affected by network topology changes and does not suffer from packet loss. In second phase, the mobile devices are grafted into the skeleton graph for seamless communication. Through the grafting process, the routes are established in advance for mobile device to communicate with nearby static devices in anywhere. The simulation results show that the packet delivery ratio is improved when the graph is constructed through the HRGC.
A New Algebraic Solution for Acoustic Emission Source Localization without Premeasuring Wave Velocity
Authors: Zhou Zilong , Lan Riyan , Rui Yichao , Dong Longjun , Cai Xin
Published: January 2021

The technique of acoustic emission (AE) source localization is critical for studying material failure mechanism and predicting the position of potential hazards. Most existing positioning methods heavily depend on the premeasured wave velocity and are not suitable for complex engineering practices where the wave velocity changes dynamically. To reduce the influence of measurement error of wave velocity on location accuracy, this paper proposes a new algebraic solution for AE source localization without premeasuring wave velocity. In this method, the nonlinear TDOA equations are established and linearized by introducing two intermediate variables. Then, by minimizing the sum of squared residuals of the linear TDOA equations with respect to the AE source coordinate and two intermediate variables separately, the optimal algebraic solution of the AE source coordinate in the least squares sense is obtained. A pencil-lead breaks experiment is performed to validate the positioning effectiveness of the proposed method. The results show that the new method improves the positioning accuracy by more than 40% compared with two pre-existing methods, and the minimum positioning accuracy of the proposed method can reach 1.12 mm. Moreover, simulation tests are conducted to further verify the location performance of the proposed method under different TDOA errors and the number of sensors.
Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction
Authors: Chen Yun-Hsuan , Sawan Mohamad
Published: January 2021

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.
A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering
Authors: Sharma Sparsh , Ahmed Suhaib , Naseem Mohd , Alnumay Waleed S. , Singh Saurabh , Cho Gi Hwan
Published: January 2021

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.
High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach
Authors: Ma Wei , Qian Sean
Published: January 2021

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.
Ultra-Stable Molecular Sensors by Sub-Micron Referencing and Why They Should Be Interrogated by Optical Diffraction—Part I. The Concept of a Spatial Affinity Lock-in Amplifier
Authors: Frutiger Andreas , Fattinger Christof , Vörös János
Published: January 2021

Label-free optical biosensors, such as surface plasmon resonance, are sensitive and well-established for the characterization of molecular interactions. Yet, these sensors require stabilization and constant conditions even with the use of reference channels. In this paper, we use tools from signal processing to show why these sensors are so cross-sensitive and how to overcome their drawbacks. In particular, we conceptualize the spatial affinity lock-in as a universal design principle for sensitive molecular sensors even in the complete absence of stabilization. The spatial affinity lock-in is analogous to the well-established time-domain lock-in. Instead of a time-domain signal, it modulates the binding signal at a high spatial frequency to separate it from the low spatial frequency environmental noise in Fourier space. In addition, direct sampling of the locked-in sensor’s response in Fourier space enabled by diffraction has advantages over sampling in real space as done by surface plasmon resonance sensors using the distributed reference principle. This paper and part II hint at the potential of spatially locked-in diffractometric biosensors to surpass state-of-the-art temperature-stabilized refractometric biosensors. Even simple, miniaturized and non-stabilized sensors might achieve the performance of bulky lab instruments. This may enable new applications in label-free analysis of molecular binding and point-of-care diagnostics.