bluuebunny/crossref_metadata_2025 huggingface dataset:
Electrochemical Properties of Phytosynthesized Gold Nanoparticles for Electrosensing
Authors: Stozhko Natalia Yu. , Bukharinova Maria A. , Khamzina Ekaterina I. , Tarasov Aleksey V.
Published: January 2022

Gold nanoparticles are widely used in electrosensing. The current trend is to phytosynthesize gold nanoparticles (phyto-AuNPs) on the basis of the “green” chemistry approach. Phyto-AuNPs are biologically and catalytically active, stable and biocompatible, which opens up broad perspectives in a variety of applications, including tactile, wearable (bio)sensors. However, the electrochemistry of phytosynthesized nanoparticles is not sufficiently studied. This work offers a comprehensive study of the electrochemical activity of phyto-AuNPs depending on the synthesis conditions. It was found that with an increase in the aliquot of the plant extract, its antioxidant activity (AOA) and pH, the electrochemical activity of phyto-AuNPs grows, which is reflected in the peak potential decrease and an increase in the peak current of phyto-AuNPs electrooxidation. It has been shown that AOA is an important parameter for obtaining phyto-AuNPs with desired properties. Electrodes modified with phyto-AuNPs have demonstrated better analytical characteristics than electrodes with citrate AuNPs in detecting uric and ascorbic acids under model conditions. The data about the phyto-AuNPs’ electrochemistry may be useful for creating highly effective epidermal sensors with good biocompatibility.
Generalized Frequency Division Multiplexing-Based Low-Power Underwater Acoustic Image Transceiver
Authors: Lin Chin-Feng , Wu Cheng-Fong , Hsieh Ching-Lung , Chang Shun-Hsyung , Parinov Ivan A. , Shevtsov Sergey
Published: January 2022

In this paper, a low-power underwater acoustic (UWA) image transceiver based on generalized frequency division multiplexing (GFDM) modulation for underwater communication is proposed. The proposed transceiver integrates a low-density parity-check code error protection scheme, adaptive 4-quadrature amplitude modulation (QAM) and 16-QAM strategies, GFDM modulation, and a power assignment mechanism in an UWA image communication environment. The transmission bit error rates (BERs), the peak signal-to-noise ratios (PSNRs) of the received underwater images, and the power-saving ratio (PSR) of the proposed transceiver obtained using 4-QAM and 16-QAM, with perfect channel estimation, and channel estimation errors (CEEs) of 5%, 10%, and 20% were simulated. The PSNR of the received underwater image is 44.46 dB when using 4-QAM with a CEE of 10%. In contrast, PSNR is 48.79 dB when using 16-QAM with a CEE of 10%. When BER is 10−4, the received UW images have high PSNR values and high resolutions, indicating that the proposed transceiver is suitable for underwater image sensor signal transmission.
The Application of Microelectromechanical Systems (MEMS) Accelerometers to the Assessment of Blast Threat to Armored Vehicle Crew
Authors: Kciuk Sławomir , Krzystała Edyta , Mężyk Arkadiusz , Szmidt Paweł
Published: January 2022

This paper describes the development and application of an autonomous register and measurement system (ARMS), and the application of microelectromechanical systems (MEMS) accelerometers to the assessment of blast threat to armored vehicle crews. Taking measurements with reference to an explosion is one of the principal issues in the protection of crews of special vehicles. The proposed ARMS reduces research costs and contributes to the development of an autonomous, wireless test stand, applicable in various research areas and industry. The ARMS performs data acquisition with simultaneous measurement in multiple channels. The maximum sampling rate is 100 kHz and the sensor range is ±500 g. This solution is an alternative to cable systems, which have a high energy demand. The functionality of the developed autonomous measuring system is demonstrated experimentally. The paper concludes with a field study of the proposed system and the application of MEMS accelerometers via a mine blast test of a military vehicle at level 4 of STANAG 4569.
Fault Identification in Electric Servo Actuators of Robot Manipulators Described by Nonstationary Nonlinear Dynamic Models Using Sliding Mode Observers
Authors: Zuev Alexander , Zhirabok Alexey N. , Filaretov Vladimir , Protsenko Alexander
Published: January 2022

The problem of fault identification in electric servo actuators of robot manipulators described by nonstationary nonlinear dynamic models under disturbances is considered. To solve the problem, sliding mode observers are used. The suggested approach is based on the reduced order model of the original system having different sensitivity to faults and disturbances. This model is realized in canonical form that enables relaxing the limitation imposed on the original system. Theoretical results are illustrated by practical example.
An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals
Authors: Palumbo Arrigo , Ielpo Nicola , Calabrese Barbara
Published: January 2022

Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.
Human Activity Recognition via Hybrid Deep Learning Based Model
Authors: Khan Imran Ullah , Afzal Sitara , Lee Jong Weon
Published: January 2022

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.
Proposal for an IIoT Device Solution According to Industry 4.0 Concept
Authors: Vaclavova Andrea , Strelec Peter , Horak Tibor , Kebisek Michal , Tanuska Pavol , Huraj Ladislav
Published: January 2022

Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client’s criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process.
Evaluation of a Yeast–Polypyrrole Biocomposite Used in Microbial Fuel Cells
Authors: Zinovicius Antanas , Rozene Juste , Merkelis Timas , Bruzaite Ingrida , Ramanavicius Arunas , Morkvenaite-Vilkonciene Inga
Published: January 2022

Electrically conductive polymers are promising materials for charge transfer from living cells to the anodes of electrochemical biosensors and biofuel cells. The modification of living cells by polypyrrole (PPy) causes shortened cell lifespan, burdens the replication process, and diminishes renewability in the long term. In this paper, the viability and morphology non-modified, inactivated, and PPy-modified yeasts were evaluated. The results displayed a reduction in cell size, an incremental increase in roughness parameters, and the formation of small structural clusters of polymers on the yeast cells with the increase in the pyrrole concentration used for modification. Yeast modified with the lowest pyrrole concentration showed minimal change; thus, a microbial fuel cell (MFC) was designed using yeast modified by a solution containing 0.05 M pyrrole and compared with the characteristics of an MFC based on non-modified yeast. The maximal generated power of the modified system was 47.12 mW/m2, which is 8.32 mW/m2 higher than that of the system based on non-modified yeast. The open-circuit potentials of the non-modified and PPy-modified yeast-based cells were 335 mV and 390 mV, respectively. Even though applying a PPy layer to yeast increases the charge-transfer efficiency towards the electrode, the damage done to the cells due to modification with a higher concentration of PPy diminishes the amount of charge transferred, as the current density drops by 846 μA/cm2. This decrease suggests that modification by PPy may have a cytotoxic effect that greatly hinders the metabolic activity of yeast.
Centralized Threshold Key Generation Protocol Based on Shamir Secret Sharing and HMAC Authentication
Authors: Abdel Hakeem Shimaa A. , Kim HyungWon
Published: January 2022

Many group key management protocols have been proposed to manage key generation and distribution of vehicular communication. However, most of them suffer from high communication and computation costs due to the complex elliptic curve and bilinear pairing cryptography. Many shared secret protocols have been proposed using polynomial evaluation and interpolation to solve the previous complexity issues. This paper proposes an efficient centralized threshold shared secret protocol based on the Shamir secret sharing technique and supporting key authentication using Hashed Message Authentication Code Protocol (HMAC). The proposed protocol allows the group manager to generate a master secret key for a group of n vehicles and split this key into secret shares; each share is distributed securely to every group member. t-of-n vehicles must recombine their secret shares and recover the original secret key. The acceptance of the recovered key is based on the correctness of the received HMAC signature to verify the group manager’s identity and ensure the key confidentiality. The proposed protocol is unconditionally secure and unbreakable using infinite computing power as t, or more than t secret shares are required to reconstruct the key. In contrast, attackers with t−1 secret shares cannot leak any information about the original secret key. Moreover, the proposed protocol reduces the computation cost due to using polynomial evaluation to generate the secret key and interpolation to recover the secret key, which is very simple and lightweight compared with the discrete logarithm computation cost in previous protocols. In addition, utilizing a trusted group manager that broadcasts some public information is important for the registered vehicles to reconstruct the key and eliminate secure channels between vehicles. The proposed protocol reduces the communication cost in terms of transmitted messages between vehicles from 2(t−1) messages in previous shared secret protocols to zero messages. Moreover, it reduces the received messages at vehicles from 2t to two messages. At the same time, it allows vehicles to store only a single secret share compared with other shared secret protocols that require storage of t secret shares. The proposed protocol security level outperforms the other shared secret protocols security, as it supports key authentication and confidentiality using HMAC that prevents attackers from compromising or faking the key.
An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society
Authors: Khan Shahzad , Iqbal Waseem , Waheed Abdul , Mehmood Gulzar , Khan Shawal , Zareei Mahdi , Biswal Rajesh Roshan
Published: January 2022

The ever-growing ecosystem of the Internet of Things (IoT) integrating with the ever-evolving wireless communication technology paves the way for adopting new applications in a smart society. The core concept of smart society emphasizes utilizing information and communication technology (ICT) infrastructure to improve every aspect of life. Among the variety of smart services, eHealth is at the forefront of these promises. eHealth is rapidly gaining popularity to overcome the insufficient healthcare services and provide patient-centric treatment for the rising aging population with chronic diseases. Keeping in view the sensitivity of medical data, this interfacing between healthcare and technology has raised many security concerns. Among the many contemporary solutions, attribute-based encryption (ABE) is the dominant technology because of its inherent support for one-to-many transfer and fine-grained access control mechanisms to confidential medical data. ABE uses costly bilinear pairing operations, which are too heavy for eHealth’s tiny wireless body area network (WBAN) devices despite its proper functionality. We present an efficient and secure ABE architecture with outsourcing intense encryption and decryption operations in this work. For practical realization, our scheme uses elliptic curve scalar point multiplication as the underlying technology of ABE instead of costly pairing operations. In addition, it provides support for attribute/users revocation and verifiability of outsourced medical data. Using the selective-set security model, the proposed scheme is secure under the elliptic curve decisional Diffie–Hellman (ECDDH) assumption. The performance assessment and top-ranked value via the help of fuzzy logic’s evaluation based on distance from average solution (EDAS) method show that the proposed scheme is efficient and suitable for access control in eHealth smart societies.
DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
Authors: Ibrahem Hatem , Salem Ahmed , Kang Hyun-Soo
Published: January 2022

We propose Depth-to-Space Net (DTS-Net), an effective technique for semantic segmentation using the efficient sub-pixel convolutional neural network. This technique is inspired by depth-to-space (DTS) image reconstruction, which was originally used for image and video super-resolution tasks, combined with a mask enhancement filtration technique based on multi-label classification, namely, Nearest Label Filtration. In the proposed technique, we employ depth-wise separable convolution-based architectures. We propose both a deep network, that is, DTS-Net, and a lightweight network, DTS-Net-Lite, for real-time semantic segmentation; these networks employ Xception and MobileNetV2 architectures as the feature extractors, respectively. In addition, we explore the joint semantic segmentation and depth estimation task and demonstrate that the proposed technique can efficiently perform both tasks simultaneously, outperforming state-of-art (SOTA) methods. We train and evaluate the performance of the proposed method on the PASCAL VOC2012, NYUV2, and CITYSCAPES benchmarks. Hence, we obtain high mean intersection over union (mIOU) and mean pixel accuracy (Pix.acc.) values using simple and lightweight convolutional neural network architectures of the developed networks. Notably, the proposed method outperforms SOTA methods that depend on encoder–decoder architectures, although our implementation and computations are far simpler.
An Efficient Probe-Based Routing for Content-Centric Networking
Authors: Tsai Pei-Hsuan , Zhang Jun-Bin , Tsai Meng-Hsun
Published: January 2022

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.
Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning
Authors: Zhang Yanbin , Huang Long-Ting , Li Yangqing , Zhang Kai , Yin Changchuan
Published: January 2022

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.
System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics
Authors: de Melo Francisco , Neto Horácio C. , da Silva Hugo Plácido
Published: January 2022

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.
An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
Authors: Al Shinwan Mohammad , Abualigah Laith , Huy Trong-Dinh , Younes Shdefat Ahmed , Altalhi Maryam , Kim Chulsoo , El-Sappagh Shaker , Abd Elaziz Mohamed , Kwak Kyung Sup
Published: January 2022

Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.
Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach
Authors: Huang Chenhui , Fukushi Kenichiro , Wang Zhenwei , Nihey Fumiyuki , Kajitani Hiroshi , Nakahara Kentaro
Published: January 2022

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects.
Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study
Authors: Akiduki Takuma , Nagasawa Jun , Zhang Zhong , Omae Yuto , Arakawa Toshiya , Takahashi Hirotaka
Published: January 2022

This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.
Comparing the Currents Measured by CARTHE, CODE and SVP Drifters as a Function of Wind and Wave Conditions in the Southwestern Mediterranean Sea
Authors: Poulain Pierre-Marie , Centurioni Luca , Özgökmen Tamay
Published: January 2022

Instruments drifting at the ocean surface are quasi-Lagrangian, that is, they do not follow exactly the near-surface ocean currents. The currents measured by three commonly-used drifters (CARTHE, CODE and SVP) are compared in a wide range of sea state conditions (winds up to 17 m/s and significant wave height up to 3 m). Nearly collocated and simultaneous drifter measurements in the southwestern Mediterranean reveal that the CARTHE and CODE drifters measure the currents in the first meter below the surface in approximately the same way. When compared to SVP drogued at 15 m nominal depth, the CODE and CARTHE currents are essentially downwind (and down-wave), with a typical speed of 0.5–1% of the wind speed. However, there is a large scatter in velocity differences between CODE/CARTHE and SVP for all wind and sea state conditions encountered, principally due to vertical and horizontal shears not related to the wind. For the CODE drifter with wind speed larger than 10 m/s and significant wave height larger than 1 m, about 30–40% of this difference can be explained by Stokes drift.
CAT: Centerness-Aware Anchor-Free Tracker
Authors: Ma Haoyi , Acton Scott T. , Lin Zongli
Published: January 2022

Accurate and robust scale estimation in visual object tracking is a challenging task. To obtain a scale estimation of the target object, most methods rely either on a multi-scale searching scheme or on refining a set of predefined anchor boxes. These methods require heuristically selected parameters, such as scale factors of the multi-scale searching scheme, or sizes and aspect ratios of the predefined candidate anchor boxes. On the contrary, a centerness-aware anchor-free tracker (CAT) is designed in this work. First, the location and scale of the target object are predicted in an anchor-free fashion by decomposing tracking into parallel classification and regression problems. The proposed anchor-free design obviates the need for hyperparameters related to the anchor boxes, making CAT more generic and flexible. Second, the proposed centerness-aware classification branch can identify the foreground from the background while predicting the normalized distance from the location within the foreground to the target center, i.e., the centerness. The proposed centerness-aware classification branch improves the tracking accuracy and robustness significantly by suppressing low-quality state estimates. The experiments show that our centerness-aware anchor-free tracker, with its appealing features, achieves salient performance in a wide variety of tracking scenarios.
Cost Effective Synthesis of Graphene Nanomaterials for Non-Enzymatic Electrochemical Sensors for Glucose: A Comprehensive Review
Authors: Balkourani Georgia , Damartzis Theodoros , Brouzgou Angeliki , Tsiakaras Panagiotis
Published: January 2022

The high conductivity of graphene material (or its derivatives) and its very large surface area enhance the direct electron transfer, improving non-enzymatic electrochemical sensors sensitivity and its other characteristics. The offered large pores facilitate analyte transport enabling glucose detection even at very low concentration values. In the current review paper we classified the enzymeless graphene-based glucose electrocatalysts’ synthesis methods that have been followed into the last few years into four main categories: (i) direct growth of graphene (or oxides) on metallic substrates, (ii) in-situ growth of metallic nanoparticles into graphene (or oxides) matrix, (iii) laser-induced graphene electrodes and (iv) polymer functionalized graphene (or oxides) electrodes. The increment of the specific surface area and the high degree reduction of the electrode internal resistance were recognized as their common targets. Analyzing glucose electrooxidation mechanism over Cu- Co- and Ni-(oxide)/graphene (or derivative) electrocatalysts, we deduced that glucose electrochemical sensing properties, such as sensitivity, detection limit and linear detection limit, totally depend on the route of the mass and charge transport between metal(II)/metal(III); and so both (specific area and internal resistance) should have the optimum values.
Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads
Authors: Yahyaoui Imene , de la Peña Natalia Vidal
Published: January 2022

This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads’ operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.
Support Vector Regression for Mobile Target Localization in Indoor Environments
Authors: Jondhale Satish R. , Mohan Vijay , Sharma Bharat Bhushan , Lloret Jaime , Athawale Shashikant V.
Published: January 2022

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.
Mobile Charging Strategy for Wireless Rechargeable Sensor Networks
Authors: Chen Tzung-Shi , Chen Jen-Jee , Gao Xiang-You , Chen Tzung-Cheng
Published: January 2022

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.
Haptic Feedback to Assist Blind People in Indoor Environment Using Vibration Patterns
Authors: Khusro Shah , Shah Babar , Khan Inayat , Rahman Sumayya
Published: January 2022

Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with speech feedback. The vibration feedback could effectively be used as an alternative approach to these mediums. This paper proposes a real-time feedback system specifically designed for blind people to convey information to them based on vibration patterns. The proposed solution has been evaluated through an empirical study by collecting data from 24 blind people through a mixed-mode survey using a questionnaire. Results show the average recognition accuracy for 10 different vibration patterns are 90%, 82%, 75%, 87%, 65%, and 70%.
Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission
Authors: Ahmed Hosameldin O. A. , Yu Yuexiao , Wang Qinghua , Darwish Mohamed , Nandi Asoke K.
Published: January 2022

Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC-side three-phase current, and the upper and lower bridges’ currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extraction. Then, two classification algorithms—multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)—are used for classification. The effectiveness of the proposed framework is validated by a two-terminal simulation model of the MMC-high-voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published results.
Adaptive Scheme for Detecting Induction Motor Incipient Broken Bar Faults at Various Load and Inertia Conditions
Authors: Atta Mohamed Esam El-Dine , Ibrahim Doaa Khalil , Gilany Mahmoud , Zobaa Ahmed F.
Published: January 2022

This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters.
Analysis of the Stability of the Body in a Standing Position When Shooting at a Stationary Target―A Randomized Controlled Trial
Authors: Krawczyk-Suszek Marlena , Martowska Blanka , Sapuła Rafał
Published: January 2022

Postural stability of the body depends on many factors. One of them is physical activity. It is especially important in the case of sports or professional work, which combine mobility with the accuracy of a shot in a standing position. The smaller the body fatigue, the more accurate the shot. The aim of the study was the assessment of the impact of physical effort on the center of gravity deflection and length of the COP (center of pressure) path, as well as the reaction of ground forces in people who do not engage in systematic physical activity. The study group included 139 people (23.1 ± 5.2 yr; M: 46.8%; F: 53.2%). The test consisted of performing a static test twice, shooting at the target in a multimedia shooting range. Group X performed the Harvard test between the static tests. Group Y made no effort. The reaction parameters of the ground forces were assessed using the Zebris PDM-L Platform. In Group X performing the Harvard test, an increase in the average COP, VCOP, and 95% confidence ellipse area was noted. The path length and the average velocity of COP speed increased. There were no differences in Group Y (p > 0.05). Physical effort significantly affected the postural stability of the studied people, increasing the average parameters assessing balance when adopting static firing position.
Robust Security Beamforming for SWIPT-Assisted Relay System with Channel Uncertainty
Authors: Guo Ruijie , Fu Chunling , Jin Yong , Hu Zhentao , Zhou Lin
Published: January 2022

This paper considers the physical layer security (PLS) of a simultaneous wireless information and power transfer (SWIPT) relay communication system composed of a legitimate source–destination pair and some eavesdroppers. Supposing a disturbance of channel status information (CSI) between relay and eavesdroppers in a bounded ellipse, we intend to design a robust beamformer to maximum security rate in the worst case on the constraints of relay energy consumption. To handle this non-convex optimization problem, we introduce a slack variable to transform the original problem into two sub-problems firstly, then an algorithm employing a semidefinite relaxation (SDR) technique and S-procedure is proposed to tackle above two sub-problems. Although our study was conducted in the scene of a direct link among source, destination, and eavesdroppers that is non-existing, we demonstrate that our conclusions can be easily extended to the scene for which a direct link among source, destination and eavesdroppers exist. Numerical simulation results compared with the benchmark scheme are provided to prove the effectiveness and superior performance of our algorithm.
Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections
Authors: Shin Kiyoung , McConville Ryan , Metatla Oussama , Chang Minhye , Han Chiyoung , Lee Junhaeng , Roudaut Anne
Published: January 2022

One of the major challenges for blind and visually impaired (BVI) people is traveling safely to cross intersections on foot. Many countries are now generating audible signals at crossings for visually impaired people to help with this problem. However, these accessible pedestrian signals can result in confusion for visually impaired people as they do not know which signal must be interpreted for traveling multiple crosses in complex road architecture. To solve this problem, we propose an assistive system called CAS (Crossing Assistance System) which extends the principle of the BLE (Bluetooth Low Energy) RSSI (Received Signal Strength Indicator) signal for outdoor and indoor location tracking and overcomes the intrinsic limitation of outdoor noise to enable us to locate the user effectively. We installed the system on a real-world intersection and collected a set of data for demonstrating the feasibility of outdoor RSSI tracking in a series of two studies. In the first study, our goal was to show the feasibility of using outdoor RSSI on the localization of four zones. We used a k-nearest neighbors (kNN) method and showed it led to 99.8% accuracy. In the second study, we extended our work to a more complex setup with nine zones, evaluated both the kNN and an additional method, a Support Vector Machine (SVM) with various RSSI features for classification. We found that the SVM performed best using the RSSI average, standard deviation, median, interquartile range (IQR) of the RSSI over a 5 s window. The best method can localize people with 97.7% accuracy. We conclude this paper by discussing how our system can impact navigation for BVI users in outdoor and indoor setups and what are the implications of these findings on the design of both wearable and traffic assistive technology for blind pedestrian navigation.
Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network
Authors: Lapusan Ciprian , Hancu Olimpiu , Rad Ciprian
Published: January 2022

The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.
Time-Domain Joint Training Strategies of Speech Enhancement and Intent Classification Neural Models
Authors: Ali Mohamed Nabih , Falavigna Daniele , Brutti Alessio
Published: January 2022

Robustness against background noise and reverberation is essential for many real-world speech-based applications. One way to achieve this robustness is to employ a speech enhancement front-end that, independently of the back-end, removes the environmental perturbations from the target speech signal. However, although the enhancement front-end typically increases the speech quality from an intelligibility perspective, it tends to introduce distortions which deteriorate the performance of subsequent processing modules. In this paper, we investigate strategies for jointly training neural models for both speech enhancement and the back-end, which optimize a combined loss function. In this way, the enhancement front-end is guided by the back-end to provide more effective enhancement. Differently from typical state-of-the-art approaches employing on spectral features or neural embeddings, we operate in the time domain, processing raw waveforms in both components. As application scenario we consider intent classification in noisy environments. In particular, the front-end speech enhancement module is based on Wave-U-Net while the intent classifier is implemented as a temporal convolutional network. Exhaustive experiments are reported on versions of the Fluent Speech Commands corpus contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, shedding light and providing insight about the most promising training approaches.