bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal Imaging Shows Submarine Groundwater Discharge Plumes Associated with Ancient Settlements on Rapa Nui (Easter Island, Chile)
Authors: DiNapoli Robert J. , Lipo Carl P. , de Smet Timothy S. , Hunt Terry L.
Published: June 2021

Submarine groundwater discharge (SGD) is an important component of many coastal environments and hydrologic processes, providing sources of nutrients to marine ecosystems, and potentially, an important source of fresh water for human populations. Here, we use a combination of unpiloted aerial systems (UAS) thermal infrared (TIR) imaging and salinity measurements to characterize SGD on the remote East Polynesian island of Rapa Nui (Easter Island, Chile). Previous research has shown that coastal freshwater seeps are abundant on Rapa Nui and strongly associated with the locations of ancient settlement sites. We currently lack, however, information on the differential magnitude or quality of these sources of fresh water. Our UAS-based TIR results from four locations on Rapa Nui suggest that locations of variably-sized SGD plumes are associated with many ancient settlement sites on the island and that these water sources are resilient to drought events. These findings support previous work indicating that ancient Rapa Nui communities responded to the inherent and climate-induced hydrological challenges of the island by focusing on these abundant and resilient freshwater sources. Our results highlight the efficacy of using UAS-based TIR for detecting relatively small SGD locations and provide key insights on the potential uses of these water sources for past and current Rapa Nui communities.
Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series
Authors: Sagheer Alaa , Hamdoun Hala , Youness Hassan
Published: June 2021

Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the hierarchy levels based on their dimensional features. The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures. Toward this target, we develop the DLSTM model in auto-encoder (AE) fashion and take full advantage of the hierarchical architecture for better time series forecasting. DLSTM-AE works as an alternative approach to traditional and machine learning approaches that have been used to manipulate hierarchical forecasting. However, training a DLSTM in hierarchical architectures requires updating the weight vectors for each LSTM cell, which is time-consuming and requires a large amount of data through several dimensions. Transfer learning can mitigate this problem by training first the time series at the bottom level of the hierarchy using the proposed DLSTM-AE approach. Then, we transfer the learned features to perform synchronous training for the time series of the upper levels of the hierarchy. To demonstrate the efficiency of the proposed approach, we compare its performance with existing approaches using two case studies related to the energy and tourism domains. An evaluation of all approaches was based on two criteria, namely, the forecasting accuracy and the ability to produce coherent forecasts through through the hierarchy. In both case studies, the proposed approach attained the highest accuracy results among all counterparts and produced more coherent forecasts.
Combining Inertial Sensors and Machine Learning to Predict vGRF and Knee Biomechanics during a Double Limb Jump Landing Task
Authors: Chaaban Courtney R. , Berry Nathaniel T. , Armitano-Lago Cortney , Kiefer Adam W. , Mazzoleni Michael J. , Padua Darin A.
Published: June 2021

(1) Background: Biomechanics during landing tasks, such as the kinematics and kinetics of the knee, are altered following anterior cruciate ligament (ACL) injury and reconstruction. These variables are recommended to assess prior to clearance for return to sport, but clinicians lack access to the current gold-standard laboratory-based assessment. Inertial sensors serve as a potential solution to provide a clinically feasible means to assess biomechanics and augment the return to sport testing. The purposes of this study were to (a) develop multi-sensor machine learning algorithms for predicting biomechanics and (b) quantify the accuracy of each algorithm. (2) Methods: 26 healthy young adults completed 8 trials of a double limb jump landing task. Peak vertical ground reaction force, peak knee flexion angle, peak knee extension moment, and peak sagittal knee power absorption were assessed using 3D motion capture and force plates. Shank- and thigh- mounted inertial sensors were used to collect data concurrently. Inertial data were submitted as inputs to single- and multiple- feature linear regressions to predict biomechanical variables in each limb. (3) Results: Multiple-feature models, particularly when an accelerometer and gyroscope were used together, were valid predictors of biomechanics (R2 = 0.68–0.94, normalized root mean square error = 4.6–10.2%). Single-feature models had decreased performance (R2 = 0.16–0.60, normalized root mean square error = 10.0–16.2%). (4) Conclusions: The combination of inertial sensors and machine learning provides a valid prediction of biomechanics during a double limb landing task. This is a feasible solution to assess biomechanics for both clinical and real-world settings outside the traditional biomechanics laboratory.
Estimation of Soil Surface Roughness Using Stereo Vision Approach
Authors: Azizi Afshin , Abbaspour-Gilandeh Yousef , Mesri-Gundoshmian Tarahom , Farooque Aitazaz A. , Afzaal Hassan
Published: June 2021

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.
The Heterostructures of CuO and SnOx for NO2 Detection
Authors: Paleczek Anna , Szafraniak Bartłomiej , Fuśnik Łukasz , Brudnik Andrzej , Grochala Dominik , Kluska Stanisława , Jurzecka-Szymacha Maria , Maciak Erwin , Kałużyński Piotr , Rydosz Artur
Published: June 2021

Controlling environmental pollution is a burning problem for all countries more than ever. Currently, due to the increasing industrialization, the number of days when the limits of air pollutants are over the threshold levels exceeds 80–85% of the year. Therefore, cheap and effective sensors are always welcome. One idea is to combine such solutions with cars and provide real-time information about the current pollution level. However, the environmental conditions are demanding, and thus the developed sensors need to be characterized by the high 3S parameters: sensitivity, stability and selectivity. In this paper, we present the results on the heterostructure of CuO/SnOx and SnOx/CuO as a possible approach for selective NO2 detection. The developed gas sensors exhibited lower operating temperature and high response in the wide range of NO2 and in a wide range of relative humidity changes. Material characterizations and impedance spectroscopy measurements were also conducted to analyze the chemical and electrical behavior.
Path Planning for Mobile Robot Based on Improved Bat Algorithm
Authors: Yuan Xin , Yuan Xinwei , Wang Xiaohu
Published: June 2021

Bat algorithm has disadvantages of slow convergence rate, low convergence precision and weak stability. In this paper, we designed an improved bat algorithm with a logarithmic decreasing strategy and Cauchy disturbance. In order to meet the requirements of global optimal and dynamic obstacle avoidance in path planning for a mobile robot, we combined bat algorithm (BA) and dynamic window approach (DWA). An undirected weighted graph is constructed by setting virtual points, which provide path switch strategies for the robot. The simulation results show that the improved bat algorithm is better than the particle swarm optimization algorithm (PSO) and basic bat algorithm in terms of the optimal solution. Hybrid path planning methods can significantly reduce the path length compared with the dynamic window approach. Path switch strategy is proved effective in our simulations.
Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint
Authors: Yuan Debao , Zhang Jian , Wang Jian , Cui Ximin , Liu Fei , Zhang Yalei
Published: June 2021

At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios.
Multi-Channel Fusion Classification Method Based on Time-Series Data
Authors: Jin Xue-Bo , Yang Aiqiang , Su Tingli , Kong Jian-Lei , Bai Yuting
Published: June 2021

Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, deep learning and broad learning systems (BLSs) are the basic methods used to explore the classification of multi-modal time-series data features. Long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks are used to learn and test the original time-series data, and a Gramian angular field and recurrence plot are used to encode time-series data to images, and a BLS is employed for image learning and testing. Finally, to obtain the final classification results, Dempster–Shafer evidence theory (D–S evidence theory) is considered to fuse the probability outputs of the two categories. Through the testing of public datasets, the method proposed in this paper obtains competitive results, compensating for the deficiencies of using only time-series data or images for different types of datasets.
Distributed Watchdogs Based on Blockchain for Securing Industrial Internet of Things
Authors: Lee JongHyup , Kwon Taekyoung
Published: June 2021

The Industrial Internet of Things (IIoT) could enhance automation and analytics in industrial environments. Despite the promising benefits of IIoT, securely managing software updates is a challenging problem for those critical applications. This is due to at least the intrinsic lack of software protection mechanisms in legacy industrial systems. In this paper, to address the challenges in building a secure software supply chain for industrial environments, we propose a new approach that leverages distributed watchdogs with blockchain systems in protecting software supply chains. For this purpose, we bind every entity with a unique identity in the blockchain and employ the blockchain as a delegated authenticator by mapping every reporting action to a non-fungible token transfer. Moreover, we present a detailed specification to clearly define the behavior of systems and to apply model checking.
Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments
Authors: Antić Miloš , Zdešar Andrej , Škrjanc Igor
Published: June 2021

This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner.
System for Visualizing Surface Potential Distribution to Eliminate Electrostatic Charge
Authors: Kikunaga Kazuya
Published: June 2021

A mixture of positive and negative static charges exists in the same plane on an insulator surface, and this can cause production quality problems at manufacturing sites. This study developed a system with a vibration array sensor to rapidly measure the surface potential distribution of an object in a non-contact and non-destructive manner and with a high spatial resolution of 1 mm. The measurement accuracy differed greatly depending on the scanning speed of the array sensor, and an optimum scanning speed of 10 mm/s enabled rapid measurements (within <3 s) of the surface potential distribution of a charged insulator (area of 30 mm × 30 mm) with an accuracy of 15%. The relationship between charge and dust on the surface was clarified to easily visualize the uneven static charges present on it and thereby eliminate static electricity.
Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance
Authors: Hobbs Steven , Lambert Andrew , Ryan Michael J. , Paull David J. , Haythorpe John
Published: June 2021

Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary.
Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions
Authors: Nam Youngja , Lee Chankyu
Published: June 2021

Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional speech denoising. In this study, a cascaded denoising CNN (DnCNN)–CNN architecture is proposed to classify emotions from Korean and German speech in noisy conditions. The proposed architecture consists of two stages. In the first stage, the DnCNN exploits the concept of residual learning to perform denoising; in the second stage, the CNN performs the classification. The classification results for real datasets show that the DnCNN–CNN outperforms the baseline CNN in overall accuracy for both languages. For Korean speech, the DnCNN–CNN achieves an accuracy of 95.8%, whereas the accuracy of the CNN is marginally lower (93.6%). For German speech, the DnCNN–CNN has an overall accuracy of 59.3–76.6%, whereas the CNN has an overall accuracy of 39.4–58.1%. These results demonstrate the feasibility of applying the DnCNN with residual learning to speech denoising and the effectiveness of the CNN-based approach in speech emotion recognition. Our findings provide new insights into speech emotion recognition in adverse conditions and have implications for language-universal speech emotion recognition.
Convolutional Neural Networks Cascade for Automatic Pupil and Iris Detection in Ocular Proton Therapy
Authors: Antonioli Luca , Pella Andrea , Ricotti Rosalinda , Rossi Matteo , Fiore Maria Rosaria , Belotti Gabriele , Magro Giuseppe , Paganelli Chiara , Orlandi Ester , Ciocca Mario , Baroni Guido
Published: June 2021

Eye tracking techniques based on deep learning are rapidly spreading in a wide variety of application fields. With this study, we want to exploit the potentiality of eye tracking techniques in ocular proton therapy (OPT) applications. We implemented a fully automatic approach based on two-stage convolutional neural networks (CNNs): the first stage roughly identifies the eye position and the second one performs a fine iris and pupil detection. We selected 707 video frames recorded during clinical operations during OPT treatments performed at our institute. 650 frames were used for training and 57 for a blind test. The estimations of iris and pupil were evaluated against the manual labelled contours delineated by a clinical operator. For iris and pupil predictions, Dice coefficient (median = 0.94 and 0.97), Szymkiewicz–Simpson coefficient (median = 0.97 and 0.98), Intersection over Union coefficient (median = 0.88 and 0.94) and Hausdorff distance (median = 11.6 and 5.0 (pixels)) were quantified. Iris and pupil regions were found to be comparable to the manually labelled ground truths. Our proposed framework could provide an automatic approach to quantitatively evaluating pupil and iris misalignments, and it could be used as an additional support tool for clinical activity, without impacting in any way with the consolidated routine.
BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment
Authors: Gan Vincent J.L. , Luo Han , Tan Yi , Deng Min , Kwok H.L.
Published: June 2021

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based real-time HVAC control, and other potential applications to maximize indoor thermal comfort.
A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image
Authors: Lu Huimin , Wang Yifan , Gao Ruoran , Zhao Chengcheng , Li Yang
Published: June 2021

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.
Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach
Authors: Li Xiangjun , Cui Qimei , Zhai Jinli , Huang Xueqing
Published: June 2021

The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.
AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task
Authors: Zoghlami Feryel , Kaden Marika , Villmann Thomas , Schneider Germar , Heinrich Harald
Published: June 2021

Sensor fusion has gained a great deal of attention in recent years. It is used as an application tool in many different fields, especially the semiconductor, automotive, and medical industries. However, this field of research, regardless of the field of application, still presents different challenges concerning the choice of the sensors to be combined and the fusion architecture to be developed. To decrease application costs and engineering efforts, it is very important to analyze the sensors’ data beforehand once the application target is defined. This pre-analysis is a basic step to establish a working environment with fewer misclassification cases and high safety. One promising approach to do so is to analyze the system using deep neural networks. The disadvantages of this approach are mainly the required huge storage capacity, the big training effort, and that these networks are difficult to interpret. In this paper, we focus on developing a smart and interpretable bi-functional artificial intelligence (AI) system, which has to discriminate the combined data regarding predefined classes. Furthermore, the system can evaluate the single source signals used in the classification task. The evaluation here covers each sensor contribution and robustness. More precisely, we train a smart and interpretable prototype-based neural network, which learns automatically to weight the influence of the sensors for the classification decision. Moreover, the prototype-based classifier is equipped with a reject option to measure classification certainty. To validate our approach’s efficiency, we refer to different industrial sensor fusion applications.
Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
Authors: Salehi Hikouei Iman , Kim S. Sonny , Mishra Deepak R.
Published: June 2021

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.
An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments
Authors: Xiong Lu , Fu Zhiqiang , Zeng Dequan , Leng Bo
Published: June 2021

This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference road. First, a vehicle kinematic model with road coordinates is established to describe the lateral movement of the vehicle. Then, nonlinear optimization based on a vehicle kinematic model in the space domain is employed to smooth the reference road. Second, a multilayered search algorithm is applied in the lateral-space domain to deal with obstacles and find a suitable path boundary. Then, the optimized path planner calculates the optimal path by considering the distance to the reference road and the curvature constraints. Furthermore, the optimized speed planner takes into account the speed boundary in the space domain and the constraints on vehicle acceleration. The optimal speed profile is obtained by using a numerical optimization method. Furthermore, a motion controller based on a kinematic error model is proposed to follow the desired trajectory. Finally, the experimental results show the effectiveness of the proposed trajectory planner and motion controller framework in handling typical scenarios and avoiding obstacles safely and smoothly on the reference road and in unstructured environments.
Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data
Authors: Gareev Albert , Protsenko Vladimir , Stadnik Dmitriy , Greshniakov Pavel , Yuzifovich Yuriy , Minaev Evgeniy , Gimadiev Asgat , Nikonorov Artem
Published: June 2021

This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, dramatically reducing the time needed to spend with the actual hardware to build a high-quality fault detection model. Our fault detection model was validated on a test bench and showed accuracy of more than 99% of correctly recognized hydraulic system states with a 10-s sampling window. This model can be also leveraged to examine the decision boundaries of the classifier in the two-dimensional embedding space.
Reconstruction of the Image Metric of Periodic Structures in an Opto-Digital Angle Measurement System
Authors: Korolev Alexander N. , Lukin Alexander Ya. , Filatov Yurii V. , Venediktov Vladimir Yu.
Published: June 2021

Measurement of the object angular position and its change is one of the important tasks in measurement technique. Our method is based on determination of the angular position of a 2D periodical optical pattern (2D mark) at the object, captured by the sensor of a digital camera. System performance can be frustrated by errors in determination of the spot coordinates on the camera sensor; by the presence of lens aberrations; by deviations from the parallelism of the pattern planes and the camera sensor; and by differences between the actual spots positions and the ideal grid. In the paper we discuss the effect of these errors and the way to correct or eliminate them. We have developed the mathematical routine and the corresponding numerical codes for correction of the said errors. The code and the routine we checked in a real experiment. It has shown that the correction decreases the standard deviation in 15 times.
An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications
Authors: Ajani Taiwo Samuel , Imoize Agbotiname Lucky , Atayero Aderemi A.
Published: June 2021

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.
Gas Monitoring in Human Frontal Sinuses–Stability Considerations and Gas Exchange Studies
Authors: Zhang Han , Han Ning , Lin Yueyu , Huang Jiawen , Svanberg Sune , Svanberg Katarina
Published: June 2021

Acute rhinosinusitis is a common infectious disease, which, in more than 90% of cases, is caused by viruses rather than by bacteria. Even so, antibiotics are often unnecessarily prescribed, and in the long run this contributes to the alarming level of antibiotics resistance. The reason is that there are no good guiding tools for defining the background reason of the infection. One main factor for the clearance of the infection is if there is non-obstructed ventilation from the sinus to the nasal cavity. Gas in Scattering Media Absorption Spectroscopy (GASMAS) has potential for diagnosing this. We have performed a study of frontal sinuses of volunteers with a focus on signal stability and reproducibility over time, accurate oxygen concentration determination, and assessment of gas transport through passages, naturally and after decongestant spray administration. Different from earlier studies on frontal sinuses, water vapor, serving the purpose of oxygen signal normalization, was measured at 818 nm rather than earlier at 937 nm, now closer to the 760 nm oxygen absorption band and thus resulting in more reliable results. In addition, the action of decongestants was objectively demonstrated for the first time. Evaluated oxygen concentration values for left- and right-hand side sinus cavities were found to agree within 0.3%, and a left-right geometrical asymmetry parameter related to anatomical differences was stable within 10%.
Rice Husk-Derived Cellulose Nanofibers: A Potential Sensor for Water-Soluble Gases
Authors: Shahi Naresh , Lee Eunji , Min Byungjin , Kim Dong-Joo
Published: June 2021

Cellulose and its derivatives have evoked much attention in sensor technology as host-matrices for conducting materials because of their versatility, renewability, and biocompatibility. However, only a few studies have dealt with the potential utilization of cellulose as a sensing material without a composite structure. In this study, cellulose nanofibers (CNF) and 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofibers (TOCNF) extracted from rice husks by using ultrasonic-assisted methods are introduced as a potential gas sensing material with highly sensitive performance. To fabricate nanocellulose-based films, CNF, TOCNF, and TOCNF with glycerol (TOCNF/G) were dispersed in water and applied on polyimide substrate with digital electrodes to form self-standing thin films by a drop-casting method. A transparent coating layer on the surface of the plate after drying is used for the detection of water-soluble gases such as acetone, ammonia, methane, and hydrogen sulfide gases at room temperature at 52% relative humidity. The sensor prototypes exhibited high sensitivity, and the detection limit was between 1 ppm and 5 ppm, with less than 10 min response and recovery time. The results indicate that both the CNF- and the TOCNF-coated sensors show good sensitivity toward ammonia and acetone, compared to other gases. A TOCNF/G-coated sensor exhibited minimum time in regard to response/recovery time, compared to a CNF-coated sensor. In this study, nanocellulose-based sensors were successfully fabricated using a low-cost process and a bio-based platform. They showed good sensitivity for the detection of various gases under ambient conditions. Therefore, our study results should further propel in-depth research regarding various applications of cellulose-based sensors in the future.
Main Uncertainties in the RF Ultrasound Scanning Simulation of the Standard Ultrasound Phantoms
Authors: Makūnaitė Monika , Jurkonis Rytis , Lukoševičius Arūnas , Baranauskas Mindaugas
Published: June 2021

Ultrasound echoscopy technologies are continuously evolving towards new modalities including quantitative parameter imaging, elastography, 3D scanning, and others. The development and analysis of new methods and algorithms require an adequate digital simulation of radiofrequency (RF) signal transformations. The purpose of this paper is the quantitative evaluation of RF signal simulation uncertainties in resolution and contrast reproduction with the model of a phased array transducer. The method is based on three types of standard physical phantoms. Digital 3D models of those phantoms are composed of point scatterers representing the weak backscattering of the background material and stronger backscattering from inclusions. The simulation results of echoscopy with sector scanning transducer by Field II software are compared with the RF output of the Ultrasonix scanner after scanning standard phantoms with 2.5 MHz phased array. The quantitative comparison of axial, lateral, and elevation resolutions have shown uncertainties from 9 to 22% correspondingly. The echoscopy simulation with two densities of scatterers is compared with contrast phantom imaging on the backscattered RF signals and B-scan reconstructed image, showing that the main sources of uncertainties limiting the echoscopy RF signal simulation adequacy are an insufficient knowledge of the scanner and phantom’s parameters. The attempt made for the quantitative evaluation of simulation uncertainties shows both problems and the potential of echoscopy simulation in imaging technology developments. The analysis presented could be interesting for researchers developing quantitative ultrasound imaging and elastography technologies looking for simulated raw RF signals comparable to those obtained from real ultrasonic scanning.
Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities
Authors: Rosero-Montalvo Paul D. , Fuentes-Hernández Edison A. , Morocho-Cayamcela Manuel E. , Sierra-Martínez Luz M. , Peluffo-Ordóñez Diego H.
Published: June 2021

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.
Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling
Authors: Akkoyun Fatih , Ercetin Ali , Aslantas Kubilay , Pimenov Danil Yurievich , Giasin Khaled , Lakshmikanthan Avinash , Aamir Muhammad
Published: June 2021

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation.
IoU Regression with H+L-Sampling for Accurate Detection Confidence
Authors: Wang Dong , Wu Huaming
Published: June 2021

It is a common paradigm in object detection frameworks that the samples in training and testing have consistent distributions for the two main tasks: Classification and bounding box regression. This paradigm is popular in sampling strategy for training an object detector due to its intuition and practicability. For the task of localization quality estimation, there exist two ways of sampling: The same sampling with the main tasks and the uniform sampling by manually augmenting the ground-truth. The first method of sampling is simple but inconsistent for the task of quality estimation. The second method of uniform sampling contains all IoU level distributions but is more complex and difficult for training. In this paper, we propose an H+L-Sampling strategy, selecting the high and low IoU samples simultaneously, to effectively and simply train the branch of quality estimation. This strategy inherits the effectiveness of consistent sampling and reduces the training difficulty of uniform sampling. Finally, we introduce accurate detection confidence, which combines the classification probability and the localization accuracy, as the ranking keyword of NMS. Extensive experiments show the effectiveness of our method in solving the misalignment between classification confidence and localization accuracy and improving the detection performance.
Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid In-Line Analysis Tool
Authors: Al Ktash Mohammad , Stefanakis Mona , Boldrini Barbara , Ostertag Edwin , Brecht Marc
Published: June 2021

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.
Correlates of Non-Partner Physical Violence among Ethiopian Women
Authors: Ebrahim Nasser B.
Published: June 2021

Intimate and non-partner violence against women are major societal and public health problems afflicting many women around the world. Unlike intimate partner violence (IPV), non-partner violence (NPV) has not been well researched in Ethiopia. Thus, individual, physical, social, and environmental factors associated with NPV are largely unknown. For the study, we used national data collected in the 2016 Ethiopia Demographic and Health Survey. The survey sample was selected with two-stage stratified cluster sampling. Multivariate logistic regression model was fitted to understand the association between NPV and its correlates. Overall, 45% of the women reported having experienced physical NPV in Ethiopia. Women who owned assets, solely or jointly (land, house, or both), were 70% less likely to report NPV than women who had no such assets. Thus, structural interventions that integrate women’s asset ownership and provide special microfinancing services for women along with community-based gender education programs may have a potential to reduce violence against women in Ethiopia.