bluuebunny/crossref_metadata_2025 huggingface dataset:
Dual-Branch Fourier-Mixing Transformer Network for Hyperspectral Target Detection
Authors: Jiao Jinyue , Gong Zhiqiang , Zhong Ping
Published: September 2023

In recent years, transformers have shown great potential in hyperspectral image processing and have also been gradually applied in hyperspectral target detection (HTD). Nonetheless, applying a typical transformer to HTD remains challenging. The heavy computation burden of the multi-head self-attention (MSA) in transformers limits its efficient HTD, while the limited ability to extract local spectral features can reduce the discrimination of the learned spectral features. To further explore the potential of transformers for HTD, for balance of representation ability and computational efficiency, we propose a dual-branch Fourier-mixing transformer network for hyperspectral target detection (DBFTTD). First, this work explores a dual-branch Fourier-mixing transformer network. The transformer-style network replaces the MSA sublayer in the transformer with a Fourier-mixing sublayer, which shows advantages in improving computational efficiency and learning valuable spectral information effectively for HTD. Second, this work proposes learnable filter ensembles in the Fourier domain that are inspired by ensemble learning to improve detection performance. Third, a simple but efficient dropout strategy is proposed for data augmentation. Sufficient and balanced training samples are constructed for training the dual-branch network, and training samples for balanced learning can further improve detection performance. Experiments on four data sets indicate that our proposed detector is superior to the state-of-the-art detectors.
GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China
Authors: Chen Tianyi , Yang Changbao , Han Liguo , Guo Senmiao
Published: September 2023

Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field.
Studying Tropical Dry Forests Secondary Succession (2005–2021) Using Two Different LiDAR Systems
Authors: Liu Chenzherui , Sanchez-Azofeifa Arturo , Bax Connor
Published: September 2023

Chronosequence changes among Tropical Dry Forests (TDFs) are essential for understanding this unique ecosystem, which is characterized by its seasonality (wet and dry) and a high diversity of deciduous trees and shrubs. From 2005 to 2021, we used two different airborne LiDAR systems to quantify structural changes in the forest at Santa Rosa National Park. Line- and shape-based waveform metrics were used to record the overall changes in the TDF structure. Based on a 16-year growth analysis, notable variations in height-related profiles were observed, particularly for RH50, RH100, and waveform-produced canopy heights. The results showed that Cy and RG have increased since the forests have been growing, whereas Cx has decreased. The decrease in Cx is because ground returns are lower when the canopy density i and canopy height increase. A positive relationship was observed between Cy and CH, RG, and RH100, particularly for the wet season data collected in 2021. These findings provide important insights into the growth dynamics of TDFs in Santa Rosa National Park and could inform future conservation efforts.
PLI-SLAM: A Tightly-Coupled Stereo Visual-Inertial SLAM System with Point and Line Features
Authors: Teng Zhaoyu , Han Bin , Cao Jie , Hao Qun , Tang Xin , Li Zhaoyang
Published: September 2023

Point feature-based visual simultaneous localization and mapping (SLAM) systems are prone to performance degradation in low-texture environments due to insufficient extraction of point features. In this paper, we propose a tightly-coupled stereo visual-inertial SLAM system with point and line features (PLI-SLAM) to enhance the robustness and reliability of systems in low-texture environments. We improve Edge Drawing lines (EDlines) for line feature detection by introducing curvature detection and a new standard for minimum line segment length to improve the accuracy of the line features, while reducing the line feature detection time. We contribute also with an improved adapting factor based on experiment to adjust the error weight of line features, which further improves the localization accuracy of the system. Our system has been tested on the EuRoC dataset. Tests on public datasets and in real environments have shown that PLI-SLAM achieves high accuracy. Furthermore, PLI-SLAM could still operate robustly even in some challenging environments. The processing time of our method is reduced by 28%, compared to the ORB-LINE-SLAM based on point and line, when using Line Segment Detector (LSD).
On the Validation of the Rotation Procedure from HEE to MEMFA Reference Frame in the Presence of Alfvén Waves in the Interplanetary Medium
Authors: Carnevale Giuseppina , Regi Mauro
Published: September 2023

Alfvén waves play an important role in the stability, heating, and transport of magnetized plasmas. They are found to be ubiquitous in solar winds (SW), which mainly propagate outward from the Sun, especially in high-speed streams that originate from coronal holes. When high-speed streams impinge on the Earth’s magnetosphere, the impact of Alfvénic fluctuations can cause magnetic reconnections between the intermittent southward Interplanetary Magnetic Field (IMF) and the geomagnetic field, resulting in energy injection from the SW into the Earth’s magnetosphere. In this work, we tested a rotation procedure from the Heliocentric Earth Ecliptic (HEE) to the Mean ElectroMagnetic Fields Aligned (MEMFA) reference frame. This is achieved by means of the Empirical Mode Decomposition (EMD) method for both the SW velocity and IMF at 1 AU. Our aim is to check the reliability of the method and its limitations in identifying Alfvénic fluctuations through the spectral analysis of time series in the MEMFA coordinate system. With this procedure, we studied the fluctuations in the main-field-aligned direction and those in the orthogonal plane to the main field. To highlight the peculiarities of each case of study and be able to better identify Alfvén waves when applying this procedure to real data, we reproduced the magnetic and velocity fields of a typical corotating high-speed stream. We tested the procedure in several cases by varying the amplitude of Alfvén waves and noise. We performed the spectral analysis of the Mean Field Aligned (MFA) component of both the magnetic and velocity fields to define the power related to the two main directions: the one aligned to the ambient magnetic field and the one orthogonal to it. The efficiency of the procedure and the results’ reliability are supported by Monte Carlo (MC) tests. The method is also applied to a real case that is represented by a selected corotating SW stream that occurred during August 2008, which fell in the solar minimum of solar cycle 23. The results are also compared with those obtained by using Elsässer variables to analyze the Alfvénicity of fluctuations via the normalized cross helicity and the normalized residual energy.
Remote Sensing Image Haze Removal Based on Superpixel
Authors: He Yufeng , Li Cuili , Bai Tiecheng
Published: September 2023

The presence of haze significantly degrades the quality of remote sensing images, resulting in issues such as color distortion, reduced contrast, loss of texture, and blurred image edges, which can ultimately lead to the failure of remote sensing application systems. In this paper, we propose a superpixel-based visible remote sensing image dehazing algorithm, namely SRD. To begin, the remote sensing haze images are divided into content-aware patches using superpixels, which cluster adjacent pixels considering their similarities in color and brightness. We assume that each superpixel region shares the same atmospheric light and transmission properties. Subsequently, methods to estimate local atmospheric light and transmission within each superpixel are proposed. Unlike existing dehazing algorithms that assume a globally constant atmospheric light, our approach considers the global heterogeneous distribution of the atmospheric ambient light, which allows us to model it as a global non-uniform variable. Furthermore, we introduce an effective atmospheric light estimation method inspired by the maximum reflectance prior. Moreover, recognizing the wavelength-dependent nature of light transmission, we independently estimate the transmittance for each RGB channel of the input image. The quantitative and qualitative evaluation results of comprehensive experiments on synthetic datasets and real-world samples demonstrate the superior performance of the proposed algorithm compared to state-of-the-art methods for remote sensing image dehazing.
Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform
Authors: Liu Weichao , Huo Hongyuan , Zhou Ping , Li Mingyue , Wang Yuzhen
Published: September 2023

The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.
Unified Transformer with Cross-Modal Mixture Experts for Remote-Sensing Visual Question Answering
Authors: Liu Gang , He Jinlong , Li Pengfei , Zhong Shenjun , Li Hongyang , He Genrong
Published: September 2023

Remote-sensing visual question answering (RSVQA) aims to provide accurate answers to remote sensing images and their associated questions by leveraging both visual and textual information during the inference process. However, most existing methods ignore the significance of the interaction between visual and language features, which typically adopt simple feature fusion strategies and fail to adequately model cross-modal attention, struggling to capture the complex semantic relationships between questions and images. In this study, we introduce a unified transformer with cross-modal mixture expert (TCMME) model to address the RSVQA problem. Specifically, we utilize the vision transformer (VIT) and BERT to extract visual and language features, respectively. Furthermore, we incorporate cross-modal mixture experts (CMMEs) to facilitate cross-modal representation learning. By leveraging the shared self-attention and cross-modal attention within CMMEs, as well as the modality experts, we effectively capture the intricate interactions between visual and language features and better focus on their complex semantic relationships. Finally, we conduct qualitative and quantitative experiments on two benchmark datasets: RSVQA-LR and RSVQA-HR. The results demonstrate that our proposed method surpasses the current state-of-the-art (SOTA) techniques. Additionally, we perform an extensive analysis to validate the effectiveness of different components in our framework.
Evaluation of Camera Recognition Performance under Blockage Using Virtual Test Drive Toolchain
Authors: Son Sungho , Lee Woongsu , Jung Hyungi , Lee Jungki , Kim Charyung , Lee Hyunwoo , Park Hyungwon , Lee Hyunmi , Jang Jeongah , Cho Sungwan , Ryu Han-Cheol
Published: September 2023

This study is the first to develop technology to evaluate the object recognition performance of camera sensors, which are increasingly important in autonomous vehicles owing to their relatively low price, and to verify the efficiency of camera recognition algorithms in obstruction situations. To this end, the concentration and color of the blockage and the type and color of the object were set as major factors, with their effects on camera recognition performance analyzed using a camera simulator based on a virtual test drive toolkit. The results show that the blockage concentration has the largest impact on object recognition, followed in order by the object type, blockage color, and object color. As for the blockage color, black exhibited better recognition performance than gray and yellow. In addition, changes in the blockage color affected the recognition of object types, resulting in different responses to each object. Through this study, we propose a blockage-based camera recognition performance evaluation method using simulation, and we establish an algorithm evaluation environment for various manufacturers through an interface with an actual camera. By suggesting the necessity and timing of future camera lens cleaning, we provide manufacturers with technical measures to improve the cleaning timing and camera safety.
Fabrications of the Flexible Non-Enzymatic Glucose Sensors Using Au-CuO-rGO and Au-CuO-rGO-MWCNTs Nanocomposites as Carriers
Authors: Liao Shu-Han , Shiau Kai-Yi , Wang Fang-Hsing , Yang Cheng-Fu
Published: September 2023

A flexible, non-enzymatic glucose sensor was developed and tested on a polyethylene terephthalate (PET) substrate. The sensor’s design involved printing Ag (silver) as the electrode and utilizing mixtures of either gold–copper oxide-modified reduced graphene oxide (Au-CuO-rGO) or gold–copper oxide-modified reduced graphene oxide-multi-walled carbon nanotubes (Au-CuO-rGO-MWCNTs) as the carrier materials. A one-pot synthesis method was employed to create a nanocomposite material, consisting of Au-CuO-rGO mixtures, which was then printed onto pre-prepared flexible electrodes. The impact of different weight ratios of MWCNTs (0~75 wt%) as a substitute for rGO was also investigated on the sensing characteristics of Au-CuO-rGO-MWCNTs glucose sensors. The fabricated electrodes underwent various material analyses, and their sensing properties for glucose in a glucose solution were measured using linear sweep voltammetry (LSV). The LSV measurement results showed that increasing the proportion of MWCNTs improved the sensor’s sensitivity for detecting low concentrations of glucose. However, it also led to a significant decrease in the upper detection limit for high-glucose concentrations. Remarkably, the research findings revealed that the electrode containing 60 wt% MWCNTs demonstrated excellent sensitivity and stability in detecting low concentrations of glucose. At the lowest concentration of 0.1 μM glucose, the nanocomposites with 75 wt% MWCNTs showed the highest oxidation peak current, approximately 5.9 μA. On the other hand, the electrode without addition of MWCNTs displayed the highest detection limit (approximately 1 mM) and an oxidation peak current of about 8.1 μA at 1 mM of glucose concentration.
Detection and Feature Extraction in Acoustic Sensor Signals
Authors: Li Yuxing , Fredianelli Luca
Published: September 2023

Our advances in detection and feature extraction in the processing of acoustic signals allow us to capture more information about a target and extract features with separability [...]
Integrating Target and Shadow Features for SAR Target Recognition
Authors: Zhao Zhiyuan , Xue Xiaorong , Mariam Iqra , Zhou Xing
Published: September 2023

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.
Modelling Weather Precipitation Intensity on Surfaces in Motion with Application to Autonomous Vehicles
Authors: Carvalho Mateus , Hangan Horia
Published: September 2023

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or cause water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modeling covers a wide range of parameters, such as varying the wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modeling results were partially validated against direct measurements carried out using a test vehicle. The model outputs showed a strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed, and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. A dimensional analysis of the problem highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface for the perceived precipitation level. The proposed model was used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.
FPGA-Based Feature Extraction and Tracking Accelerator for Real-Time Visual SLAM
Authors: Zhang Jie , Xiong Shuai , Liu Cheng , Geng Yongchao , Xiong Wei , Cheng Song , Hu Fang
Published: September 2023

Due to its advantages of low latency, low power consumption, and high flexibility, FPGA-based acceleration technology has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end. For the first time, we implement a hardware solution that combines features from accelerated segment test (FAST) feature points with Gunnar Farneback (GF) dense optical flow to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problems of FAST features, an efficient pyramid module with a five-layer thumbnail structure was designed and implemented. The accelerator was implemented on a modern Xilinx Zynq FPGA. The evaluation results showed that the accelerator could achieve stable tracking of features of violently shaking images and were consistent with the results from MATLAB code running on PCs. Compared to PC CPUs, which require seconds of processing time, the processing latency was greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.
Adapting the Time-Domain Synthetic Aperture Focusing Technique (T-SAFT) to Laser Ultrasonics for Imaging the Subsurface Defects
Authors: Karuppasamy Sundara Subramanian , Yang Che-Hua
Published: September 2023

Traditional ultrasonic testing uses a single probe or phased array probe to investigate and visualize defects by adapting certain imaging algorithms. The time-domain synthetic aperture focusing technique (T-SAFT) is an imaging algorithm that employs a single probe to scan along the test specimen in various positions, to generate inspection images with better resolution. Both the T-SAFT and phased array probes are contact methods with limited bandwidth. This work aims to combine the advantages of the T-SAFT and phased array in a noncontact way with the aid of laser ultrasonics. Here, a pulsed laser beam is employed to generate ultrasonic waves in both thermoelastic and ablation regimes, whereas the laser Doppler vibrometer is used to acquire the generated signals. These two lasers are focused on the test specimen and, to avoid the plasma and crater influence in the ablation regime, the transmission beam and reception beam are separated by 5 mm. By moving the test specimen with a step size of 0.5 mm, a 1D linear phased array (41 and 43 elements) with a pitch of 0.5 mm was synthesized, and three side-drilled holes (Ø 8 mm—thermoelastic regime, Ø 10 mm and Ø 2 mm—ablation regime) were introduced for inspection. The A-scan data obtained from these elements were processed via the T-SAFT algorithm to generate the inspection images in various grid sizes. The results showed that the defect reflections obtained in the ablation regime have better visibility than those from the thermoelastic regime. This is due to the high-amplitude signals obtained in the ablation regime, which pave the way for enhancing the pixel intensity of each grid. Moreover, the separation distance (5 mm) does not have any significant effect on the defect location during the reconstruction process.
Infrared Thermography for Real-Time Assessment of the Effectiveness of Scoliosis Braces
Authors: Angrisani Leopoldo , De Benedetto Egidio , Duraccio Luigi , Lo Regio Fabrizio , Ruggiero Roberto , Tedesco Annarita
Published: September 2023

This work proposes an innovative method, based on the use of low-cost infrared thermography (IRT) instrumentation, to assess in real time the effectiveness of scoliosis braces. Establishing the effectiveness of scoliosis braces means deciding whether the pressure exerted by the brace on the patient’s back is adequate for the intended therapeutic purpose. Traditionally, the evaluation of brace effectiveness relies on empirical, qualitative assessments carried out by orthopedists during routine follow-up examinations. Hence, it heavily depends on the expertise of the orthopedists involved. In the state of the art, the only objective methods used to confirm orthopedists’ opinions are based on the evaluation of how scoliosis progresses over time, often exposing people to ionizing radiation. To address these limitations, the method proposed in this work aims to provide a real-time, objective assessment of the effectiveness of scoliosis braces in a non-harmful way. This is achieved by exploiting the thermoelastic effect and correlating temperature changes on the patient’s back with the mechanical pressure exerted by the braces. A system based on this method is implemented and then validated through an experimental study on 21 patients conducted at an accredited orthopedic center. The experimental results demonstrate a classification accuracy slightly below 70% in discriminating between adequate and inadequate pressure, which is an encouraging result for further advancement in view of the clinical use of such systems in orthopedic centers.
A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli
Authors: Alessandrini Michele , Falaschetti Laura , Biagetti Giorgio , Crippa Paolo , Luzzi Simona , Turchetti Claudio
Published: September 2023

In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extract functional brain responses from EEG signals, prolonged and repeated stimuli are needed because of the artifacts generated in recordings which adversely impact the stimulus-response analysis. To mitigate the artifact effect, correlation analysis (CA) methods are applied in the literature, where the predominant approaches focus on enhancing stimulus-response correlations through the use of linear analysis methods like canonical correlation analysis (CCA). This paper introduces a novel CA framework based on a neural network with a loss function specifically designed to maximize correlation between EEG and speech stimuli. Compared with other deep learning CA approaches (DCCAs) in the literature, this framework introduces a single multilayer perceptron (MLP) network instead of two networks for each stimulus. To validate the proposed approach, a comparison with linear CCA (LCCA) and DCCA was performed, using a dataset containing the EEG traces of subjects listening to speech stimuli. The experimental results show that the proposed method improves the overall Pearson correlation by 10.56% compared with the state-of-the-art DCCA method.
Joint Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Networks and Beyond
Authors: Alghazali Qusay , Al-Amaireh Husam , Cinkler Tibor
Published: September 2023

Spectral efficiency is a crucial metric in wireless communication systems, as it defines how much information can be transmitted over a given amount of spectrum resources. Non-orthogonal multiple access (NOMA) is a promising technology that has captured the interest of the wireless research community because of its capacity to enhance spectral efficiency. NOMA allows multiple users to share the same frequency band and time slot by assigning different power levels and modulation schemes to different users. Furthermore, channel assignment is a critical challenge in OFDMA-NOMA systems that must be addressed to achieve optimal performance. In this context, we propose a solution for both channel and power assignment based on channel condition by splitting the problem into two parts: first, we introduce a novel algorithm to solve the channel user allocation problem, which we refer to as Channel User Sorting and Filling (CUSF). Then, we solve the power allocation problem in two steps: we apply the water filling algorithm at the power assignment and then we implement the Fractional Transmit Power Control (FTPC) algorithm in the NOMA power assignment.
Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System
Authors: Tesfaw Belayneh Abebe , Juang Rong-Terng , Tai Li-Chia , Lin Hsin-Piao , Tarekegn Getaneh Berie , Nathanael Kabore Wendenda
Published: September 2023

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network.
Effect of Tryptic Digestion on Sensitivity and Specificity in MALDI-TOF-Based Molecular Diagnostics through Machine Learning
Authors: Sarkar Sumon , Squire Abigail , Diab Hanin , Rahman Md. Kaisar , Perdomo Angela , Awosile Babafela , Calle Alexandra , Thompson Jonathan
Published: September 2023

The digestion of protein into peptide fragments reduces the size and complexity of protein molecules. Peptide fragments can be analyzed with higher sensitivity (often > 102 fold) and resolution using MALDI-TOF mass spectrometers, leading to improved pattern recognition by common machine learning algorithms. In turn, enhanced sensitivity and specificity for bacterial sorting and/or disease diagnosis may be obtained. To test this hypothesis, four exemplar case studies have been pursued in which samples are sorted into dichotomous groups by machine learning (ML) software based on MALDI-TOF spectra. Samples were analyzed in ‘intact’ mode in which the proteins present in the sample were not digested with protease prior to MALDI-TOF analysis and separately after the standard overnight tryptic digestion of the same samples. For each case, sensitivity (sens), specificity (spc), and the Youdin index (J) were used to assess the ML model performance. The proteolytic digestion of samples prior to MALDI-TOF analysis substantially enhanced the sensitivity and specificity of dichotomous sorting. Two exceptions were when substantial differences in chemical composition between the samples were present and, in such cases, both ‘intact’ and ‘digested’ protocols performed similarly. The results suggest proteolytic digestion prior to analysis can improve sorting in MALDI/ML-based workflows and may enable improved biomarker discovery. However, when samples are easily distinguishable protein digestion is not necessary to obtain useful diagnostic results.
Impact of the Number of Needle Tip Bevels on the Exerted Forces and Energy in Insulin Pen Injections
Authors: Ponsiglione Alfonso Maria , Ricciardi Carlo , Bonora Enzo , Amato Francesco , Romano Maria
Published: September 2023

Patients affected with type 1 diabetes and a non-negligible number of patients with type 2 diabetes are insulin dependent. Both the injection technique and the choice of the most suitable needle are fundamental for allowing them to have a good injection experience. The needles may differ in several parameters, from the length and diameter, up to the forces required to perform the injection and to some geometrical parameters of the needle tip (e.g., number of facets or bevels). The aim of the research is to investigate whether an increased number of bevels could decrease forces and energy involved in the insertion–extraction cycle, thus potentially allowing patients to experience lower pain. Two needle variants, namely, 31 G × 5 mm and 32 G × 4 mm, are considered, and experimental tests are carried out to compare 3-bevels with 5-bevels needles for both the variants. The analysis of the forces and energy for both variants show that the needles with 5 bevels require a statistically significant lower drag or sliding force (p-value = 0.040 for the 31 G × 5 mm needle and p-value < 0.001 for 32 G × 4 mm), extraction force (p-value < 0.001 for both variants), and energy (p-value < 0.001 for both variants) during the insertion–extraction cycle. As a result, 3-bevels needles do not have the same functionality of 5-bevels needles, show lower capacity of drag and extraction, and can potentially be related to more painful injection experience for patients.
ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks
Authors: Mahalingam Anandaraj , Perumal Ganeshkumar , Subburayalu Gopalakrishnan , Albathan Mubarak , Altameem Abdullah , Almakki Riyad Saleh , Hussain Ayyaz , Abbas Qaisar
Published: September 2023

The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.
A Vibration Sensing Device Using a Six-Axis IMU and an Optimized Beam Structure for Activity Monitoring
Authors: Try Pieter , Gebhard Marion
Published: September 2023

Activity monitoring of living creatures based on the structural vibration of ambient objects is a promising method. For vibration measurement, multi-axial inertial measurement units (IMUs) offer a high sampling rate and a small size compared to geophones, but have higher intrinsic noise. This work proposes a sensing device that combines a single six-axis IMU with a beam structure to enable measurement of small vibrations. The beam structure is integrated into the PCB of the sensing device and connects the IMU to the ambient object. The beam is designed with finite element method (FEM) and optimized to maximize the vibration amplitude. Furthermore, the beam oscillation creates simultaneous translation and rotation of the IMU, which is measured with its accelerometers and gyroscopes. On this basis, a novel sensor fusion algorithm is presented that adaptively combines IMU data in the wavelet domain to reduce intrinsic sensor noise. In experimental evaluation, the proposed sensing device using a beam structure achieves a 6.2-times-higher vibration amplitude and an increase in signal energy of 480% when compared to a directly mounted IMU without a beam. The sensor fusion algorithm provides a noise reduction of 5.6% by fusing accelerometer and gyroscope data at 103 Hz.
Probabilistic Modeling of Multicamera Interference for Time-of-Flight Sensors
Authors: Rodriguez Bryan , Zhang Xinxiang , Rajan Dinesh
Published: September 2023

The behavior of multicamera interference in 3D images (e.g., depth maps), which is based on infrared (IR) light, is not well understood. In 3D images, when multicamera interference is present, there is an increase in the amount of zero-value pixels, resulting in a loss of depth information. In this work, we demonstrate a framework for synthetically generating direct and indirect multicamera interference using a combination of a probabilistic model and ray tracing. Our mathematical model predicts the locations and probabilities of zero-value pixels in depth maps that contain multicamera interference. Our model accurately predicts where depth information may be lost in a depth map when multicamera interference is present. We compare the proposed synthetic 3D interference images with controlled 3D interference images captured in our laboratory. The proposed framework achieves an average root mean square error (RMSE) of 0.0625, an average peak signal-to-noise ratio (PSNR) of 24.1277 dB, and an average structural similarity index measure (SSIM) of 0.9007 for predicting direct multicamera interference, and an average RMSE of 0.0312, an average PSNR of 26.2280 dB, and an average SSIM of 0.9064 for predicting indirect multicamera interference. The proposed framework can be used to develop and test interference mitigation techniques that will be crucial for the successful proliferation of these devices.
An Enhanced Spatial Smoothing Technique of Coherent DOA Estimation with Moving Coprime Array
Authors: Yang Meng , Zhang Yu , Sun Yuxin , Zhang Xiaofei
Published: September 2023

This paper investigates the direction of arrival (DOA) estimation of coherent signals with a moving coprime array (MCA). Spatial smoothing techniques are often used to deal with the covariance matrix of coherent signals, but they cannot be used in sparse arrays. Therefore, super-resolution algorithms such as multiple signal classification (MUSIC) cannot be applied in the DOA estimation of coherent signals in sparse arrays. In this study, we propose an enhanced spatial smoothing method specifically designed for MCA. Firstly, we combine the signals received by the MCA at different times, which can be regarded as a sparse array with a larger number of array sensors. Secondly, we describe how to compute the covariance matrix, derive the signal subspace by eigenvalue decomposition, and prove that the signal subspace is also equivalent to a received signal. Thirdly, we apply enhanced spatial smoothing to the signal subspace and construct a rank recovered covariance matrix. Finally, the DOA of coherent signals are well estimated by the MUSIC algorithm. The simulation results validate the improved performance of the proposed algorithm compared with traditional methods, particularly in scenarios with low signal-to-noise ratios.
Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets
Authors: Luo Chenglong , Kim Sung-Woo , Park Hun-Young , Lim Kiwon , Jung Hoeryong
Published: September 2023

Issues of fairness and consistency in Taekwondo poomsae evaluation have often occurred due to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network–based action recognition model for an objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variations in the viewpoints by reducing the discrepancy between the training and test images. It uses 3D skeletons of poomsae unit actions collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from desired viewpoints. The 2D skeletons obtained from diverse viewpoints form the training dataset, on which the model is trained to ensure consistent recognition performance regardless of the viewpoint. The performance of the model was evaluated against various test datasets, including projected 2D skeletons and RGB images captured from diverse viewpoints. Comparison of the performance of the proposed model with those of previously reported action recognition models demonstrated the superiority of the proposed model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.
Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction
Authors: Kim Jonghoek
Published: September 2023

This paper considers a multi-agent foraging problem, where multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location, called the base. This article considers the case where autonomous agents move in unknown 3-D workspace with many obstacles. This article describes 3-D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. This paper proposes a 3-D foraging strategy which has the following two steps. The first step is to detect all pucks inside the 3-D cluttered unknown workspace, such that every puck in the workspace is detected in a provably complete manner. The next step is to generate a path from the base to every puck, followed by collecting every puck to the base. Since an agent cannot use global localization, each agent depends on local interaction to bring every puck to the base. In this article, every agent on a path to a puck is used for guiding an agent to reach the puck and to bring the puck to the base. To the best of our knowledge, this article is novel in letting multiple agents perform foraging and puck carrying in 3-D cluttered unknown workspace, while not relying on global localization of an agent. In addition, the proposed search strategy is provably complete in detecting all pucks in the 3-D cluttered bounded workspace. MATLAB simulations demonstrate the outperformance of the proposed multi-agent foraging strategy in 3-D cluttered workspace.
Pressure Time Dose as a Representation of Intracranial Pressure Burden and Its Dependency on Intracranial Pressure Waveform Morphology at Different Time Intervals
Authors: Schönenberg-Tu Anna-Li , Cysarz Dirk , Petzold Benjamin , Blümel Carl Benjamin , Raak Christa , Fricke Oliver , Edelhäuser Friedrich , Scharbrodt Wolfram
Published: September 2023

Intracranial pressure (ICP) burden or pressure time dose (PTD) is a valuable clinical indicator for pending intracranial hypertension, mostly based on threshold exceedance. Pulse frequency and waveform morphology (WFM) of the ICP signal contribute to PTD. The temporal resolution of the ICP signal has a great influence on PTD calculation but has not been systematically studied yet. Hence, the temporal resolution of the ICP signal on PTD calculation is investigated. We retrospectively analysed continuous 48 h ICP recordings with high temporal resolution obtained from 94 patients at the intensive care unit who underwent neurosurgery due to an intracranial haemorrhage and received an intracranial pressure probe (43 females, median age: 72 years, range: 23 to 88 years). The cumulative area under the curve above the threshold of 20 mmHg was compared for different temporal resolutions of the ICP signal (beat-to-beat, 1 s, 300 s, 1800 s, 3600 s). Events with prolonged ICP elevation were compared to those with few isolated threshold exceedances. PTD increased for lower temporal resolutions independent of WFM and frequency of threshold exceedance. PTDbeat-to-beat best reflected the impact of frequency of threshold exceedance and WFM. Events that could be distinguished in PTDbeat-to-beat became magnified more than 7-fold in PTD1s and more than 104 times in PTD1h, indicating an overestimation of PTD. PTD calculation should be standardised, and beat-by-beat PTD could serve as an easy-to-grasp indicator for the impact of frequency and WFM of ICP elevations on ICP burden.
Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements
Authors: Khudhair Mustafa , Gucunski Nenad
Published: September 2023

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing machine-learning algorithms developed using data from multiple nondestructive evaluation (NDE) techniques. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. Subsequently, this algorithm was applied to data collected from an actual bridge deck in the BEAST® facility, showcasing a significant advancement in ER measurement interpretation through the incorporation of information from other NDE technologies. Such strides are pivotal in advancing the reliability of assessments of structural elements for their durability and safety.