bluuebunny/crossref_metadata_2025 huggingface dataset:
Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals
Authors: Haque Chowdhury Azimul , Kwon Tae-Ho , Kim Ki-Doo
Published: February 2022

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals.
A Crest Factor Reduction Technique for LTE Signals with Target Relaxation in Power Amplifier Linearization
Authors: Cárdenas-Valdez José Ricardo , Galaviz-Aguilar Jose Alejandro , Vargas-Rosales Cesar , Inzunza-González Everardo , Flores-Hernández Leonardo
Published: February 2022

The signal conditioning treatment to achieve good relation of power with radio-frequency (RF) conversion in conventional transceiver systems require precise baseband models. A developed framework is built to provide a demonstration of the modeling figures of merit with orthogonal frequency division multiplexing (OFDM) support under signal conditioning and transmission restrictions to waveforms with high peak to average power ratio (PAPR) in practical applications. Therefore, peak and average power levels have to be limited to correct high PAPR for a better suited correction power from the amplifier that can lead to compression or clipping in the signal of interest. This work presents an alternative joint crest factor reduction (CFR) algorithm to correct the performance of PAPR. A real-time field-programmable gate array (FPGA) testbed is developed to characterize and measure the behavior of an amplifier using a single-carrier 64–QAM OFDM based on long-term evolution (LTE) downlink at 2.40 GHz as stimulus, across wide modulation bandwidths. The results demonstrate that the CFR accuracy capabilities for the signal conditioning show a reliable clipping reduction to give a smooth version of the clipping signal and provide a factor of correction for the unwanted out-of-band emission validated according to the adjacent channel power ratio (ACPR), PAPR, peak power, complementary cumulative distribution function (CCDF), and error vector magnitude (EVM) figures of merit.
State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy
Authors: Navega Vieira Rômulo , Mauricio Villanueva Juan Moises , Sales Flores Thommas Kevin , Tavares de Macêdo Euler Cássio
Published: February 2022

Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA’s research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA’s research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.
Probabilistic Modeling of Motion Blur for Time-of-Flight Sensors
Authors: Rodriguez Bryan , Zhang Xinxiang , Rajan Dinesh
Published: February 2022

Synthetically creating motion blur in two-dimensional (2D) images is a well-understood process and has been used in image processing for developing deblurring systems. There are no well-established techniques for synthetically generating arbitrary motion blur within three-dimensional (3D) images, such as depth maps and point clouds since their behavior is not as well understood. As a prerequisite, we have previously developed a method for generating synthetic motion blur in a plane that is parallel to the sensor detector plane. In this work, as a major extension, we generalize our previously developed framework for synthetically generating linear and radial motion blur along planes that are at arbitrary angles with respect to the sensor detector plane. Our framework accurately captures the behavior of the real motion blur that is encountered using a Time-of-Flight (ToF) sensor. This work uses a probabilistic model that predicts the location of invalid pixels that are typically present within depth maps that contain real motion blur. More specifically, the probabilistic model considers different angles of motion paths and the velocity of an object with respect to the image plane of a ToF sensor. Extensive experimental results are shown that demonstrate how our framework can be applied to synthetically create radial, linear, and combined radial-linear motion blur. We quantify the accuracy of the synthetic generation method by comparing the resulting synthetic depth map to the experimentally captured depth map with motion. Our results indicate that our framework achieves an average Boundary F1 (BF) score of 0.7192 for invalid pixels for synthetic radial motion blur, an average BF score of 0.8778 for synthetic linear motion blur, and an average BF score of 0.62 for synthetic combined radial-linear motion blur.
Electrical and Low Frequency Noise Characterization of Graphene Chemical Sensor Devices Having Different Geometries
Authors: Nah JongBong , Perkins Frank Keith , Lock Evgeniya H. , Nath Anindya , Boyd Anthony , Myers-Ward Rachael L. , Gaskill David Kurt , Osofsky Michael , Rao Mulpuri V.
Published: February 2022

Chemiresistive graphene sensors are promising for chemical sensing applications due to their simple device structure, high sensitivity, potential for miniaturization, low-cost, and fast response. In this work, we investigate the effect of (1) ZnO nanoparticle functionalization and (2) engineered defects onto graphene sensing channel on device resistance and low frequency electrical noise. The engineered defects of interest include 2D patterns of squares, stars, and circles and 1D patterns of slots parallel and transverse to the applied electric potential. The goal of this work is to determine which devices are best suited for chemical sensing applications. We find that, relative to pristine graphene devices, nanoparticle functionalization leads to reduced contact resistance but increased sheet resistance. In addition, functionalization lowers 1/f current noise on all but the uniform mesa device and the two devices with graphene strips parallel to carrier transport. The strongest correlations between noise and engineering defects, where normalized noise amplitude as a function of frequency f is described by a model of AN/fγ, are that γ increases with graphene area and contact area but decreases with device total perimeter, including internal features. We did not find evidence of a correlation between the scalar amplitude, AN, and the device channel geometries. In general, for a given device area, the least noise was observed on the least-etched device. These results will lead to an understanding of what features are needed to obtain the optimal device resistance and how to reduce the 1/f noise which will lead to improved sensor performance.
Industry 4.0 Readiness Calculation—Transitional Strategy Definition by Decision Support Systems
Authors: Trstenjak Maja , Opetuk Tihomir , Cajner Hrvoje , Hegedić Miro
Published: February 2022

The digitization of the manufacturing industry, 10 years after the introduction of the Industry 4.0 concept, is still one of the most demanding tasks for the companies, especially for SMEs. As one of the biggest barriers in new business model implementation, the lack of strategy and workforce skills is frequently mentioned in the literature. The high level of investments it requires and the perception of high risks with unclear future benefits can be avoided with readiness factor calculation. This paper presents a novel model for readiness factor calculation, oriented to process planning and based on decision support systems. The model enables the definition of the optimal strategic plan for the digitization with the use of decision support systems (analytic hierarchy process) and through the use of statistical methods implemented within the model it minimizes the influence of human subjectivity and quantification of qualitative criteria. This innovative approach enables the understanding of the transition process to new technology-enabled business models, in this case oriented towards process planning. The useability and reliability of the model is proven in a case study of a metal machining company.
Assessing Perceptual Load and Cognitive Load by Fixation-Related Information of Eye Movements
Authors: Liu Jung-Chun , Li Kuei-An , Yeh Su-Ling , Chien Shao-Yi
Published: February 2022

Assessing mental workload is imperative for avoiding unintended negative consequences in critical situations such as driving and piloting. To evaluate mental workload, measures of eye movements have been adopted, but unequivocal results remain elusive, especially those related to fixation-related parameters. We aimed to resolve the discrepancy of previous results by differentiating two kinds of mental workload (perceptual load and cognitive load) and manipulated them independently using a modified video game. We found opposite effects of the two kinds of mental workload on fixation-related parameters: shorter fixation durations and more fixations when participants played an episode with high (vs. low) perceptual load, and longer fixation durations and fewer fixations when they played an episode with high (vs. low) cognitive load. Such opposite effects were in line with the load theory and demonstrated that fixation-related parameters can be used to index mental workload at different (perceptual and cognitive) stages of mental processing.
Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
Authors: Ma Jun , Li Jiateng , Huang Hongwei
Published: February 2022

V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.
From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art
Authors: Medina Sánchez Carlos , Zella Matteo , Capitán Jesús , Marrón Pedro J.
Published: February 2022

Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments.
Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait
Authors: Hellec Justine , Chorin Frédéric , Castagnetti Andrea , Guérin Olivier , Colson Serge S.
Published: February 2022

The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.
Proactive Handover Decision for UAVs with Deep Reinforcement Learning
Authors: Jang Younghoon , Raza Syed M. , Kim Moonseong , Choo Hyunseung
Published: February 2022

The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity. The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online learning of UAV handover decisions. The proposed scheme is evaluated in a 3D-emulated UAV mobility environment where it reduces up to 76 and 73% of unnecessary handovers compared to greedy and Q-learning-based UAV handover decision schemes, respectively. Furthermore, this scheme ensures reliable communication with the UAV by maintaining the RSSI above −75 dBm more than 80% of the time.
Degradation of Sub-Micrometer Sensitive Polymer Layers of Acoustic Sensors Exposed to Chlorpyrifos Water-Solution
Authors: Rabus David , Lotthammer Fanny , Degret Joscelyn , Arapan Lilia , Palmino Frank , Friedt Jean-Michel , Cherioux Frédéric
Published: February 2022

The detection of organophosphates, a wide class of pesticides, in water-solution has a huge impact in environmental monitoring. Acoustic transducers are used to design passive wireless sensors for the direct detection of pesticides in water-solution by using tailored polymers as sensitive layers. We demonstrate by combining analytical chemistry tools that organophosphate molecules strongly alter polymer layers widely used in acoustic sensors in the presence of water. This chemical degradation can limit the use of these polymers in detection of organophosphates in water-solution.
A CMOS Image Sensor Based Refractometer without Spectrometry
Authors: Yang Haechang , Shin Sanghoon , Kumar Samir , Seo Dongmin , Oh Sangwoo , Lee Moonjin , Seo Sungkyu
Published: February 2022

The refractive index (RI), an important optical property of a material, is measured by commercial refractometers in the food, agricultural, chemical, and manufacturing industries. Most of these refractometers must be equipped with a prism for light dispersion, which drastically limits the design and size of the refractometer. Recently, there have been several reports on the development of a surface plasmon resonance (SPR)-based RI detector, which is characterized by its high sensitivity and simplicity. However, regardless of the prism, an expensive spectrometer is required to analyze the resonance wavelength or angle of incidence. This paper proposes a method that eliminates the need for the prism and other conventional spectrometer components. For this purpose, total internal reflection SPR technology was used on an Ag thin film, and RI analysis was combined with a lens-free CMOS image sensor or a smartphone camera. A finite-difference time-domain (FDTD) numerical simulation was performed to evaluate the relationship between the output power intensity and Ag film thickness for different RIs at three wavelengths of commercial light-emitting diodes (LEDs). The maximum sensitivity of −824.54 RIU−1 was achieved with AG20 at an incident wavelength of 559 nm. Due to its simple design and cost effectiveness, this prism-less, SPR-based refractometer combined with a lens-free CMOS image sensor or a smartphone could be a superior candidate for a point-of-care device that can determine the RIs of various analytes in the field of biological or chemical sensing.
Vibration-Based Loosening Detection of a Multi-Bolt Structure Using Machine Learning Algorithms
Authors: Eraliev Oybek , Lee Kwang-Hee , Lee Chul-Hee
Published: February 2022

Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening detection and identification technique is proposed using ML algorithms, for a motor fastened with four bolts (M8 × 1.5) to a stationary support. First, several cases with fastened and loosened bolts were established, and the motor was operated in three different types of working condition (800 rpm, 1000 rpm, and 1200 rpm), in order to obtain enough vibration data. Second, for feature extraction of the dataset, the short-time Fourier transform (STFT) method was performed. Third, different types of classifier of ML were trained, and a new test dataset was applied to evaluate the performance of the classifiers. Finally, the classifier with the greatest accuracy was identified. The test results showed that the capability of the classifier was satisfactory for detecting bolt loosening and identifying which bolt or bolts started to lose their preload in each working condition. The identified classifier will be implemented for online monitoring of the early stage of bolt loosening of a multi-bolt structure in future works.
Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System
Authors: Liu Dunwen , Chen Haofei , Tang Yu , Liu Chao , Cao Min , Gong Chun , Jiang Shulin
Published: February 2022

The rapid development of highway engineering has made slope stability an important issue in infrastructure construction. To meet the needs of green vegetation growth, ecological recovery, landscape beautification and the economy, long-term monitoring research on high-slope micrometeorology has important practical significance. Because of that, we designed and created a new slope micrometeorological monitoring and predicting system (SMMPS). We innovatively upgraded the cloud platform system, by adding an ARIMA prediction system and data-fitting system. From regularly sensor-monitored slope micrometeorological factors (soil temperature and humidity, slope temperature and humidity, and slope rainfall), a data-fitting system was used to fit atmospheric data with slope micrometeorological data, the trend of which ARIMA predicted. The slope was protected in time to prevent severe weather damage to the slope vegetation on a large scale. The SMMPS, which upgrades its cloud platform, significantly reduces the cost of long-term monitoring, protects slope stability, and improves the safety of rail and road projects.
Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors
Authors: Attestog Sveinung , Senanayaka Jagath Sri Lal , Van Khang Huynh , Robbersmyr Kjell G.
Published: February 2022

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.
Feasibility and Reliability of Smartwatch to Obtain Precordial Lead Electrocardiogram Recordings
Authors: Sprenger Nora , Sepehri Shamloo Alireza , Schäfer Jonathan , Burkhardt Sarah , Mouratis Konstantinos , Hindricks Gerhard , Bollmann Andreas , Arya Arash
Published: February 2022

The Apple Watch is capable of recording single-lead electrocardiograms (ECGs). To incorporate such devices in routine medical care, the reliability of such devices to obtain precordial leads needs to be validated. The purpose of this study was to assess the feasibility and reliability of a smartwatch (SW) to obtain precordial leads compared to standard ECGs. We included 100 participants (62 male, aged 62.8 ± 13.1 years) with sinus rhythm and recorded a standard 12-lead ECG and the precordial leads with the Apple Watch. The ECGs were quantitively compared. A total of 98 patients were able to record precordial leads without assistance. A strong correlation was observed between the amplitude of the standard and SW-ECGs’ waves, in terms of P waves, QRS-complexes, and T waves (all p-values < 0.01). A significant correlation was observed between the two methods regarding the duration of the ECG waves (all p-values < 0.01). Assessment of polarity showed a significant and a strong concordance between the ECGs’ waves in all six leads (91–100%, all p-values < 0.001). In conclusion, 98% of patients were able to record precordial leads using a SW without assistance. The SW is feasible and reliable for obtaining valid precordial-lead ECG recordings as a validated alternative to a standard ECG.
Vehicle Bump Testing Parameters Influencing Modal Identification of Long-Span Segmental Prestressed Concrete Bridges
Authors: Hernandez Wilson , Viviescas Alvaro , Riveros-Jerez Carlos Alberto
Published: February 2022

In-service prestressed concrete box girder bridges have received increasing attention in recent years due to a large number of bridges reaching decades in service. Therefore, the ageing of infrastructure demands the development of robust condition assessment methodologies based on affordable technology such as vehicle-induced vibration tests (VITs) in contrast with more expensive existing technologies such as tests using hammers or shakers. Ambient vibration tests (AVTs) have been widely used worldwide, taking advantage of freely available ambient excitation sources. However, the literature has commonly reported insufficient input energy to excite the structure to obtain satisfactory modal identification results, especially in long-span concrete bridges. On the other hand, the use of forced vibration tests (FVTs) requires more economic resources. This paper presents the results of field measurements at optimally selected locations in VITs consisting of a 32-ton truck and a springboard with a height of 50 mm. AVTs using optimal sensor placement (OSP) provide similar results to VITs without considering OSP locations. Additionally, the VIT/AVT cost ratio is reduced to 2 since a shorter data collection time is achieved within a one-day (8 h) test framework, which minimizes temperature effects, thus leading to improvements in AVT identification results, especially in vertical modes.
GEANT4 Simulation for Radioactive Particle Tracking (RPT) Technique
Authors: Alghamdi Ahmed A. , Aljuwaya Thaar M. , Alomari Abdullah S. , Al-Dahhan Muthanna H.
Published: February 2022

In the past two decades, the radioactive particle tracking (RPT) measurement technique has been proven to visualize flow fields of most multiphase flow systems of industrial interest. The accuracy of RPT, and hence the data obtained, depend largely on the calibration process, which stands here as a basis for two subsequent processes: tracking and reconstruction. However, limitations in the RPT calibration process can be found in different experimental constrains and in assumptions made in the classical Monte Carlo approach used to simulate number of counts received by the detectors. Therefore, in this work, we applied a GEANT4-based Monte Carlo code to simulate the RPT calibration process for an investigated multiphase flow system (i.e., gas–liquid bubble column). The GEANT4 code was performed to simulate the number of counts received by 28 scintillation detectors for 931 known tracer positions while capturing all the types of photon interaction and overcoming solids’ angle limitations in classical approaches. The results of the simulation were validated against experimental data obtained using an automated RPT calibration device. The results showed a good agreement between the simulated and experimental counts, where the maximum absolute average relative deviation detected was about 5%. The GEANT4 model typically predicted the number of counts received by all the detectors; however, it over-estimated the counts when the number of primary events applied in the model was not the optimal.
Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data
Authors: Akhonda M. A. B. S. , Levin-Schwartz Yuri , Calhoun Vince D. , Adali Tülay
Published: February 2022

It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders.
Robust Ship Detection in Infrared Images through Multiscale Feature Extraction and Lightweight CNN
Authors: Miao Rui , Jiang Hongxu , Tian Fangzheng
Published: February 2022

The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of multi-scene, multi-resolution and multi-type target ships. To overcome these challenges, a ship detection method based on multiscale feature extraction and lightweight CNN is proposed. Firstly, the candidate-region extraction method, based on a multiscale model, can cover the potential targets under different backgrounds accurately. Secondly, the multiple feature fusion method is employed to achieve ship classification, in which, Fourier global spectrum features are applied to discriminate between targets and simple interference, and the targets in complex interference scenarios are further distinguished by using lightweight CNN. Thirdly, the cascade classifier training algorithm and an improved non-maximum suppression method are used to minimise the classification error rate and maximise generalisation, which can achieve final-target confirmation. Experimental results validate our method, showing that it significantly outperforms the available alternatives, reducing the model size by up to 2.17 times while improving detection performance be improved by up to 5.5% in multi-interference scenarios. Furthermore, the robustness ability was verified by three indicators, among which the F-measure score and true–false-positive rate can increase by up to 5.8% and 4.7% respectively, while the mean error rate can decrease by up to 38.2%.
Using Voice Activity Detection and Deep Neural Networks with Hybrid Speech Feature Extraction for Deceptive Speech Detection
Authors: Mihalache Serban , Burileanu Dragos
Published: February 2022

In this work, we first propose a deep neural network (DNN) system for the automatic detection of speech in audio signals, otherwise known as voice activity detection (VAD). Several DNN types were investigated, including multilayer perceptrons (MLPs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), with the best performance being obtained for the latter. Additional postprocessing techniques, i.e., hysteretic thresholding, minimum duration filtering, and bilateral extension, were employed in order to boost performance. The systems were trained and tested using several data subsets of the CENSREC-1-C database, with different simulated ambient noise conditions, and additional testing was performed on a different CENSREC-1-C data subset containing actual ambient noise, as well as on a subset of the TIMIT database. An accuracy of up to 99.13% was obtained for the CENSREC-1-C datasets, and 97.60% for the TIMIT dataset. We proceed to show how the final VAD system can be adapted and employed within an utterance-level deceptive speech detection (DSD) processing pipeline. The best DSD performance is achieved by a novel hybrid CNN-MLP network leveraging a fusion of algorithmically and automatically extracted speech features, and reaches an unweighted accuracy (UA) of 63.7% on the RLDD database, and 62.4% on the RODeCAR database.
Optimal Architecture of Floating-Point Arithmetic for Neural Network Training Processors
Authors: Junaid Muhammad , Arslan Saad , Lee TaeGeon , Kim HyungWon
Published: February 2022

The convergence of artificial intelligence (AI) is one of the critical technologies in the recent fourth industrial revolution. The AIoT (Artificial Intelligence Internet of Things) is expected to be a solution that aids rapid and secure data processing. While the success of AIoT demanded low-power neural network processors, most of the recent research has been focused on accelerator designs only for inference. The growing interest in self-supervised and semi-supervised learning now calls for processors offloading the training process in addition to the inference process. Incorporating training with high accuracy goals requires the use of floating-point operators. The higher precision floating-point arithmetic architectures in neural networks tend to consume a large area and energy. Consequently, an energy-efficient/compact accelerator is required. The proposed architecture incorporates training in 32 bits, 24 bits, 16 bits, and mixed precisions to find the optimal floating-point format for low power and smaller-sized edge device. The proposed accelerator engines have been verified on FPGA for both inference and training of the MNIST image dataset. The combination of 24-bit custom FP format with 16-bit Brain FP has achieved an accuracy of more than 93%. ASIC implementation of this optimized mixed-precision accelerator using TSMC 65nm reveals an active area of 1.036 × 1.036 mm2 and energy consumption of 4.445 µJ per training of one image. Compared with 32-bit architecture, the size and the energy are reduced by 4.7 and 3.91 times, respectively. Therefore, the CNN structure using floating-point numbers with an optimized data path will significantly contribute to developing the AIoT field that requires a small area, low energy, and high accuracy.
Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function
Authors: Petmezas Georgios , Cheimariotis Grigorios-Aris , Stefanopoulos Leandros , Rocha Bruno , Paiva Rui Pedro , Katsaggelos Aggelos K. , Maglaveras Nicos
Published: February 2022

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.
Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles
Authors: Fevgas Georgios , Lagkas Thomas , Argyriou Vasileios , Sarigiannidis Panagiotis
Published: February 2022

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research.
Over-the-Air Testing of a Massive MIMO Antenna with a Full-Rank Channel Matrix
Authors: Honda Kazuhiro
Published: February 2022

This paper presents an over-the-air testing method in which a full-rank channel matrix is created for a massive multiple-input multiple-output (MIMO) antenna system utilizing a fading emulator with a small number of scatterers. In the proposed method, in order to mimic a fading emulator with a large number of scatterers, the scatterers are virtually positioned by rotating the massive MIMO antenna. The performance of a 64-element quasi-half-wavelength dipole circular array antenna was evaluated using a two-dimensional fading emulator. The experimental results reveal that a large number of available eigenvalues are obtained from the channel response matrix, confirming that the proposed method, which utilizes a full-rank channel matrix, can be used to assess a massive MIMO antenna system.
A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques
Authors: Bal Prasanta Kumar , Mohapatra Sudhir Kumar , Das Tapan Kumar , Srinivasan Kathiravan , Hu Yuh-Chung
Published: February 2022

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization’s profit as well as users’ satisfaction. A customer’s request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.
Recent Strategies for Using Monolithic Materials in Glycoprotein and Glycopeptide Analysis
Authors: Alla Allan J. , Stine Keith J.
Published: February 2022

There is continuous effort towards developing monolithic materials as solid supports for the separation, enrichment, and digestion of glycoproteins. The intention of this review is to discuss and summarize work reported in this area during the period 2015–2021 as a follow-up to our prior review. Reports from the past three decades have already proven the advantages of monolithic materials, such as the ease with which they can be prepared and functionalized, their high permeability and low resistance to mass transfer, and their stability over a wide range of pH. Recent works on glycoprotein analysis introduce different strategies in using monolithic materials specifically in separation, enrichment, and identification of glycoproteins, glycopeptides, and free glycans. A majority of these are focused on boronic acid affinity-based technique and others on lectin affinity and HILIC-based techniques. There are also newly developed ligands that utilize different interactions with glycans, such as encapsulation into β-cyclodextrin vesicles, CH- or OH-π interactions with fullerenes, immunoaffinity with monoclonal antibodies, H-bonding interactions with metallophthalocyanines, coordination interactions with cobalt phthalocyanine tetracarboxylic acid, and hydrophilic interaction with cyclodextrin molecular tubes, zwitterionic iminodiacetic acid, and boric acid. Recent strategies for developing on-line, multidimensional systems use immobilized monolithic enzyme reactors (IMERs) for high-throughput glycoprotein analysis. These works serve as contributions to better understand glycan structure-function relationship, as glycoproteins are now widely accepted disease biomarkers.
A General Description of Growth Trends
Authors: Elitzur Moshe
Published: February 2022

Time series that display periodicity can be described with a Fourier expansion. In a similar vein, a recently developed formalism enables the description of growth patterns with the optimal number of parameters. The method has been applied to the growth of national GDP, population and the COVID-19 pandemic; in all cases, the deviations of long-term growth patterns from purely exponential required no more than two additional parameters, mostly only one. Here, I utilize the new framework to develop a unified formulation for all functions that describe growth deceleration, wherein the growth rate decreases with time. The result offers the prospects for a new general tool for trend removal in time-series analysis.
Selection of Auxiliary Variables for Three-Fold Linking Models in Small Area Estimation: A Simple and Effective Method
Authors: Cai Song , Rao J.N.K.
Published: February 2022

Model-based estimation of small area means can lead to reliable estimates when the area sample sizes are small. This is accomplished by borrowing strength across related areas using models linking area means to related covariates and random area effects. The effective selection of variables to be included in the linking model is important in small area estimation. The main purpose of this paper is to extend the earlier work on variable selection for area level and two-fold subarea level models to three-fold sub-subarea models linking sub-subarea means to related covariates and random effects at the area, sub-area, and sub-subarea levels. The proposed variable selection method transforms the sub-subarea means to reduce the linking model to a standard regression model and applies commonly used criteria for variable selection, such as AIC and BIC, to the reduced model. The resulting criteria depend on the unknown sub-subarea means, which are then estimated using the sample sub-subarea means. Then, the estimated selection criteria are used for variable selection. Simulation results on the performance of the proposed variable selection method relative to methods based on area level and two-fold subarea level models are also presented.
Wafer-Scale Polishing of Polycrystalline MPACVD-Diamond
Authors: Huang Xuerun , Zhou Changjie , Wu Bo , Geng Zhiming , Zhang Xing
Published: February 2022

Diamond offers great potential for use as a thermal spreader in various applications, including power electronics and radio-frequency (RF) applications. However, to be used as an efficient thermal spreader, the atomically smooth surface of the diamond is critical to be bonded with chips. Herein, a polishing technique for a 2-inch diameter wafer-scale bulk polycrystalline diamond substrate is proposed. In this work, 350 μm thick polycrystalline diamond is grown by the microwave plasma-assisted chemical vapor deposition (MPACVD) technique on a Si substrate at a growth rate of 8 µm/h. Thereafter, a three-step polishing process was applied to achieve an atomically smooth surface, consisting of grinding using a diamond slurry with an iron plate, ICP etching using the SF6 gas, and final mechanical polishing using a resin-bonded diamond wheel. Surface roughness of diamond characterized by atomic force microscopy showed the significantly reduced from 900 nm to 0.3 nm. Hence, this study provide the practical methods for obtaining atomically smooth diamond films suitable for thermal management in various areas including power electronics and RF devices.