bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Representative View Selection of a 3D Cultural Relic Using Depth Variation Entropy and Depth Distribution Entropy
Authors: Zeng Sheng , Geng Guohua , Zhou Mingquan
Published: November 2021

Automatically selecting a set of representative views of a 3D virtual cultural relic is crucial for constructing wisdom museums. There is no consensus regarding the definition of a good view in computer graphics; the same is true of multiple views. View-based methods play an important role in the field of 3D shape retrieval and classification. However, it is still difficult to select views that not only conform to subjective human preferences but also have a good feature description. In this study, we define two novel measures based on information entropy, named depth variation entropy and depth distribution entropy. These measures were used to determine the amount of information about the depth swings and different depth quantities of each view. Firstly, a canonical pose 3D cultural relic was generated using principal component analysis. A set of depth maps obtained by orthographic cameras was then captured on the dense vertices of a geodesic unit-sphere by subdividing the regular unit-octahedron. Afterwards, the two measures were calculated separately on the depth maps gained from the vertices and the results on each one-eighth sphere form a group. The views with maximum entropy of depth variation and depth distribution were selected, and further scattered viewpoints were selected. Finally, the threshold word histogram derived from the vector quantization of salient local descriptors on the selected depth maps represented the 3D cultural relic. The viewpoints obtained by the proposed method coincided with an arbitrary pose of the 3D model. The latter eliminated the steps of manually adjusting the model’s pose and provided acceptable display views for people. In addition, it was verified on several datasets that the proposed method, which uses the Bag-of-Words mechanism and a deep convolution neural network, also has good performance regarding retrieval and classification when dealing with only four views.
Cooling Cycle Optimization for a Vuilleumier Refrigerator
Authors: Paul Raphael , Khodja Abdellah , Fischer Andreas , Hoffmann Karl Heinz
Published: November 2021

Vuilleumier refrigerators are a special type of heat-driven cooling machines. Essentially, they operate by using heat from a hot bath to pump heat from a cold bath to an environment at intermediate temperatures. In addition, some external energy in the form of electricity can be used as an auxiliary driving mechanism. Such refrigerators are, for example, advantageous in situations where waste heat is available and cooling power is needed. Here, the question of how the performance of Vuilleumier refrigerators can be improved is addressed with a particular focus on the piston motion and thus the thermodynamic cycle of the refrigerator. In order to obtain a quantitative estimate of the possible cooling power gain, a special class of piston movements (the AS motion class explained below) is used, which was already used successfully in the context of Stirling engines. We find improvements of the cooling power of more than 15%.
On Epistemics in Expected Free Energy for Linear Gaussian State Space Models
Authors: Koudahl Magnus T. , Kouw Wouter M. , de Vries Bert
Published: November 2021

Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents). In this paper we show that Expected Free Energy (EFE) minimisation, a core feature of the framework, does not lead to purposeful explorative behaviour in linear Gaussian dynamical systems. We provide a simple proof that, due to the specific construction used for the EFE, the terms responsible for the exploratory (epistemic) drive become constant in the case of linear Gaussian systems. This renders AIF equivalent to KL control. From a theoretical point of view this is an interesting result since it is generally assumed that EFE minimisation will always introduce an exploratory drive in AIF agents. While the full EFE objective does not lead to exploration in linear Gaussian dynamical systems, the principles of its construction can still be used to design objectives that include an epistemic drive. We provide an in-depth analysis of the mechanics behind the epistemic drive of AIF agents and show how to design objectives for linear Gaussian dynamical systems that do include an epistemic drive. Concretely, we show that focusing solely on epistemics and dispensing with goal-directed terms leads to a form of maximum entropy exploration that is heavily dependent on the type of control signals driving the system. Additive controls do not permit such exploration. From a practical point of view this is an important result since linear Gaussian dynamical systems with additive controls are an extensively used model class, encompassing for instance Linear Quadratic Gaussian controllers. On the other hand, linear Gaussian dynamical systems driven by multiplicative controls such as switching transition matrices do permit an exploratory drive.
Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition
Authors: Dhandapani Ragavesh , Mitiche Imene , McMeekin Scott , Mallela Venkateswara Sarma , Morison Gordon
Published: November 2021

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.
Human Synchronization Maps—The Hybrid Consciousness of the Embodied Mind
Authors: Orsucci Franco
Published: November 2021

We examine the theoretical implications of empirical studies developed over recent years. These experiments have explored the biosemiotic nature of communication streams from emotional neuroscience and embodied mind perspectives. Information combinatorics analysis enabled a deeper understanding of the coupling and decoupling dynamics of biosemiotics streams. We investigated intraindividual and interpersonal relations as coevolution dynamics of hybrid couplings, synchronizations, and desynchronizations. Cluster analysis and Markov chains produced evidence of chimaera states and phase transitions. A probabilistic and nondeterministic approach clarified the properties of these hybrid dynamics. Thus, multidimensional theoretical models can represent the hybrid nature of human interactions.
Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population
Authors: Marcos-Martínez Diego , Martínez-Cagigal Víctor , Santamaría-Vázquez Eduardo , Pérez-Velasco Sergio , Hornero Roberto
Published: November 2021

Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI–NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject’s scores from Luria tests performed before and after MI–NFT. We found that MI–NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI–NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI–NFT.
What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models
Authors: García-Medina Andrés , Luu Duc Huynh Toan
Published: November 2021

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins’ price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.
A Review of the System-Intrinsic Nonequilibrium Thermodynamics in Extended Space (MNEQT) with Applications
Authors: Gujrati Purushottam D.
Published: November 2021

The review deals with a novel approach (MNEQT) to nonequilibrium thermodynamics (NEQT) that is based on the concept of internal equilibrium (IEQ) in an enlarged state space SZ involving internal variables as additional state variables. The IEQ macrostates are unique in SZ and have no memory just as EQ macrostates are in the EQ state space SX⊂SZ. The approach provides a clear strategy to identify the internal variables for any model through several examples. The MNEQT deals directly with system-intrinsic quantities, which are very useful as they fully describe irreversibility. Because of this, MNEQT solves a long-standing problem in NEQT of identifying a unique global temperature T of a system, thus fulfilling Planck’s dream of a global temperature for any system, even if it is not uniform such as when it is driven between two heat baths; T has the conventional interpretation of satisfying the Clausius statement that the exchange macroheatdeQflows from hot to cold, and other sensible criteria expected of a temperature. The concept of the generalized macroheat dQ=deQ+diQ converts the Clausius inequality dS≥deQ/T0 for a system in a medium at temperature T0 into the Clausius equalitydS≡dQ/T, which also covers macrostates with memory, and follows from the extensivity property. The equality also holds for a NEQ isolated system. The novel approach is extremely useful as it also works when no internal state variables are used to study nonunique macrostates in the EQ state space SX at the expense of explicit time dependence in the entropy that gives rise to memory effects. To show the usefulness of the novel approach, we give several examples such as irreversible Carnot cycle, friction and Brownian motion, the free expansion, etc.
Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator
Authors: Aydın Dursun , Ahmed Syed Ejaz , Yılmaz Ersin
Published: November 2021

This paper focuses on the adaptive spline (A-spline) fitting of the semiparametric regression model to time series data with right-censored observations. Typically, there are two main problems that need to be solved in such a case: dealing with censored data and obtaining a proper A-spline estimator for the components of the semiparametric model. The first problem is traditionally solved by the synthetic data approach based on the Kaplan–Meier estimator. In practice, although the synthetic data technique is one of the most widely used solutions for right-censored observations, the transformed data’s structure is distorted, especially for heavily censored datasets, due to the nature of the approach. In this paper, we introduced a modified semiparametric estimator based on the A-spline approach to overcome data irregularity with minimum information loss and to resolve the second problem described above. In addition, the semiparametric B-spline estimator was used as a benchmark method to gauge the success of the A-spline estimator. To this end, a detailed Monte Carlo simulation study and a real data sample were carried out to evaluate the performance of the proposed estimator and to make a practical comparison.
Proposal to Test a Transient Deviation from Quantum Mechanics’ Predictions for Bell’s Experiment
Authors: Hnilo Alejandro Andrés , Agüero Monica Beatriz , Kovalsky Marcelo Gregorio
Published: November 2021

Quantum mechanics predicts correlations between measurements performed in distant regions of a spatially spread entangled state to be higher than allowed by intuitive concepts of Locality and Realism. These high correlations forbid the use of nonlinear operators of evolution (which would be desirable for several reasons), for they may allow faster-than-light signaling. As a way out of this situation, it has been hypothesized that the high quantum correlations develop only after a time longer than L/c has elapsed (where L is the spread of the entangled state and c is the velocity of light). In shorter times, correlations compatible with Locality and Realism would be observed instead. A simple hidden variables model following this hypothesis is described. It is based on a modified Wheeler–Feynman theory of radiation. This hypothesis has not been disproved by any of the experiments performed to date. A test achievable with accessible means is proposed and described. It involves a pulsed source of entangled states and stroboscopic record of particle detection during the pulses. Data recorded in similar but incomplete optical experiments are analyzed, and found consistent with the proposed model. However, it is not claimed, in any sense, that the hypothesis has been validated. On the contrary, it is stressed that a complete, specific test is absolutely needed.
Decreased Resting-State Functional Complexity in Elderly with Subjective Cognitive Decline
Authors: Ni Huangjing , Song Zijie , Liang Lei , Xing Qiaowen , Qin Jiaolong , Wu Xiaochuan
Published: November 2021

Individuals with subjective cognitive decline (SCD) are at high risk of developing preclinical or clinical state of Alzheimer’s disease (AD). Resting state functional magnetic resonance imaging, which can indirectly reflect neuron activities by measuring the blood-oxygen-level-dependent (BOLD) signals, is promising in the early detection of SCD. This study aimed to explore whether the nonlinear complexity of BOLD signals can describe the subtle differences between SCD and normal aging, and uncover the underlying neuropsychological implications of these differences. In particular, we introduce amplitude-aware permutation entropy (AAPE) as the novel measure of brain entropy to characterize the complexity in BOLD signals in each brain region of the Brainnetome atlas. Our results demonstrate that AAPE can reflect the subtle differences between both groups, and the SCD group presented significantly decreased complexities in subregions of the superior temporal gyrus, the inferior parietal lobule, the postcentral gyrus, and the insular gyrus. Moreover, the results further reveal that lower complexity in SCD may correspond to poorer cognitive performance or even subtle cognitive impairment. Our findings demonstrated the effectiveness and sensitiveness of the novel brain entropy measured by AAPE, which may serve as the potential neuroimaging marker for exploring the subtle changes in SCD.
Entropic Uncertainty for Two Coupled Dipole Spins Using Quantum Memory under the Dzyaloshinskii–Moriya Interaction
Authors: Khedr Ahmad N. , Mohamed Abdel-Baset A. , Abdel-Aty Abdel-Haleem , Tammam Mahmoud , Abdel-Aty Mahmoud , Eleuch Hichem
Published: November 2021

In the thermodynamic equilibrium of dipolar-coupled spin systems under the influence of a Dzyaloshinskii–Moriya (D–M) interaction along the z-axis, the current study explores the quantum-memory-assisted entropic uncertainty relation (QMA-EUR), entropy mixedness and the concurrence two-spin entanglement. Quantum entanglement is reduced at increased temperature values, but inflation uncertainty and mixedness are enhanced. The considered quantum effects are stabilized to their stationary values at high temperatures. The two-spin entanglement is entirely repressed if the D–M interaction is disregarded, and the entropic uncertainty and entropy mixedness reach their maximum values for equal coupling rates. Rather than the concurrence, the entropy mixedness can be a proper indicator of the nature of the entropic uncertainty. The effect of model parameters (D–M coupling and dipole–dipole spin) on the quantum dynamic effects in thermal environment temperature is explored. The results reveal that the model parameters cause significant variations in the predicted QMA-EUR.
A Multi-Layered Framework for Analyzing Primary Students’ Multimodal Reasoning in Science
Authors: Xu Lihua , van Driel Jan , Healy Ryan
Published: November 2021

Classroom communication is increasingly accepted as multimodal, through the orchestrated use of different semiotic modes, resources, and systems. There is growing interest in examining the meaning-making potential of other modes (e.g., gestural, visual, kinesthetic) beyond the semiotic mode of language, in classroom communication and in student reasoning in science. In this paper, we explore the use of a multi-layered analytical framework in an investigation of student reasoning during an open inquiry into the physical phenomenon of dissolving in a primary classroom. The 24 students, who worked in pairs, were video recorded in a facility purposefully designed to capture their verbal and non-verbal interactions during the science session. By employing a multi-layered analytical framework, we were able to identify the interplays between the different semiotic modes and the level of reasoning undertaken by the students as they worked through the tasks. This analytical process uncovered a variety of ways in which the students negotiated ideas and coordinated semiotic resources in their exploration of dissolving. This paper highlights the affordances and challenges of this multi-layered analytical framework for identifying the dynamic inter-relationships between different modes that the students drew on to grapple with the complexity of the physical phenomenon of dissolving.
Reflecting upon 30 Years of STEM Partnerships between Industry, University, and Public Schools: Past Lessons, Current Successes, and Future Dreams
Authors: Dieker Lisa A. , Butler Malcolm B. , Ortiz Enrique , Gao Su
Published: November 2021

The importance of partnerships is critical in educational arenas, but information on how partnerships form with the involvement of corporations, districts, and universities working in harmony is limited in the current literature. The teacher preparation program described in this paper is a “built-to-last” partnership model with over 650 teachers prepared to be teacher-leaders in science, technology, engineering, and mathematics (STEM) education. The authors provide a history of the program’s development, the sustainability of the program over time, the content of the various components of the partnership, and the evolution of the program, including its current status.
Localization of Multi-Class On-Road and Aerial Targets Using mmWave FMCW Radar
Authors: Gupta Khushi , Joshi Soumya , Srinivas M. B. , Boppu Srinivas , Manikandan M. Sabarimalai , Cenkeramaddi Linga Reddy
Published: November 2021

mmWave radars play a vital role in autonomous systems, such as unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), ground station control and monitoring systems. The challenging task when using mmWave radars is to estimate the accurate angle of arrival (AoA) of the targets, due to the limited number of receivers. In this paper, we present a novel AoA estimation technique, using mmWave FMCW radars operating in the frequency range 77–81 GHz by utilizing the mechanical rotation. Rotating the radar also increases the field of view in both azimuth and elevation. The proposed method estimates the AoA of the targets, using only a single transmitter and receiver. The measurements are carried out in a variety of practical scenarios including pedestrians, a car, and an UAV, also known as a drone. With measured data, range-angle maps are created, and morphological operators are used to estimate the AoA of the targets. We also process radar range-angle images for improved visual representation. The proposed method will be extremely beneficial for practical ground stations, traffic control and monitoring frameworks for both on-ground and airborne vehicles.
Gate-Level Static Approximate Adders: A Comparative Analysis
Authors: Balasubramanian Padmanabhan , Nayar Raunaq , Maskell Douglas L.
Published: November 2021

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.
Methods of Fast Analysis of DC–DC Converters—A Review
Authors: Górecki Paweł , Górecki Krzysztof
Published: November 2021

The paper discusses the methods of fast analysis of DC–DC converters dedicated to computer programmes. Literature methods of such an analysis are presented, which enable determination of the characteristics of the considered converters in the steady state and in the transient states. The simplifications adopted at the stage of developing these methods are discussed, and their influence on the accuracy of computations is indicated. Particular attention is paid to the methods of fast analysis of DC–DC converters, taking into account thermal phenomena in semiconductor devices. The sample results of computations of the DC–DC boost type converter obtained with the use of the selected methods are presented. The scope of application of particular computation methods and their duration times are discussed. Computations were performed with the use of SPICE and PLECS.
Intervention Programs for the Problematic Use of the Internet and Technological Devices: A Systematic Review
Authors: Cañas Elizabeth , Estévez Estefanía
Published: November 2021

The intensive use of the Internet and communication technologies among adolescents has increased addiction and/or their problematic use. The innovative and revolutionary development of this technology can have negative effects on the mental and physical health of its users, and it seems to have a greater impact on adolescents. As this is causing a public health problem, the objective of this study was to review the different intervention and prevention programs for this problem in adolescents. A total of 14 programs met the inclusion criteria. The analysis of the programs allows for the identification of effective intervention designs for prevention, and also for the treatment of the current problems derived from the use of the Internet and technological devices among adolescent users.
Geometric Midpoint Algorithm for Device-Free Localization in Low-Density Wireless Sensor Networks
Authors: Sun Chao , Zhou Biao , Yang Shangyi , Kim Youngok
Published: November 2021

Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.
Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier
Authors: Majeed Rizwan , Abdullah Nurul Azma , Faheem Mushtaq Muhammad , Umer Muhammad , Nappi Michele
Published: November 2021

Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results.
Triangular Cavity Multi-Passband HMSIW Filter Based on Odd-Even Mode Analysis
Authors: Zhang Luhua , Wu Aiting , Zhang Pengquan , Zhang Zhonghai
Published: November 2021

This letter proposes a multi-passband half-mode substrate integrated waveguide (HMSIW) filter based on the theory of odd and even mode analysis. The filter adopts a triangular HMSIW cavity cut along the diagonal of the rectangle. By etching two dual-mode resonators, the resonant mode of the HMSIW resonator is coupled with the odd-even mode of the dual-mode resonator to achieve multiple passbands. The defected ground structure (DGS) of the filter can reduce the resonance frequency of the HMSIW cavity without increasing the volume of the HMSIW cavity, making it easier to couple with the odd and even mode frequencies of the resonator. The input and output ports are directly coupled through a microstrip line. In this way, it adds an additional coupling path to the filter, which increases the out-of-band suppression without changing the performance in the passband, and improves the overall performance of the filter. To prove the feasibility of the above method, a multi-passband HMSIW filter was fabricated and tested. The center frequencies of the three passbands of the filter are 2.98 GHz, 4.78 GHz, and 6.62 GHz, respectively. The return loss in the passband is better than −15 dB, and the insertion loss is better than 2 dB. The measured results have a good agreement with the simulation results.
Prospects of Wireless Energy-Aware Sensors for Smart Factories in the Industry 4.0 Era
Authors: Kanoun Olfa , Khriji Sabrine , Naifar Slim , Bradai Sonia , Bouattour Ghada , Bouhamed Ayda , El Houssaini Dhouha , Viehweger Christian
Published: November 2021

Advanced sensors are becoming essential for modern factories, as they contribute by gathering comprehensive data about machines, processes, and human-machine interaction. They play an important role in improving manufacturing performance, in-factory logistics, predictive maintenance, supply chains, and digitalization in general. Wireless sensors and wireless sensor networks (WSNs) provide, in this context, significant advantages as they are flexible and easily deployable. They have reduced installation and maintenance costs and contributed by reducing cables and preinstalled infrastructure, leading to improved reliability. WSNs can be retrofitted in machines to provide direct information from inside the processes. Recent developments have revealed exciting possibilities to enhance energy harvesting (EH) and wireless energy transmission, enabling a reliable use of wireless sensors in smart factories. This review provides an overview of the potential of energy aware WSNs for industrial applications and shows relevant techniques for realizing a sustainable energy supply based on energy harvesting and energy transfer. The focus is on high-performance converter solutions and improvement of frequency, bandwidth, hybridization of the converters, and the newest trends towards flexible converters. We report on possibilities to reduce the energy consumption in wireless communication on the node level and on the network level, enabling boosting network efficiency and operability. Based on the existing technologies, energy aware WSNs can nowadays be realized for many applications in smart factories. It can be expected that they will play a great role in the future as an enabler for digitalization in this decisive economic sector.
Compact 3-bit Frequency Reconfigurable Monopole Antenna Realized with a Switchable Three-Line Section
Authors: Yang Yueyou , Liu Yun
Published: November 2021

This work proposes a compact 3-bit frequency-reconfigurable monopole antenna covering a broad reconfigurable range by inserting a switchable three-line section (STLS). The design starts with a conventional quarter-wavelength monopole line antenna, which is then replaced by a novel structure, the STLS. The STLS is composed of three parallel-connected lines with different lengths. Accordingly, three RF p–i–n diodes are introduced in the STLS to achieve binary reconfiguration. After all parameters of the antenna have been optimized, it will eventually output 2N = 8 (N is the number of switches) independent working states with different equivalent lengths and a reconfigurable working frequency. The number of states in a binary reconfigurable antenna is optimally large in relation to the number of switches used, which means that it can be extremely convenient for digital control of switching all the states and capable of decreasing the number of RF p–i–n diodes we used, thereby minimizing the manufacturing cost and loss of diodes. A prototype antenna is fabricated and tested, and the measurement results agree well with the simulation results, validating the good features, such as a large reconfigurable switchable frequency range from 0.95 GHz to 2.45 GHz with considerable working bandwidth varying from 40 MHz to 540 MHz for each state, simple structure, and a compact size of 70 × 40 mm2, which can be appropriately used for a multi-radio wireless system and handheld devices. All the states have a similar monopole radiation pattern with a good maximum efficiency and an acceptable peak gain according to its compact size.
4 × 4 MIMO Antenna System for Smart Eyewear in Wi-Fi 5G and Wi-Fi 6e Wireless Communication Applications
Authors: Chung Ming-An , Hsiao Cheng-Wei , Yang Chih-Wei , Chuang Bing-Ruei
Published: November 2021

This paper proposes a small-slot antenna system (50 mm × 9 mm × 2.7 mm) for 4 × 4 multiple-input multiple-output (MIMO) on smart glasses devices. The antenna is set on the plastic temple, and the inverted F antenna radiates through the slot in the ground plane of the sputtered copper layer outside the temple. Two symmetrical antennas and slots on the same temple and series capacitive elements enhance the isolation between the two antenna ports. When both temples are equipped with the proposed antennas, 4 × 4 MIMO transmission can be achieved. The antenna substrate is made of polycarbonate (PC), and its thickness is 2.7 mm εr=2.85, tanδ=0.0092. According to the actual measurement results, this antenna has two working frequency bands when the reflection coefficient is lower than −10dB, its working frequency bandwidth at 4.58–5.72 GHz and 6.38–7.0 GHz. The proposed antenna has a peak gain of 4.3 dBi and antenna efficiency of 85.69% at 5.14 GHz. In addition, it also can obtain a peak gain of 3.3 dBi and antenna efficiency of 82.78% at 6.8 GHz. The measurement results show that this antenna has good performance, allowing future smart eyewear devices to be applied to Wi-Fi 5G (5.18–5.85 GHz) and Wi-Fi 6e (5.925–7.125 GHz).
Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet
Authors: Kim Minho , Kwon Hyuk-Chul
Published: November 2021

Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the χ2 statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).
FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems
Authors: Lee Geon-Hui , Akpudo Ugochukwu Ejike , Hur Jang-Wook
Published: November 2021

Gear pump failures in industrial settings are common due to their exposure to uneven high-pressure outputs within short time periods of machine operation and uncertainty. Improving the field and line clam are considered as the solutions for these failures, yet they are quite insufficient for optimal reliability. This research, therefore, suggests a method for early wear detection in gear pumps following an extensive failure modes, effects, and criticality analysis (FMECA) of an AP3.5/100 external gear pump manufactured by BESCO. To replicate this condition, fine particles of iron oxide (Fe2O3) were mixed with the experimental fluid, and the resulting vibration data were collected, processed, and exploited for wear detection. The intelligent wear detection process was explored using various machine learning algorithms following a mel-frequency cepstral coefficient (MFCC)-based discriminative feature extraction process. Among these algorithms, extensive performance evaluation reveals that the random forest classifier returned the highest test accuracy of 95.17%, while the k-nearest neighbour was the most cost efficient following cross validations. This study is expected to contribute to improved evaluations of gear pump failure diagnosis and prognostics.
The Grid Independence of an Electric Vehicle Charging Station with Solar and Storage
Authors: Caines Alex , Ghosh Aritra , Bhattacharjee Ankur , Feldman Adam
Published: November 2021

The UK government has set a ban on the sale of new petrol and diesel cars and vans by 2030. This will create a shift to electric vehicles. which will present a substantial impact on the grid. Therefore, methods to reduce the charging station’s impact on the grid have to be developed. This paper’s objective is to evaluate how integrating solar and storage affects a charging station’s dependence on the grid. A photovoltaic electric vehicle charging station (PVEVCS) is first designed, and then four charging profiles are selected to assess the station through a simulation using MATLAB. The array produces 3257 MWh/yr which, on average, offsets 40% of the electric vehicle (EV) load experienced by the station. Furthermore, with the integration of storage, the dependence is further reduced by 10% on average. The system also exported energy to the grid, offsetting close to all the energy imported.
An Efficient Carrier Synchronization Scheme for Demodulation Systems
Authors: Hu Wanru , Wang Zhugang , Mei Ruru , Lin Meiyan
Published: November 2021

A simple data-aided carrier synchronization scheme is proposed for variable modulation (VM) communication systems under the initial conditions of a low signal-to-noise ratio (SNR) and normalized carrier frequency offset (CFO) symbol rate of 20%. The proposed carrier synchronization scheme is simplified into two steps; a reconfigurable L&R (RLR) algorithm and pilot-aided (PA) phase linear interpolation algorithm is applied for carrier frequency recovery (CFR) and carrier phase recovery (CPR), respectively. Furthermore, the autocorrelation values of multi-pilot blocks are superimposed to improve the accuracy of the CFR algorithm, and the algorithm formulas are decomposed and modularized to simplify the implementation complexity of the RLR algorithm. Simulation results show that the RLR algorithm can track and lock the CFO up to a 33.2% symbol rate and reduce the CFO to 0.024%. The bit error rate (BER) performance of the carrier synchronization scheme almost coincides with the theoretical curve results. Comparison of hardware complexity shows that the multiplication resource consumption can be reduced by at least 72.47%.
Fabrication of Superconducting Nb–AlN–NbN Tunnel Junctions Using Electron-Beam Lithography
Authors: Fominsky Mikhail Yu. , Filippenko Lyudmila V. , Chekushkin Artem M. , Dmitriev Pavel N. , Koshelets Valery P.
Published: November 2021

Mixers based on superconductor–insulator–superconductor (SIS) tunnel junctions are the best input devices at frequencies from 0.1 to 1.2 THz. This is explained by both the extremely high nonlinearity of such elements and their extremely low intrinsic noise. Submicron tunnel junctions are necessary to realize the ultimate parameters of SIS receivers, which are used as standard devices on both ground and space radio telescopes around the world. The technology for manufacturing submicron Nb–AlN–NbN tunnel junctions using electron-beam lithography was developed and optimized. This article presents the results on the selection of the exposure dose, development time, and plasma chemical etching parameters to obtain high-quality junctions (the ratio of the resistances below and above the gap Rj/Rn). The use of a negative-resist ma-N 2400 with lower sensitivity and better contrast in comparison with a negative-resist UVN 2300-0.5 improved the reproducibility of the structure fabrication process. Submicron (area from 2.0 to 0.2 µm2) Nb–AlN–NbN tunnel junctions with high current densities and quality parameters Rj/Rn > 15 were fabricated. The spread of parameters of submicron tunnel structures across the substrate and the reproducibility of the cycle-to-cycle process of tunnel structure fabrication were measured.
PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach
Authors: Alsharif Roaa , Al-Issa Yazan , Alqudah Ali Mohammad , Qasmieh Isam Abu , Mustafa Wan Azani , Alquran Hiam
Published: November 2021

Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than others. Radiological images such as Chest X-ray (CXR) images provide early detection and prompt action, where typical CXR for such a disease is characterized by radiopaque appearance or seemingly solid segment at the affected parts of the lung due to inflammatory exudate formation replacing the air in the alveoli. The early and accurate detection of pneumonia is crucial to avoid fatal ramifications, particularly in children and seniors. In this paper, we propose a novel 50 layers Convolutional Neural Network (CNN)-based architecture that outperforms the state-of-the-art models. The suggested framework is trained using 5852 CXR images and statistically tested using five-fold cross-validation. The model can distinguish between three classes: viz viral, bacterial, and normal; with 99.7% ± 0.2 accuracy, 99.74% ± 0.1 sensitivity, and 0.9812 Area Under the Curve (AUC). The results are promising, and the new architecture can be used to recognize pneumonia early with cost-effectiveness and high accuracy, especially in remote areas that lack proper access to expert radiologists, and therefore, reduces pneumonia-caused mortality rates.
Classifying Emotions in Film Music—A Deep Learning Approach
Authors: Ciborowski Tomasz , Reginis Szymon , Weber Dawid , Kurowski Adam , Kostek Bozena
Published: November 2021

The paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the subjective test results is performed. The application employs a deep convolutional neural network (CNN), which classifies emotions based on 30 s excerpts of music works presented to the CNN input using mel-spectrograms. Examples of classification results of the selected neural networks used to create the system are shown.
Self-Adaptive Approximate Mobile Deep Learning
Authors: Knez Timotej , Machidon Octavian , Pejović Veljko
Published: November 2021

Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential to facilitate the flexible deployment of deep learning models in real-world applications. However, their actual suitability and performance in ubiquitous or edge computing applications has not, to this date, been evaluated. In this context, our work aims to bridge the gap between the theoretical resource savings promised by such approaches and the requirements of a real-world mobile application by introducing algorithms that dynamically guide the compression rate of a neural network according to the continuously changing context in which the mobile computation is taking place. Through an in-depth trace-based investigation, we confirm the feasibility of our adaptation algorithms in offering a scalable trade-off between the inference accuracy and resource usage. We then implement our approach on real-world edge devices and, through a human activity recognition application, confirm that it offers efficient neural network compression adaptation in highly dynamic environments. The results of our experiment with 21 participants show that, compared to using static network compression, our approach uses 2.18× less energy with only a 1.5% drop in the average accuracy of the classification.