bluuebunny/crossref_metadata_2025 huggingface dataset:
Faster Deep Inertial Pose Estimation with Six Inertial Sensors
Authors: Xia Di , Zhu Yeqing , Zhang Heng
Published: September 2022

We propose a novel pose estimation method that can predict the full-body pose from six inertial sensors worn by the user. This method solves problems encountered in vision, such as occlusion or expensive deployment. We address several complex challenges. First, we use the SRU network structure instead of the bidirectional RNN structure used in previous work to reduce the computational effort of the model without losing its accuracy. Second, our model does not require joint position supervision to achieve the best results of the previous work. Finally, since sensor data tend to be noisy, we use SmoothLoss to reduce the impact of inertial sensors on pose estimation. The faster deep inertial poser model proposed in this paper can perform online inference at 90 FPS on the CPU. We reduce the impact of each error by more than 10% and increased the inference speed by 250% compared to the previous state of the art.
Flow-Enhanced Photothermal Spectroscopy
Authors: Radeschnig Ulrich , Bergmann Alexander , Lang Benjamin
Published: September 2022

Photothermal spectroscopy (PTS) is a promising sensing technique for the measurement of gases and aerosols. PTS systems using a Fabry–Pérot interferometer (FPI) are considered particularly promising owing to their robustness and potential for miniaturization. However, limited information is available on viable procedures for signal improvement through parameter tuning. In our work, we use an FPI-based PTS configuration, in which the excitation laser irradiates the target collinearly to the flowing gas. We demonstrate that the generated thermal wave, and thus the signal intensity, is significantly affected by the ratio between excitation modulation frequency and gas flow velocity towards another. We provide an analytical model that predicts the signal intensity with particular considerations of these two parameter settings and validate the findings experimentally. The results reveal the existence of an optimal working regime, depending on the modulation frequency and flow velocity.
The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation
Authors: Salilew Waleligne Molla , Abdul Karim Zainal Ambri , Lemma Tamiru Alemu , Fentaye Amare Desalegn , Kyprianidis Konstantinos G.
Published: September 2022

A gas path analysis approach of dynamic modelling was used to examine the gas turbine performance. This study presents an investigation of the effect of physical faults on the performance of a three-shaft gas turbine at full-load and part-load operation. A nonlinear steady state performance model was developed and validated. The datasheet from the engine manufacturer was used to gather the input and validation data. Some engineering judgement and optimization were used. Following validation of the engine performance model with the engine manufacturer data using physical fault and component health parameter relationships, physical faults were implanted into the performance model to evaluate the performance characteristics of the gas turbine at degradation state at full- and part-load operation. The impact of erosion and fouling on the gas turbine output parameters, component measurement parameters, and the impact of degraded components on another primary component of the engine have been investigated. The simulation results show that the deviation in the output parameters and component isentropic efficiency due to compressor fouling and erosion is linear with the load variation, but it is almost nonlinear for the downstream components. The results are discussed following the plots.
Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp
Authors: Rogers Bruce , Schaffarczyk Marcelle , Gronwald Thomas
Published: September 2022

Monitoring of the physiologic metric, respiratory frequency (RF), has been shown to be of value in health, disease, and exercise science. Both heart rate (HR) and variability (HRV), as represented by variation in RR interval timing, as well as analysis of ECG waveform variability, have shown potential in its measurement. Validation of RF accuracy using newer consumer hardware and software applications have been sparse. The intent of this report is to assess the precision of the RF derived using Kubios HRV Premium software version 3.5 with the Movesense Medical sensor single-channel ECG (MS ECG) and the Polar H10 (H10) HR monitor. Gas exchange data (GE), RR intervals (H10), and continuous ECG (MS ECG) were recorded from 21 participants performing an incremental cycling ramp to failure. Results showed high correlations between the reference GE and both the H10 (r = 0.85, SEE = 4.2) and MS ECG (r = 0.95, SEE = 2.6). Although median values were statistically different via Wilcoxon testing, adjusted median differences were clinically small for the H10 (RF about 1 breaths/min) and trivial for the MS ECG (RF about 0.1 breaths/min). ECG based measurement with the MS ECG showed reduced bias, limits of agreement (maximal bias, −2.0 breaths/min, maximal LoA, 6.1 to −10.0 breaths/min) compared to the H10 (maximal bias, −3.9 breaths/min, maximal LoA, 8.2 to −16.0 breaths/min). In conclusion, RF derived from the combination of the MS ECG sensor with Kubios HRV Premium software, tracked closely to the reference device through an exercise ramp, illustrates the potential for this system to be of practical usage during endurance exercise.
Recover User’s Private Training Image Data by Gradient in Federated Learning
Authors: Gong Haimei , Jiang Liangjun , Liu Xiaoyang , Wang Yuanqi , Wang Lei , Zhang Ke
Published: September 2022

Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that gradient inversion technique can reconstruct the input images on the pixel level. In this study, we review the research work of data leakage by gradient inversion technique and categorize existing works into three groups: (i) Bias Attacks, (ii) Optimization-Based Attacks, and (iii) Linear Equation Solver Attacks. According to the characteristics of these algorithms, we propose one privacy attack system, i.e., Single-Sample Reconstruction Attack System (SSRAS). This system can carry out image reconstruction regardless of whether the label can be determined. It can extends gradient inversion attack from a fully connected layer with bias terms to attack a fully connected layer and convolutional neural network with or without bias terms. We also propose Improved R-GAP Alogrithm, which can utlize DLG algorithm to derive ground truth. Furthermore, we introduce Rank Analysis Index (RA-I) to measure the possible of whether the user’s raw image data can be reconstructed. This rank analysis derive virtual constraints Vi from weights. Compared with the most representative attack algorithms, this reconstruction attack system can recover a user’s private training image with high fidelity and attack success rate. Experimental results also show the superiority of the attack system over some other state-of-the-art attack algorithms.
ACF: An Armed CCTV Footage Dataset for Enhancing Weapon Detection
Authors: Hnoohom Narit , Chotivatunyu Pitchaya , Jitpattanakul Anuchit
Published: September 2022

Thailand, like other countries worldwide, has experienced instability in recent years. If current trends continue, the number of crimes endangering people or property will expand. Closed-circuit television (CCTV) technology is now commonly utilized for surveillance and monitoring to ensure people’s safety. A weapon detection system can help police officers with limited staff minimize their workload through on-screen surveillance. Since CCTV footage captures the entire incident scenario, weapon detection becomes challenging due to the small weapon objects in the footage. Due to public datasets providing inadequate information on our interested scope of CCTV image’s weapon detection, an Armed CCTV Footage (ACF) dataset, the self-collected mockup CCTV footage of pedestrians armed with pistols and knives, was collected for different scenarios. This study aimed to present an image tilling-based deep learning for small weapon object detection. The experiments were conducted on a public benchmark dataset (Mock Attack) to evaluate the detection performance. The proposed tilling approach achieved a significantly better mAP of 10.22 times. The image tiling approach was used to train different object detection models to analyze the improvement. On SSD MobileNet V2, the tiling ACF Dataset achieved an mAP of 0.758 on the pistol and knife evaluation. The proposed method for enhancing small weapon detection by using the tiling approach with our ACF Dataset can significantly enhance the performance of weapon detection.
Novel Electrochemical Sensors Based on L-Proline Assisted LDH for H2O2 Determination in Healthy and Diabetic Urine
Authors: Tomassetti Mauro , Pezzilli Riccardo , Prestopino Giuseppe , Di Natale Corrado , Medaglia Pier Gianni
Published: September 2022

In this paper, a novel non-enzymatic modified glassy carbon (GC) sensor, of the (GC-Agpaste)-catalytic proline-assisted LDH type, for H2O2 determination was fabricated, studied, characterized and employed to determine the hydrogen peroxide content in healthy and diabetic human urine. LDH (whose composition can be schematized as [ZnIIAlIII (OH)2]+ NO3−·nH2O) is glued to glassy carbon by means of silver paste, while proline, which increases the catalytic properties of LDH, is used free in solution in the phosphate buffer. A voltametric survey was first conducted to ascertain the positive effect induced by the presence of proline, i.e., the increase of sensor sensitivity. Then a deep study of the new three-electrode amperometric proline-assisted LDH sensor, whose working electrode was of the same type as the one used to perform the cyclic voltammetry, was carried out, working at first in static air, then in a nitrogen atmosphere. Possible interferences from various substances, both oxidants and antioxidants, were also investigated. Lastly, the new amperometric sensor was successfully used to determine the H2O2 level in human urine from both healthy and diabetic subjects. The effect of proline in enhancing the properties of the sensor system was also investigated. The limit of detection (LOD) of the new catalytic sensor was of the order of 0.15 mmol L−1, working in air, and of 0.05 µmol L−1, working in nitrogen atmosphere.
A Non-Contact and Real-Time Measurement Technique of Human Body Potential Using Electrostatic Induction Current Accompanied by Human Body Motion
Authors: Kurita Koichi
Published: September 2022

This paper describes a non-contact and real-time measurement technique of human body potential using ultra-sensitive electrostatic induction. When a participant moves his/her palm to a position approximately 30 cm away from an electrostatic induction sensor, electrostatic induction current flows transiently. It is clarified whether estimation of the human body potential is possible by simultaneously measuring the velocity of the participant’s palm and distance between the participant’s palm and sensor. In addition, even when the participant walks at a position approximately 50 cm away from the electrostatic induction sensor, it is confirmed that the estimation of human body potential is possible.
Camera-LiDAR Fusion Method with Feature Switch Layer for Object Detection Networks
Authors: Kim Taek-Lim , Park Tae-Hyoung
Published: September 2022

Object detection is an important factor in the autonomous driving industry. Object detection for autonomous vehicles requires robust results, because various situations and environments must be considered. A sensor fusion method is used to implement robust object detection. A sensor fusion method using a network should effectively meld two features, otherwise, there is concern that the performance is substantially degraded. To effectively use sensors in autonomous vehicles, data analysis is required. We investigated papers in which the camera and LiDAR data change for effective fusion. We propose a feature switch layer for a sensor fusion network for object detection in cameras and LiDAR. Object detection performance was improved by designing a feature switch layer that can consider its environment during network feature fusion. The feature switch layer extracts and fuses features while considering the environment in which the sensor data changes less than during the learning network. We conducted an evaluation experiment using the Dense Dataset and confirmed that the proposed method improves the object detection performance.
Volatile Olfactory Profiles of Umbrian Extra Virgin Olive Oils and Their Discrimination through MOX Chemical Sensors
Authors: Mariotti Roberto , Núñez-Carmona Estefanía , Genzardi Dario , Pandolfi Saverio , Sberveglieri Veronica , Mousavi Soraya
Published: September 2022

Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography–mass spectrometry with solid-phase micro-extraction detection (GC–MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC–MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods.
High-Accuracy Height-Independent 3D VLP Based on Received Signal Strength Ratio
Authors: Xu Yihuai , Hu Xin , Sun Yimao , Yang Yanbing , Zhang Lei , Deng Xiong , Chen Liangyin
Published: September 2022

Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED’s emission power and the receiver’s height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED’s emission power is normally time-varying due to the fact that the LED’s optical output power is prone to changing with the LED’s temperature, and the receiver’s height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED’s emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver’s height and LED’s emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D.
Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal
Authors: Ding Cheng , Pereira Tania , Xiao Ran , Lee Randall J. , Hu Xiao
Published: September 2022

Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representations of the PPG, including features defined by domain experts, 1D raw signal and 2D image. Three different classifiers are tested on the noisy training data, including support vector machine (SVM), XGBoost, 1D Resnet and 2D Resnet, which handle three representations, respectively. The results showed that the two deep learning models were more robust than the two traditional machine learning models for both the random and class-dependent label noise. From the representation perspective, the 2D image shows better robustness compared to the 1D raw signal. The logits from three classifiers are also analyzed, the predicted probabilities intend to be more dispersed when more label noise is introduced. From this work, we investigated various factors related to label noise, including representations, label noise type, and data imbalance, which can be a good guidebook for designing more robust methods for label noise in future work.
Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation
Authors: Thirumalaisamy Manikandan , Basheer Shajahan , Selvarajan Shitharth , Althubiti Sara A. , Alenezi Fayadh , Srivastava Gautam , Lin Jerry Chun-Wei
Published: September 2022

There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.
Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream
Authors: Chen Shih-Lun , Chen Tsung-Yi , Lin Ting-Lan , Chen Chiung-An , Lin Szu-Yin , Chiang Yu-Liang , Tung Kun-Hsien , Chiang Wei-Yuan
Published: September 2022

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only −0.001~0.008 W.
Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System
Authors: Saeed Umer , Shah Syed Aziz , Khan Muhammad Zakir , Alotaibi Abdullah Alhumaidi , Althobaiti Turke , Ramzan Naeem , Abbasi Qammer H.
Published: September 2022

Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods such as radar is non-line-of-sight and multi-floor environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we have utilised the publicly available online database based on the intelligent reflecting surface (IRS) system developed at the Communications, Sensing and Imaging group at the University of Glasgow, UK (references 39 and 40). The IRS system works better in the multi-floor and non-line-of-sight environments. This work for the first time uses algorithms such as support vector machine Bagging and Decision Tree on the publicly available IRS data and achieves better accuracy when a subset of the available data is considered along specific human activities. Additionally, the work also considers the processing time taken by the classier in training stage when exposed to the IRS data which was not previously explored.
Deep-Learning-Based Adaptive Symbol Decision for Visual MIMO System with Variable Channel Modeling
Authors: Kim Jai-Eun , Kwon Tae-Ho , Kim Ki-Doo
Published: September 2022

A channel modeling method and deep-learning-based symbol decision method are proposed to improve the performance of a visual MIMO system for communication between a variable-color LED array and camera. Although image processing algorithms using color clustering are available to correct distorted color information in a channel, color-similarity-based approaches are limited by real-world distortions; to overcome such limitations, symbol decision is defined as a multiclass classification problem. Further, to learn a robust classifier against channel distortion, a deep neural network learning technique is applied to adaptively determine symbols from channel distortion. The network designed herein comprises the channel identification and symbol decision modules; the channel identification module extracts a channel identification vector for symbol determination from an input image using a two-dimensional deep convolutional neural network (CNN); the symbol decision module then generates a feature map by combining the channel identification vector and information on adjacent symbols to determine the symbol via learning correlations between adjacent symbols using a one-dimensional CNN. The two modules are connected together and learned simultaneously in an end-to-end manner. We also propose a new channel modeling method that intuitively reflects real-world distortion factors rather than the conventional additive white Gaussian noise channel to efficiently train deep-learning networks. Lastly, in the proposed channel distortion environment, the proposed method shows performance improvement by an average of about 41.8% (up to about 54.8%) compared to the existing Euclidean distance method, and about 6.3% (up to about 9.2%) on average compared to the SVM method.
Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network
Authors: Jeon Hyeon-Ju , Choi Min-Woo , Lee O-Joun
Published: September 2022

Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.
Detecting Gait Events from Accelerations Using Reservoir Computing
Authors: Chiasson-Poirier Laurent , Younesian Hananeh , Turcot Katia , Sylvestre Julien
Published: September 2022

Segmenting the gait cycle into multiple phases using gait event detection (GED) is a well-researched subject with many accurate algorithms. However, the algorithms that are able to perform accurate and robust GED for real-life environments and physical diseases tend to be too complex for their implementation on simple hardware systems limited in computing power and memory, such as those used in wearable devices. This study focuses on a numerical implementation of a reservoir computing (RC) algorithm called the echo state network (ESN) that is based on simple computational steps that are easy to implement on portable hardware systems for real-time detection. RC is a neural network method that is widely used for signal processing applications and uses a fast-training method based on a ridge regression adapted to the large quantity and variety of IMU data needed to use RC in various real-life environment GED. In this study, an ESN was used to perform offline GED with gait data from IMU and ground force sensors retrieved from three databases for a total of 28 healthy adults and 15 walking conditions. Our main finding is that despite its low complexity, ESN is robust for GED, with performance comparable to other state-of-the-art algorithms. Our results show the ESN is robust enough to obtain good detection results in all conditions if the algorithm is trained with variable data that match those conditions. The distribution of the mean absolute errors (MAE) between the detection times from the ESN and the force sensors were between 40 and 120 ms for 6 defined gait events (95th percentile). We compared our ESN with four different state-of-the-art algorithms from the literature. The ESN obtained a MAE not more than 10 ms above three other reference algorithms for normal walking indoor and outdoor conditions and yielded the 2nd lowest MAE and the 2nd highest true positive rate and specificity when applied to outdoor walking and running conditions. Our work opens the door to using the ESN as a GED for applications in wearable sensors for long-term patient monitoring.
Analysis of OSTBC-OFDM Combined with Dual-Polarization and Time-Diversity in Millimeter-Wave MIMO Channels with Rain Distortions
Authors: Elgam Avner , Peretz Yossi , Pinhasi Yosef
Published: September 2022

Various destructive weather and physical phenomena affect many parameters in the radio layer (i.e., affecting the wireless paths Over-The-Air (OTA)) of many outdoor-to-outdoor wireless systems. These destructive effects create polarization torsion and rotation of the signals propagating in space and cause the scattering of wireless spatial paths. The direct meaning is a significant degradation in system performance, especially in the Quality-of-Service (QoS). Under these challenging scenarios, intelligent utilization of advanced Multiple-Input-Multiple-Output (MIMO) techniques such as polarization-diversity and time-diversity at the transmitter, as well as at the receiver, and intelligent use of the Cross-Polarization-Isolation (XPI) mechanism, are essential. We prove that combining these techniques with the tuning of the XPI of the antennas creates optimal conditions in the wireless MIMO channels. This combination does not only improve the system’s performance, but also turns the destructive physical phenomena in the spatial-domain, into an advantage. In this article, we focus on formulating a wireless communication MIMO model in millimeter-Wave (mmWave) channels under rain distortions. We demonstrate the optimal use of combining Orthogonal-Space-Time-Coding (OSTBC) and Maximal-Ratio-Receive-Combiner (MRRC) with cross-polarization diversity techniques, that utilize the tuning of the XPI. An analytical exact optimal solution is proposed, that allows the tuning of the leading parameters to achieve global optimal performance, in terms of channel-capacity and Bit-Error-Rate (BER). In addition, we propose a process of approximation of feedback-closed-loop-MIMO. The feedback is employed between the transmitter and the receiver, in the scenario of changes in the channel-response-matrix in-between successive symbol-times. The feedback was designed to acheive global-maximum channel-capacity, while preserving the channel-path orthogonality in order to minimize the BER.
Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data
Authors: Mahlberg Justin A. , Li Howell , Cheng Yi-Ting , Habib Ayman , Bullock Darcy M.
Published: September 2022

The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way.
Additively Manufactured Detection Module with Integrated Tuning Fork for Enhanced Photo-Acoustic Spectroscopy
Authors: Viola Roberto , Liberatore Nicola , Mengali Sandro
Published: September 2022

Starting from Quartz-Enhanced Photo-Acoustic Spectroscopy (QEPAS), we have explored the potential of a tightly linked method of gas/vapor sensing, from now on referred to as Tuning-Fork-Enhanced Photo-Acoustic Spectroscopy (TFEPAS). TFEPAS utilizes a non-piezoelectric metal or dielectric tuning fork to transduce the photoacoustic excitation and an optical interferometric readout to measure the amplitude of the tuning fork vibration. In particular, we have devised a solution based on Additive Manufacturing (AM) for the Absorption Detection Module (ADM). The novelty of our solution is that the ADM is entirely built monolithically by Micro-Metal Laser Sintering (MMLS) or other AM techniques to achieve easier and more cost-effective customization, extreme miniaturization of internal volumes, automatic alignment of the tuning fork with the acoustic micro-resonators, and operation at high temperature. This paper reports on preliminary experimental results achieved with ammonia at parts-per-million concentration in nitrogen to demonstrate the feasibility of the proposed solution. Prospectively, the proposed TFEPAS solution appears particularly suited for hyphenation to micro-Gas Chromatography and for the analysis of complex solid and liquid traces samples, including compounds with low volatility such as illicit drugs, explosives, and persistent chemical warfare agents.
Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review
Authors: Pinheiro Cristiana , Figueiredo Joana , Cerqueira João , Santos Cristina P.
Published: September 2022

This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating the technical and clinical specifications of biofeedback systems (BSs), including the complementary use with assistive devices and/or physiotherapist-oriented cues. A literature search was conducted from January 2019 to September 2022 on Cochrane, Embase, PubMed, PEDro, Scopus, and Web of Science databases. Data regarding technical (sensors, biofeedback parameters, actuators, control strategies, assistive devices, physiotherapist-oriented cues) and clinical (participants’ characteristics, protocols, outcome measures, BSs’ effects) specifications of BSs were extracted from the relevant studies. A total of 31 studies were reviewed, which included 660 stroke survivors. Most studies reported visual biofeedback driven according to the comparison between real-time kinetic or spatiotemporal data from wearable sensors and a threshold. Most studies achieved statistically significant improvements on sensor-based and clinical outcomes between at least two evaluation time points. Future research should study the effectiveness of using multiple wearable sensors and actuators to provide personalized biofeedback to users with multiple sensorimotor deficits. There is space to explore BSs complementing different assistive devices and physiotherapist-oriented cues according to their needs. There is a lack of randomized-controlled studies to explore post-stroke stage, mental and sensory effects of BSs.
A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material
Authors: Chen Ning , Hu Fuhai , Chen Jiayao , Wang Kai , Yang Chunhua , Gui Weihua
Published: September 2022

In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods.
Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises
Authors: Villa Murillo , Ferreira Bruno , Cruz Nuno
Published: September 2022

In source localization problems, the relative geometry between sensors and source will influence the localization performance. The optimum configuration of sensors depends on the measurements used for the source location estimation, how these measurements are affected by noise, the positions of the source, and the criteria used to evaluate the localization performance. This paper addresses the problem of optimum sensor placement in a plane for the localization of an underwater vehicle moving in 3D. We consider sets of sensors that measure the distance to the vehicle and model the measurement noises with distance dependent covariances. We develop a genetic algorithm and analyze both single and multi-objective problems. In the former, we consider as the evaluation metric the arithmetic average along the vehicle trajectory of the maximum eigenvalue of the inverse of the Fisher information matrix. In the latter, we estimate the Pareto front of pairs of common criteria based on the Fisher information matrix and analyze the evolution of the sensor positioning for the different criteria. To validate the algorithm, we initially compare results with a case with a known optimal solution and constant measurement covariances, obtaining deviations from the optimal less than 0.1%. Posterior, we present results for an underwater vehicle performing a lawn-mower maneuver and a spiral descent maneuver. We also present results restricting the allowed positions for the sensors.
Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks
Authors: Konings Daniel , Alam Fakhrul , Faulkner Nathaniel , de Jong Calum
Published: September 2022

In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
Validation of Easy Fabrication Methods for PDMS-Based Microfluidic (Bio)Reactors
Authors: Vogt Josef , Rosenthal Katrin
Published: September 2022

The common method for producing casting molds for the fabrication of polydimethylsiloxane (PDMS) chips is standard photolithography. This technique offers high resolution from hundreds of nanometers to a few micrometers. However, this mold fabrication method is costly, time-consuming, and might require clean room facilities. Additionally, there is a need for non-micromechanics experts, who do not have specialized equipment to easily and quickly prototype chips themselves. Simple, so-called, makerspace technologies are increasingly being explored as alternatives that have potential to enable anyone to fabricate microfluidic structures. We therefore tested simple fabrication methods for a PDMS-based microfluidic device. On the one hand, channels were replicated from capillaries and tape. On the other hand, different mold fabrication methods, namely laser cutting, fused layer 3D printing, stereolithographic 3D printing, and computer numerical control (CNC) milling, were validated in terms of machine accuracy and tightness. Most of these methods are already known, but the incorporation and retention of particles with sizes in the micrometer range have been less investigated. We therefore tested two different types of particles, which are actually common carriers for the immobilization of enzymes, so that the resulting reactor could ultimately be used as a microfluidic bioreactor. Furthermore, CNC milling provide the most reliable casting mold fabrication method. After some optimization steps with regard to manufacturing settings and post-processing polishing, the chips were tested for the retention of two different particle types (spherical and non-spherical particles). In this way, we successfully tested the obtained PDMS-based microfluidic chips for their potential applicability as (bio)reactors with enzyme immobilization carrier beads.
Recent Progress in Photocatalytic Removal of Environmental Pollution Hazards in Water Using Nanostructured Materials
Authors: Alahmadi Nadiyah
Published: September 2022

Water pollution has become a critical issue because of the Industrial Revolution, growing populations, extended droughts, and climate change. Therefore, advanced technologies for wastewater remediation are urgently needed. Water contaminants are generally classified as microorganisms and inorganic/organic pollutants. Inorganic pollutants are toxic and some of them are carcinogenic materials, such as cadmium, arsenic, chromium, cadmium, lead, and mercury. Organic pollutants are contained in various materials, including organic dyes, pesticides, personal care products, detergents, and industrial organic wastes. Nanostructured materials could be potential candidates for photocatalytic reduction and for photodegradation of organic pollutants in wastewater since they have unique physical, chemical, and optical properties. Enhanced photocatalytic performance of nanostructured semiconductors can be achieved using numerous techniques; nanostructured semiconductors can be doped with different species, transition metals, noble metals or nonmetals, or a luminescence agent. Furthermore, another technique to enhance the photocatalytic performance of nanostructured semiconductors is doping with materials that have a narrow band gap. Nanostructure modification, surface engineering, and heterojunction/homojunction production all take significant time and effort. In this review, I report on the synthesis and characterization of nanostructured materials, and we discuss the photocatalytic performance of these nanostructured materials in reducing environmental pollutants.
Perceptions of Colombian Teachers about the Didactics of Social Sciences
Authors: Palacios Mena Nancy , García Monteagudo Diego , Pizzinato Liliana
Published: September 2022

The article seeks to answer the question about the perceptions that teachers have in training and in practice on the didactics of the social sciences in Colombia. To this end, a method with an interpretative approach with a mixed design is used to integrate the quantitative and qualitative analysis of the data produced on these perceptions in the context of basic and secondary education. The results present from the perceptions of teachers some correlations between the didactics they claim to use and the way in which it is expressed in their pedagogical practice. The conclusions contribute to the design, implementation, and evaluation of educational policies in education, especially those aimed at improving the teaching and learning processes of the social sciences at different educational levels.
2.0 Society Convergences: Coexistence, Otherness, Communication and Edutainment
Authors: Mateus De Oro Cirit , Campis Carrillo Rodrigo Mario , Aguaded Ignacio , Jabba Molinares Daladier , Erazo Coronado Ana María
Published: September 2022

The research finds justification given the incidence and magnitude they currently have in the human social sphere in the framework of the so-called Society 2.0. Derived from this framework, this approach must specially consider education as a vital social process. The same happens with information and communication technologies since they are frequently and increasingly used as mediation in educational contexts and because of their undeniable mediation in human interactions. Consequently, one of the most relevant questions tackled in this research refers to the disciplines required to provide a sufficiently broad theoretical and conceptual background for the scientific basis of the convergence between communication, edutainment, coexistence and otherness to effectively transform the last two concepts into measurable variables. The paper results from the content and a systemic analysis using ATLAS.ti software of published scientific documents for the last two decades about the phenomena of coexistence and otherness. As a main result, the researchers present a taxonomy that includes dimensions and indicators that enable the conversion of both constructs into measurable variables. Thus, a convergence to address the scientific study of coexistence and otherness from communication and edutainment emerged. The review also provides a theoretical basis for designing intervention models aimed at promoting coexistence. Edutainment is also incorporated as a novel tool to promote pro-social attitudes.
Sustainable Textile Marketing—Editorial
Authors: Memon Hafeezullah , Jin Xiaoke , Tian Wei , Zhu Chengyan
Published: September 2022

Sustainability has become a vital issue in the textile and garment industry [...]