bluuebunny/crossref_metadata_2025 huggingface dataset:
Suppressing Decoherence in Quantum State Transfer with Unitary Operations
Authors: Gavreev Maxim A. , Kiktenko Evgeniy O. , Mastiukova Alena S. , Fedorov Aleksey K.
Published: January 2023

Decoherence is the fundamental obstacle limiting the performance of quantum information processing devices. The problem of transmitting a quantum state (known or unknown) from one place to another is of great interest in this context. In this work, by following the recent theoretical proposal, we study an application of quantum state-dependent pre- and post-processing unitary operations for protecting the given (multi-qubit) quantum state against the effect of decoherence acting on all qubits. We observe the increase in the fidelity of the output quantum state both in a quantum emulation experiment, where all protecting unitaries are perfect, and in a real experiment with a cloud-accessible quantum processor, where protecting unitaries themselves are affected by the noise. We expect the considered approach to be useful for analyzing capabilities of quantum information processing devices in transmitting known quantum states. We also demonstrate the applicability of the developed approach for suppressing decoherence in the process of distributing a two-qubit state over remote physical qubits of a quantum processor.
Review of Novel High-Entropy Protective Materials: Wear, Irradiation, and Erosion Resistance Properties
Authors: Feltrin Ana C. , Xing Qiuwei , Akinwekomi Akeem Damilola , Waseem Owais Ahmed , Akhtar Farid
Published: January 2023

By their unique compositions and microstructures, recently developed high-entropy materials (HEMs) exhibit outstanding properties and performance above the threshold of traditional materials. Wear- and erosion-resistant materials are of significant interest for different applications, such as industrial devices, aerospace materials, and military equipment, related to their capability to tolerate heavy loads during sliding, rolling, or impact events. The high-entropy effect and crystal lattice distortion are attributed to higher hardness and yield stress, promoting increased wear and erosion resistance in HEMs. In addition, HEMs have higher defect formation/migration energies that inhibit the formation of defect clusters, making them resistant to structural damage after radiation. Hence, they are sought after in the nuclear and aerospace industries. The concept of high-entropy, applied to protective materials, has enhanced the properties and performance of HEMs. Therefore, they are viable candidates for today’s demanding protective materials for wear, erosion, and irradiation applications.
Superior Resilience of Non-Gaussian Entanglement against Local Gaussian Noises
Authors: Filippov Sergey , Termanova Alena
Published: January 2023

Entanglement distribution task encounters a problem of how the initial entangled state should be prepared in order to remain entangled the longest possible time when subjected to local noises. In the realm of continuous-variable states and local Gaussian channels it is tempting to assume that the optimal initial state with the most robust entanglement is Gaussian too; however, this is not the case. Here we prove that specific non-Gaussian two-mode states remain entangled under the effect of deterministic local attenuation or amplification (Gaussian channels with the attenuation factor/power gain κi and the noise parameter μi for modes i=1,2) whenever κ1μ22+κ2μ12<14(κ1+κ2)(1+κ1κ2), which is a strictly larger area of parameters as compared to where Gaussian entanglement is able to tolerate noise. These results shift the “Gaussian world” paradigm in quantum information science (within which solutions to optimization problems involving Gaussian channels are supposed to be attained at Gaussian states).
News Stance Discrimination Based on a Heterogeneous Network of Social Background Information Fusion
Authors: Ren Yanze , Liu Yan , Chen Jing , Guo Xiaoyu , Shi Junyu , Jia Mengmeng
Published: January 2023

Media with partisan tendencies publish news articles to support their preferred political parties to guide the direction of public opinion. Therefore, discovering political bias in news texts has important practical significance for national election prediction and public opinion management. Some biased news often has obscure expressions and ambiguous writing styles. By bypassing the language model, the accuracy of methods that rely on news semantic information for position discrimination is low. This manuscript proposes a news standpoint discrimination method based on social background information fusion heterogeneous network. This method expands the judgment ability of creators and topics on news standpoints from external information and fine-grained topics based on news semantics. Multi-attribute features of nodes enrich the feature representation of nodes, and joint representation of heterogeneous networks can reduce the dependence of position discrimination on the news semantic information. To effectively deal with the position discrimination of new news, the design of a multi-attribute fusion heterogeneous network is extended to inductive learning, avoiding the cost of model training caused by recomposition. Based on the Allsides dataset, this manuscript expands the information of its creator’s social background and compares the model for discriminating political positions based on news content. In the experiment, the best transductive attribute fusion heterogeneous network achieved an accuracy of 92.24% and a macro F1 value of 92.05%. The effect is improved based purely on semantic information for position discrimination, which proves the effectiveness of the model design.
Testing Nonlinearity with Rényi and Tsallis Mutual Information with an Application in the EKC Hypothesis
Authors: Tuna Elif , Evren Atıf , Ustaoğlu Erhan , Şahin Büşra , Şahinbaşoğlu Zehra Zeynep
Published: January 2023

The nature of dependence between random variables has always been the subject of many statistical problems for over a century. Yet today, there is a great deal of research on this topic, especially focusing on the analysis of nonlinearity. Shannon mutual information has been considered to be the most comprehensive measure of dependence for evaluating total dependence, and several methods have been suggested for discerning the linear and nonlinear components of dependence between two variables. We, in this study, propose employing the Rényi and Tsallis mutual information measures for measuring total dependence because of their parametric nature. We first use a residual analysis in order to remove linear dependence between the variables, and then we compare the Rényi and Tsallis mutual information measures of the original data with that the lacking linear component to determine the degree of nonlinearity. A comparison against the values of the Shannon mutual information measure is also provided. Finally, we apply our method to the environmental Kuznets curve (EKC) and demonstrate the validity of the EKC hypothesis for Eastern Asian and Asia-Pacific countries.
Influence of Infiltrations on the Recharge of the Nkoabang Aquifer Located in the Center Region, Cameroon
Authors: Tagne Monique Makuate , Simo-Tagne Merlin , Nwakuba Nnaemeka R. , Effoudou-Priso Estelle Ndome , Ngoupayou Jules Rémy Ndam , Mbessa Michel , Tagne Ablain Tagne , Kharchi Razika
Published: January 2023

Due to the limited reach of the drinking-water delivery network, Yaoundé’s surrounding communities (such as Nkoabang) rely on well water, boreholes, and springs. This study conducted a Thornthwaite water-balance analysis in the watershed in order to understand its hydrology capability and investigated the influence of the flows and infiltrations on the recharge of the aquifer of Nkoabang (Centre Region, Cameroon). The methodology of this work consisted of updating the rainfall and temperature data of the Mvan meteorological station in Yaoundé to carry out the hydrological and water balances of the Nkoabang aquifer and performing the piezometric monitoring of seven wells and one spring in the dry season and the rainy season. The average rainfall height for the period between 1951–2017 was 1577 ± 222 mm, while the monthly temperatures ranged from 22.8 (July) to 25.4 °C (February) for an average of 24.1 °C ± 0.8 °C. The average interannual infiltration was 137 mm, corresponding to an infiltration coefficient of 8.68%. The value of the piezometric levering varies between 706 and 718 m for an average of 711.76 m during the dry season. It fluctuates between 706 and 719 m during the rainy season for an average of 712.95 ± 4.09 m. Irrespective of the season, the highest and lowest values are those of P6 and P3, respectively. Piezometric level values vary little from one season to another but are higher in the rainy season than in the dry season. Wells in the study area generally show small variations in piezometric level amplitude, ranging from 0.4 to 3.3 m with an average of 1.19 ± 1 m. The analysis of the piezometric map of the Nkoabang aquifer shows a flow in the NE-SW direction; storage areas south of the study areas and the water supply area in the peaks and NE of Nkoabang. The daily indicative flow rates of the spring (S) are 0.15 m3 (dry season), which can supply in the dry and rainy seasons 36 to 46 people, respectively, based on a ratio of 0.1 m3 per day per habitant.
Nonlinear Interactive Stories as an Educational Resource
Authors: Černý Michal , Kalmárová Kristýna , Martonová Monika , Mazáčová Pavlína , Škyřík Petr , Štěpánek Jan , Vokřál Jan
Published: January 2023

Working with digitized cultural heritage is an important aspect of education. This article offers research on the interaction of pupils and teachers with digitized cultural heritage processed in the Twine2 environment, focusing on exploring the potential use of digital nonlinear stories in the teaching of humanities in high schools. The aim of this study is to describe the design process that led to the creation of 16 innovative educational resources, and to present the results of their pilot testing and evaluation, which subsequently took place among participating teachers and pupils. For the evaluation, we used a mix of design methods, combining a quantitative and qualitative approach. Both the teachers and the pupils were asked about their experience and takeaways from the interaction with the resources offered. The findings show that presenting educational content in the form of a nonlinear interactive story is a suitable alternative to the traditional frontal educational approach, as both teachers and pupils perceived the resources as intuitive, appealing, and effective in helping to achieve the set educational goals. The study also highlights some key recommendations for future designers of nonlinear interactive stories, which emerged from the respondents’ feedback.
Serious Games in Cultural Heritage: A Review of Practices and Considerations in the Design of Location-Based Games
Authors: DaCosta Boaventura , Kinsell Carolyn
Published: January 2023

The overwhelming growth and popularity of mobile devices and games have motivated initiatives to explore mobile and augmented reality to bring the physical and virtual together. While several domains have been the recipient of such focus, particular importance has been placed on the cultural heritage (CH) field, with examples demonstrating the promise of location-based games (LBGs) at enhancing the CH experience aimed at promoting awareness. Games like Pokémon GO have exacerbated the appeal of LBGs, showing that they can be as popular as blockbuster video games. However, as much potential as LBGs have in raising CH awareness, they can be challenging to build because, unlike their commercial counterparts, their primary aim is learning, emphasizing the cultural significance of the real-world location rather than augmented assets. With limited research that explicitly identifies elements in creating these games in CH contexts, this work aimed to identify design practices and considerations in the design of LBGs. This work is intended for educators, researchers, instructional designers, game developers, and those in the CH field interested in exploring creative ways to embody a deeper understanding of and appreciation for CH.
Nudging Autonomous Learning Behavior: Three Field Experiments
Authors: Weijers Robert , de Koning Björn , Vermetten Yvonne , Paas Fred
Published: January 2023

Autonomous learning behavior is an important skill for students, but they often do not master it sufficiently. We investigated the potential of nudging as a teaching strategy in tertiary education to support three important autonomous learning behaviors: planning, preparing for class, and asking questions. Nudging is a strategy originating from behavioral economics used to influence behavior by changing the environment, and consists of altering the choice environment to steer human behavior. In this study, three nudges were designed by researchers in co-creation with teachers. A video booth to support planning behavior (n = 95), a checklist to support class preparation (n = 148), and a goal-setting nudge to encourage students to ask questions during class (n = 162) were tested in three field experiments in teachers’ classrooms with students in tertiary education in the Netherlands. A mixed-effects model approach revealed a positive effect of the goal-setting nudge on students’ grades and a marginal positive effect on the number of questions asked by students. Additionally, evidence for increased self-reported planning behavior was found in the video booth group—but no increase in deadlines met. No significant effects were found for the checklist. We conclude that, for some autonomous learning behaviors, primarily asking questions, nudging has potential as an easy, effective teaching strategy.
Factors Associated with Job Satisfaction in Medical Laboratory Professionals during the COVID-19 Pandemic: An Exploratory Study in Ontario, Canada
Authors: Lo Joyce , Fayyaz Yusra , Jaswal Sharan , Gohar Basem , Yazdani Amin , Chattu Vijay Kumar , Nowrouzi-Kia Behdin
Published: January 2023

Job satisfaction has been widely studied across several healthcare disciplines and is correlated with important outcomes such as job performance and employee mental health. However, there is limited research on job satisfaction among medical laboratory professionals (MLPs), a key healthcare group that aids in diagnosis, treatment, and patient care. The objective of this study is to examine the demographic and psychosocial factors associated with job satisfaction for MLPs in Ontario, Canada during the COVID-19 pandemic. A survey was administered to medical laboratory technologists (MLTs) and medical laboratory technicians/assistants (MLT/As) in Ontario, Canada. The survey included demographic questions and items from the Copenhagen Psychosocial Questionnaire, third edition. Binary logistic regressions were used to examine the association between job satisfaction and demographic variables and psychosocial work factors. There were 688 MLPs included in the analytic sample (72.12% response rate). Having a higher sense of community at work was correlated with higher job satisfaction in both MLT (OR = 2.22, 95% CI: 1.07–4.77) and MLT/A (OR = 3.85, 95% CI: 1.12–14.06). In addition, having higher stress was correlated with lower job satisfaction in both MLT (OR = 0.32, 95% CI: 0.18–0.57) and MLT/A (OR = 0.26, 95% CI: 0.10–0.66). This study provides preliminary evidence on factors associated with job satisfaction in MLT and MLT/A. The findings can be used to support organizational practices and policies to improve psychosocial work factors.
Double-Split Rectangular Dual-Ring DNG Metamaterial for 5G Millimeter Wave Applications
Authors: Alam M. Jubaer , Latif Saeed I.
Published: January 2023

This article presents the design and analysis of a low profile double-negative (DNG) metamaterial unit structure for 5G mmWave (millimeter wave) applications. The structure, comprised of double-slotted rectangular ring patches, experiences the peak current value near the magnetic resonance, causing the metamaterial to resonate at 28 GHz where it exhibits negative effective permittivity and permeability. The 3.05 mm × 2.85 mm compact structure is designed over a substrate Rogers RT/Duroid 5880 to attain better effective medium ratio (EMR) in the 5G frequency range (27.1–29.2 GHz). A rigorous parametric study is conducted to obtain the proposed design. Full-wave electromagnetic simulation software tools CST and HFSS are used to generate the scattering parameters for the analysis. The Nicolson–Ross–Wier method is used to observe the negative effective permittivity and permeability. In addition, different output quantities, e.g., surface current and electric and magnetic field distribution, are investigated. The structure is further tested with 1 × 2, 2 × 2, and 4 × 4 arrays, where the results show adequate agreement to be considered for 5G mmWave applications.
A Reconfigurable Hybrid RF Front-End Rectifier for Dynamic PCE Enhancement of Ambient RF Energy Harvesting Systems
Authors: Lian Wen Xun , Yong Jack Kee , Chong Gabriel , Churchill Kishore Kumar Pakkirisami , Ramiah Harikrishnan , Chen Yong , Mak Pui-In , Martins Rui P.
Published: January 2023

This paper presents a reconfigurable hybrid Radio Frequency (RF) rectifier designed to efficiently convert AC RF power to DC voltages for an energy harvesting system. The proposed reconfigurable rectifier adopts the advantage of low conduction loss in the switch-connected rectifier and low reverse current loss in the diode-connection rectifier topology to enhance its power conversion efficiency (PCE). Capable of reconfiguring into different rectifier topologies, the proposed circuit can reconfigure into a switch-based cross-coupling differential drive (CCDD) at low input power and a diode-based hybrid rectifier at higher input power for a wide dynamic range operation. Designed and implemented on a CMOS 65 nm technology, the post-layout result records a peak PCE of 88.7% and a wide PCE dynamic range (PDR) of 16 dBm for PCE >40%. The proposed circuit also demonstrates a −21 dBm sensitivity output across a 1 MΩ output load.
Rule-Based Embedded HMMs Phoneme Classification to Improve Qur’anic Recitation Recognition
Authors: Alqadasi Ammar Mohammed Ali , Sunar Mohd Shahrizal , Turaev Sherzod , Abdulghafor Rawad , Hj Salam Md Sah , Alashbi Abdulaziz Ali Saleh , Salem Ali Ahmed , Ali Mohammed A. H.
Published: January 2023

Phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. A mispronunciation of Arabic short vowels or long vowels can change the meaning of a complete sentence. However, correctly distinguishing phonemes with vowels in Quranic recitation (the Holy book of Muslims) is still a challenging problem even for state-of-the-art classification methods, where the duration of the phonemes is considered one of the important features in Quranic recitation, which is called Medd, which means that the phoneme lengthening is governed by strict rules. These features of recitation call for an additional classification of phonemes in Qur’anic recitation due to that the phonemes classification based on Arabic language characteristics is insufficient to recognize Tajweed rules, including the rules of Medd. This paper introduces a Rule-Based Phoneme Duration Algorithm to improve phoneme classification in Qur’anic recitation. The phonemes of the Qur’anic dataset contain 21 Ayats collected from 30 reciters and are carefully analyzed from a baseline HMM-based speech recognition model. Using the Hidden Markov Model with tied-state triphones, a set of phoneme classification models optimized based on duration is constructed and integrated into a Quranic phoneme classification method. The proposed algorithm achieved outstanding accuracy, ranging from 99.87% to 100% according to the Medd type. The obtained results of the proposed algorithm will contribute significantly to Qur’anic recitation recognition models.
Design of 1 × 2 MIMO Palm Tree Coplanar Vivaldi Antenna in the E-Plane with Different Patch Structure
Authors: Nurhayati Nurhayati , Setijadi Eko , de Oliveira Alexandre Maniçoba , Kurniawan Dayat , Yasin Mohd Najib Mohd
Published: January 2023

In this paper, 1 × 2 MIMO of Palm Tree Coplanar Vivaldi Antenna is presented that simulated at 0.5–4.5 GHz. Some GPR applications require wideband antennas starting from a frequency below 1 GHz to overcome high material loss and achieve deeper penetration. However, to boost the gain, antennas are set up in MIMO and this is costly due to the large size of the antenna. When configuring MIMO antenna in the E-plane, there is occasionally uncertainty over which antenna model may provide the optimum performance in terms of return loss, mutual coupling, directivity, beam squint, beam width, and surface current using a given substrate size. However, the configuration of E-plane antenna in MIMO has an issue of mutual coupling if the distance between elements is less than 0.5λ. Furthermore, it produces grating lobes at high frequencies.We implement several types of patch structures by incorporating the truncated, tilt shape, Hlbert and Koch Fractal, Exponential slot, Wave slot, the lens with elips, and metamaterial slot to the radiator by keeping the width of the substrate and the shape of the feeder. The return loss, mutual coupling, directivity, beam squint, beamwidth, and surface current of the antenna are compared for 1 × 2 MIMO CVA. A continuous patch MIMO has a spacing of 0.458λ at 0.5 GHz, which is equivalent to its element width. From the simulation, we found that Back Cut Palm Tree (BCPT) and Horizontale Wave Structure Palm Tree (HWSPT) got the best performance of return loss and mutual scattering at low-end frequency respectively. The improvement of directivity got for Metamaterial Lens Palm Tree (MLPT) of 4.453 dBi if compared with Regular Palm Tree-Coplanar Vivaldi Antena (RPT) at 4 GHz. Elips Lens Palm Tree (ELPT) has the best beam squint performance across all frequencies of 0°. It also gots the best beamwidth at 4.5 GHz of 3.320. In addition, we incorporate the MLPT into the radar application.
Patch It If You Can: Increasing the Efficiency of Patch Generation Using Context
Authors: Heo Jinseok , Jeong Hohyeon , Lee Eunseok
Published: January 2023

Although program repair is a tremendous aspect of a software system, it can be extremely challenging. An Automated Program Repair (APR) technique has been proposed to solve this problem. Among them, template-based APR shows good performance. One of the key properties of the template-based APR technique for practical use is its efficiency. However, because the existing techniques mainly focus on performance improvement, they do not sufficiently consider the efficiency. In this study, we propose EffiGenC, which efficiently explores the patch ingredient search space to improve the overall efficiency of the template-based APR. EffiGenC defines the context using the concept of extended reaching definition from compiler theory. EffiGenC constructs the search space by collecting the ingredient required for patching in the context. We evaluated EffiGenC on the Defects4j benchmark. EffiGenC decreases the number of candidate patches from 27% to 86% compared to existing techniques. EffiGenC also correctly/plausibly fixes 47/72 bugs. For Future work, we will solve the search space problem that exists in multiline bugs using context.
Research on Analog Circuit Soft Fault Diagnosis Method Based on Mathematical Morphology Fractal Dimension
Authors: Lu Xinmiao , Yang Cunfang , Wu Qiong , Wang Jiaxu , Lu Zihan , Sun Shuai , Liu Kaiyi , Shao Dan
Published: January 2023

It is difficult for traditional circuit-fault feature-extraction methods to accurately distinguish between nonlinear analog-circuit faults and analog-circuit faults with high fault rates and high diagnostic costs. To solve this problem, this paper proposes a method of mathematical morphology fractal dimension (VMD-MMFD) based on variational mode decomposition for soft-fault feature extraction in analog circuits. First, the signal is decomposed into variational modes to suppress the influence of environmental noise, and multiple high-dimensional eigenmode functions with different center frequencies are obtained. The fractal dimension of the signal feature information component IMF is calculated, and then, KPCA (Kernel Principal Component Analysis) is used to remove the overlapping and redundant parts of the data. The fault set obtained is used as the basis for judging the working state and the fault type of the circuit. The experimental results of the simulation circuits show that this method can be effectively used for circuit-fault diagnosis.
A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks
Authors: Siddiqui Maraj Uddin Ahmed , Qamar Faizan , Kazmi Syed Hussain Ali , Hassan Rosilah , Arfeen Asad , Nguyen Quang Ngoc
Published: January 2023

The quantum leap in mobile data traffic and high density of wireless electronic devices, coupled with the advancements in industrial radio monitoring and autonomous systems, have created great challenges for smooth wireless network operations. The fifth-generation and beyond (B5G) (also being referred to as sixth-generation (6G)) wireless communication technologies, due to their compatibility with the previous generations, are expected to overcome these unparalleled challenges. Accompanied by traditional and new techniques, the massive multiple input multiple output (mMIMO) approach is one of the evolving technologies for B5G/6G systems used to control the ever-increasing user stipulations and the emergence of new cases efficiently. However, the major challenges in deploying mMIMO systems are their high computational intricacy and high computing time latencies, as well as difficulties in fully exploiting the multi-antenna multi-frequency channels. Therefore, to optimize the current and B5G/6G wireless network elements proficiently, the use of the mMIMO approach in a HetNet structure with artificial intelligence (AI) techniques, e.g., machine learning (ML), distributed learning, federated learning, deep learning, and neural networks, has been considered as the prospective efficient solution. This work analyzes the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks. To provide a complete insight into the mMIMO systems with emerging antenna and propagation precoding techniques, we address and identify various relevant topics in each section that may help to make the future wireless systems robust. Overall, this work is designed to guide all B5G/6G stakeholders, including researchers and operators, aiming to understand the functional behavior and associated techniques to make such systems more agile for future communication purposes.
Allocation of Charging Stations for Electric Vehicles Considering Spatial Difference in Urban Land Price and Fixed Budget
Authors: Fu Dingtao , Xia Yongxiang , Bi Xiaowen , Gu Xuan
Published: January 2023

In this paper, the allocation of charging station (CS) is optimized to alleviate the “range anxiety” of electric vehicle (EV) drivers by reducing the time of medium-to-long distance travel, which is raised due to the potential en-route charging. The problem is defined to explicitly consider the spatial differences in urban land price. Although many works take spatial land price into consideration, few of them notice what the gap of spatial land price bring to the charging system. Our objective function is the expected traveling time under an optimized distribution of urban EV flows, and models of spatial network and CSs allocation are then established. Based on Tabu Search algorithm (TSA), a fixed budget charging resources planning algorithm (FBCRPA) is proposed. The proposed method is compared with methods based on betweeness centrality, and results show that our method can find more effective allocation strategy. It is found that users’ traveling time would decrease with increase in difference in land price. Meanwhile, budget would transfer from central region to other regions and carrying capacity of charging system would improve in the above situation. This paper also finds that increase in budget is beneficial to a reduction in drivers’ time, but the improvement is limited.
On the Selectivity of Planar Microwave Glucose Sensors with Multicomponent Solutions
Authors: Juan Carlos G. , Bronchalo Enrique , Potelon Benjamin , Quendo Cédric , Muñoz Víctor F. , Ferrández-Vicente José M. , Sabater-Navarro José M.
Published: January 2023

The development of glucose concentration sensors by means of microwave planar resonant technology is an active field attracting considerable attention from the scientific community. Although showing promising results, the current experimental sensors are facing some fundamental challenges. Among them, the most critical one seems to be the selectivity of glucose concentration against the variations of the concentrations of other components or parameters. In this article, we investigate the selectivity of microwave planar resonant sensors when measuring multicomponent solutions. Three sensors are involved, two of them having been designed looking for a more simplified system with a reduced size, and the third one has been specially developed to improve the sensitivity. The performance of these sensors is thoroughly assessed with a large set of measurements involving multicomponent solutions composed of pure water, NaCl, albumin at different concentrations and glucose at different concentrations. The impact of the simultaneous variations of the concentrations of glucose and albumin on the final measurements is analyzed, and the effective selectivity of the sensors is discussed. The results show a clear influence of the albumin concentration on the measurements of the glucose concentration, thereby pointing to a lack of selectivity for all sensors. This influence has been modeled, and strategies to manage this selectivity challenge are inferred.
HBCA: A Toolchain for High-Accuracy Branch-Fused CNN Accelerator on FPGA with Dual-Decimal-Fused Technique
Authors: Li Zhengjie , Hou Lingli , Tao Xinxuan , Wang Jian , Lai Jinmei
Published: January 2023

The programmability of FPGA suits the constantly changing convolutional neural network (CNN). However, several challenges arise when the previous FPGA-based accelerators update CNN. Firstly, although the model of RepVGG can balance accuracy and speed, it solely supports two types of kernels. Meanwhile, 8-bit integer-only quantization of PyTorch which can support various CNNs is seldom successfully supported by the FPGA-based accelerators. In addition, Winograd F(4 × 4, 3 × 3) uses less multiplication, but its transformation matrix contains irregular decimals, which could lead to accuracy problems. To tackle these issues, this paper proposes High-accuracy Branch-fused CNN Accelerator (HBCA): a toolchain and corresponding FPGA-based accelerator. The toolchain proposes inception-based branch–fused technique, which can support more types of kernels. Meanwhile, the accelerator proposes Winograd-quantization dual decimal–fuse techniques to balance accuracy and speed. In addition, this accelerator supports multi-types of kernels and proposes Winograd decomposed-part reuse, multi-mode BRAM & DSP and data reuse to increase power efficiency. Experiments show that HBCA is capable of supporting seven CNNs with different types of kernels and more branches. The accuracy loss is within 0.1% when compared to the quantized model. Furthermore, the power efficiency (GOPS/W) of Inception, ResNet and VGG is up to 226.6, 188.1 and 197.7, which are better than other FPGA-based CNN accelerators.
3D Heterogenous Integrated Wideband Switchable Bandpass Filter Bank for Millimeter Wave Applications
Authors: Wang Zhiyu , Shu Yujian , Ma Siyuan , Guo Xi , Yang Wei , Ding Xu , Lyu Xiaofeng , Yu Faxin
Published: January 2023

This article proposes a three-dimensional heterogenous-integrated (3DHI) switchable bandpass filter bank with two independent wideband filter channels that cover 26–40 GHz and 32.5–40 GHz, respectively. An accurate wafer-level process with a high hollowed ratio of the applied 8-inch high-resistivity-silicon (HR-Si) interposer wafers is presented to form both compact filter channels. Above the interdigital filter patterns fabricated on the bottom interposer wafer, deep cavities are etched in the cap interposer wafer to improve the quality factor of the filter bank. Besides the cavities, the cap interposer wafer is 35% hollow inside, which two bare dies of GaAs single-pole double-throw (SPDT) switches and two thin film resistors are attached to the bottom interposer after the wafer-to-wafer (W2W) bonding. To ensure good out-of-band performance, a 3D EM co-simulation of the switch layout at the chip level and filter patterns at the package level is applied. Measurement results show that the switchable filter bank achieves a high isolation of 50 dB and a competitive shape factor (BW30dB/BW3dB) of about 1.3. In addition, the size of the switchable filter bank is only 7.0 mm × 3.5 mm × 0.6 mm, and the weight is only 0.1 g.
A Novel Wearable Upper-Limb Rehabilitation Assistance Exoskeleton System Driven by Fluidic Muscle Actuators
Authors: Chiou Shean-Juinn , Chu Hsien-Ru , Li I-Hsum , Lee Lian-Wang
Published: January 2023

This paper proposed a novel design using a torsion spring mechanism with a single fluidic muscle actuator (FMA) to drive a joint with one degree-of-freedom (DOF) through a steel wire and a proportional pressure regulating valve (PRV). We developed a 4-DOF wearable upper-limb rehabilitation assistance exoskeleton system (WURAES) that is suitable for assisting in the rehabilitation of patients with upper-limb injuries. This system is safe, has a simple mechanism, and exhibits upper-limb motion compliance. The developed WURAES enables patients with upper-limb musculoskeletal injuries and neurological disorders to engage in rehabilitation exercises. Controlling the joint is difficult because of the time-varying hysteresis properties of the FMA and the nonlinear motion between standard extension and flexion. To solve this problem, a proxy-based output feedback sliding mode control (POFSC) was developed to provide appropriate rehabilitation assistance power for the upper-limb exoskeleton and to maintain smooth and safe contact between the WURAES and the patient. The POFSC enables the overdamped dynamic of the WURAES to recover motion to be aligned with the target trajectory without a significant error overshoot caused by actuator saturation. The experimental results indicate that the proposed POFSC can control the designed WURAES effectively. The POFSC can monitor the exoskeleton system’s total disturbance and unknown state online and adapt to the exterior environment to enhance the control capability of the designed system. The results indicate that a single FMA with a torsion spring module exhibits a control response similar to a dual FMA configuration.
Voltage Based Electronic Control Unit (ECU) Identification with Convolutional Neural Networks and Walsh–Hadamard Transform
Authors: Baldini Gianmarco
Published: January 2023

This paper proposes an identification approach for the Electronic Control Units (ECUs) in the vehicle, which are based on the physical characteristics of the ECUs extracted from their voltage output. Then, the identification is not based on cryptographic means, but it could be used as an alternative or complementary means to strengthen cryptographic solutions for vehicle cybersecurity. While previous research has used hand-crafted features such as mean voltage, max voltage, skew or variance, this study applies Convolutional Neural Networks (CNNs) in combination with the Walsh–Hadamard Transform (WHT), which has useful properties of compactness and robustness to noise. These properties are exploited by the CNN, and in particular, the pooling layers, to reduce the size of the feature maps in the CNN. The proposed approach is applied to a recently public data set of ECU voltage fingerprints extracted from different automotive vehicles. The results show that the combination of CNN and the WHT outperforms, in terms of identification accuracy, robustness to noise and computing times, and other approaches proposed in the literature based on shallow machine learning and tailor-made features, as well as CNN with other linear transforms such as the Discrete Fourier Transform (DFT) or CNN with the original time domain representations.
Study on Thermal Runaway Propagation Characteristics of Lithium Iron Phosphate Battery Pack under Different SOCs
Authors: Zhu Minghao , Yao Jiajie , Qian Feiyu , Luo Weiyi , Chen Yin , Zhao Luyao , Chen Mingyi
Published: January 2023

Thermal runaway (TR) of lithium-ion batteries (LIBs) has always been the most important problem for battery development, and the TR characteristics of large LIBs need more research. In this paper, the thermal runaway propagation (TRP) characteristics and TR behavior changes of three lithium iron phosphate (LFP) batteries (numbered 1 to 3) under different states of charge (SOCs) were studied. The main parameters discussed include temperature, temperature rise rate, mass, mass change rate, and TRP flue gas ejection behavior. The experimental results indicate that with the increase in SOC, the TRP behavior of the battery is more obvious. The higher the temperature, more blocked temperature rise rate, mass loss rate, and greater mass loss, the shorter the TRP time that can be observed. The TRP interval of 100% SOC battery 1 to 2 is 71.4% smaller than that of 50% SOC, while the TRP interval of battery 2 to 3 is reduced by 87.2%. In addition, a 100% SOC battery pack exhibits spark ejection, while 50% SOC and 0% SOC battery pack exhibit flue gas generation.
Hausdorff Distance and Similarity Measures for Single-Valued Neutrosophic Sets with Application in Multi-Criteria Decision Making
Authors: Ali Mehboob , Hussain Zahid , Yang Miin-Shen
Published: January 2023

Hausdorff distance is one of the important distance measures to study the degree of dissimilarity between two sets that had been used in various fields under fuzzy environments. Among those, the framework of single-valued neutrosophic sets (SVNSs) is the one that has more potential to explain uncertain, inconsistent and indeterminate information in a comprehensive way. And so, Hausdorff distance for SVNSs is important. Thus, we propose two novel schemes to calculate the Hausdorff distance and its corresponding similarity measures (SMs) for SVNSs. In doing so, we firstly develop the two forms of Hausdorff distance between SVNSs based on the definition of Hausdorff metric between two sets. We then use these new distance measures to construct several SMs for SVNSs. Some mathematical theorems regarding the proposed Hausdorff distances for SVNSs are also proven to strengthen its theoretical properties. In order to show the exact calculation behavior and distance measurement mechanism of our proposed methods in accordance with the decorum of Hausdorff metric, we utilize an intuitive numerical example that demonstrate the novelty and practicality of our proposed measures. Furthermore, we develop a multi-criteria decision making (MCDM) method under single-valued neutrosophic environment using the proposed SMs based on our defined Hausdorff distance measures, called as a single-valued neutrosophic MCDM (SVN-MCDM) method. In this connection, we employ our proposed SMs to compute the degree of similarity of each option with the ideal choice to identify the best alternative as well as to perform an overall ranking of the alternatives under study. We then apply our proposed SVN-MCDM scheme to solve two real world problems of MCDM under single-valued neutrosophic environment to show its effectiveness and application.
Design Automation of Low Dropout Voltage Regulators: A General Approach
Authors: Mohamed Karimeldeen , Nafea Sherif , Omran Hesham
Published: January 2023

Analog design is an inherently intricate process comprising many trade-offs; as a result, it is an iterative time-consuming operation. A low dropout voltage regulator (LDO) is an example of such analog blocks that involve a myriad of trade-offs. In this paper, we present an automated design procedure for LDOs using precomputed look-up tables (LUTs) and the gm/ID methodology. Using a symbolic solver and the precomputed LUTs, a design database for an LDO that contains one million design points is generated in a few seconds. The database provides visualization of the design space and exploration of the trade-offs across different corners and load currents. A design example is provided to demonstrate the procedure using 40 nm technology and the results are verified using Cadence Spectre simulator. The approach is holistic in the sense that it uses an accurate symbolic solver to capture the small signal model complexities, incorporates LUTs for accurate calculation of the large signal solution and the small signal parameters, is fast because the simulator in the loop scenario is omitted, and almost all the specifications of LDOs are incorporated.
Far-Field Wireless Power Transfer for the Internet of Things
Authors: Nusrat Tasin , Roy Sayan , Lotfi-Neyestanak Abbas Ali , Noghanian Sima
Published: January 2023

A complete end-to-end far-field wireless power transfer (WPT) is proposed and studied in this paper for the application of the Internet of Things (IoT) at the industrial, scientific, and medical (ISM) band of 2.4 GHz. The radiative WPT has achieved a remarkable attraction for the capability to transfer power in the long range. We propose two approaches. In the first approach, a 2×4 microstrip patch transmitter antenna array with a high gain and a narrow beamwidth is proposed that is rotated toward the IoT device using a small stepper motor. The performance of the rectifier in the receiving circuit was separately analyzed, and 17.54% efficiency was achieved with a load of 0.6 kΩ for the circuit, while the input power was 10 dBm. The overall system test was performed and the targeted result was investigated considering the distance between the transmitter and the receiver, and an input radio frequency (RF) power of 5 dBm to 15 dBm at 2.4 GHz. The second approach uses a 1×4 transmitter antenna array fed through a Butler matrix to provide four individual beams with a 22.5∘ angular separation, and 90∘ total angular coverage. The goal was to focus the power into four angular locations and to reduce the power waste in other directions. A mobile app was developed to control the direction of the beam. A system efficiency of as much as 19% was measured for an input RF power of 0 dBm and a resistive load of 62 kΩ.
Blockchain Technologies: Smart Contracts for Consumer Electronics Data Sharing and Secure Payment
Authors: John William Alfred Daniel , Rajendran Santhosh , Pranam Pradish , Berry Yosuva , Sreedharan Anuj , Gul Junaid , Paul Anand
Published: January 2023

Blockchain may be an optimal solution when a detailed and transparent record of assets is necessary. It is imperative to manage and safeguard digital interactions or maintain a decentralized and shared system of records in applications, such as those used for electricity production, transmission, distribution, and consumption and those used for data sharing and secure payments. Such applications can benefit from blockchain technology to resolve these problems. In the proposed blockchain-based consumer electronics data sharing and safe payment framework, an innovative IoT meter detects monthly consumption and transmits the data to a decentralized application that is stored in the blockchain. This decentralized platform will generate the bill and provide incentives for legitimate consumers. Finally, the end-to-end latency and throughput were used to evaluate the performance of the proposed approach.
ENEX-FP: A BERT-Based Address Recognition Model
Authors: Li Min , Liu Zeyu , Li Gang , Zhou Mingle , Han Delong
Published: January 2023

In e-commerce logistics, government registration, financial transportation and other fields, communication addresses are required. Analyzing the communication address is crucial. There are various challenges in address recognition due to the address text’s features of free writing, numerous aliases and significant text similarity. This study shows an ENEX-FP address recognition model, which consists of an entity extractor (ENEX) and a feature processor (FP) for address recognition, as a solution to the issues mentioned. This study uses adversarial training to enhance the model’s robustness and a hierarchical learning rate setup and learning rate attenuation technique to enhance recognition accuracy. Compared with traditional named entity recognition models, our model achieves an F1-score of 93.47% and 94.59% in the dataset, demonstrating the ENEX-FP model’s effectiveness in recognizing addresses.
K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor
Authors: Subburaj Sathishkumar , Yeh Chih-Ho , Patel Brijesh , Huang Tsung-Han , Hung Wei-Song , Chang Ching-Yuan , Wu Yu-Wei , Lin Po Ting
Published: January 2023

Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively.
Adaptive Dead-Time Control Design with Low Dead-Time Error in 20 MHz 90 V GaN Gate Driver
Authors: Hu Yifan , Wang Yong , Wang Ying , Peng Ling , Kong Ying
Published: January 2023

This paper presents an adaptive dead-time control circuit for a maximum work frequency 20 MHz, maximum voltage level 90 V GaN gate driver. The dead-time is set to prevent straight-through of the upper and lower power transistors of the bridge arm structure and ensure the reliability of the motor driver. For GaN drivers on the market today, fixed or configurable dead-time is widely used in more-than-10 MHz application scenarios. However, the switching loss caused by insufficient dead-time and reverse turn-on loss caused by excessive dead-time of GaN devices have a hazardous influence on the efficiency of drivers, which fixed or configurable dead-time cannot avoid. The gate driver with the proposed adaptive dead-time control circuit has been implemented in a 0.18 um BCD process. The proposed adaptive control circuit dissipates 56.3 uA quiescent current in a simulation situation and is able to provide maximum 5 ns dead-time error for a GaN gate driver.