bluuebunny/crossref_metadata_2025 huggingface dataset:
Antipodal Linearly Tapered Slot Antenna with Quasi-Hemispherical Pattern Using Subwavelength Elements
Authors: Wang Rui , Liao Dashuang , Yang Feng
Published: January 2023

Antennas with quasi-hemispherical radiation patterns are preferred in many wide−area wireless communication systems which require the signals to uniformly cover a wide two−dimensional region. In this work, a simple but effective beamwidth broadening technique based on an antipodal linearly tapered slot antenna (ALTSA) is first proposed and then experimentally verified. Compared with most of the reported designs, the proposed antenna can significantly widen beamwidth and achieve a quasi-hemispherical radiation pattern without increasing the overall size and structural complexity. Only two rows of subwavelength metallic elements (eight elements in total) are simply and skillfully printed at specified positions on the dielectric substrate (relative permittivity εr = 2.94 and thickness h = 1.5 mm) of a general ALTSA whose peak gain is 11.7 dBi, approximately 200% half-power beamwidth (HPBW) enlargement can be obtained in all cut-planes containing the end-fire direction at the central frequency of 15 GHz, and the HPBW extensions in different cut-planes have good consistency. Thus, a quasi-hemispherical beam pattern can be acquired. Thanks to the simplicity of this method, the antenna size and structural complexity do not increase, resulting in the characteristics of easy fabrication and integration, being lightweight, and high reliability. This proposed method provides a good choice for wide−beam antenna design and will have a positive effect on the potential applications of wide-area wireless communication systems.
Distributed Finite-Time Control of Islanded Microgrid for Ancillary Services Provision
Authors: Shrivastava Sonam , Subudhi Bidyadhar , Sahu Jambeswar
Published: January 2023

This paper presents a hierarchical cyber-physical multi-agent model for an AC microgrid (MG). A new distributed finite-time secondary controller is designed in the provision of ancillary services, such as voltage and frequency synchronization and active and reactive power regulation. The control algorithm developed is fully distributed and intended to restore the system voltage and frequency to their nominal value finite time. The existing distributed controllers achieve a consensus in an infinite-time horizon, whereas the proposed control provides a quick convergence to a consensus even in the face of disturbances and restores the voltage and frequency in less than 0.25 s. For accurate active and reactive power regulation, a distributed control algorithm is proposed that corrects the power mismatch among neighboring distributed generator units and eventually for the entire MG network. A Lyapunov analysis is used to establish the upper limit on the convergence time. The proposed control scheme is evaluated and compared with the previously reported asymptotic control technique based on a neighborhood tracking error using time-domain simulations under load variation and communication constraints. The controller withstands a communication link delay up to 2 s with a small deviation and settles down to the nominal values within 0.4 s. Further, the convergence analysis shows that the performance of the proposed algorithm depends exclusively on the controller parameters and communication network connectivity, not on the line and load parameters of the AC MG. The proposed controller enables the plug-and-play capability of the AC MG and effectively reduces the load change-induced disturbance.
Towards Deploying DNN Models on Edge for Predictive Maintenance Applications
Authors: Pandey Rick , Uziel Sebastian , Hutschenreuther Tino , Krug Silvia
Published: January 2023

Almost all rotating machinery in the industry has bearings as their key building block and most of these machines run 24 × 7. This makes bearing health prediction an active research area for predictive maintenance solutions. Many state of the art Deep Neural Network (DNN) models have been proposed to solve this. However, most of these high performance models are computationally expensive and have high memory requirements. This limits their use to very specific industrial applications with powerful hardwares deployed close the the machinery. In order to bring DNN-based solutions to a potential use in the industry, we need to deploy these models on Microcontroller Units (MCUs) which are cost effective and energy efficient. However, this step is typically neglected in literature as it poses new challenges. The primary concern when inferencing the DNN models on MCUs is the on chip memory of the MCU that has to fit the model, the data and additional code to run the system. Almost all the state of the art models fail this litmus test since they feature too many parameters. In this paper, we show the challenges related to the deployment, review possible solutions and evaluate one of them showing how the deployment can be realized and what steps are needed. The focus is on the steps required for the actual deployment rather than finding the optimal solution. This paper is among the first to show the deployment on MCUs for a predictive maintenance use case. We first analyze the gap between State Of The Art benchmark DNN models for bearing defect classification and the memory constraint of two MCU variants. Additionally, we review options to reduce the model size such as pruning and quantization. Afterwards, we evaluate a solution to deploy the DNN models by pruning them in order to fit them into microcontrollers. Our results show that most models under test can be reduced to fit MCU memory for a maximum loss of 3% in average accuracy of the pruned models in comparison to the original models. Based on the results, we also discuss which methods are promising and which combination of model and feature work best for the given classification problem.
Design of A Smart Tourism Management System through Multisource Data Visualization-Based Knowledge Discovery
Authors: Qin Zhicong , Pan Younghwan
Published: January 2023

Nowadays, tourism management is a universal concern in the world. It is important for generating tourism characteristics for travelers, so as to digitally facilitate tourism business scheduling. Currently, there is still a lack of technologies that are competent in managing tourism business affairs. Therefore, in this paper a smart tourism management system is designed through multisource data visualization-based knowledge discovery. Firstly, this work presents the total architecture of a tourism management system with respect to three modules: data collection, data visualization, and knowledge discovery. Then, multisource business data are processed with the use of visualization techniques so as to output statistical analysis results for different individuals. On this basis, characterized knowledge can be found from previous visualization results and demonstrated for travelers or administrators. In addition, a case study on real data is conducted to test running performance of the proposed tourism management system. The main body of public service tourism is the government or other social organizations that do not regard profit as the main purpose; public service tourism a general term for products and services with obvious public nature. The testing results show that user preferences can be mined and corresponding travelling plans can be suggested via multisource data visualization-based knowledge discovery means.
Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies
Authors: Cesari Paola , Cristani Matteo , Demrozi Florenc , Pascucci Francesco , Picotti Pietro Maria , Pravadelli Graziano , Tomazzoli Claudio , Turetta Cristian , Workneh Tewabe Chekole , Zenti Luca
Published: January 2023

In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups.
The Inflection Point of Single Event Transient in SiGe HBT at a Cryogenic Temperature
Authors: Pan Xiaoyu , Guo Hongxia , Lu Chao , Zhang Hong , Liu Yinong
Published: January 2023

Basing our findings on our previous pulsed laser testing results, we have experimentally demonstrated that there is an inflection point of a single event transient (SET) in the silicon-germanium heterojunction bipolar transistors (SiGe HBTs) with a decreasing temperature from +20 °C to −180 °C. Additionally, the changes in the parasitic resistivity of the carrier collection pathway due to incomplete ionization could play a key role. In this paper, we found that the incident-heavy ion’s parameters could also have an important impact on the SET inflection point by introducing the ion track structures generated by Geant4 simulation to the TCAD transient simulation. Heavy ion with a low linear energy transfer (LET) will not trigger the ion shunt effect of SiGe HBT and the inflection point will not occur until −200 °C. For high LET ions’ incidence, the high-density electron-hole pairs (EHPs) could significantly affect the parasitic resistivity on the pathway and lead to an earlier inflection point. The present results and methods could provide a new reference for the effective evaluation of single-event effects in bipolar transistors and circuits at cryogenic temperatures and provide new evidence of the SiGe technology’s potential for applications in extreme cryogenic environments.
Segmentation of Nucleus and Cytoplasm from H&E-Stained Follicular Lymphoma
Authors: Saxena Pranshu , Goyal Anjali , Bivi Mariyam Aysha , Singh Sanjay Kumar , Rashid Mamoon
Published: January 2023

This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.
Multidimensional Domain Knowledge Framework for Poet Profiling
Authors: Zhou Ai , Zhang Yijia , Lu Mingyu
Published: January 2023

Authorship profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text’s stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution.
Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE
Authors: Palanca Javier , Rincon Jaime Andres , Carrascosa Carlos , Julian Vicente Javier , Terrasa Andrés
Published: January 2023

Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper.
Lightweight Video Super-Resolution for Compressed Video
Authors: Kwon Ilhwan , Li Jun , Prasad Mukesh
Published: January 2023

Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.
A Multi-Objective Approach to Robust Control of Air Handling Units for Optimized Energy Performance
Authors: Wani Mubashir , Hafiz Faizal , Swain Akshya , Ukil Abhisek
Published: January 2023

This paper presents a robust control framework with meta-heuristic intelligence to optimize the energy performance of air handling units (AHUs) and to maximize the thermal comfort of occupants by judiciously selecting the temperature set points of two controllers (i.e., the H∞ controller and the boiler controller). The selection of these set points is formulated as a multi-objective optimization problem, where the goal is to balance energy consumption with thermal comfort. Furthermore, the uncertainty weights of the H∞ controller are estimated to minimize oscillations in the outflow air temperature of the AHU plant. The performance of the proposed framework is investigated by considering the real-time weather data of Auckland, New Zealand. The results of the simulation show that the proposed robust control framework could significantly reduce oscillations in the outflow air temperature compared with the conventional case, where the temperature set points are selected empirically. Moreover, annual energy savings of 49.13% are achieved without compromising the thermal comfort.
Detection and Classification of Printed Circuit Boards Using YOLO Algorithm
Authors: Glučina Matko , Anđelić Nikola , Lorencin Ivan , Car Zlatan
Published: January 2023

Printed circuit boards (PCBs) are an indispensable part of every electronic device used today. With its computing power, it performs tasks in much smaller dimensions, but the process of making and sorting PCBs can be a challenge in PCB factories. One of the main challenges in factories that use robotic manipulators for “pick and place” tasks are object orientation because the robotic manipulator can misread the orientation of the object and thereby grasp it incorrectly, and for this reason, object segmentation is the ideal solution for the given problem. In this research, the performance, memory size, and prediction of the YOLO version 5 (YOLOv5) semantic segmentation algorithm are tested for the needs of detection, classification, and segmentation of PCB microcontrollers. YOLOv5 was trained on 13 classes of PCB images from a publicly available dataset that was modified and consists of 1300 images. The training was performed using different structures of YOLOv5 neural networks, while nano, small, medium, and large neural networks were used to select the optimal network for the given challenge. Additionally, the total dataset was cross validated using 5-fold cross validation and evaluated using mean average precision, precision, recall, and F1-score classification metrics. The results showed that large, computationally demanding neural networks are not required for the given challenge, as demonstrated by the YOLOv5 small model with the obtained mAP, precision, recall, and F1-score in the amounts of 0.994, 0.996, 0.995, and 0.996, respectively. Based on the obtained evaluation metrics and prediction results, the obtained model can be implemented in factories for PCB sorting applications.
FPGA/AI-Powered Architecture for Anomaly Network Intrusion Detection Systems
Authors: Pham-Quoc Cuong , Bao Tran Hoang Quoc , Thinh Tran Ngoc
Published: January 2023

This paper proposes an architecture to develop machine learning/deep learning models for anomaly network intrusion detection systems on reconfigurable computing platforms. We build two models to validate the framework: Anomaly Detection Autoencoder (ADA) and Artificial Neural Classification (ANC) in the NetFPGA-sume platform. Three published data sets NSL-KDD, UNSW-NB15, and CIC-IDS2017 are used to test the deployed models’ throughput, latency, and accuracy. Experimental results with the NetFPGA-SUME show that the ADA model uses 20.97% LUTs, 15.16% FFs, 19.42% BRAM, and 6.81% DSP while the ANC model requires 21.39% LUTs, 15.19% FFS, 14.59% BRAM, and 3.67% DSP. ADA and ANC achieve a bandwidth of up to 28.7 Gbps and 34.74 Gbps, respectively. In terms of throughput, ADA can process at up to 18.7 Gops, while ADA can offer 10 Gops with different datasets. With the NSL-KDD dataset, the ADA model achieves 90.87% accuracy and a false negative rate of 4.86%. The ANC model with UNSW-NB15 and CIC-IDS2017 obtains accuracy of 87.49% and 98.22%, respectively, with the false negative rates achieving 2.0% and 6.2%, respectively.
A Low-Loss Impedance Transformer-Less Fish-Tail-Shaped MS-to-WG Transition for K-/Ka-/Q-/U-Band Applications
Authors: Varshney Atul , Sharma Vipul , Nayak Chittaranjan , Goyal Amit Kumar , Massoud Yehia
Published: January 2023

This paper presents a low-loss, high-transmission, broadside-coupled, transverse, reciprocal, two-port, and nature-inspired Ka-band transition design to move the electromagnetic energy of a rectangular waveguide (RWG) to the microstrip (MS) line. The proposed transition is simple in structure, with an excellent insertion loss, S12/S21, (IL) near −0.40 dB and return loss, S11/S22, of <−21 dB, while the VSWR value is very close to one. Thus, this transition is an outstanding candidate for MIC/MMIC-based millimeter wave, military, and RADAR applications, as well as in wireless and satellite communications as a compatible connector. This transition also provides a bandwidth of 21.50 GHz (23.52–45.0 GHz) for the abovementioned microwave applications, at a <−10 dB return loss (RL). The proposed transition model also exhibits a −15 dB absolute bandwidth of 27.06–23.44 GHz, with an insertion loss < −0.60 dB. Due to a return loss of <−15 dB over an ultra-wide bandwidth, the proposed transition is not only a good candidate for full Ka-band (26–40 GHz) applications but also covers applications for K-band from 23.74 GHz to 26.0 GHz, Q-band applications from 33.0 to 45.0 GHz, and U-band applications from 40.0 GHz to 45 GHz, with approximately 97% power transmission between the transmission lines and only 3% power reflections. The impedance matching at the designed frequency between the RWG and MS line is achieved by flaring one end of the MS line inside the RWG in a fishtail shape, without the need for a quarter-wave/tapered/exponential/Binomial, or multi-section Chebyshev transformer. The main goal of this research was to design a multi-section impedance-transformer-free, simple, and easy-to-fabricate MS line, to share electromagnetic (EM) energy between an MS line and RWG in 30 GHz satellite applications and 30 GHz high-frequency applications, for interconnects screen printed on an organic substrate for flexible, wearable, textile conformal antennas. This work also presents an exact RLC electrical equivalence model of the MS line (fishtail) to RWG transition at 30 GHz. The novelty of this work is that the proposed transition can be used for four microwave bands of electromagnetic energy transmission, with extremely low reflection, and with a compact, simple-design MS line, and simple RWG.
Two-Dimensional Positioning with Machine Learning in Virtual and Real Environments
Authors: Kóczi Dávid , Németh József , Sárosi József
Published: January 2023

In this paper, a ball-on-plate control system driven only by a neural network agent is presented. Apart from reinforcement learning, no other control solution or support was applied. The implemented device, driven by two servo motors, learned by itself through thousands of iterations how to keep the ball in the center of the resistive sensor. We compared the real-world performance of agents trained in both a real-world and in a virtual environment. We also examined the efficacy of a virtually pre-trained agent fine-tuned in the real environment. The obtained results were evaluated and compared to see which approach makes a good basis for the implementation of a control task implemented purely with a neural network.
Pruning Multi-Scale Multi-Branch Network for Small-Sample Hyperspectral Image Classification
Authors: Bai Yu , Xu Meng , Zhang Lili , Liu Yuxuan
Published: January 2023

In recent years, the use of deep learning models has developed rapidly in the field of hyperspectral image (HSI) classification. However, most network models cannot make full use of the rich spatial-spectral features in hyperspectral images, being disadvantaged by their complex models and low classification accuracy for small-sample data. To address these problems, we present a lightweight multi-scale multi-branch hybrid convolutional network for small-sample classification. The network contains two new modules, a pruning multi-scale multi-branch block (PMSMBB) and a 3D-PMSMBB, each of which contains a multi-branch part and a pruning part. Each branch of the multi-branch part contains a convolutional kernel of different scales. In the training phase, the multi-branch part can extract rich feature information through different perceptual fields using the asymmetric convolution feature, which can effectively improve the classification accuracy of the model. To make the model lighter, pruning is introduced in the master branch of each multi-branch module, and the pruning part can remove the insignificant parameters without affecting the learning of the multi-branch part, achieving a light weight model. In the testing phase, the multi-branch part and the pruning part are jointly transformed into one convolution, without adding any extra parameters to the network. The study method was tested on three datasets: Indian Pines (IP), Pavia University (PU), and Salinas (SA). Compared with other advanced classification models, this pruning multi-scale multi-branch hybrid convolutional network (PMSMBN) had significant advantages in HSI small-sample classification. For instance, in the SA dataset with multiple crops, only 1% of the samples were selected for training, and the proposed method achieved an overall accuracy of 99.70%.
Lightweight-BIoV: Blockchain Distributed Ledger Technology (BDLT) for Internet of Vehicles (IoVs)
Authors: Laghari Asif Ali , Khan Abdullah Ayub , Alkanhel Reem , Elmannai Hela , Bourouis Sami
Published: January 2023

The vast enhancement in the development of the Internet of Vehicles (IoV) is due to the impact of the distributed emerging technology and topology of the industrial IoV. It has created a new paradigm, such as the security-related resource constraints of Industry 5.0. A new revolution and dimension in the IoV popup raise various critical challenges in the existing information preservation, especially in node transactions and communication, transmission, trust and privacy, and security-protection-related problems, which have been analyzed. These aspects pose serious problems for the industry to provide vehicular-related data integrity, availability, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects against the increasing number of multiple transactions. In addition, there has been a lot of research interest that intersects with blockchain and Internet of Vehicles association. In this regard, the inadequate performance of the Internet of Vehicles and connected nodes and the high resource requirements of the consortium blockchain ledger have not yet been tackled with a complete solution. The introduction of the NuCypher Re-encryption infrastructure, hashing tree and allocation, and blockchain proof-of-work require more computational power as well. This paper contributes in two different folds. First, it proposes a blockchain sawtooth-enabled modular architecture for protected, secure, and trusted execution, service delivery, and acknowledgment with immutable ledger storage and security and peer-to-peer (P2P) network on-chain and off-chain inter-communication for vehicular activities. Secondly, we design and create a smart contract-enabled data structure in order to provide smooth industrial node streamlined transactions and broadcast content. Substantially, we develop and deploy a hyperledger sawtooth-aware customized consensus for multiple proof-of-work investigations. For validation purposes, we simulate the exchange of information and related details between connected devices on the IoV. The simulation results show that the proposed architecture of BIoV reduces the cost of computational power down to 37.21% and the robust node generation and exchange up to 56.33%. Therefore, only 41.93% and 47.31% of the Internet of Vehicles-related resources and network constraints are kept and used, respectively.
Hybrid Backstepping-Super Twisting Algorithm for Robust Speed Control of a Three-Phase Induction Motor
Authors: Ali Sadia , Prado Alvaro , Pervaiz Mahmood
Published: January 2023

This paper proposes a Hybrid Backstepping Super Twisting Algorithm for robust speed control of a three-phase Induction Motor in the presence of load torque uncertainties. First of all, a three-phase squirrel cage Induction Motor is modeled in MATLAB/Simulink. This is then followed by the design of different non-linear controllers, such as sliding mode control (SMC), super twisting SMC, and backstepping control. Furthermore, a novel controller is designed by the synergy of two methods, such as backstepping and super twisting SMC (Back-STC), to obtain the benefits of both techniques and, thereby, improve robustness. The sigmoid function is used with an exact differentiator to minimize the high-speed discontinuities present in the input channel. The efficacy of this novel design and its performance were evidenced in comparison with other methods, carried out by simulations in MATLAB/Simulink. Regression parameters, such as ISE (Integral Square error), IAE (Integral Absolute error) and ITAE (Integral Time Absolute error), were calculated in three different modes of operation: SSM (Start-Stop Mode), NOM (Normal Operation Mode) and DRM (Disturbance Rejection Mode). In the end, the numerical values of the regression parameters were quantitatively analyzed to draw conclusions regarding the tracking performance and robustness of the implemented non-linear control techniques.
Attention Mechanism Trained with Small Datasets for Biomedical Image Segmentation
Authors: Weng Weihao , Zhu Xin , Jing Lei , Dong Mianxiong
Published: January 2023

The understanding of long-range pixel–pixel dependencies plays a vital role in image segmentation. The use of a CNN plus an attention mechanism still has room for improvement, since existing transformer-based architectures require many thousands of annotated training samples to model long-range spatial dependencies. This paper presents a smooth attention branch (SAB), a novel architecture that simplifies the understanding of long-range pixel–pixel dependencies for biomedical image segmentation in small datasets. The SAB is essentially a modified attention operation that implements a subnetwork via reshaped feature maps instead of directly calculating a softmax value over the attention score for each input. The SAB fuses multilayer attentive feature maps to learn visual attention in multilevel features. We also introduce position blurring and inner cropping specifically for small-scale datasets to prevent overfitting. Furthermore, we redesign the skip pathway for the reduction of the semantic gap between every captured feature of the contracting and expansive path. We evaluate the architecture of U-Net with the SAB (SAB-Net) by comparing it with the original U-Net and widely used transformer-based models across multiple biomedical image segmentation tasks related to the Brain MRI, Heart MRI, Liver CT, Spleen CT, and Colonoscopy datasets. Our training set was made of random 100 images of the original training set, since our goal was to adopt attention mechanisms for biomedical image segmentation tasks with small-scale labeled data. An ablation study conducted on the brain MRI test set demonstrated that every proposed method achieved an improvement in biomedical image segmentation. Integrating the proposed methods helped the resulting models consistently achieve outstanding performance on the above five biomedical segmentation tasks. In particular, the proposed method with U-Net improved its segmentation performance over that of the original U-Net by 13.76% on the Brain MRI dataset. We proposed several novel methods to address the need for modeling long-range pixel–pixel dependencies in small-scale biomedical image segmentation. The experimental results illustrated that each method could improve the medical image segmentation accuracy to various degrees. Moreover, SAB-Net, which integrated all proposed methods, consistently achieved outstanding performance on the five biomedical segmentation tasks.
Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path
Authors: Sharma Kaushlendra , Doriya Rajesh , Shastri Sameer , Aljrees Turki , Singh Kamred Udham , Pandey Saroj Kumar , Singh Teekam , Samriya Jitendra Kumar , Kumar Ankit
Published: January 2023

With the development of computer technology and artificial intelligence (AI), service robots are widely used in our daily life. At the same time, the manufacturing cost of the robots is too expensive for almost all small companies. The greatest technical limitations are the design of the service robot and the resource sharing of the robot groups. Path planning for robots is one of the issues playing an important role in every application of service robots. Path optimization, fast computation, and minimum computation time are required in all applications. This paper aims to propose the Google Cloud Computing Platform and Amazon Web Service (AWS) platforms for robot path planning. The aim is to identify the effect and impact of using a cloud computing platform for service robots. The cloud approach shifts the computation load from robots to the cloud server. Three different path-planning algorithms were considered to find the path for robots using the Google Cloud Computing Platform, while with AWS, three different types of environments, namely dense, moderate, and sparse, were selected to run the path-planning algorithms for robots. The paper presents the comparison and analysis of the results carried out for robot path planning using cloud services with that of the traditional approach. The proposed approach of using a cloud platform performs better in this case. The time factor is crucially diagnosed and presented in the paper. The major advantage derived from this experiment is that as the size of the environment increases, the respective relative delay decreases. This proves that increasing the scale of work can be beneficial by using cloud platforms. The result obtained using the proposed methodology proves that using cloud platforms improves the efficiency of path planning. The result reveals that using the cloud computing platform for service robots can change the entire perspective of using service robots in the future. The main advantage is that with the increase in the scale of services, the system remains stable, while the traditional system starts deteriorating in terms of performance.
Deep Feature Meta-Learners Ensemble Models for COVID-19 CT Scan Classification
Authors: Thomas Jibin B. , K. V. Shihabudheen , Sulthan Sheik Mohammed , Al-Jumaily Adel
Published: January 2023

The infectious nature of the COVID-19 virus demands rapid detection to quarantine the infected to isolate the spread or provide the necessary treatment if required. Analysis of COVID-19-infected chest Computed Tomography Scans (CT scans) have been shown to be successful in detecting the disease, making them essential in radiology assessment and screening of infected patients. Single-model Deep CNN models have been used to extract complex information pertaining to the CT scan images, allowing for in-depth analysis and thereby aiding in the diagnosis of the infection by automatically classifying the chest CT scan images as infected or non-infected. The feature maps obtained from the final convolution layer of the Deep CNN models contain complex and positional encoding of the images’ features. The ensemble modeling of these Deep CNN models has been proved to improve the classification performance, when compared to a single model, by lowering the generalization error, as the ensemble can meta-learn from a broader set of independent features. This paper presents Deep Ensemble Learning models to synergize Deep CNN models by combining these feature maps to create deep feature vectors or deep feature maps that are then trained on meta shallow and deep learners to improve the classification. This paper also proposes a novel Attentive Ensemble Model that utilizes an attention mechanism to focus on significant feature embeddings while learning the Ensemble feature vector. The proposed Attentive Ensemble model provided better generalization, outperforming Deep CNN models and conventional Ensemble learning techniques, as well as Shallow and Deep meta-learning Ensemble CNNs models. Radiologists can use the presented automatic Ensemble classification models to assist identify infected chest CT scans and save lives.
Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications
Authors: Tovar Nathaniel , Kwon Sean (Seok-Chul) , Jeong Jinseong
Published: January 2023

Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.
Sustainability Reporting Based on GRI Standards within Organizations in Romania
Authors: Mihai Florin , Aleca Ofelia Ema
Published: January 2023

This article aims to establish an overview of sustainable development practices within organizations in Romania, to analyze how these practices are connected to GRI (Global Reporting Initiative) indexes, and to identify the connection between the efforts made to ensure sustainable processes and the economic performance of the enterprise. Given the ever more prevalent digitalization of the company processes, implementing a sustainability management system and a sustainability monitoring system becomes more accessible, and there is increasing transparency of the information on the activities supporting sustainability. Using the methodology, the authors carried out a quantitative and qualitative analysis of the content of the sustainability reports published by Romanian companies in the top 500 by the number of employees. The economic and financial data on these companies were taken from the topfirme.ro platform. Based on the sustainability reports published by the companies, the sustainability score was calculated using the reported data, according to the GRI (Global Reporting Initiative) standard and analyzed considering the industry in which the organizations operate, specific industries being more sensitive to environmental issues due to certain specificities of their economic activity that is based on processes prone to hurt the environment. During the research, hypotheses were formulated and tested regarding the factors that could influence the reporting of activities in support of sustainability (the sustainability report score and the page count of the sustainability report) and the economic performance of organizations (profit, income). One of the study’s conclusions was related to the financial performance of companies expressed by the net profit. It is positively influenced, although to a small degree, but promising in terms of the future of company policies on freedom of association and collective bargaining, expressed in the sustainability report through the GRI index. Regarding the company belonging to a particular industry sector, the research proved that the companies’ sustainability policies are not influenced by it, which could point to the idea that sustainability issues are equally crucial for all industries. Furthermore, an essential conclusion of the research is that the performance and the reliability of sustainability policies must be monitored and implemented within an integrated sustainability management system, which would ensure a high level of coherence and transparency of the guidelines, the provided data, but also of the monitoring within companies or by the stakeholders. Finally, the research offers results regarding the interest of Romanian organizations in the measures and actions taken to ensure the sustainable development of the economic environment.
Physical Activity Recognition Based on Deep Learning Using Photoplethysmography and Wearable Inertial Sensors
Authors: Hnoohom Narit , Mekruksavanich Sakorn , Jitpattanakul Anuchit
Published: January 2023

Human activity recognition (HAR) extensively uses wearable inertial sensors since this data source provides the most information for non-visual datasets’ time series. HAR research has advanced significantly in recent years due to the proliferation of wearable devices with sensors. To improve recognition performance, HAR researchers have extensively investigated other sources of biosignals, such as a photoplethysmograph (PPG), for this task. PPG sensors measure the rate at which blood flows through the body, and this rate is regulated by the heart’s pumping action, which constantly occurs throughout the body. Even though detecting body movement and gestures was not initially the primary purpose of PPG signals, we propose an innovative method for extracting relevant features from the PPG signal and use deep learning (DL) to predict physical activities. To accomplish the purpose of our study, we developed a deep residual network referred to as PPG-NeXt, designed based on convolutional operation, shortcut connections, and aggregated multi-branch transformation to efficiently identify different types of daily life activities from the raw PPG signal. The proposed model achieved more than 90% prediction F1-score from experimental results using only PPG data on the three benchmark datasets. Moreover, our results indicate that combining PPG and acceleration signals can enhance activity recognition. Although, both biosignals—electrocardiography (ECG) and PPG—can differentiate between stationary activities (such as sitting) and non-stationary activities (such as cycling and walking) with a level of success that is considered sufficient. Overall, our results propose that combining features from the ECG signal can be helpful in situations where pure tri-axial acceleration (3D-ACC) models have trouble differentiating between activities with relative motion (e.g., walking, stair climbing) but significant differences in their heart rate signatures.
Academic Framing as a Cause of Eco-Anxiety
Authors: Chiolero Arnaud
Published: January 2023

Eco-anxiety, a highly mediatized emotion that is complex to characterize [...]
Effect of Landscape Elements on Public Psychology in Urban Park Waterfront Green Space: A Quantitative Study by Semantic Segmentation
Authors: Li Junyi , Huang Ziluo , Zheng Dulai , Zhao Yujie , Huang Peilin , Huang Shanjun , Fang Wenqiang , Fu Weicong , Zhu Zhipeng
Published: January 2023

Urban park waterfront green spaces provide positive mental health benefits to the public. In order to further explore the specific influence mechanism between landscape elements and public psychological response, 36 typical waterfront green areas in Xihu Park and Zuohai Park in Gulou District, Fuzhou City, Fujian Province, China, were selected for this study. We used semantic segmentation technology to quantitatively decompose the 36 scenes of landscape elements and obtained a public psychological response evaluation using virtual reality technology combined with questionnaire interviews. The main results showed that: (1) the Pyramid Scene Parsing Network (PSPNet) is a model suitable for quantitative decomposition of landscape elements of urban park waterfront green space; (2) the public’s overall evaluation of psychological responses to the 36 scenes was relatively high, with the psychological dimension scoring the highest; (3) different landscape elements showed significant differences in four dimensions. Among the elements, plant layer, pavement proportion, and commercial facilities all have an impact on the four dimensions; and (4) the contribution rate of the four element types to the public’s psychological response is shown as spatial element (37.9%) > facility element (35.1%) > natural element (25.0%) > construction element (2.0%). The obtained results reveal the influence of different landscape elements in urban park waterfront green spaces on public psychology and behavior. Meanwhile, it provides links and methods that can be involved in the planning and design of urban park waterfront green space, and also provides emerging technical support and objective data reference for subsequent research.