bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of the Multi-Location External Workload Profile in the Most Common Movements in Basketball
Authors: Gómez-Carmona Carlos D. , Feu Sebastián , Pino-Ortega José , Ibáñez Sergio J.
Published: May 2021

The present study analyzed the multi-location external workload profile in basketball players using a previously validated test battery and compared the demands among anatomical locations. A basketball team comprising 13 semi-professional male players was evaluated in five tests (abilities/skills/tests): (a) aerobic, linear movement, 30-15 IFT; (b) lactic anaerobic, acceleration and deceleration, 16.25 m RSA (c) alactic anaerobic, curvilinear movement, 6.75 m arc (d) elastic, jump, Abalakov test (e) physical-conditioning, small-sided game, 10’ 3 vs.3 10 × 15 m. PlayerLoadRT was evaluated at six anatomical locations simultaneously (interscapular line, lumbar region, knees and ankles) by six WIMU PROTM inertial devices attached to the player using an ad hoc integral suit. Statistical analysis was composed of an ANOVA of repeated measures and partial eta squared effect sizes. Significant differences among anatomical locations were found in all tests with higher values in the location nearer to ground contact (p < 0.01). However, differences between lower limb locations were only found in curvilinear movements, with a higher workload in the outside leg (p < 0.01). Additionally, high between-subject variability was found in team players, especially at lower limb locations. In conclusion, multi-location evaluation in sports movements will make it possible to establish an individual external workload profile and design specific strategies for training and injury prevention programs.
A Surface Plasmon Resonance Plastic Optical Fiber Biosensor for the Detection of Pancreatic Amylase in Surgically-Placed Drain Effluent
Authors: Pasquardini Laura , Cennamo Nunzio , Malleo Giuseppe , Vanzetti Lia , Zeni Luigi , Bonamini Deborah , Salvia Roberto , Bassi Claudio , Bossi Alessandra Maria
Published: May 2021

Postoperative pancreatic fistula (POPF), the major driver of morbidity and mortality following pancreatectomy, is caused by an abnormal communication between the pancreatic ductal epithelium and another epithelial surface containing pancreas-derived, enzyme-rich fluid. There is a strong correlation between the amylase content in surgically-placed drains early in the postoperative course and the development of POPF. A simple and cheap method to determine the amylase content from the drain effluent has been eagerly advocated. Here, we developed an amylase optical biosensor, based on a surface plasmon resonance (SPR) plastic optical fiber (POF), metallized with a 60 nm layer of gold and interrogated with white light. The sensor was made specific by coupling it with an anti-amylase antibody. Each surface derivatization step was optimized and studied by XPS, contact angle, and fluorescence. The POF-biosensor was tested for its response to amylase in diluted drain effluents. The volume of sample required was 50 µL and the measurement time was 8 min. The POF-biosensor showed selectivity for amylase, a calibration curve log-linear in the range of 0.8–25.8 U/L and a limit of detection (LOD) of ~0.5 U/L. In preliminary tests, the POF-biosensor allowed for the measurement of the amylase content of diluted surgically-placed drain effluents with an accuracy of >92% with respect to the gold standard. The POF-biosensor allows for reliable measurement and could be implemented to allow for a rapid bedside assessment of amylase value in drains following pancreatectomy.
Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases
Authors: Bowman Thomas , Gervasoni Elisa , Arienti Chiara , Lazzarini Stefano , Negrini Stefano , Crea Simona , Cattaneo Davide , Carrozza Maria
Published: May 2021

Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson’s n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions.
Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features
Authors: Ćwian Krzysztof , Nowicki Michał R. , Wietrzykowski Jan , Skrzypczyński Piotr
Published: May 2021

Although visual SLAM (simultaneous localization and mapping) methods obtain very accurate results using optimization of residual errors defined with respect to the matching features, the SLAM systems based on 3-D laser (LiDAR) data commonly employ variants of the iterative closest points algorithm and raw point clouds as the map representation. However, it is possible to extract from point clouds features that are more spatially extended and more meaningful than points: line segments and/or planar patches. In particular, such features provide a natural way to represent human-made environments, such as urban and mixed indoor/outdoor scenes. In this paper, we perform an analysis of the advantages of a LiDAR-based SLAM that employs high-level geometric features in large-scale urban environments. We present a new approach to the LiDAR SLAM that uses planar patches and line segments for map representation and employs factor graph optimization typical to state-of-the-art visual SLAM for the final map and trajectory optimization. The new map structure and matching of features make it possible to implement in our system an efficient loop closure method, which exploits learned descriptors for place recognition and factor graph for optimization. With these improvements, the overall software structure is based on the proven LOAM concept to ensure real-time operation. A series of experiments were performed to compare the proposed solution to the open-source LOAM, considering different approaches to loop closure computation. The results are compared using standard metrics of trajectory accuracy, focusing on the final quality of the estimated trajectory and the consistency of the environment map. With some well-discussed reservations, our results demonstrate the gains due to using the high-level features in the full-optimization approach in the large-scale LiDAR SLAM.
A Scaling Law for SPAD Pixel Miniaturization
Authors: Morimoto Kazuhiro , Charbon Edoardo
Published: May 2021

The growing demands on compact and high-definition single-photon avalanche diode (SPAD) arrays have motivated researchers to explore pixel miniaturization techniques to achieve sub-10 μm pixels. The scaling of the SPAD pixel size has an impact on key performance metrics, and it is, thereby, critical to conduct a systematic analysis of the underlying tradeoffs in miniaturized SPADs. On the basis of the general assumptions and constraints for layout geometry, we performed an analytical formulation of the scaling laws for the key metrics, such as the fill factor (FF), photon detection probability (PDP), dark count rate (DCR), correlated noise, and power consumption. Numerical calculations for various parameter sets indicated that some of the metrics, such as the DCR and power consumption, were improved by pixel miniaturization, whereas other metrics, such as the FF and PDP, were degraded. Comparison of the theoretically estimated scaling trends with previously published experimental results suggests that the scaling law analysis is in good agreement with practical SPAD devices. Our scaling law analysis could provide a useful tool to conduct a detailed performance comparison between various process, device, and layout configurations, which is essential for pushing the limit of SPAD pixel miniaturization toward sub-2 μm-pitch SPADs.
Design of Metamaterial Based Efficient Wireless Power Transfer System Utilizing Antenna Topology for Wearable Devices
Authors: Shaw Tarakeswar , Samanta Gopinath , Mitra Debasis , Mandal Bappaditya , Augustine Robin
Published: May 2021

In this article, the design of an efficient wireless power transfer (WPT) system using antenna-based topology for the applications in wearable devices is presented. To implement the wearable WPT system, a simple circular patch antenna is initially designed on a flexible felt substrate by placing over a three-layer human tissue model to utilize as a receiving element. Meanwhile, a high gain circular patch antenna is also designed in the air environment to use as a transmitter for designing the wearable WPT link. The proposed WPT system is built to operate at the industrial, scientific and medical (ISM) band of 2.40–2.48 GHz. In addition, to improve the power transfer efficiency (PTE) of the system, a metamaterial (MTM) slab built with an array combination of 3 × 3 unit cells has been employed. Further, the performance analysis of the MTM integrated system is performed on the different portions of the human body like hand, head and torso model to present the versatile applicability of the system. Moreover, analysis of the specific absorption rate (SAR) has been performed in different wearable scenarios to show the effect on the human body under the standard recommended limits. Regarding the practical application issues, the performance stability analysis of the proposed system due to the misalignment and flexibility of the Rx antenna is executed. Finally, the prototypes are fabricated and experimental validation is performed on several realistic wearable platforms like three-layer pork tissue slab, human hand, head and body. The simulated and measured result confirms that by using the MTM slab, a significant amount of the PTE improvement is obtained from the proposed system.
Dimensionally Stable Anode Based Sensor for Urea Determination via Linear Sweep Voltammetry
Authors: Vasconcellos Maria de Lourdes S. , Silva Luiz Ricardo G. , Lee Chung-Seop , Fajardo Ana Sofia , Garcia-Segura Sergi , Ribeiro Josimar
Published: May 2021

Urea is an added value chemical with wide applications in the industry and agriculture. The release of urea waste to the environment affects ecosystem health despite its low toxicity. Online monitoring of urea for industrial applications and environmental health is an unaddressed challenge. Electroanalytical techniques can be a smart integrated solution for online monitoring if sensors can overcome the major barrier associated with long-term stability. Mixed metal oxides have shown excellent stability in environmental conditions with long lasting operational lives. However, these materials have been barely explored for sensing applications. This work presents a proof of concept that demonstrates the applicability of an indirect electroanalytical quantification method of urea. The use of Ti/RuO2-TiO2-SnO2 dimensional stable anode (DSA®) can provide accurate and sensitive quantification of urea in aqueous samples exploiting the excellent catalytic properties of DSA® on the electrogeneration of active chlorine species. The cathodic reduction of accumulated HClO/ClO− from anodic electrogeneration presented a direct relationship with urea concentration. This novel method can allow urea quantification with a competitive LOD of 1.83 × 10−6 mol L−1 within a linear range of 6.66 × 10−6 to 3.33 × 10−4 mol L−1 of urea concentration.
XCycles Backprojection Acoustic Super-Resolution
Authors: Almasri Feras , Vandendriessche Jurgen , Segers Laurent , da Silva Bruno , Braeken An , Steenhaut Kris , Touhafi Abdellah , Debeir Olivier
Published: May 2021

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.
BER and Channel Capacity Performance of an FSO Communication System over Atmospheric Turbulence with Different Types of Noise
Authors: Xu Zixuan , Xu Guanjun , Zheng Zhengqi
Published: May 2021

The propagation performance of a free-space optical (FSO) communication system in an atmospheric environment is restricted and degraded due to the influence of atmospheric turbulence. In this paper, both the lognormal and Gamma–Gamma channel models are employed to characterize this turbulence under weak-to-strong conditions. In addition, the average bit error rate and average channel capacity of an FSO communication system under the influence of background noise, thermal noise and quantum noise (resulting from the environment, the device, manual operation, etc.) are considered. Moreover, the comparison of system performance under different turbulence conditions and various noises are conducted. Simulation results reveal that thermal noise has a dominant effect on the FSO system. In addition, both the channel parameters and the system parameters have a significant influence on the performance of an FSO communication system.
Non-Contact Respiration Measurement Method Based on RGB Camera Using 1D Convolutional Neural Networks
Authors: Hwang Hyeon-Sang , Lee Eui-Chul
Published: May 2021

Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would reduce discomfort and help detect and prevent fatal diseases. Therefore, we propose a respiration measurement method using a learning-based region-of-interest detector and a clustering-based respiration pixel estimation technique. The proposed method consists of a model for classifying whether a pixel conveys respiration information based on its variance and a method for classifying pixels with clear breathing components using the symmetry of the respiration signals. The proposed method was evaluated with the data of 14 men and women acquired in an actual environment, and it was confirmed that the average error was within approximately 0.1 bpm. In addition, a Bland–Altman analysis confirmed that the measurement result had no error bias, and regression analysis confirmed that the correlation of the results with the reference is high. The proposed method, designed to be inexpensive, fast, and robust to noise, is potentially suitable for practical use in clinical scenarios.
Novel Weight-Based Approach for Soil Moisture Content Estimation via Synthetic Aperture Radar, Multispectral and Thermal Infrared Data Fusion
Authors: Yahia Oualid , Guida Raffaella , Iervolino Pasquale
Published: May 2021

Though current remote sensing technologies, especially synthetic aperture radars (SARs), exhibit huge potential for soil moisture content (SMC) retrievals, such technologies also present several performance disadvantages. This study explored the merits of proposing a novel data fusion methodology (partly decision level and partly feature level) for SMC estimation. Initially, individual estimations were derived from three distinct methods: the inversion of an Empirically Adapted Integral Equation Model (EA-IEM) applied to SAR data, the Perpendicular Drought Index (PDI), and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat-8 data. Subsequently, three feature level fusions were performed to produce three different novel salient feature combinations where said features were extracted from each of the previously mentioned methods to be the input of an artificial neural network (ANN). The latter underwent a modification of its performance function, more specifically from absolute error to root mean square error (RMSE). Eventually, all SMC estimations, including the feature level fusion estimation, were fused at the decision level through a novel weight-based estimation. The performance of the proposed system was analysed and validated by measurements collected from three study areas, an agricultural field in Blackwell farms, Guildford, United Kingdom, and two different agricultural fields in Sidi Rached, Tipasa, Algeria. Those measurements contained SMC levels and surface roughness profiles. The proposed SMC estimation system yielded stronger correlations and lower RMSE values than any of the considered SMC estimation methods in the order of 0.38%, 1.4%, and 1.09% for the Blackwell farms, Sidi Rached 1, and Sidi Rached 2 datasets, respectively.
Blind Interleaver Parameters Estimation Using Kolmogorov–Smirnov Test
Authors: Wee Seungwoo , Choi Changryoul , Jeong Jechang
Published: May 2021

The use of error-correcting codes (ECCs) is essential for designing reliable digital communication systems. Usually, most systems correct errors under cooperative environments. If receivers do not know interleaver parameters, they must first find out them to decode. In this paper, a blind interleaver parameters estimation method is proposed using the Kolmogorov–Smirnov (K–S) test. We exploit the fact that rank distributions of square matrices of linear codes differ from those of random sequences owing to the linear dependence of linear codes. We use the K–S test to make decision whether two groups are extracted from the same distribution. The K–S test value is used as a measure to find the most different rank distribution for the blind interleaver parameters estimation. In addition to control false alarm rates, multinomial distribution is used to calculate the probability that the most different rank distribution will occur. By exploiting those, we can estimate the interleaver period with relatively low complexity. Experimental results show that the proposed algorithm outperforms previous methods regardless of the bit error rate.
Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications
Authors: Boursianis Achilles D. , Papadopoulou Maria S. , Koulouridis Stavros , Rocca Paolo , Georgiadis Apostolos , Tentzeris Manos M. , Goudos Sotirios K.
Published: May 2021

A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shaped patch antenna is used. The receiving module (antenna) of the rectenna system is optimized in terms of its reflection coefficient to match the RF-to-DC rectifier. The final geometry of the proposed antenna is derived by the application of the Moth Search Algorithm and a commercial electromagnetic solver. The impedance matching network of the proposed system is obtained based on a three-step process, including the minimization of the reflection coefficient versus frequency, as well as the minimization of the reflection coefficient variations and the maximization of the DC output voltage versus RF input power. The proposed RF-to-DC rectifier is designed based on the Greinacher topology. The designed rectenna is fabricated on a single layer of FR-4 substrate. Measured results show that our proposed rectenna can harvest RF energy from outdoor (ambient and dedicated) sources with an efficiency of greater than 52%.
Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation
Authors: Tao Shengxin , Jiang Yun , Cao Simin , Wu Chao , Ma Zeqi
Published: May 2021

The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model.
Implementation of Lightweight Convolutional Neural Networks via Layer-Wise Differentiable Compression
Authors: Diao Huabin , Hao Yuexing , Xu Shaoyun , Li Gongyan
Published: May 2021

Convolutional neural networks (CNNs) have achieved significant breakthroughs in various domains, such as natural language processing (NLP), and computer vision. However, performance improvement is often accompanied by large model size and computation costs, which make it not suitable for resource-constrained devices. Consequently, there is an urgent need to compress CNNs, so as to reduce model size and computation costs. This paper proposes a layer-wise differentiable compression (LWDC) algorithm for compressing CNNs structurally. A differentiable selection operator OS is embedded in the model to compress and train the model simultaneously by gradient descent in one go. Instead of pruning parameters from redundant operators by contrast to most of the existing methods, our method replaces the original bulky operators with more lightweight ones directly, which only needs to specify the set of lightweight operators and the regularization factor in advance, rather than the compression rate for each layer. The compressed model produced by our method is generic and does not need any special hardware/software support. Experimental results on CIFAR-10, CIFAR-100 and ImageNet have demonstrated the effectiveness of our method. LWDC obtains more significant compression than state-of-the-art methods in most cases, while having lower performance degradation. The impact of lightweight operators and regularization factor on the compression rate and accuracy also is evaluated.
SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams
Authors: Abdrakhmanova Madina , Kuzdeuov Askat , Jarju Sheikh , Khassanov Yerbolat , Lewis Michael , Varol Huseyin Atakan
Published: May 2021

We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human–computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases. Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (∼3.8 TB). For technical validation, we demonstrate two baseline examples. The first baseline shows classification by gender, utilizing different combinations of the three data streams in both clean and noisy environments. The second example consists of thermal-to-visual facial image translation, as an instance of domain transfer.
Recent Progress on Semiconductor-Interface Facing Clinical Biosensing
Authors: Zhang Mingrui , Adkins Mitchell , Wang Zhe
Published: May 2021

Semiconductor (SC)-based field-effect transistors (FETs) have been demonstrated as amazing enhancer gadgets due to their delicate interface towards surface adsorption. This leads to their application as sensors and biosensors. Additionally, the semiconductor material has enormous recognizable fixation extends, high affectability, high consistency for solid detecting, and the ability to coordinate with other microfluidic gatherings. This review focused on current progress on the semiconductor-interfaced FET biosensor through the fundamental interface structure of sensor design, including inorganic semiconductor/aqueous interface, photoelectrochemical interface, nano-optical interface, and metal-assisted interface. The works that also point to a further advancement for the trademark properties mentioned have been reviewed here. The emergence of research on the organic semiconductor interface, integrated biosensors with Complementary metal–oxide–semiconductor (CMOS)-compatible, metal-organic frameworks, has accelerated the practical application of biosensors. Through a solid request for research along with sensor application, it will have the option to move forward the innovative sensor with the extraordinary semiconductor interface structure.
BIFNOM: Binary-Coded Features on Normal Maps
Authors: Miyashita Leo , Nakamura Akihiro , Odagawa Takuto , Ishikawa Masatoshi
Published: May 2021

We propose a novel method for detecting features on normal maps and describing binary features, called BIFNOM, which is three-dimensionally rotation invariant and detects and matches interest points at high speed regardless of whether a target is textured or textureless and rigid or non-rigid. Conventional methods of detecting features on normal maps can also be applied to textureless targets, in contrast with features on luminance images; however, they cannot deal with three-dimensional rotation between each pair of corresponding interest points due to the definition of orientation, or they have difficulty achieving fast detection and matching due to a heavy-weight descriptor. We addressed these issues by introducing a three dimensional local coordinate system and converting a normal vector to a binary code, and achieved more than 750fps real-time feature detection and matching. Furthermore, we present an extended descriptor and criteria for real-time tracking, and evaluate the performance with both simulation and actual system.
Noise Floor Reduction in Frequency Delta-Sigma Modulation Microphone Sensors
Authors: Maezawa Koichi , Mori Masayuki , Andoh Hiroya
Published: May 2021

Frequency delta-sigma modulator (FDSM) employing a variable frequency oscillator is a novel replacement of the classical delta-sigma modulators. This is advantageous for application to sensors, because an ADC can be intrinsically integrated with the sensors. We have already proposed to use this technique to various sensors. However, the signal-to-noise ratio was significantly degraded by noise floor, in the previous papers. In this paper, we have investigated the origin of the noise floor in the FDSM microphone sensors as a promising example. It was demonstrated that improving the phase noise of the oscillator can drastically reduce the noise floor. For this reduction we improved the Q-factor of the cavity resonator, and the design of the oscillator circuit. With these improvements, the phase noise, and, hence, the noise floor, were improved by approximately 40 dB. In addition, we obtained an SNR of 57 dB for 114 dBSPL sound input with 96 kHz bandwidth, which corresponds to the dynamic range of 87 dB for maximum 140 dBSPL. A much larger dynamic range of around 120 dB is expected by increasing the sampling rate and decreasing the Al diaphragm thickness. These results also indicate the promise of the FDSM to varieties of physical sensors.
CosG: A Graph-Based Contrastive Learning Method for Fact Verification
Authors: Chen Chonghao , Zheng Jianming , Chen Honghui
Published: May 2021

Fact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous methods can’t well distinguish semantic-similar claims and evidences with distinct authenticity labels. In addition, the performances of graph-based models are limited by the over-smoothing problem of graph neural networks. To this end, we propose a graph-based contrastive learning method for fact verification abbreviated as CosG, which introduces a contrastive label-supervised task to help the encoder learn the discriminative representations for different-label claim-evidence pairs, as well as an unsupervised graph-contrast task, to alleviate the unique node features loss in the graph propagation. We conduct experiments on FEVER, a large benchmark dataset for fact verification. Experimental results show the superiority of our proposal against comparable baselines, especially for the claims that need multiple-evidences to verify. In addition, CosG presents better model robustness on the low-resource scenario.
A People-Counting and Speed-Estimation System Using Wi-Fi Signals
Authors: Tian Liping , Chen Liangqin , Xu Zhimeng , Chen Zhizhang (David)
Published: May 2021

Counting the number of people and estimating their walking speeds are essential in crowd control and flow. In this work, we propose a system that uses prevalent Wi-Fi signals to identify the number of people entering and leaving a room through a door. It selects the best subcarrier of Wi-Fi signals and applies the Hampel filter to remove outlier information first. Then, it employs a double threshold method to determine the start and end times of entering or leaving. Afterward, it compares the detected signals with the precollected database using the dynamic time-warping algorithm and determines the number of people. It uses a variance threshold method to identify the states of entering or leaving. It also employs a nonlinear fitting approach to calculate the walking speeds. The experiments show that, in a large empty laboratory, the accuracy rates in determining the number of people are 100% for one person, 81% for two persons, and 95% for three persons. In a small office, the accuracy rates for detecting the number of people are 98% for one or two persons, 82% for three persons, 93% for four, and 75% for five persons. For the walking speed estimation, the accuracy rate for a speed error of less than 0.2410 m/s is 75% for a single person.
WS-RCNN: Learning to Score Proposals for Weakly Supervised Instance Segmentation
Authors: Ou Jia-Rong , Deng Shu-Le , Yu Jin-Gang
Published: May 2021

Weakly supervised instance segmentation (WSIS) provides a promising way to address instance segmentation in the absence of sufficient labeled data for training. Previous attempts on WSIS usually follow a proposal-based paradigm, critical to which is the proposal scoring strategy. These works mostly rely on certain heuristic strategies for proposal scoring, which largely hampers the sustainable advances concerning WSIS. Towards this end, this paper introduces a novel framework for weakly supervised instance segmentation, called Weakly Supervised R-CNN (WS-RCNN). The basic idea is to deploy a deep network to learn to score proposals, under the special setting of weak supervision. To tackle the key issue of acquiring proposal-level pseudo labels for model training, we propose a so-called Attention-Guided Pseudo Labeling (AGPL) strategy, which leverages the local maximal (peaks) in image-level attention maps and the spatial relationship among peaks and proposals to infer pseudo labels. We also suggest a novel training loss, called Entropic OpenSet Loss, to handle background proposals more effectively so as to further improve the robustness. Comprehensive experiments on two standard benchmarking datasets demonstrate that the proposed WS-RCNN can outperform the state-of-the-art by a large margin, with an improvement of 11.6% on PASCAL VOC 2012 and 10.7% on MS COCO 2014 in terms of mAP50, which indicates that learning-based proposal scoring and the proposed WS-RCNN framework might be a promising way towards WSIS.
Parallel Fish School Tracking Based on Multiple Appearance Feature Detection
Authors: Wang Zhitao , Xia Chunlei , Lee Jangmyung
Published: May 2021

A parallel fish school tracking based on multiple-feature fish detection has been proposed in this paper to obtain accurate movement trajectories of a large number of zebrafish. Zebrafish are widely adapted in many fields as an excellent model organism. Due to the non-rigid body, similar appearance, rapid transition, and frequent occlusions, vision-based behavioral monitoring is still a challenge. A multiple appearance feature based fish detection scheme was developed by examining the fish head and center of the fish body based on shape index features. The proposed fish detection has the advantage of locating individual fishes from occlusions and estimating their motion states, which could ensure the stability of tracking multiple fishes. Moreover, a parallel tracking scheme was developed based on the SORT framework by fusing multiple features of individual fish and motion states. The proposed method was evaluated in seven video clips taken under different conditions. These videos contained various scales of fishes, different arena sizes, different frame rates, and various image resolutions. The maximal number of tracking targets reached 100 individuals. The correct tracking ratio was 98.60% to 99.86%, and the correct identification ratio ranged from 97.73% to 100%. The experimental results demonstrate that the proposed method is superior to advanced deep learning-based methods. Nevertheless, this method has real-time tracking ability, which can acquire online trajectory data without high-cost hardware configuration.
Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms
Authors: Rozhon Jan , Rezac Filip , Jalowiczor Jakub , Behan Ladislav
Published: May 2021

With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.
Blind Deblurring Based on Sigmoid Function
Authors: Sun Shuhan , Duan Lizhen , Xu Zhiyong , Zhang Jianlin
Published: May 2021

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.
A Smart Walker for People with Both Visual and Mobility Impairment
Authors: Mostofa Nafisa , Feltner Christopher , Fullin Kelly , Guilbe Jonathan , Zehtabian Sharare , Bacanlı Salih Safa , Bölöni Ladislau , Turgut Damla
Published: May 2021

In recent years, significant work has been done in technological enhancements for mobility aids (smart walkers). However, most of this work does not cover the millions of people who have both mobility and visual impairments. In this paper, we design and study four different configurations of smart walkers that are specifically targeted to the needs of this population. We investigated different sensing technologies (ultrasound-based, infrared depth cameras and RGB cameras with advanced computer vision processing), software configurations, and user interface modalities (haptic and audio signal based). Our experiments show that there are several engineering choices that can be used in the design of such assistive devices. Furthermore, we found that a holistic evaluation of the end-to-end performance of the systems is necessary, as the quality of the user interface often has a larger impact on the overall performance than increases in the sensing accuracy beyond a certain point.
ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature
Authors: Gu Bo , Liu Jianxun , Xiong Huiyuan , Li Tongtong , Pan Yuelong
Published: May 2021

In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.
Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus
Authors: Akib Tarik Bin Abdul , Mou Samia Ferdous , Rahman Md. Motiur , Rana Md. Masud , Islam Md. Rabiul , Mehedi Ibrahim M. , Mahmud M. A. Parvez , Kouzani Abbas Z.
Published: May 2021

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.
Study of a Long-Gauge FBG Strain Sensor with Enhanced Sensitivity and Its Application in Structural Monitoring
Authors: Yang Jing , Hou Peng , Yang Caiqian , Yang Ning
Published: May 2021

A long-gauge fiber Bragg grating (FBG) strain sensor with enhanced strain sensitivity is proposed, which is encapsulated with two T-shaped metal blocks. Its fabrication method is described briefly, and the strain sensitivity can be flexibly adjusted through changing its packaging method. A series of experiments are carried out to study the packaging and its sensing properties. The experimental results show that the strain and temperature sensitivity coefficient of the sensor are three times larger than the common FBG sensors. The linearity coefficients of the FBG sensor are larger than 0.999, and the relative error of the repeatability of all sensor samples is less than 1%. Through the stability test on the actual bridge, it is revealed that the long-term stability of the sensor is excellent, and the maximum error is less than 1.5%. In addition, the proposed FBG strain sensors are used to conduct a shear strengthening experiment on a reinforced concrete (RC) beam to verify its working performance. The experimental results show that the strain change and crack propagation of the RC beam are well monitored by the sensors during the loading process.