bluuebunny/crossref_metadata_2025 huggingface dataset:
Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns
Authors: Mousavi Parastoo
Published: July 2021

With the prominent role of government debt in economic growth in recent decades, one would expect that government debt alongside economic growth to be a risk factor priced in the time series of stock returns. In this paper, this idea is investigated by applying a nonparametric model, namely, a local-linear kernel smoother with the aim of forecasting long-term stock returns where the model and smoothing parameters are chosen by cross-validation. While a wide range of predictive variables are examined, we find that our newly introduced debt-by-price ratio and the third to fourth quarter economic growth are robust predictors of stock returns, beating the well-known predictive variables in the literature by a significant difference. The combination of these two covariates can explain almost 30% variation of stock returns at a one-year horizon. This is very crucial considering the difficulty in capturing even a small proportion of movements in stock returns.
Metagenomic Insight into the Community Structure of Maize-Rhizosphere Bacteria as Predicted by Different Environmental Factors and Their Functioning within Plant Proximity
Authors: Akinola Saheed , Ayangbenro Ayansina , Babalola Olubukola
Published: July 2021

The rhizosphere microbiota contributes immensely to nutrient sequestration, productivity and plant growth. Several studies have suggested that environmental factors and high nutrient composition of plant’s rhizosphere influence the structural diversity of proximal microorganisms. To verify this assertion, we compare the functional diversity of bacteria in maize rhizosphere and bulk soils using shotgun metagenomics and assess the influence of measured environmental variables on bacterial diversity. Our study showed that the bacterial community associated with each sampling site was distinct, with high community members shared among the samples. The bacterial community was dominated by Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Bacteroidetes and Verrucomicrobia. In comparison, genera such as Gemmatimonas, Streptomyces, Conexibacter, Burkholderia, Bacillus, Gemmata, Mesorhizobium, Pseudomonas and Micromonospora were significantly (p ≤ 0.05) high in the rhizosphere soils compared to bulk soils. Diversity indices showed that the bacterial composition was significantly different across the sites. The forward selection of environmental factors predicted N-NO3 (p = 0.019) as the most influential factor controlling the variation in the bacterial community structure, while other factors such as pH (p = 1.00) and sulfate (p = 0.50) contributed insignificantly to the community structure of bacteria. Functional assessment of the sampling sites, considering important pathways viz. nitrogen metabolism, phosphorus metabolism, stress responses, and iron acquisition and metabolism could be represented as Ls > Rs > Rc > Lc. This revealed that functional hits are higher in the rhizosphere soil than their controls. Taken together, inference from this study shows that the sampling sites are hotspots for biotechnologically important microorganisms.
The Bucherer–Bergs Multicomponent Synthesis of Hydantoins—Excellence in Simplicity
Authors: Kalník Martin , Gabko Peter , Bella Maroš , Koóš Miroslav
Published: July 2021

Hydantoins and their hybrids with other molecules represent a very important group of heterocycles because they exhibit diverse biological and pharmacological activities in medicinal and agrochemical applications. They also serve as key precursors in the chemical or enzymatic synthesis of significant nonnatural α-amino acids and their conjugates with medical potential. This review provides a comprehensive treatment of the synthesis of hydantoins via the Bucherer–Bergs reaction including the Hoyer modification but limited to free carbonyl compounds or carbonyl compounds protected as acetals (ketals) and cyanohydrins used as starting reaction components. In this respect, the Bucherer–Bergs reaction provides an efficient and simple method in the synthesis of important natural products as well as for the preparation of new organic compounds applicable as potential therapeutics. The scope and limitations, as well as a comparison with some other methods for preparing hydantoins, are also discussed.
Theoretical Considerations of Photonic Crystal Fiber with All Uniform-Sized Air Holes for Liquid Sensing
Authors: Maidi Abdul Mu’iz , Abas Pg Emeroylarffion , Petra Pg Iskandar , Kaijage Shubi , Zou Nianyu , Begum Feroza
Published: July 2021

A novel liquid-infiltrated photonic crystal fiber model applicable in liquid sensing for different test liquids—water, ethanol and benzene—has been proposed. One core hole and three air hole rings have been designed and a full vector finite element method has been used for numerical investigation to give the best results in terms of relative sensitivity, confinement loss, power fraction, dispersion, effective area, nonlinear coefficient, numerical aperture and V-Parameter. Specially, the assessed relative sensitivities of the proposed fiber with water, ethanol and benzene are 94.26%, 95.82% and 99.58%, respectively, and low confinement losses of 1.52 × 10−11 dB/m with water, 1.21 × 10−12 dB/m with ethanol and 6.01 × 10−16 dB/m with benzene, at 1.0 μm operating wavelength. This novel PCF design is considered simple and can be easily fabricated for practical use, and the assessed waveguide properties has determined the potential applicability in real liquid sensing applications.
Design and Analysis of Trench-Assisted Low-Bending-Loss Large-Mode-Field-Area Multi-Core Fiber with an Air Hole
Authors: Jia Renjie , Lian Yudong , Wang Yulei , Lu Zhiwei
Published: July 2021

In this paper, a trench-assisted low-bending-loss large-mode-field-area multi-core fiber with air hole is proposed, which can achieve dual-mode transmission. The influence of structural parameters on fiber performance is analyzed systematically, and the structure of the trench, with a lower refractive index than the cladding, is also analyzed and optimized. By adjusting the structural parameters, the effective mode field area of the fundamental mode can reach 2003.24 um2 at 1550 nm, and when the bending radius is 1 cm, the bending loss is 2.57 × 10−3 dB/m. The practical implementation of the proposed fiber is feasible using existing fabrication technology and is applicable to the transmission of large-capacity optical communication systems and high-power lasers.
Measurement of Focal Length and Radius of Curvature for Spherical Lenses and Mirrors by Using Digital-Grating Moiré Effect
Authors: Han Chien-Yuan , Lo Wen-Tai , Chen Kun-Huang , Lee Ju-Yi , Yeh Chien-Hung , Chen Jing-Heng
Published: July 2021

This paper proposes the use of digital-grating moiré effect for measuring the focal lengths and radius of curvatures of biconvex and biconcave spherical simple lenses and spherical mirrors. Based on Fresnel diffraction, the equation for the electric field of propagated light passing through the test samples was derived. Through digital image post processing, the recorded intensity on an observation screen was superimposed on a digital grating to generate a moiré pattern. On substituting the slant angle of the moiré pattern into the derived equation, the focal lengths and radius of curvatures could be determined. The experimental results successfully demonstrated the feasibility of the proposed method; the percent errors for focal length and radius of curvature measurement were less than 0.5%. The measurement uncertainty was analyzed and the correctness of the derived equation was confirmed through simulation. Because of the use of digital image post processing, the proposed method has advantages such as a simple set up, easy operation, high stability, high accuracy, and low cost. Thus, the method has considerable potential in relevant application.
Invasive and Non-Invasive Observation of Occluded Fast Transient Events: Computational Tools
Authors: Ng Soon Hock , Anand Vijayakumar , Katkus Tomas , Juodkazis Saulius
Published: July 2021

Industrial processes involving thermal plasma such as cutting, welding, laser machining with ultra-short laser pulses (nonequilibrium conditions), high temperature melting using electrical discharge or ion-beams, etc., generate non-repeatable fast transient events which can reveal valuable information about the processes. In such industrial environments containing high temperature and radiation, it is often difficult to install conventional lens-based imaging windows and components to observe such events. In this study, we compare imaging requirements and performances with invasive and non-invasive modes when a fast transient event is occluded by a metal window consisting of numerous holes punched through it. Simulation studies were carried out for metal windows with different types of patterns, reconstructed for both invasive and non-invasive modes and compared. Sparks were generated by rapid electrical discharge behind a metal window consisting of thousands of punched through-holes and the time sequence was recorded using a high-speed camera. The time sequence was reconstructed with and without the spatio-spectral point spread functions and compared. Commented MATLAB codes are provided for both invasive and non-invasive modes of reconstruction.
Morpho-Physio-Biochemical and Molecular Responses of Maize Hybrids to Salinity and Waterlogging during Stress and Recovery Phase
Authors: Mahmood Umer , Hussain Saddam , Hussain Sadam , Ali Basharat , Ashraf Umair , Zamir Shahid , Al-Robai Sami Asir , Alzahrani Fatima Omari , Hano Christophe , El-Esawi Mohamed A.
Published: July 2021

Maize is one of the most economically important cereal crops worldwide. Salinity coupled with waterlogging is a major challenge for successful crop production. Understanding the underlying mechanisms and impacts of individual and combined salinity and waterlogging stress on the morpho-physio-biochemical and molecular responses and oxidative metabolism of maize during stress and recovery periods is essential. The present study was carried out to assess the response of four hybrid maize cultivars viz. DK-6142, FH-1231, FH-949, and MALKA-2016 under individual and combined salinity and waterlogging conditions. The treatments comprised the control (no stress), NaCl (salinity with 10 dSm−1), WL (waterlogged conditions with 3 cm flooding), and NaCl + WL (combined salinity and waterlogging stress). The data regarding morpho-physiological attributes were collected at 22 days after sowing (DAS; stress phase) and 30 DAS (recovery phase). The results revealed that both stresses, either individually or in combination, substantially reduced the root-shoot length, root-shoot fresh and dry weights, leaf width, and the number of leaves per plant as well as the leaf chlorophyll (Chl) and carotenoids contents; however, the inhibitory effects were more severe in combined stresses than for individual stress factors in many cultivars. Both individual and combined stress conditions enhanced hydrogen peroxide (H2O2) accumulation, whereas the antioxidant enzyme activities, i.e., superoxide dismutase (SOD), peroxidase (POD) catalase (CAT), and ascorbate peroxidase (APX), remained higher under stress conditions compared to the control. The expression levels of antioxidant genes (CAT and POD) were also upregulated under stress conditions. All of the cultivars recovered better from individual stresses than combined stress conditions; however, the hybrid DK-6142 performed better than the other maize hybrids under stress conditions and showed faster recovery.
CFD Performance Analysis of a Dish-Stirling System for Microgeneration
Authors: Papurello Davide , Bertino Davide , Santarelli Massimo
Published: July 2021

The sustainable transition towards renewable energy sources has become increasingly important nowadays. In this work, a microgeneration energy system was investigated. The system is composed of a solar concentrator system coupled with an alpha-type Stirling engine. The aim was to maximize the production of electrical energy. By imposing a mean value of the direct irradiance on the system, the model developed can obtain the temperature of the fluid contained inside the Stirling engine. The heat exchanger of the microgenerator system was analyzed, focusing on the solar coupling with the engine, with a multiphysical approach (COMSOL v5.3). A real Stirling cycle was implemented using two methods for comparison: the first-order empirical Beale equation and the Schmidt isothermal method. Results demonstrated that a concentrator of 2.4 m in diameter can generate, starting from 800 W/m2 of mean irradiance, a value of electrical energy equal to 0.99 kWe.
A Review of the Extraction and Closed-Loop Spray Drying-Assisted Micro-Encapsulation of Algal Lutein for Functional Food Delivery
Authors: Lei Zexin , Langrish Timothy
Published: July 2021

In this study, the physical and chemical properties and bioavailability of lutein have been summarized, with the novelty of this work being the review of lutein from production to extraction, through to preservation and drying, in order to deliver a functional food ingredient. The potential health functions of lutein have been introduced in detail. By comparing algae and marigold flowers, the advantages of algae extraction technology have been discussed. In this article, we have introduced the use of closed-loop spray drying technology to microencapsulate lutein to improve its stability and solubility. Microencapsulation of unstable substances by spray drying is a potentially useful direction that is worth exploring further.
CFD-Based Structural Optimization of Rotor Cage for High-Efficiency Rotor Classifier
Authors: Mou Xinliang , Jia Fangchao , Fang Ying , Chen Chuanwen
Published: July 2021

Due to the uneven materials dispersion and high dust concentration in industrial applications of turbo air classifiers, a high-efficiency rotor classifier was designed. Numerical simulations by ANSYS-Fluent 19.0, the effects of rotor cage shape, the number of blades, and the blade profile on the inner flow field, as well as classification performance, were investigated. The simulation results indicated a significant improvement in flow field distribution near the classification surface with the conical rotor cage. Furthermore, there was an average reduction of 10.1% in cut size, as well as a 23.6% increase in classification accuracy. When the number of blades was 36, the flow field distribution between the blades was relatively uniform and a smaller cut size was obtained at a higher classification accuracy. A streamline blade with 52° as the inlet installation angle effectively reduced the impact of the airflow on the blade and eliminated the inertia anti-vortex between blades. The cut size reduction was 4.7–6.3%, with a basically unchanged classification accuracy. The material classification experimental results were in agreement with the simulated results. The discrete phase model (DPM) could well-predict the cut sizes and classification accuracy, but it could not present the fishhook effect. The present study provides theoretical guidance for the structural optimization of an air classifier with a rotor cage.
Quantitative Assessment of Organic and Inorganic Contaminants in Charcoal
Authors: Jelonek Zbigniew , Fabiańska Monika , Jelonek Iwona
Published: July 2021

Thirty-one batches of commercial charcoal from various regions of Poland and Germany were tested for the presence of 20 toxic elements and polycyclic aromatic hydrocarbons (PAHs) using gas chromatography-mass spectrometry (GC-MS). Elements that are toxic to living organisms were determined using atomic absorption spectroscopy (AAS). They were classified as elements representing a very high degree of hazard (As, Cd, Cu, Hg, and Pb), high degree of hazard (Zn, Ba, Cr, Mn, and Mo), moderate degree of hazard (Co, Ni, Sn, and Te), and a low degree of hazard for living organisms and the environment (Ag, Bi, Ce, Se, Sr, and Zr). In regard to the most toxic elements, the highest concentration in the whole tested material was recorded for Cu. In addition, considerable amounts of Ba, Mn, and Sr, i.e., elements representing a high or moderate degree of hazard, were found in the tested charcoals. Moreover, all charcoals contained a wide range of PAHs, from naphthalene to benzo(ghi)perylene, with concentrations in a range between 12.55 and 3554.11 ng/g charcoal. In total, 25 unsubstituted PAHs were identified in the charcoal extracts. PAHs distributions were dominated by five-ring PAHs. The results indicate high carcinogenicity with ∑PAHcarc/∑PAHtot close to 1, as well as high TEQ and MEQ values. Thus, prolonged exposure to charcoal and charcoal dust might cause serious health problems. This applies to employees actively involved in the production and transport of charcoal and, to a lesser extent, users of this fuel.
A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
Authors: Wang Sen , Chen Xiaohe , Ding Guanyu , Li Yongyao , Xu Wenchang , Zhao Qinglei , Gong Yan , Song Qi
Published: July 2021

This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.
Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks
Authors: Alhussain Alanoud , Kurdi Heba , Altoaimy Lina
Published: July 2021

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.
Representation Learning for Fine-Grained Change Detection
Authors: O’Mahony Niall , Campbell Sean , Krpalkova Lenka , Carvalho Anderson , Walsh Joseph , Riordan Daniel
Published: July 2021

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.
Measurement Noise Model for Depth Camera-Based People Tracking
Authors: Korkalo Otto , Takala Tapio
Published: July 2021

Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement noise model for depth camera-based people tracking. In our tracking solution, we utilise the so-called plan-view approach, where the 3D measurements are transformed to the floor plane, and the tracking problem is solved in 2D. We directly model the measurement noise in the plan-view domain, and the errors that originate from the imaging process and the geometric transformations of the 3D data are combined. We also present a method for directly defining the noise models from the observations. Together with our depth sensor network self-calibration routine, the approach allows fast and practical deployment of depth-based people tracking systems.
Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation
Authors: Lo Justin , Nithiyanantham Saiee , Cardinell Jillian , Young Dylan , Cho Sherwin , Kirubarajan Abirami , Wagner Matthias W. , Azma Roxana , Miller Steven , Seed Mike , Ertl-Wagner Birgit , Sussman Dafna
Published: July 2021

Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more accurate and user independent. We propose a deep learning (DL) framework for 2D fetal MRI segmentation using a Cross Attention Squeeze Excitation Network (CASE-Net) for research and clinical applications. CASE-Net is an end-to-end segmentation architecture with relevant modules that are evidence based. The goal of CASE-Net is to emphasize localization of contextual information that is relevant in biomedical segmentation, by combining attention mechanisms with squeeze-and-excitation (SE) blocks. This is a retrospective study with 34 patients. Our experiments have shown that our proposed CASE-Net achieved the highest segmentation Dice score of 87.36%, outperforming other competitive segmentation architectures.
Decaying Post-Seismic Deformation Observed on the Korean Peninsula Following the 2011 Tohoku-Oki Earthquake
Authors: Sohn Dong-Hyo , Choi Byung-Kyu , Kim Sungshil , Park Sun-Cheon , Lee Won-Jin , Park Pil-Ho
Published: July 2021

We investigated decaying post-seismic deformation observed on the Korean Peninsula associated with the 2011 Mw 9.0 Tohoku-Oki earthquake using Global Navigation Satellite System (GNSS). The GNSS velocity vectors were estimated in five periods from 2005 to 2019. A co-seismic offset of the Korean Peninsula caused by the 2011 earthquake was inversely proportional to epicentral distances. According to the temporal variations of two components (magnitude and direction) of the GNSS velocity vector with the epicentral distance, the difference between the eastern and western regions for the two components becomes smaller over time. For approximately nine years after the 2011 event, the direction for the crustal movement in South Korea showed a recovery pattern returning to the pre-earthquake motion. In addition, the recovery patterns of the crustal movement were observed differently with the regional geologic structure (e.g., the crustal thickness) and each period. Our estimates of the decay in post-seismic deformation of the Korean Peninsula suggest that post-seismic relaxation will be complete within 5–20 years after the 2011 earthquake. The results suggest that the crustal movement on the Korean Peninsula is gradually recovering to its pre-earthquake motion.
BBNet: A Novel Convolutional Neural Network Structure in Edge-Cloud Collaborative Inference
Authors: Zhou Hongbo , Zhang Weiwei , Wang Chengwei , Ma Xin , Yu Haoran
Published: July 2021

Edge-cloud collaborative inference can significantly reduce the delay of a deep neural network (DNN) by dividing the network between mobile edge and cloud. However, the in-layer data size of DNN is usually larger than the original data, so the communication time to send intermediate data to the cloud will also increase end-to-end latency. To cope with these challenges, this paper proposes a novel convolutional neural network structure—BBNet—that accelerates collaborative inference from two levels: (1) through channel-pruning: reducing the number of calculations and parameters of the original network; (2) through compressing the feature map at the split point to further reduce the size of the data transmitted. In addition, This paper implemented the BBNet structure based on NVIDIA Nano and the server. Compared with the original network, BBNet’s FLOPs and parameter achieve up to 5.67× and 11.57× on the compression rate, respectively. In the best case, the feature compression layer can reach a bit-compression rate of 512×. Compared with the better bandwidth conditions, BBNet has a more obvious inference delay when the network conditions are poor. For example, when the upload bandwidth is only 20 kb/s, the end-to-end latency of BBNet is increased by 38.89× compared with the cloud-only approach.
Palm Temperature Differences after Static and Dynamic Load on High Bar
Authors: Šibanc Karmen , Čuk Ivan , Pajek Maja , Pušnik Igor
Published: July 2021

Thermal imaging is used in various fields of industry and research to measure temperature and its possible differences. Since there is a lack of research and literature on palm temperatures and prevention of blisters on hands, our question was how palm temperature differs in human hands after different loads (Hang and Swing in Hang) for 30 s on a high bar. Thirty-eight students from the Faculty of Sport at the University of Ljubljana were measured with a high-quality thermal imaging camera. Palm temperatures were measured before the load was applied, immediately after and every 30 s for a period of 5 min after the load. Each hand was divided into nine different regions of interest (ROIs). Mean (XA), standard deviation (SD), maximum and minimum, and number of pixels were calculated. We found that there was no difference between the left and right hand. The temperature right after the load was applied decreased significantly for both loads and then increased above the level before the load was applied. After the static load, the temperature reached a constant higher level after 3 min. After the dynamic load, the temperatures continued to increase throughout the measurement period. Further investigation is needed to determine the time period in which the hand temperature reaches the temperature before the load is applied.
A Comprehensive Design of Six-Axis Force/Moment Sensor
Authors: Ahmad Anton Royanto , Wynn Terrence , Lin Chyi-Yeu
Published: July 2021

Strain gage type six-axis force/moment (F/M) sensors have been largely studied and implemented in industrial applications by using an external data acquisition board (DAQ). The use of external DAQs will ill-affect accuracy and crosstalk due to the possibility of voltage drop through the wire length. The most recent research incorporated DAQ within a relatively small F/M sensor, but only for sensors of the capacitance and optical types. This research establishes the integration of a high-efficiency DAQ on six-axis F/M sensor with a revolutionary arrangement of 32 strain gages. The updated structural design was optimized using the sequential quadratic programming method and validated using Finite Element Analysis (FEA). A new, integrated DAQ system was designed, tested, and compared to commercial DAQ systems. The proposed six-axis F/M sensor was examined with the calibrated jig. The results show that the measurement error and crosstalk have been significantly reduced to 1.15% and 0.68%, respectively, the best published combination at this moment.
LFM: A Lightweight LCD Algorithm Based on Feature Matching between Similar Key Frames
Authors: Zhu Zuojun , Xu Xiangrong , Liu Xuefei , Jiang Yanglin
Published: July 2021

Loop Closure Detection (LCD) is an important technique to improve the accuracy of Simultaneous Localization and Mapping (SLAM). In this paper, we propose an LCD algorithm based on binary classification for feature matching between similar images with deep learning, which greatly improves the accuracy of LCD algorithm. Meanwhile, a novel lightweight convolutional neural network (CNN) is proposed and applied to the target detection task of key frames. On this basis, the key frames are binary classified according to their labels. Finally, similar frames are input into the improved lightweight feature matching network based on Transformer to judge whether the current position is loop closure. The experimental results show that, compared with the traditional method, LFM-LCD has higher accuracy and recall rate in the LCD task of indoor SLAM while ensuring the number of parameters and calculation amount. The research in this paper provides a new direction for LCD of robotic SLAM, which will be further improved with the development of deep learning.
A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies
Authors: Hussain Tazar , Nugent Chris , Moore Adrian , Liu Jun , Beard Alfie
Published: July 2021

The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based analytics module, where an ensemble-based data analytic approach, called Calibrated Random Forest (CRF), is proposed to extract useful information while quantifying and managing the uncertainty associated with predictions, by using confidence scores. Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner. The proposed CRF method is firstly evaluated and demonstrated on a HAR scenario in a Smart Home environment in case study I and is further evaluated and illustrated with a remote health monitoring scenario for a diabetes use case in case study II. The experimental results indicate that using the framework’s raw sensor data can be converted into meaningful actions despite several sources of uncertainty. The comparison of the proposed framework to existing approaches highlights the key metrics that make decision making more rational and transparent.
Instance Sequence Queries for Video Instance Segmentation with Transformers
Authors: Xu Zhujun , Vivet Damien
Published: July 2021

Existing methods for video instance segmentation (VIS) mostly rely on two strategies: (1) building a sophisticated post-processing to associate frame level segmentation results and (2) modeling a video clip as a 3D spatial-temporal volume with a limit of resolution and length due to memory constraints. In this work, we propose a frame-to-frame method built upon transformers. We use a set of queries, called instance sequence queries (ISQs), to drive the transformer decoder and produce results at each frame. Each query represents one instance in a video clip. By extending the bipartite matching loss to two frames, our training procedure enables the decoder to adjust the ISQs during inference. The consistency of instances is preserved by the corresponding order between query slots and network outputs. As a result, there is no need for complex data association. On TITAN Xp GPU, our method achieves a competitive 34.4% mAP at 33.5 FPS with ResNet-50 and 35.5% mAP at 26.6 FPS with ResNet-101 on the Youtube-VIS dataset.
Flexible Multiscale Pore Hybrid Self-Powered Sensor for Heart Sound Detection
Authors: Liu Boyan , Han Liuyang , Pan Lyuming , Li Hongzheng , Zhao Jingjing , Dong Ying , Wang Xiaohao
Published: July 2021

This research introduces an idea of producing both nanoscale and microscale pores in piezoelectric material, and combining the properties of the molecular β-phase dipoles in ferroelectric material and the space charge dipoles in order to increase the sensitivity of the sensor and modulate the response frequency bandwidth of the material. Based on this idea, a bi-nano-micro porous dual ferro-electret hybrid self-powered flexible heart sound detection sensor is proposed. Acid etching and electrospinning were the fabrication processes used to produce a piezoelectric film with nanoscale and microscale pores, and corona poling was used for air ionization to produce an electret effect. In this paper, the manufacturing process of the sensor is introduced, and the effect of the porous structure and corona poling on improving the performance of the sensor is discussed. The proposed flexible sensor has an equivalent piezoelectric coefficient d33 of 3312 pC/N, which is much larger than the piezoelectric coefficient of the common piezoelectric materials. Experiments were carried out to verify the function of the flexible sensor together with the SS17L heart sound sensor (BIOPAC, Goleta, CA, USA) as a reference. The test results demonstrated its practical application for wearable heart sound detection and the potential for heart disease detection. The proposed flexible sensor in this paper could realize batch production, and has the advantages of flexibility, low production cost and a short processing time compared with the existing heart sound detection sensors.
IoT-Enabled Smart Cities: Evolution and Outlook
Authors: Bauer Martin , Sanchez Luis , Song JaeSeung
Published: July 2021

For the last decade the Smart City concept has been under development, fostered by the growing urbanization of the world’s population and the need to handle the challenges that such a scenario raises. During this time many Smart City projects have been executed–some as proof-of-concept, but a growing number resulting in permanent, production-level deployments, improving the operation of the city and the quality of life of its citizens. Thus, Smart Cities are still a highly relevant paradigm which needs further development before it reaches its full potential and provides robust and resilient solutions. In this paper, the focus is set on the Internet of Things (IoT) as an enabling technology for the Smart City. In this sense, the paper reviews the current landscape of IoT-enabled Smart Cities, surveying relevant experiences and city initiatives that have embedded IoT within their city services and how they have generated an impact. The paper discusses the key technologies that have been developed and how they are contributing to the realization of the Smart City. Moreover, it presents some challenges that remain open ahead of us and which are the initiatives and technologies that are under development to tackle them.
An Electro-Oculogram Based Vision System for Grasp Assistive Devices—A Proof of Concept Study
Authors: Roy Rinku , Mahadevappa Manjunatha , Nazarpour Kianoush
Published: July 2021

Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple ‘signature’ extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients.
A Reversible Low Frequency Alternating Current Nerve Conduction Block Applied to Mammalian Autonomic Nerves
Authors: Muzquiz M. Ivette , Mintch Landan , Horn M. Ryne , Alhawwash Awadh , Bashirullah Rizwan , Carr Michael , Schild John H. , Yoshida Ken
Published: July 2021

Electrical stimulation can be used to modulate activity within the nervous system in one of two modes: (1) Activation, where activity is added to the neural signalling pathways, or (2) Block, where activity in the nerve is reduced or eliminated. In principle, electrical nerve conduction block has many attractive properties compared to pharmaceutical or surgical interventions. These include reversibility, localization, and tunability for nerve caliber and type. However, methods to effect electrical nerve block are relatively new. Some methods can have associated drawbacks, such as the need for large currents, the production of irreversible chemical byproducts, and onset responses. These can lead to irreversible nerve damage or undesirable neural responses. In the present study we describe a novel low frequency alternating current blocking waveform (LFACb) and measure its efficacy to reversibly block the bradycardic effect elicited by vagal stimulation in anaesthetised rat model. The waveform is a sinusoidal, zero mean(charge balanced), current waveform presented at 1 Hz to bipolar electrodes. Standard pulse stimulation was delivered through Pt-Black coated PtIr bipolar hook electrodes to evoke bradycardia. The conditioning LFAC waveform was presented either through a set of CorTec® bipolar cuff electrodes with Amplicoat® coated Pt contacts, or a second set of Pt Black coated PtIr hook electrodes. The conditioning electrodes were placed caudal to the pulse stimulation hook electrodes. Block of bradycardic effect was assessed by quantifying changes in heart rate during the stimulation stages of LFAC alone, LFAC-and-vagal, and vagal alone. The LFAC achieved 86.2±11.1% and 84.3±4.6% block using hook (N = 7) and cuff (N = 5) electrodes, respectively, at current levels less than 110 µAp (current to peak). The potential across the LFAC delivering electrodes were continuously monitored to verify that the blocking effect was immediately reversed upon discontinuing the LFAC. Thus, LFACb produced a high degree of nerve block at current levels comparable to pulse stimulation amplitudes to activate nerves, resulting in a measurable functional change of a biomarker in the mammalian nervous system.
Resonant Subwavelength and Nano-Scale Grating Structures for Biosensing Application: A Comparative Study
Authors: Abutoama Mohammad , Abuleil Marwan , Abdulhalim Ibrahim
Published: July 2021

Resonant-based sensors are attractive optical structures due to the easy detection of shifts in the resonance location in response to variations in the analyte refractive index (RI) in comparison to non-resonant-based sensors. In particular, due to the rapid progress of nanostructures fabrication methods, the manufacturing of subwavelength and nano-scale gratings in a large area and at a low cost has become possible. A comparative study is presented involving analysis and experimental work on several subwavelength and nanograting structures, highlighting their nano-scale features’ high potential in biosensing applications, namely: (i) Thin dielectric grating on top of thin metal film (TDGTMF), which can support the excitation of extended surface plasmons (ESPs), guided mode resonance, or leaky mode; (ii) reflecting grating for conventional ESP resonance (ESPR) and cavity modes (CMs) excitation; (iii) thick dielectric resonant subwavelength grating exhibiting guided mode resonance (GMR) without a waveguide layer. Among the unique features, we highlight the following: (a) Self-referenced operation obtained using the TDGTMF geometry; (b) multimodal operation, including ESPR, CMs, and surface-enhanced spectroscopy using reflecting nanograting; (c) phase detection as a more sensitive approach in all cases, except the case of reflecting grating where phase detection is less sensitive than intensity or wavelength detection. Additionally, intensity and phase detection modes were experimentally demonstrated using off-the-shelf grating-based optical compact discs as a low-cost sensors available for use in a large area. Several flexible designs are proposed for sensing in the visible and infrared spectral ranges based on the mentioned geometries. In addition, enhanced penetration depth is also proposed for sensing large entities such as cells and bacteria using the TDGTMF geometry.
High Accuracy Ultrasound Micro-Distance Measurements with PMUTs under Liquid Operation
Authors: Zamora Iván , Ledesma Eyglis , Uranga Arantxa , Barniol Núria
Published: July 2021

Ultrasonic systems driven by multi-frequency continuous waves (MFCW) have been used for range distance measurement, offering high accuracy in long and medium range distance estimation. However, the use of continuous waves in very short-distance measurements causes large errors due to multipath reflections. This paper presents a new strategy to estimate very short relative distances with high accuracy based on the use of multi-frequency pulsed waves (MFPW). The proposed strategy allows to avoid the multipath reflections that appear when continuous waves are used, and it improves the achieved accuracy compared to the original MFCW method. To validate it, an 80 µm square AlScN piezoelectric micromachined ultrasonic transducer (PMUT) was chosen as a transmitter while a hydrophone was utilized as a target and receiver, immersed in fluorinert (FC-70) as a propagation medium. Three independent and consecutive tone-burst signals were transmitted successively. The selected frequencies are f1 = 2.3962 MHz, f2 = 2.327 MHz and f3 = 2.1195 MHz, giving first and second-order resolutions of 6.88 and 0.79 µm/°, respectively. Experimental results show a ±6.2 μm measured range error in a range of 3.5 mm, and therefore it represents a good candidate for ultrasound micro-profilometer applications under liquid operation.
Development of a Novel Gas-Sensing Platform Based on a Network of Metal Oxide Nanowire Junctions Formed on a Suspended Carbon Nanomesh Backbone
Authors: Kim Taejung , Lee Seungwook , Cho Wootaek , Kwon Yeong Min , Baik Jeong Min , Shin Heungjoo
Published: July 2021

Junction networks made of longitudinally connected metal oxide nanowires (MOx NWs) have been widely utilized in resistive-type gas sensors because the potential barrier at the NW junctions leads to improved gas sensing performances. However, conventional MOx–NW-based gas sensors exhibit limited gas access to the sensing sites and reduced utilization of the entire NW surfaces because the NW networks are grown on the substrate. This study presents a novel gas sensor platform facilitating the formation of ZnO NW junction networks in a suspended architecture by growing ZnO NWs radially on a suspended carbon mesh backbone consisting of sub-micrometer-sized wires. NW networks were densely formed in the lateral and longitudinal directions of the ZnO NWs, forming additional longitudinally connected junctions in the voids of the carbon mesh. Therefore, target gases could efficiently access the sensing sites, including the junctions and the entire surface of the ZnO NWs. Thus, the present sensor, based on a suspended network of longitudinally connected NW junctions, exhibited enhanced gas response, sensitivity, and lower limit of detection compared to sensors consisting of only laterally connected NWs. In addition, complete sensor structures consisting of a suspended carbon mesh backbone and ZnO NWs could be prepared using only batch fabrication processes such as carbon microelectromechanical systems and hydrothermal synthesis, allowing cost-effective sensor fabrication.