bluuebunny/crossref_metadata_2025 huggingface dataset:
Surround-Net: A Multi-Branch Arbitrary-Oriented Detector for Remote Sensing
Authors: Luo Junkun , Hu Yimin , Li Jiadong
Published: April 2022

With the development of oriented object detection technology, especially in the area of remote sensing, significant progress has been made, and multiple excellent detection architectures have emerged. Oriented detection architectures can be broadly divided into five-parameter systems and eight-parameter systems that encounter the periodicity problem of angle regression and the discontinuous problem of vertex regression during training, respectively. Therefore, we propose a new multi-branch anchor-free one-stage model that can effectively alleviate the corner case when representing rotating objects, called Surround-Net. The creative contribution submitted in this paper mainly includes three aspects. Firstly, a multi-branch strategy is adopted to make the detector choose the best regression path adaptively for the discontinuity problem. Secondly, to address the inconsistency between classification and quality estimation (location), a modified high-dimensional Focal Loss and a new Surround IoU Loss are proposed to enhance the unity ability of the features. Thirdly, in the refined process after backbone feature extraction, a center vertex attention mechanism is adopted to deal with the environmental noise introduced in the remote sensing images. This type of auxiliary module is able to focus the model’s attention on the boundary of the bounding box. Finally, extensive experiments were carried out on the DOTA dataset, and the results demonstrate that Surround-Net can solve regression boundary problems and can achieve a more competitive performance (e.g., 75.875 mAP) than other anchor-free one-stage detectors with higher speeds.
Resolution Enhancement of SMAP Passive Soil Moisture Estimates
Authors: Brown Jordan P. , Long David G.
Published: April 2022

The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 km to 36 km shortly after the mission started to return data. To improve this, we employed the Scatterometer Image Reconstruction algorithm in its radiometer form (rSIR) to enhance the resolution of the radiometer brightness temperature measurements from which the soil moisture was derived. This paper compares the soil moisture estimates created from the rSIR-enhanced brightness temperatures with SMAP project radiometer L2_SM_SP and SMAP-Sentinel L2_SM_P products reported on 9 km and 3 km grids, respectively. We find that the difference of the rSIR-enhanced passive soil moisture product is generally within 0.020 cm3 cm−3 RMS of the 9 km SMAP radiometer L2_SM_SP and 0.045 cm3 cm−3 RMS of the 3 km SMAP-Sentinel L2_SM_P soil moisture products. The accuracy of the rSIR soil moisture can be improved by including better antenna pattern correction methods applied to the input TB measurements.
Downscaling Satellite-Based Estimates of Ocean Bottom Pressure for Tracking Deep Ocean Mass Transport
Authors: Delman Andrew , Landerer Felix
Published: April 2022

Gravimetry measurements from the GRACE and GRACE-Follow-On satellites provide observations of ocean bottom pressure (OBP), which can be differenced between basin boundaries to infer mass transport variability at a given level in the deep ocean. However, GRACE data products are limited in spatial resolution, and conflate signals from many depth levels along steep continental slopes. To improve estimates of OBP variability near steep bathymetry, ocean bottom pressure observations from a JPL GRACE mascon product are downscaled using an objective analysis procedure, with OBP covariance information from an ocean model with horizontal grid spacing of ∼18 km. In addition, a depth-based adjustment was applied to enhance correlations at similar depths. Downscaled GRACE OBP shows realistic representations of sharp OBP gradients across bathymetry contours and strong currents, albeit with biases in the shallow ocean. In validations at intraannual (3–12 month) timescales, correlations of downscaled GRACE data (with depth adjustment) and in situ bottom pressure recorder time series were improved in ∼79% of sites, compared to correlations that did not involve downscaled GRACE. Correlations tend to be higher at sites where the amplitude of the OBP signal is larger, while locations where surface eddy kinetic energy is high (e.g., Gulf Stream extension) are more likely to have no improvement from the downscaling procedure. The downscaling procedure also increases the amplitude (standard deviation) of OBP variability compared to the non-downscaled GRACE at most sites, resulting in standard deviations that are closer to in situ values. A comparison of hydrography-based transport from RAPID with estimates based on downscaled GRACE data suggests substantial improvement from the downscaling at intraannual timescales, though this improvement does not extend to longer interannual timescales. Possible efforts to improve the downscaling technique through process studies and analysis of alongtrack GRACE/GRACE-FO observations are discussed.
Short-Term and Long-Term Replenishment of Water Storage Influenced by Lockdown and Policy Measures in Drought-Prone Regions of Central India
Authors: Bhanja Soumendra N. , Sekhar M.
Published: April 2022

Central India faces a freshwater shortage due to its diverse terrain, sudden change in precipitation patterns and crystalline rock covered subsurface. Here, we investigate the patterns in terrestrial water storage anomaly (TWSA) over the last two decades, and also study the influence of the COVID-19 lockdown on TWSA in the drought-prone regions of central India, mostly covering the Vidarbha region of the Indian state of Maharashtra. The Vidarbha region is arguably the most drought-affected region in terms of farmer suicides due to crop failure. Our forecast data using multiple statistical approaches show a net TWSA rise in the order of 3.65 to 19.32 km3 in the study area in May 2020. A short-term rise in TWSA in April–May of 2020 is associated with lockdown influenced human activity reduction. A long-term rise in TWSA has been observed in the study region in recent years; the rising TWSA trend is not directly associated with precipitation patterns, rather it may be attributed to the implementation of water management policies.
An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets
Authors: Yang Yanqing , Bao Zhenxin , Wu Houfa , Wang Guoqing , Liu Cuishan , Wang Jie , Zhang Jianyun
Published: April 2022

Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from the SSM has long been the focus of most attention. Data assimilation methods are promising techniques for RZSM estimation, developing numerous assimilated reanalysis datasets, e.g., ERA5 and the latest Soil Moisture Active and Passive (SMAP) L4 SM product. However, data latency and large computation during data collecting and processing often inhibits further applications. This work proposes a rapid estimation scheme for estimating RZSM with short latency and small computations, based on the Exponential Filter (EF) method. The EF model with single parameter T was firstly calibrated and validated using the SSM and RZSM of ERA5 reanalysis dataset, obtaining the optimum parameter T map for each grid. Then, the fast-updating SMAP L3 SSM product together with the scale-matched optimum T were adopted as inputs into the EF model to retrieve RZSM estimation of each grid. Specifically, such estimation scheme was tested over the central and eastern agricultural areas of China, using a dense monitoring network of 796 SM observation sites, which contains various land uses, as well as meteorological and hydrological conditions. The calibrated optimum parameter T presented an increasing trend with good physical explanations. Furthermore, all the estimated RZSMs were found to have good performances on capturing the temporal-spatial variations of RZSM and well reflecting seasonal RZSM changes. Overall, such an estimation scheme was proven to be a desirable alternative for estimating RZSM over large agricultural areas.
Design of a Planner-Based Intervention to Facilitate Diet Behaviour Change in Type 2 Diabetes
Authors: Cradock Kevin A. , Quinlan Leo R. , Finucane Francis M. , Gainforth Heather L. , Ginis Kathleen A. Martin , Sanders Elizabeth B.-N. , ÓLaighin Gearóid
Published: April 2022

Diet behaviour is influenced by the interplay of the physical and social environment as well as macro-level and individual factors. In this study, we focus on diet behaviour at an individual level and describe the design of a behaviour change artefact to support diet behaviour change in persons with type 2 diabetes. This artefact was designed using a human-centred design methodology and the Behaviour Change Wheel framework. The designed artefact sought to support diet behaviour change through the addition of healthy foods and the reduction or removal of unhealthy foods over a 12-week period. These targeted behaviours were supported by the enabling behaviours of water consumption and mindfulness practice. The artefact created was a behaviour change planner in calendar format, that incorporated behaviour change techniques and which focused on changing diet behaviour gradually over the 12-week period. The behaviour change planner forms part of a behaviour change intervention which also includes a preparatory workbook exercise and one-to-one action planning sessions and can be customised for each participant.
Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach
Authors: Kumar Rajesh , Subbiah Geetha
Published: April 2022

Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augmented by reducing false negatives and false positives. In this paper, the performance of bagging and boosting machine learning models is enhanced by reducing misclassification. Shapley values of features are a true representation of the amount of contribution of features and help detect top features for any prediction by the ML model. Shapley values are transformed to probability scale to correlate with a prediction value of ML model and to detect top features for any prediction by a trained ML model. The trend of top features derived from false negative and false positive predictions by a trained ML model can be used for making inductive rules. In this work, the best performing ML model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. The best performing ML model is used to make effective inductive rules using waterfall plots based on the probability scale of features. This work helps improve cyber security scenarios by effective detection of false-negative zero-day malware.
Effects of Thermal Gradients in High-Temperature Ultrasonic Non-Destructive Tests
Authors: Slongo Juliano Scholz , Gund Jefferson , Passarin Thiago Alberto Rigo , Pipa Daniel Rodrigues , Ramos Júlio Endress , Arruda Lucia Valeria , Junior Flávio Neves
Published: April 2022

Ultrasonic inspection techniques and non-destructive tests are widely applied in evaluating products and equipment in the oil, petrochemical, steel, naval, and energy industries. These methods are well established and efficient for inspection procedures at room temperature. However, errors can be observed in the positioning and sizing of the flaws when such techniques are used during inspection procedures under high working temperatures. In such situations, the temperature gradients generate acoustic anisotropy and consequently distortion of the ultrasonic beams. Failure to consider such distortions in ultrasonic signals can result, in extreme situations, in mistaken decision making by inspectors and professionals responsible for guaranteeing product quality or the integrity of the evaluated equipment. In this scenario, this work presents a mathematical tool capable of mitigating positioning errors through the correction of focal laws. For the development of the tool, ray tracing concepts are used, as well as a model of heat propagation in solids and an experimentally defined linear approximation of dependence between sound speed and temperature. Using the focal law correction tool, the relative firing delays of the active elements are calculated considering the temperature gradients along the sonic path, and the results demonstrate a reduction of more than 68% in the error of flaw positioning.
Decision-Based Fusion for Vehicle Matching
Authors: Ghanem Sally , Kerekes Ryan A. , Tokola Ryan
Published: April 2022

In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines the extent of reliance between signatures generated from vehicle images to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. To that end, a dataset was collected that contains hundreds of thousands of side-view vehicle images under different illumination conditions and elevation angles. Experiments show that our approach could achieve better matching accuracy by taking into account the decisions made by a whole-vehicle or wheels-only matching network.
Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test
Authors: Hage Renaud , Buisseret Fabien , Houry Martin , Dierick Frédéric
Published: April 2022

Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.
A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network
Authors: Ghaleb Fuad A. , Saeed Faisal , Alkhammash Eman H. , Alghamdi Norah Saleh , Al-rimy Bander Ali Saleh
Published: April 2022

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air.
Design-Time Reliability Prediction Model for Component-Based Software Systems
Authors: Ali Awad , Bashir Mohammed Bakri , Hassan Alzubair , Hamza Rafik , Alqhtani Samar M. , Tawfeeg Tawfeeg Mohmmed , Yousif Adil
Published: April 2022

Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios.
The Resulting Effect of Flow Pulsations on Calibration Constant of Acoustic Path in Ultrasonic Flowmeters
Authors: Goltsman Anna , Saushin Ilya
Published: April 2022

The present paper compares, for the first time, the regimes of a pulsating turbulent flow in a smooth pipe in terms of 0.001 ≤ ω+ ≤ 0.0346 and 0.16 ≤ β ≤ 0.63 at Re ≈ 7000 with the uncertainty in estimating the flow rate by an ultrasonic flowmeter. It was revealed that the classification of pulsating flow regimes according to the dimensionless angular frequency ω+ does not have a direct relation with the K parameter equal to the ratio of the phase-average calibration constant in pulsating flow to the corresponding value in steady flow. The results of data processing showed that K depends on the relative amplitude of pulsations β and the position of the chord of the ultrasonic beam trajectory (L/R is distance L from the pipe center to the chord to the pipe radius R). In the coordinates β and L/R, there is a rather wide area where the uncertainty in flow rate estimation of pulsating flows should not exceed 0.5%. An increase in β or L/R leads to an increase in measurement uncertainty, which in the limiting case β, L/R → 1 can reach 5% or more. Favorable and unfavorable areas of the pipe section were identified when scanning pulsating flows and the effectiveness of using multi-path scanning schemes was estimated to reduce the resulting effect of flow pulsations on flow measurement uncertainty.
Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms
Authors: Kou Lei , Li Yang , Zhang Fangfang , Gong Xiaodong , Hu Yinghong , Yuan Quande , Ke Wende
Published: April 2022

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.
LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile
Authors: Ahmed Mohamed A. , Gallardo Jose Luis , Zuniga Marcos D. , Pedraza Manuel A. , Carvajal Gonzalo , Jara Nicolás , Carvajal Rodrigo
Published: April 2022

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.
LIPSHOK: LIARA Portable Smart Home Kit
Authors: Chapron Kévin , Thullier Florentin , Lapointe Patrick , Maître Julien , Bouchard Kévin , Gaboury Sébastien
Published: April 2022

Several smart home architecture implementations have been proposed in the last decade. These architectures are mostly deployed in laboratories or inside real habitations built for research purposes to enable the use of ambient intelligence using a wide variety of sensors, actuators and machine learning algorithms. However, the major issues for most related smart home architectures are their price, proprietary hardware requirements and the need for highly specialized personnel to deploy such systems. To tackle these challenges, lighter forms of smart home architectures known as smart homes in a box (SHiB) have been proposed. While SHiB remain an encouraging first step towards lightweight yet affordable solutions, they still suffer from few drawbacks. Indeed, some of these kits lack hardware support for some technologies, and others do not include enough sensors and actuators to cover most smart homes’ requirements. Thus, this paper introduces the LIARA Portable Smart Home Kit (LIPSHOK). It has been designed to provide an affordable SHiB solution that anyone is able to install in an existing home. Moreover, LIPSHOK is a generic kit that includes a total of four specialized sensor modules that were introduced independently, as our laboratory has been working on their development over the last few years. This paper first provides a summary of each of these modules and their respective benefits within a smart home context. Then, it mainly focus on the introduction of the LIPSHOK architecture that provides a framework to unify the use of the proposed sensors thanks to a common modular infrastructure capable of managing heterogeneous technologies. Finally, we compare our work to the existing SHiB kit solutions and outline that it offers a more affordable, extensible and scalable solution whose resources are distributed under an open-source license.
Towards Development of Specular Reflection Vascular Imaging
Authors: Burton Timothy , Saiko Gennadi , Douplik Alexandre
Published: April 2022

Specular reflection from tissue is typically considered as undesirable, and managed through device design. However, we believe that specular reflection is an untapped light-tissue interaction, which can be used for imaging subcutaneous blood flow. To illustrate the concept of subcutaneous blood flow visualization using specular reflection from the skin, we have developed a ray tracing for the neck and identified conditions under which useful data can be collected. Based on our model, we have developed a prototype Specular Reflection Vascular Imaging (SRVI) device and demonstrated its feasibility by imaging major neck vessels in a case study. The system consists of a video camera that captures a video from a target area illuminated by a rectangular LED source. We extracted the SRVI signal from 5 × 5 pixels areas (local SRVI signal). The correlations of local SRVIs to the SRVI extracted from all pixels in the target area do not appear to be randomly distributed, but rather form cohesive sub-regions with distinct boundaries. The obtained waveforms were compared with the ECG signal. Based on the time delays with respect to the ECG signal, as well as the waveforms themselves, the sub-regions can be attributed to the jugular vein and carotid artery. The proposed method, SRVI, has the potential to contribute to extraction of the diagnostic information that the jugular venous pulse can provide.
Research on UAV Robust Adaptive Positioning Algorithm Based on IMU/GNSS/VO in Complex Scenes
Authors: Dai Jun , Hao Xiangyang , Liu Songlin , Ren Zongbin
Published: April 2022

As an important component of autonomous intelligent systems, the research on autonomous positioning algorithms used by UAVs is of great significance. In order to resolve the problem whereby the GNSS signal is interrupted, and the visual sensor lacks sufficient feature points in complex scenes, which leads to difficulties in autonomous positioning, this paper proposes a new robust adaptive positioning algorithm that ensures the robustness and accuracy of autonomous navigation and positioning in UAVs. On the basis of the combined navigation model of vision/inertial navigation and satellite/inertial navigation, based on ESKF, a multi-source fusion model based on a federated Kalman filter is here established. Furthermore, a robust adaptive localization algorithm is proposed, which uses robust equivalent weights to estimate the sub-filters, and then uses the sub-filter state covariance to adaptively assign information sharing coefficients. After simulation experiments and dataset verification, the results show that the robust adaptive algorithm can effectively limit the impact of gross errors in observations and mathematical model deviations and can automatically update the information sharing coefficient online according to the sub-filter equivalent state covariance. Compared with the classical federated Kalman algorithm and the adaptive federated Kalman algorithm, our algorithm can meet the real-time requirements of navigation, and the accuracy of position, velocity, and attitude measurement is improved by 2–3 times. The robust adaptive localization algorithm proposed in this paper can effectively improve the reliability and accuracy of autonomous navigation systems in complex scenes. Moreover, the algorithm is general—it is not intended for a specific scene or a specific sensor combination– and is applicable to individual scenes with varied sensor combinations.
Robust and High-Performance Machine Vision System for Automatic Quality Inspection in Assembly Processes
Authors: Frustaci Fabio , Spagnolo Fanny , Perri Stefania , Cocorullo Giuseppe , Corsonello Pasquale
Published: April 2022

This paper addresses the problem of automatic quality inspection in assembly processes by discussing the design of a computer vision system realized by means of a heterogeneous multiprocessor system-on-chip. Such an approach was applied to a real catalytic converter assembly process, to detect planar, translational, and rotational shifts of the flanges welded on the central body. The manufacturing line imposed tight time and room constraints. The image processing method and the features extraction algorithm, based on a specific geometrical model, are described and validated. The algorithm was developed to be highly modular, thus suitable to be implemented by adopting a hardware–software co-design strategy. The most timing consuming computational steps were identified and then implemented by dedicated hardware accelerators. The entire system was implemented on a Xilinx Zynq heterogeneous system-on-chip by using a hardware–software (HW–SW) co-design approach. The system is able to detect planar and rotational shifts of welded flanges, with respect to the ideal positions, with a maximum error lower than one millimeter and one sexagesimal degree, respectively. Remarkably, the proposed HW–SW approach achieves a 23× speed-up compared to the pure software solution running on the Zynq embedded processing system. Therefore, it allows an in-line automatic quality inspection to be performed without affecting the production time of the existing manufacturing process.
An Affordable Streamflow Measurement Technique Based on Delay and Sum Beamforming
Authors: Passarella Giuseppe , Lay-Ekuakille Aimé , Djungha Okitadiowo John Peter , Masciale Rita , Brigida Silvia , Matarrese Raffaella , Portoghese Ivan , Isernia Tommaso , Blois Luciano
Published: April 2022

At the local scale, environmental parameters often require monitoring by means of affordable measuring techniques and technologies given they need to be frequently surveyed. Streamflow in riverbeds or in channels is a hydrological variable that needs to be monitored in order to keep the runoff regimes under control and somehow forecast floods, allowing prevention of damage for people and infrastructure. Moreover, measuring such a variable is always extremely important for the knowledge of the environmental status of connected aquatic ecosystems. This paper presents a new approach to assessing hydrodynamic features related to a given channel by means of a beamforming technique that was applied to video sensing. Different features have been estimated, namely the flow velocity, the temperature, and the riverbed movements. The applied beamforming technique works on a modified sum and delay method, also using the Multiple Signal Classification algorithm (MUSIC), by acting as Synthetic Aperture Radar (SAR) post-processing. The results are very interesting, especially compared to the on-site measured data and encourage the use of affordable video sensors located along the channel or river course for monitoring purposes. The paper also illustrates the use of beamforming measurements to be calibrated by means of conventional techniques with more accurate data. Certainly, the results can be improved; however, they indicate some margins of improvements and updates. As metrics of assessment, a histogram of greyscale/pixels was adopted, taking into account the example of layers and curve plots. They show changes according to the locations where the supporting videos were obtained.
An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery
Authors: Xia Haiyang , Yang Baohua , Li Yunlong , Wang Bing
Published: April 2022

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness.
Use of Genetic Algorithms for Design an FPGA-Integrated Acoustic Camera
Authors: Grubeša Sanja , Stamać Jasna , Suhanek Mia , Petošić Antonio
Published: April 2022

The goal of this paper is to design a broadband acoustic camera using micro-electromechanical system (MEMS) microphones. The paper describes how an optimization of the microphone array has been carried out. Furthermore, the final goal of the described optimization is that the gain in the desired direction and the attenuation of side lobes is maximized at a frequency up to 4 kHz. Throughout the research, various shapes of microphone arrays and their directivity patterns have been considered and analyzed using newly developed algorithms implemented in Matlab. A hemisphere algorithm, genetic algorithm, and genetic square algorithm were used to find the optimal position and number of microphones placed on an acoustic camera. The proposed acoustic camera design uses a large number of microphones for high directional selectivity, while a field programmable gate array system on a chip (FPGA SoC) is selected as the processing element of the system. According to the obtained results, three different acoustic camera prototypes were developed. This paper presents simulations of their characteristics, compares the obtained measurements, and discusses the positive and negative sides of each acoustic camera prototype.
A Review of Vehicle-to-Vulnerable Road User Collisions on Limited-Access Highways to Support the Development of Automated Vehicle Safety Assessments
Authors: Muslim Husam , Antona-Makoshi Jacobo
Published: April 2022

This study aims to provide evidence to support the development of automated vehicle (AV) safety assessments that consider the possible presence of non-motorized vulnerable road-users (VRUs) on limited-access highways. Although limited-access highways are designed to accommodate high-speed motor vehicles, collisions involving VRUs on such roadways are frequently reported. A narrative review is conducted, covering the epidemiology of VRUs crashes on limited-access highways to identify typical crash patterns considering collisions severity and the underlying reasons for the VRUs to use the highway. The review results show that occupants alighting from a disabled or crashed vehicle, people seeking help or helping others, highway maintenance zones, police stops, and people crossing a highway should be given priority to ensure VRU safety on limited-access highways. The results are summarized in figures with schematic models to generate test scenarios for AV safety assessment. Additionally, the results are discussed using two examples of traffic situations relevant to the potential AV-VRU crashes on highways and the current performance of autonomous emergency braking and autonomous emergency steering systems. These findings have important implications for producing scenarios in which AV may not produce crashes lest it performs worse than human drivers in the proposed scenarios.
Simultaneous Determination of Methyl Nicotinate and Three Salicylic Acid Derivatives in Pain Relief Spray Using HPLC–DAD
Authors: Ali Hazim M.
Published: April 2022

For the first time, the high-performance liquid chromatography–diode array detector (HPLC–DAD) approach was operated for the simultaneous assessment of methyl nicotinate (MN), methyl salicylate (MS), ethyl salicylate (ES) and 2-hydroxyethyl salicylate (HES) in one pharmaceutical formulation. The limits of detection of MN, HES, MS and ES were found to be 0.0144, 0.0455, 0.0087 and 0.0061 μg/mL. The recovery percentages and relative standard deviations ranged from 93.48 to 102.12% and 0.301 to 6.341% for all active ingredients. Accordingly, the previously described data demonstrate the sensitivity, accuracy and precision of the developed method. Therefore, the investigated approach was effectively applied for the simultaneous assessment of MN, HES, MS and ES in DEEP HEAT Spray.
Government, Premier Leader and Small Lakes’ People Vis-a-Vis Lake Governance
Authors: Brillo Bing Baltazar C.
Published: April 2022

Small lakes within social sciences’ conceptualisation are mostly wanting, less anchored and seldom scrutinised in academic literature as opposed to large lakes and natural sciences. Essentially, small lakes, from a social sciences’ perspective, are about people connecting and enhancing through lake governance. Thus, the main argument is that a small lake’s people must accept, broaden and elevate the prospect of lake governance by focusing on and embracing the central concepts of government—the most compulsory and crucial constituent—as well as premier leader—the most pre-eminent and imperative function. Accordingly, lake governance refers to engaging with and intervening in the collective people of a small lake, to undertake economic development, pursue ecological conservation and manage government. Government refers to steering a small lake’s people towards emphasising executive authority and decision-making power, whether through solutions, policies, regulations and/or implementations. The premier leader refers to the person presiding over a small lake’s people in the critical aspects of resoluteness—in establishing and sustaining the rules—and decisiveness—in settling and determining a community’s issues. Overall, as small lakes’ people are political, lake governance is consequential, and a government/premier leader is evidently the most efficacious outcome, whether for addressing problems, choosing decisions or ameliorating society.
Reflecting on the Value of Community Researchers in Criminal Justice Research Projects
Authors: Doyle Caroline , Yates Sophie , Hargrave Jen
Published: April 2022

While the importance of community researchers has long been acknowledged in disability studies, inclusive research practices such as these are less common in research about another marginalised group: people who are in prison or have spent time in prison. Over the past decade in Australia, the number and rate of people imprisoned has risen rapidly, and recidivism rates remain high, indicating a need for improved services. In this article, we draw on methodological reflections from two case studies on research with marginalised communities, one in disability studies and one in post-prison research. We apply insights from disability research to argue the importance of incorporating community researchers in qualitative research projects seeking to explore the experiences of people involved with the criminal justice system, such as people who have been released from prison.
“From All Sides”: Black-Asian Reddit Communities Identify and Expand Experiences of the Multiracial Microaggression Taxonomy
Authors: Gay Tyler McCoy , Farinu Oluyemi T. O. , Issano Jackson Monisha
Published: April 2022

The purpose of this paper is to examine the identity narratives of online Black-Asian communities concerning their multiracial microaggression experiences. As we are investigating an under-explored population, we utilize a purposive sampling method to narrow forum engagement by Black-Asian users on the popular social media network, Reddit. Our research is guided by multiracial critical theory (MultiCrit) which highlights monoracism as a ubiquitous element of the monoracial paradigm of race. In our research, we employ a content analysis using Black-Asian microaggression narratives to test the existing multiracial microaggression taxonomy. Our findings demonstrate that Black-Asian subreddit communities identified all of the existing multiracial microaggression categories: (1) exclusion or isolation; (2) exotification and objectification; (3) assumption of monoracial or mistaken identity; (4) denial of multiraciality; (5) pathologizing of identity and experiences; and (6) microaggressions based on racial stereotypes. Our analysis also found an emerging microaggression category entitled (7) white-mixed superiority. These multiracial microaggressions drew on monoracist ideologies and the monoracial paradigm of race, which exclude Black-Asian people and aid the maintenance of white supremacy in the United States. As such, we suggest further inquiry and expansion of the multiracial microaggression taxonomy to include how racism, particularly the monoracial paradigm of race, influences non-white multiracial identities.
Life Course and Emerging Adulthood: Protestant Women’s Views on Intimate Partner Violence and Divorce
Authors: Ochoa Melissa K.
Published: April 2022

There are inconsistent findings on the relationship between intimate partner violence (IPV) and religiosity or Biblical inerrancy. The Biblical text accepts divorce in cases of infidelity and desertion—but does not specify abuse or IPV as legitimate reasons. In this study, I interviewed twenty White Protestant women (ages 18–22) at a large southern university. In emerging adulthood, a critical period for young adults (ages 18–29), I examined their current levels of religious participation, beliefs in Biblical inerrancy, and their perceptions of IPV as a legitimate reason for divorce. During this process of identity formation as emerging adults, they may reevaluate their religious socialization and parents’ values as well as engage in various social relationships, including romantic ones. Emerging adult women are also at the highest risk for IPV. The findings suggest Protestant women in emerging adulthood reevaluated their religious socialization to formulate a more adaptive worldview. Their religious participation and belief in Biblical inerrancy declined during emerging adulthood and they all accepted divorce as acceptable in cases of IPV. It is an important finding because they are in a key period of potential IPV exposure in their life course.
Voter Evaluations of Biracial-Identified Political Candidates
Authors: Leslie Gregory John , Masuoka Natalie , Gaither Sarah E. , Remedios Jessica D. , Vinluan A. Chyei
Published: April 2022

Today, identity expression and acceptance represent an important area of political advocacy and representation. Yet, how responsive are voters to new racial identity cues promoted by political leaders? Using candidates with interracial backgrounds as a case study, we assess whether voters are responsive to candidates who assert a mixed-race identity or if voters primarily rely on other traits, such as the candidate’s family background, in determining their support of that candidate. Using an experimental design, this study presents participants with various hypothetical candidates who vary both in their racial heritages (i.e., candidates with Asian and White interracial parents or Black and White interracial parents) and identity choices (i.e., as single-race minority, single-race White, or biracial). We then compare how the mixed-race, single-race minority, and White participants evaluate the candidate. We expect that the mixed-race participants will be most supportive of candidates who signal a common in-group identity by identifying specifically as “biracial”. On the other hand, the single-race minority and White participants should be more likely to adhere to the one-drop rule or hypodescent in their evaluations, meaning they will provide more positive evaluations of interracial candidates who identify as a single-race minority. Our study finds that the single-race minority and White participants completely overlook racial identity cues and instead focus on the description of the candidate’s family heritage along with their own assumptions about hypodescent. The mixed-race participants, on the other hand, show strong support for biracial-identified, in-group political candidates This study adds to a burgeoning literature on racial perception and on political representation.