bluuebunny/crossref_metadata_2025 huggingface dataset:
Earth Gravity In-Orbit Sensing: MPC Formation Control Based on a Novel Constellation Model
Authors: Boggio Mattia , Colangelo Luigi , Virdis Mario , Pagone Michele , Novara Carlo
Published: June 2022

Missions finalized at measuring the space-time variations of the Earth gravity field have become of high relevance in recent years. These missions are indeed of interest for scientific purposes and applications in several fields. Precise observations of the Earth gravity field can be accomplished by measuring the distance between two satellites flying in suitable orbits. Several mission concepts foresee an active formation control to maintain the distance variations between the two satellites within given bounds. In this paper, we first present an original constellation model, called the Triangle Dynamics (TD) model, which is particularly suitable to describe the orbital dynamics of satellite pairs. Open-loop simulations are performed, where the TD model is compared with a standard model, derived from the well-known Hill–Clohessy–Wiltshire (HCW) equations. The simulation results show that the TD model provides more accurate predictions than the HCW model. Then, we propose a formation control approach based on a new Model Predictive Control (MPC) algorithm. The core of this algorithm is the TD model, which is used in real-time to predict the behavior of the satellite pair, allowing the computation of an optimal formation control command. A case study concerned with the Next Generation Gravity Mission (NGGM) is presented to demonstrate the effectiveness of the proposed MPC-TD algorithm.
Design Space Exploration of a Sparse MobileNetV2 Using High-Level Synthesis and Sparse Matrix Techniques on FPGAs
Authors: Tragoudaras Antonios , Stoikos Pavlos , Fanaras Konstantinos , Tziouvaras Athanasios , Floros George , Dimitriou Georgios , Kolomvatsos Kostas , Stamoulis Georgios
Published: June 2022

Convolution Neural Networks (CNNs) are gaining ground in deep learning and Artificial Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce efficient and low-power hardware designs. The inference process of a Deep Neural Network (DNN) is considered a computationally intensive process that requires hardware accelerators to operate in real-world scenarios due to the low latency requirements of real-time applications. As a result, High-Level Synthesis (HLS) tools are gaining popularity since they provide attractive ways to reduce design time complexity directly in register transfer level (RTL). In this paper, we implement a MobileNetV2 model using a state-of-the-art HLS tool in order to conduct a design space exploration and to provide insights on complex hardware designs which are tailored for DNN inference. Our goal is to combine design methodologies with sparsification techniques to produce hardware accelerators that achieve comparable error metrics within the same order of magnitude with the corresponding state-of-the-art systems while also significantly reducing the inference latency and resource utilization. Toward this end, we apply sparse matrix techniques on a MobileNetV2 model for efficient data representation, and we evaluate our designs in two different weight pruning approaches. Experimental results are evaluated with respect to the CIFAR-10 data set using several different design methodologies in order to fully explore their effects on the performance of the model under examination.
Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution
Authors: Maktab Dar Oghaz Mahdi , Razaak Manzoor , Remagnino Paolo
Published: June 2022

One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the coverage. State-of-the-art general purpose object detector tend to under-perform and struggle with small object detection due to loss of spatial features and weak feature representation of the small objects and sheer imbalance between objects and the background. This paper aims to address small object detection in aerial imagery by offering a Convolutional Neural Network (CNN) model that utilizes the Single Shot multi-box Detector (SSD) as the baseline network and extends its small object detection performance with feature enhancement modules including super-resolution, deconvolution and feature fusion. These modules are collectively aimed at improving the feature representation of small objects at the prediction layer. The performance of the proposed model is evaluated using three datasets including two aerial images datasets that mainly consist of small objects. The proposed model is compared with the state-of-the-art small object detectors. Experiment results demonstrate improvements in the mean Absolute Precision (mAP) and Recall values in comparison to the state-of-the-art small object detectors that investigated in this study.
Memory Offloading for Remote Attestation of Multi-Service IoT Devices
Authors: Dushku Edlira , Østergaard Jeppe Hagelskjær , Dragoni Nicola
Published: June 2022

Remote attestation (RA) is an effective malware detection mechanism that allows a trusted entity (Verifier) to detect a potentially compromised remote device (Prover). The recent research works are proposing advanced Control-Flow Attestation (CFA) protocols that are able to trace the Prover’s execution flow to detect runtime attacks. Nevertheless, several memory regions remain unattested, leaving the Prover vulnerable to data memory and mobile adversaries. Multi-service devices, whose integrity is also dependent on the integrity of any attached external peripheral devices, are particularly vulnerable to such attacks. This paper extends the state-of-the-art RA schemes by presenting ERAMO, a protocol that attests larger memory regions by adopting the memory offloading approach. We validate and evaluate ERAMO with a hardware proof-of-concept implementation using a TrustZone-capable LPC55S69 running two sensor nodes. We enhance the protocol by providing extensive memory analysis insights for multi-service devices, demonstrating that it is possible to analyze and attest the memory of the attached peripherals. Experiments confirm the feasibility and effectiveness of ERAMO in attesting dynamic memory regions.
Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs
Authors: Omar Madiah Binti , Ibrahim Rosdiazli , Mantri Rhea , Chaudhary Jhanavi , Ram Selvaraj Kaushik , Bingi Kishore
Published: June 2022

A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.
Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review
Authors: Gulzar Ahmad Saima , Iqbal Tassawar , Javaid Anam , Ullah Munir Ehsan , Kirn Nasira , Ullah Jan Sana , Ramzan Naeem
Published: June 2022

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers.
Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids
Authors: Zhai Feng , Yang Ting , Zhao Bing , Chen Hao
Published: June 2022

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.
Non-Contact Activity Monitoring Using a Multi-Axial Inertial Measurement Unit in Animal Husbandry
Authors: Try Pieter , Gebhard Marion
Published: June 2022

In this work, a novel method is presented for non-contact non-invasive physical activity monitoring, which utilizes a multi-axial inertial measurement unit (IMU) to measure activity-induced structural vibrations in multiple axes. The method is demonstrated in monitoring the activity of a mouse in a husbandry cage, where activity is classified as resting, stationary activity and locomotion. In this setup, the IMU is mounted in the center of the underside of the cage floor where vibrations are measured as accelerations and angular rates in the X-, Y- and Z-axis. The ground truth of activity is provided by a camera mounted in the cage lid. This setup is used to record 27.67 h of IMU data and ground truth activity labels. A classification model is trained with 16.17 h of data which amounts to 3880 data points. Each data point contains eleven features, calculated from the X-, Y- and Z-axis accelerometer data. The method achieves over 90% accuracy in classifying activity versus non-activity. Activity is monitored continuously over more than a day and clearly depicts the nocturnal behavior of the inhabitant. The impact of this work is a powerful method to assess activity which enables automatic health evaluation and optimization of workflows for improved animal wellbeing.
Coupling Interference between Eddy Current Sensors for the Radial Displacement Measurement of a Cylindrical Target
Authors: Zhang Weifeng , Bu Jianguo , Li Dongjie , Zhang Ke , Zhou Ming
Published: June 2022

In the radial displacement measurement of a small-sized cylindrical target, coupling interference between eddy current sensors reduces the accuracy of the measurement. In this study, finite element method (FEM) simulation based on ANSYS Maxwell was adopted to investigate the relationships between the coupling coefficient of the sensors and different parameters including the lift-off, cylinder diameter, axis angle, material, and excitation frequency. The experimental results were consistent with the simulation results. The coupling interference between the sensors increased with the decrease in the lift-off and cylinder diameter. The coupling effect decreased significantly when the probe axis angle increased to 120°, and the decrease in the sensor sensitivity was acceptable. A polynomial fitting function fitted the output signal well. A compensation method was given based on the compensation necessity evaluation. The results showed that the error of the rotor motion track was significantly decreased after compensation.
Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study
Authors: Koivisto Tero , Lahdenoja Olli , Hurnanen Tero , Vasankari Tuija , Jaakkola Samuli , Kiviniemi Tuomas , Airaksinen K. E. Juhani
Published: June 2022

Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2–90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.
Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors
Authors: Lai Wei-In , Chen Yung-Yu , Sun Jia-Hong
Published: June 2022

This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in atmospheric condition monitoring. However, their native coarse resolution and poor selectivity limit their performance. Thus, we adopted recurrent neural network (RNN) models to extract the time-series concentration data characteristics and improve the detection accuracy. Firstly, four types of RNN models, LSTM and GRU, Bi-LSTM, and Bi-GRU, were optimized to define the best-performance single weak models for CO, O3, and NO2 gases, respectively. Next, ensemble models which integrate multiple single weak models with a dynamic model were defined and trained. The testing results show that the ensemble models perform better than the single weak models. Further, a retraining procedure was proposed to make the ensemble model more flexible to adapt to environmental conditions. The significantly improved determination coefficients show that the retraining helps the ensemble models maintain long-term stable sensing performance in an atmospheric environment. The result can serve as an essential reference for the applications of IoT devices with commercial gas sensors in environment condition monitoring.
A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
Authors: Lian Bin , Wei Zhongcheng , Sun Xiang , Li Zhihua , Zhao Jijun
Published: June 2022

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.
Instance Segmentation Based on Improved Self-Adaptive Normalization
Authors: Yang Sen , Wang Xiaobao , Yang Qijuan , Dong Enzeng , Du Shengzhi
Published: June 2022

The single batch normalization (BN) method is commonly used in the instance segmentation algorithms. The batch size is concerned with some drawbacks. A too small sample batch size leads to a sharp drop in accuracy, but a too large batch may result in the memory overflow of graphic processing units (GPU). These problems make BN not feasible to some instance segmentation tasks with inappropriate batch sizes. The self-adaptive normalization (SN) method, with an adaptive weight loss layer, shows good performance in instance segmentation algorithms, such as the YOLACT. However, the parameter averaging mechanism in the SN method is prone to problems in the weight learning and assignment process. In response to such a problem, the paper proposes to replace the single BN with an adaptive weight loss layer in SN models, based on which a weight learning method is developed. The proposed method increases the input feature expression ability of the subsequent layers. By building a Pytorch deep learning framework, the proposed method is validated in the MS-COCO data set and Autonomous Driving Cityscapes data set. The experimental results prove that the proposed method is effective in processing samples independent from the batch size. The stable accuracy for all kinds of target segmentation is achieved, and the overall loss value is significantly reduced at the same time. The convergence speed of the network is also improved.
Assessing the Impacts of Autonomous Vehicles on Road Congestion Using Microsimulation
Authors: Malibari Areej , Higatani Akito , Saleh Wafaa
Published: June 2022

The introduction of autonomous vehicles has been considered as a possible option for reducing traffic congestion in many transport studies. Many types of models, in particular car-following microsimulation models have been adopted in most studies. The impacts of autonomous vehicles (AVs) on congestion, however, have not yet been concluded. This could be because different researchers use different forms of car-following models to assess these impacts, or because the utilised modelling approaches and their parameters are different in different studies. In particular, two of the important parameters that are associated with car-following models are the used values for maximum acceleration and the average desired time gaps. While the values of these parameters can be adjusted and controlled by the ACC controllers in the AV, they can also be controlled by the users. Therefore, assigning unrealistic values to these parameters could well result in unrealistic conclusions. This paper investigated the impacts of the maximum acceleration and the average desired time gaps on congestion levels using the loss-time indicator. The analysis was carried out on the Hanshin expressway in Japan and was tested and assessed using the Helly (FACC) car-following microsimulation model. This includes estimating the values of the desired time gap from real traffic time-gap distributions. The Hanshin expressway is an urban toll highway of 273 km that extends from Osaka to Kobe, representing the Hanshin area in Japan. The Hanshin highway serves a huge traffic volume that consists of private and freight vehicles that operate within the Hanshin area. This area represents one of three major municipal areas in Japan including Tokyo and Nagoya. A total of 740,000 vehicles per day travel on the expressway. As a result, there is significant congestion on the Hanshin expressway. There have been various plans put in place to ease congestion ranging from building new roads to the implementation of traffic-demand-management measures. However, the predictions of the impacts of such measures do not provide any evidence that they would ease traffic congestion. Other possible measures that could be investigated for easing traffic congestion include technology-based solutions such as autonomous vehicles. The modelling results recommend that the results obtained from microsimulation models should be taken with care, and good attention should be paid to the parameters used and their values in the model. The values assigned to driving-behaviour parameters, the maximum values of acceleration, and the time-gap settings, for example, control the final outcomes of the models.
Active Control System to Prevent Malfunctioning Caused by the Pressure Difference in Gasket Plate Heat Exchangers Applied in the Oil and Gas Industry
Authors: Martins Thiago , Spengler Anderson Wedderhoff , Oliveira Jorge Luiz Goes , de Paiva Kleber Vieira , Seman Laio Oriel
Published: June 2022

In the oil and gas industry, heat exchangers are subject to loads that cause malfunctioning. These loads are divided into thermal and mechanical stresses; however, most efforts are focused on studying thermal stresses. The present work reduces mechanical stresses by mitigating pressure events in a gasket plate heat exchanger (GPHE). GPHE requires that the hot and cold stream branches have approximately the same pressure. Thus, the work focuses on controlling the pressure difference between the branches. A test bench was used to emulate, on a small scale, the typical pressure events of an oil production plant. A control valve was used in different positions to evaluate the controller. In the experiments, it was observed that the best option to control the pressure difference is to use a hydraulic pump and control valve in the flow of the controlled thermal fluid branch. The reduction in pressure events was approximately 50%. Actuator efforts are also reduced in this configuration.
A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles
Authors: Huang Qianyong , Xu Xianyun , Chen Huifang , Xie Lei
Published: June 2022

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.
An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis
Authors: Zhu Fu , Liu Chang , Yang Jianwei , Wang Sen
Published: June 2022

In recent years, neural networks have shown good performance in terms of accuracy and efficiency. However, along with the continuous improvement in diagnostic accuracy, the number of parameters in the network is increasing and the models can often only be run in servers with high computing power. Embedded devices are widely used in on-site monitoring and fault diagnosis. However, due to the limitation of hardware resources, it is difficult to effectively deploy complex models trained by deep learning, which limits the application of deep learning methods in engineering practice. To address this problem, this article carries out research on network lightweight and performance optimization based on the MobileNet network. The network structure is modified to make it directly suitable for one-dimensional signal processing. The wavelet convolution is introduced into the convolution structure to enhance the feature extraction ability and robustness of the model. The excessive number of network parameters is a challenge for the deployment of networks and also for the running performance problems. This article analyzes the influence of the full connection layer size on the total network. A network parameter reduction method is proposed based on GAP to reduce the network parameters. Experiments on gears and bearings show that the proposed method can achieve more than 97% classification accuracy under the strong noise interference of −6 dB, showing good anti-noise performance. In terms of performance, the network proposed in this article has only one-tenth of the number of parameters and one-third of the running time of standard networks. The method proposed in this article provides a good reference for the deployment of deep learning intelligent diagnosis methods in embedded node systems.
Motor Signatures in Digitized Cognitive and Memory Tests Enhances Characterization of Parkinson’s Disease
Authors: Ryu Jihye , Torres Elizabeth B.
Published: June 2022

Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining the information of traditional clinical tests. We aim at digitizing traditional tests of cognitive and memory performance to derive motor biometrics of pen-strokes and voice, thereby complementing clinical tests with objective criteria, while enhancing the overall characterization of Parkinson’s disease (PD). 35 participants including patients with PD, healthy young and age-matched controls performed a series of drawing and memory tasks, while their pen movement and voice were digitized. We examined the moment-to-moment variability of time series reflecting the pen speed and voice amplitude. The stochastic signatures of the fluctuations in pen drawing speed and voice amplitude of patients with PD show a higher signal-to-noise ratio compared to those of neurotypical controls. It appears that contact motions of the pen strokes on a tablet evoke sensory feedback for more immediate and predictable control in PD, while voice amplitude loses its neurotypical richness. We offer new standardized data types and analytics to discover the hidden motor aspects within the cognitive and memory clinical assays.
Interdisciplinary Research to Advance Digital Imagery and Natural Compounds for Eco-Cleaning and for Preserving Textile Cultural Heritage
Authors: Ilies Dorina Camelia , Zlatev Zlatin , Ilies Alexandru , Zharas Berdenov , Pantea Emilia , Hodor Nicolaie , Indrie Liliana , Turza Alexandru , Taghiyari Hamid R. , Caciora Tudor , Costea Monica , Safarov Bahodiron , Lucian Barbu-Tudoran
Published: June 2022

The old fibers that make up heritage textiles displayed in museums are degraded by the aging process, environmental conditions (microclimates, particulate matter, pollutants, sunlight) and the action of microorganisms. In order to counteract these processes and keep the textile exhibits in good condition for as long as possible, both reactive and preventive interventions on them are necessary. Based on these ideas, the present study aims to test a natural and non-invasive method of cleaning historic textiles, which includes the use of a natural substance with a known antifungal effect (being traditionally used in various rural communities)—lye. The design of the study was aimed at examining a traditional women’s shirt that is aged between 80–100 years, using artificial intelligence techniques for Scanning Electron Microscopy (SEM) imagery analysis and X-ray powder diffraction technique in order to achieve a complex and accurate investigation and monitoring of the object’s realities. The determinations were performed both before and after washing the material with lye. SEM microscopy investigations of the ecologically washed textile specimens showed that the number of microorganism colonies, as well as the amount of dust, decreased. It was also observed that the surface cellulose fibers lost their integrity, eventually being loosened on cellulose fibers of cotton threads. This could better visualize the presence of microfibrils that connect the cellulose fibers in cotton textiles. The results obtained could be of real value both for the restorers, the textile collections of the different museums, and for the researchers in the field of cultural heritage. By applying such a methodology, cotton tests can be effectively cleaned without compromising the integrity of the material.
Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars
Authors: Lee Jeongpyo , Park Kyungeun , Kim Youngok
Published: June 2022

Indoor device-free localization (DFL) systems are used in various Internet-of-Things applications based on human behavior recognition. However, the usage of camera-based intuitive DFL approaches is limited in dark environments and disaster situations. Moreover, camera-based DFL schemes exhibit certain privacy issues. Therefore, DFL schemes with radars are increasingly being investigated owing to their efficient functioning in dark environments and their ability to prevent privacy issues. This study proposes a deep learning-based DFL scheme for simultaneous estimation of indoor location and posture using 24-GHz frequency-modulated continuous-wave (FMCW) radars. The proposed scheme uses a parallel 1D convolutional neural network structure with a regression and a classification model for localization and posture estimation, respectively. The two-dimensional location information of the target is estimated for localization, and four different postures, namely standing, sitting, lying, and absence, are estimated simultaneously. We experimentally evaluated the proposed scheme and compared its performance with that of conventional schemes under identical conditions. The results indicate that the average localization error of the proposed scheme is 0.23 m, whereas that of the conventional scheme is approximately 0.65 m. The average posture estimation error of the proposed scheme is approximately 1.7%, whereas that of the conventional correlation, CSP, and SVM schemes are 54.8%, 42%, and 10%, respectively.
Sum Rate Optimization of IRS-Aided Uplink Muliantenna NOMA with Practical Reflection
Authors: Choi Jihyun , Cantos Luiggi , Choi Jinho , Kim Yun Hee
Published: June 2022

Recently, intelligent reflecting surfaces (IRSs) have drawn huge attention as a promising solution for 6G networks to enhance diverse performance metrics in a cost-effective way. For massive connectivity toward a higher spectral efficiency, we address an intelligent reflecting surface (IRS) to an uplink nonorthogonal multiple access (NOMA) network supported by a multiantenna receiver. We maximize the sum rate of the IRS-aided NOMA network by optimizing the IRS reflection pattern under unit modulus and practical reflection. For a moderate-sized IRS, we obtain an upper bound on the optimal sum rate by solving a determinant maximization (max-det) problem after rank relaxation, which also leads to a feasible solution through Gaussian randomization. For a large number of IRS elements, we apply the iterative algorithms relying on the gradient, such as Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS algorithms for which the gradient of the sum rate is derived in a computationally efficient form. The results show that the max-det approach provides a near-optimal performance under unit modulus reflection, while the gradient-based iterative algorithms exhibit merits in performance and complexity for a large-sized IRS with practical reflection.
Compact Four-Port Circularly Polarized MIMO X-Band DRA
Authors: Ibrahim Ahmed A. , Zahra Hijab , Abbas Syed Muzahir , Ahmed Mohamed I. , Varshney Gaurav , Mukhopadhyay Subhas , Mahmoud Abdelhady
Published: June 2022

A circularly polarized (CP) multi-input multioutput (MIMO) dielectric resonator (DR) antenna (DRA) with compact size and four ports is implemented. CP radiation was achieved using the deformed DR geometry excited with aperture coupled feeding. A CPDRA with a single and two ports is investigated. The defected ground structure (DGS) was incorporated into the antenna for improving the isolation between the ports. The DGS was incorporated in such a way that the required phase difference between the generated orthogonal degenerate modes is preserved. This concept could be utilized in implementing a compact four-port CP antenna. The MIMO antenna provides a 10 dB impedance bandwidth of 38% (8.5–12.5 GHz) and a 3 dB AR bandwidth of 9.32% (9.2–10.1 GHz). The gain of the implemented antenna was around 6 dBi in the band where CP radiation was achieved. The MIMO performance parameters were calculated, and their values remained within the acceptable limits. The implemented antenna could suitably be used in X-band applications.
Using a Brain-Inspired Decision-Making System to Model a Real-Time Responsive Risk Assessment of the Dynamic Tasks Involved with Hazardous Materials
Authors: Asgari Alireza , Beauregard Yvan
Published: June 2022

Risk assessment of the operations utilized in processing products and services always deals with uncertainties and complexities. The ever-evolving complex and dynamic circumstances make it very difficult to identify and analyze potential events affecting workers’ safety and health. Our first study was on managing the risky situations of a dynamic environment, the transport and storage of residual hazardous materials with high variation in operational times. It showed that the dynamicity of operational functions has a direct relation to the risk of accidents and suggested that such environments require a system to decide whether to perform each new action on a suspected risk condition or not. A practical framework, engaged close to the variable functions involved in potential events, is needed to provide reliable measures for risk assessment. Based on these measures, this framework would help to make decisions at the right time and to take preventive actions. It would support the decision-making process by recognizing the risk-associated features of available information and offer continuously updated alternatives for appropriate actions to prevent unsafe operations. In our second study, we developed a brain-inspired decision-making system for the real-time configuration of dynamic environments. That decision-making system builds knowledge from the least to the most similarities between experienced states to determine the most appropriate action(s) to rapidly reorient risky operations to a safe condition. This paper aims to verify the second study’s proposed system performance in the simulated environment discussed in our first study on residual hazardous materials transportation. We extract information, including the effective factors, from that first study and use it in the decision-making system to prevent risky transportation. This model would be useful in daily risk management as a practical framework for establishing safe operations in today’s industrial environments that involve dangerous chemical or radioactive products.
Evaluation of the Base Stability of Hydrophilic Interaction Chromatography Columns Packed with Silica or Ethylene-Bridged Hybrid Particles
Authors: Walter Thomas H. , Alden Bonnie A. , Berthelette Kenneth
Published: June 2022

Stability as a function of mobile phase pH is an important consideration when selecting a chromatographic column. While the pH stability of reversed-phase columns is widely studied, there are relatively few reports of the stability of hydrophilic interaction chromatography (HILIC) columns. We evaluated the stability of silica and ethylene-bridged hybrid HILIC columns when used with mobile phases containing basic buffers. The predominant mode of column degradation observed in our studies was a decrease in efficiency due to voiding, resulting from the hydrolysis of the silica particles. Associated with this were increases in tailing factors. Retention factor changes were also noted but were smaller than the efficiency losses. The dependence of the rate of efficiency decrease on the key variables of temperature, mobile phase pH and water content were studied for an unbonded silica column. The effect of the acetonitrile concentration on the pH of the mixed aqueous/acetonitrile mobile phases was also investigated. Using conditions found to cause a 50% decrease in efficiency after approximately five hours of exposure to the basic solution, we evaluated eight different commercially available HILIC columns containing silica or ethylene-bridged hybrid particles. The results show large differences between the stability of the silica and ethylene-bridged hybrid particle stationary phases, with the latter exhibiting greater stability.
Natural Factors on Heterogenetic Accumulations of PTEs in Sloping Farmland in a Typical Small Mountainous Watershed in Southwest China
Authors: Gao Ya , Gu Bihan , Mao Lingchen , Zhang Daofang , Tao Hong
Published: June 2022

High potential toxic element (PTE) concentrations in soils that exceed local regulatory threshold values have been reported in non-polluted mountainous areas worldwide. However, there have been few studies that have comprehensively investigated the contribution of natural factors including the parental material, pedogenesis processes and physiochemical properties of soils on the distribution of PTEs in these soils. Therefore, in this study, we studied the distribution of 13 PTEs in sloping farmland soils collected from a mountainous watershed in Guizhou Province, Southwest China. The contributions of natural influencing factors were analyzed using a geostatistical analysis and a geographic detector method. All of the PTEs were unevenly distributed, especially Sb, and the average contents of V, Cr, Co, Ni, Cu, Zn, As, Mo, Cd, Sb, Tl, Pb and Hg were 57.15, 36.20, 4.61, 12.61, 13.36, 63.50, 11.94, 0.78, 0.37, 6.44, 0.48, 27.42 and 0.36mg/kg, respectively. The proportion of samples with Cd, Hg and As exceeding the screening value of the soil pollution risk of agricultural land in China was 46.7%, 5.9% and 4.4%, respectively. Except for Cd and Pb, the q values of the PTEs calculated from the geographical detector were above 0.05, indicating that altitude changes, which affect the pedogenesis process, have a great impact on the spatial distribution. Stratigraphic factors contributed greatly to the distribution of Co, Ni and Cu, which indicates their similarity in parental material. The combined effect of clay content, topographic factors and agricultural land types had the strongest explanatory power for V, Cr, Mo and Pb. The distributions of As, Sb, Tl and Hg are strongly associated with a potential source of mercury ore, and their accumulation is also enhanced by the adsorption on soil clay. Agricultural As also contributes to its distribution.
Evaluation of the Gauss Integral
Authors: Martila Dmitri , Groote Stefan
Published: June 2022

The normal or Gaussian distribution plays a prominent role in almost all fields of science. However, it is well known that the Gauss (or Euler–Poisson) integral over a finite boundary, as is necessary, for instance, for the error function or the cumulative distribution of the normal distribution, cannot be expressed by analytic functions. This is proven by the Risch algorithm. Regardless, there are proposals for approximate solutions. In this paper, we give a new solution in terms of normal distributions by applying a geometric procedure iteratively to the problem.
Quantitative Trading through Random Perturbation Q-Network with Nonlinear Transaction Costs
Authors: Zhu Tian , Zhu Wei
Published: June 2022

In recent years, reinforcement learning (RL) has seen increasing applications in the financial industry, especially in quantitative trading and portfolio optimization when the focus is on the long-term reward rather than short-term profit. Sequential decision making and Markov decision processes are rather suited for this type of application. Through trial and error based on historical data, an agent can learn the characteristics of the market and evolve an algorithm to maximize the cumulative returns. In this work, we propose a novel RL trading algorithm utilizing random perturbation of the Q-network and account for the more realistic nonlinear transaction costs. In summary, we first design a new near-quadratic transaction cost function considering the slippage. Next, we develop a convolutional deep Q-learning network (CDQN) with multiple price input based on this cost functions. We further propose a random perturbation (rp) method to modify the learning network to solve the instability issue intrinsic to the deep Q-learning network. Finally, we use this newly developed CDQN-rp algorithm to make trading decisions based on the daily stock prices of Apple (AAPL), Meta (FB), and Bitcoin (BTC) and demonstrate its strengths over other quantitative trading methods.
A Life for Development Cooperation, Gender Equality and Food Security—Gretchen Brandow Bloom
Authors: Gatto Andrea , Morrow Nathan
Published: June 2022

Gretchen Brandow Bloom, gender and development advisor and educator, died at her home in Dupont Circle Washington DC at the age of 76 [...]
Towards a Sustainable Metamorphosis of a Small Island Tourism: Dynamizing Capacity Building, Alternating Governance Arrangements, and Emerging Political Bargaining Power
Authors: Nurhasanah Isye Susana , Van den Broeck Pieter
Published: June 2022

This study examines how local agents (non-profit-seeking actors and residents of Pahawang Island) developed empowering methods to address ecological problems during the island’s transformation from a small, secluded island to a tourist destination. Building capacity through grassroots initiatives, bottom-linked collaboration, and improved political bargaining power were critical components, constituting the conceptual framework to analyse the data. The socio-spatial shift in Pahawang Island reflects the nature, degree of involvement, and participation of multiple stakeholders. Slowly and gradually, bottom-up and later bottom-linked institutional efforts were implemented to lay the groundwork for a more sustainable ecology, social economy, and political transformation. Capacity building was dynamized by including it in a social innovation process, bottom-linked governance, and stimulated empowerment, which altered the governance, political bargaining power and policymaking power balance. Under these conditions, collaborative grassroots learning and action efforts can be a virtuous panacea for improving the sustainable governance of small islands, which is frequently disregarded in a neoliberal society.
Sustainable Urban Regeneration of Blighted Neighborhoods: The Case of Al Ghanim Neighborhood, Doha, Qatar
Authors: Mareeva Victoria Maruanova , Ahmad Ahmad Mohammad , Ferwati M. Salim , Garba Shaibu Bala
Published: June 2022

At a time when urban expansion and regeneration are being prioritized, many cities are undergoing significant widespread urban decay. Planning issues, such as the regeneration of historical areas and the redevelopment of blighted neighborhoods, have prompted a complex agenda to be put in place for urban planning practice. The most significant district is the city’s core. It is a crucial indicator of the city’s success because it contains the Central Business District (CBD) and housing. However, due to poor governmental attention, many city cores are experiencing new development, subsequently overwhelming the old neighborhoods. Consequently, old areas are witnessing urban disfigurement and fast deterioration in their physical and visual characteristics. This research utilizes urban regeneration to address the city’s core challenges to help achieve sustainable development. To test the sustainability framework, the researchers used Qatar’s local case study of the Old Ghanim neighborhood, one of Doha’s oldest districts. As a result of the original population’s relocation to suburban developments, the district has deteriorated, lacking street activity and increasing indigent public space. The researchers examined the literature on urban regeneration, conducted site visits and observations, reviewed and described case study limitations, and identified factors that contribute to the creation of a sustainable neighborhood, based on urban qualities such as integrated networks and walkable streets, open and green public spaces, and the regeneration of the old urban fabric. As a result, they intended to address a theoretical and practical gap in current local knowledge, and they additionally intended to provide a helpful tool for urban regeneration specialists. The researchers proposed a conceptual framework for rejuvenating neglected neighborhoods and ideas for ensuring urban cohesion, which is critical for improving the lives of individuals in these areas.