bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Temporal Trade-Offs of Ecosystem Services by Production Possibility Frontiers
Authors: Jiang Wei , Gao Guangyao , Wu Xing , Lv Yihe
Published: January 2023

Ecosystems provide multiple valuable services that play an essential role in preventing meteorological risks, combating sandy land expansion, and ensuring sustainable development in the West Liao River Basin. The trade-off among ecosystem services (ES) is inevitable because of biophysical constraints and societal preferences. The production possibility frontier (PPF) is increasingly deemed an appropriate tool for representing trade-off relationships among ES. In this study, we developed a feasible approach for estimating PPF, which includes three steps. First, the annual water yield model, the sediment delivery ratio model, the carbon storage and sequestration model, and the habitat quality model of InVEST models were used to quantify temporal changes in four key ES, including water retention, soil conservation, carbon sequestration, and habitat improvement, in five-year periods from 1990 to 2020. Second, after the standardization of ES quantities, the functional forms of PPF curves for six pairs of ES trade-offs were derived by adopting a two-term exponential function of the curve fitting tool in MATLAB. Third, the trade-off intensity for each ES pair was defined and calculated based on the distance from the mean point to the PPF curve. Compared to the existing approaches, our approach has the advantage of fitting functional forms of PPF curves, handling both positive and negative values of ES, and calculating trade-off intensities. This study has three implications. First, showing the trade-offs between ES by PPF is helpful for providing knowledge on the existence of turning points and a complex relationship between certain ES pairs, thus avoiding unintended and large-scale shifts in the provision of ES. Second, PPF curves are a useful tool for visualizing the nature of ES relationships and the changes in trade-off intensity, thus supporting decision-makers to identify optimal solutions and make land use planning that can increase the overall efficiency over multiple ES. Third, socioeconomic components should be integrated into the assessment of ES trade-offs in order to understand the influences of societal choices on and examine stakeholders’ preferences regarding efficient ES combinations.
ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards
Authors: Gao Rui , Torres-Rua Alfonso F. , Nieto Hector , Zahn Einara , Hipps Lawrence , Kustas William P. , Alsina Maria Mar , Bambach Nicolas , Castro Sebastian J. , Prueger John H. , Alfieri Joseph , McKee Lynn G. , White William A. , Gao Feng , McElrone Andrew J. , Anderson Martha , Knipper Kyle , Coopmans Calvin , Gowing Ian , Agam Nurit , Sanchez Luis , Dokoozlian Nick
Published: January 2023

Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy fluxes. This partitioning has an important role in agriculture since it is related to grapevine stress, yield quality, irrigation efficiency, and growth. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale. However, medium-resolution satellite imagery from platforms such as Landsat is often insufficient for precision agricultural management at the plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah State University enable ET estimation and its partitioning over vineyards via the two-source energy balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water evaporation and plant transpiration), considering three different resistance models using ground-based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS information. This new method, coupled with the TSEB model (called TSEB-2TQ), improved sensible heat flux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different from the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with the transpiration modeled by different TSEB models, the TSEB-2TQ shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2TQ coupled with different resistance models resulted in insignificant differences. This comparison is one of the first for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods.
Reducing Model Error Effects in El Niño–Southern Oscillation Prediction Using Ensemble Coupled Data Assimilation
Authors: Gao Yanqiu , Tang Youmin , Liu Ting
Published: January 2023

Model error is an important source of uncertainty that significantly reduces the accuracy of El Niño–Southern Oscillation (ENSO) prediction. In this study, ensemble coupled data assimilation was employed to estimate the tendency error of the fifth-generation Lamont–Doherty Earth observation (LDEO5) model, which represented the comprehensive effect of different sources of errors. Then, the estimated tendency error was applied to an ensemble prediction system for ENSO prediction. Assimilation experiments showed that tendency error estimation yielded better analysis than state estimation only. With tendency error estimation, simulated state variables such as zonal wind stress anomalies and subsurface temperature anomalies in the Niño3.4 region and upper layer depth anomalies along the equator showed good agreement with their reanalyzed counterparts. The ensemble ENSO prediction system with tendency error estimation demonstrated significantly better prediction skill than the ensemble system without tendency error estimation or the original LDEO5 model, especially for long lead times. The tendency error estimation improved the prediction skill for El Niño more than for La Niña. This study provides a promising approach to further improve prediction skill by reducing model error effects in an ensemble prediction.
A Google Earth Engine-Based Framework to Identify Patterns and Drivers of Mariculture Dynamics in an Intensive Aquaculture Bay in China
Authors: Wang Peng , Wang Jian , Liu Xiaoxiang , Huang Jinliang
Published: January 2023

Although mariculture contributes significantly to regional/local economic development, it also promotes environmental degradation. Therefore, it is essential to understand mariculture dynamics before taking adaptive measures to deal with it. In the present study, a framework that integrates the Google Earth Engine (GEE) based methods and GeoDetector software was developed to identify patterns and drivers of mariculture dynamics. This framework was then applied to Zhao’an Bay, which is an intensive aquaculture bay in Coastal China, based on Landsat 8 OLI (2013–2022) and Sentinel-2 (December 2015–May 2022) data. The results show that the GEE-based method produces acceptable classification accuracy. The overall accuracy values for the interpretation are >85%, where the kappa coefficients are >0.9 for all years, excluding 2015 (0.83). Mariculture increased in the study area from 2013 to 2022, and this is characterised by distinct spatiotemporal variations. Cage mariculture is primarily concentrated around islands, whereas raft mariculture is dominant in bay areas, and pond and mudflat mariculture types are mostly in nearshore areas. The growth of mariculture in Zhao’an Bay is attributed to a combination of geographic and human factors. The initial area associated with mariculture in a grid significantly impacted the expansion of the raft, cage, and mudflat mariculture. The distance to an island, spatial proximity to similar types of mariculture and types of mariculture are the main drivers of change in mariculture. Human activities greatly contribute to the dynamics of mudflat mariculture; regulation regarding the clearing of waterways directly impacts the dynamics of mariculture. The present study demonstrates that the proposed framework facilitates the effective monitoring of the mariculture dynamics and identification of driving factors. These findings can be exploited for the local planning and management of mariculture in similar coastal bays.
Editorial for Special Issue “Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management”
Authors: Niculescu Simona , Xia Junshi , Roberts Dar
Published: January 2023

Most of the papers published in this Special Issue were presented at the international conference EUCOMARE-2022 in the framework of the European Jean Monnet Chair European Spatial Studies of Sea and Coastal zones with the support of the ERASMUS+ Programme of the European Union.
Quantitative Analysis of the Contributions of Climatic and Anthropogenic Factors to the Variation in Net Primary Productivity, China
Authors: Shi Shouhai , Zhu Luping , Luo Zhaohui , Qiu Hua
Published: January 2023

Accurate quantification of the contributions of climatic and anthropogenic factors to the variation in NPP is critical for elucidating the relevant driving mechanisms. In this study, the spatiotemporal variation in net primary productivity (NPP) in China during 2000–2020, the interactive effects of climatic and anthropogenic factors on NPP and the optimal characteristics of driving forces were explored. Our results indicate that NPP had obvious spatial differentiation, an overall increasing trend was identified and this trend will continue in the future for more than half of the pixels. Land use and Land cover and precipitation were the main factors regulating NPP variation at both the national scale and the sub-region scale, except in southwest China, which was dominated by altitude and temperature. Moreover, an interactive effect between each pair of factors was observed and the effect of any pair of driving factors was greater than that of any single factor, manifested as either bivariate enhancement or nonlinear enhancement. Furthermore, the responses and optimal characteristics of NPP concerning driving forces were diverse. The findings provide a critical understanding of the impacts of driving forces on NPP and could help to create optimal conditions for vegetation growth to mitigate and adapt to climate changes.
Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals
Authors: Ng Gabriel , Andrysek Jan
Published: January 2023

Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments.
Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map
Authors: Alsafyani Majed , Alhomayani Fahad , Alsuwat Hatim , Alsuwat Emad
Published: January 2023

Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study’s findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.
Margin-Based Modal Adaptive Learning for Visible-Infrared Person Re-Identification
Authors: Zhao Qianqian , Wu Hanxiao , Zhu Jianqing
Published: January 2023

Visible-infrared person re-identification (VIPR) has great potential for intelligent transportation systems for constructing smart cities, but it is challenging to utilize due to the huge modal discrepancy between visible and infrared images. Although visible and infrared data can appear to be two domains, VIPR is not identical to domain adaptation as it can massively eliminate modal discrepancies. Because VIPR has complete identity information on both visible and infrared modalities, once the domain adaption is overemphasized, the discriminative appearance information on the visible and infrared domains would drain. For that, we propose a novel margin-based modal adaptive learning (MMAL) method for VIPR in this paper. On each domain, we apply triplet and label smoothing cross-entropy functions to learn appearance-discriminative features. Between the two domains, we design a simple yet effective marginal maximum mean discrepancy (M3D) loss function to avoid an excessive suppression of modal discrepancies to protect the features’ discriminative ability on each domain. As a result, our MMAL method could learn modal-invariant yet appearance-discriminative features for improving VIPR. The experimental results show that our MMAL method acquires state-of-the-art VIPR performance, e.g., on the RegDB dataset in the visible-to-infrared retrieval mode, the rank-1 accuracy is 93.24% and the mean average precision is 83.77%.
Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
Authors: Ali Ali M. , Ngadi Md Asri , Sham Rohana , Al_Barazanchi Israa Ibraheem
Published: January 2023

Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things.
Active and Low-Cost Hyperspectral Imaging for the Spectral Analysis of a Low-Light Environment
Authors: Tang Yang , Song Shuang , Gui Shengxi , Chao Weilun , Cheng Chinmin , Qin Rongjun
Published: January 2023

Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system’s hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system’s hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining.
Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases
Authors: Albiges Timothy , Sabeur Zoheir , Arbab-Zavar Banafshe
Published: January 2023

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) “Healthy” or “COPD” and (2) “Healthy”, “COPD”, or “Pneumonia” classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.
Sensor Networks: Physical and Social Sensing in the IoT
Authors: De Suparna , Moessner Klaus
Published: January 2023

Advances made in the Internet of Things (IoT) and other disruptive technological trends, including big data analytics and edge computing methods, have contributed enabling solutions to the numerous challenges affecting modern communities [...]
Research on UWB Indoor Positioning System Based on TOF Combined Residual Weighting
Authors: Yang Jinmin , Zhu Chunhua
Published: January 2023

The performance of TDOA positioning based on UWB is limited by the hyperbolic characteristics of TDOA, especially for tags away from the hyperbolic asymptote. Aiming at this problem, a new UWB indoor positioning system is proposed. Firstly, TOF ranging is adopted to build the positioning equations; then the weighted centroid algorithm of four base stations is presented to compute the initial rough position of the tag; and the residual weighting is introduced to optimize the initial tag position; then, the corresponding nonlinear positioning equations, which will be algebraically transformed to one distribution function, are solved, and the optimal tag coordinates can be obtained by the Newton iteration method. Simulation experiments have verified the positioning reliability of the proposed algorithm under different noise environments and for different tag positions.
Assessment of the Structural Vibration Effect on Plasma Current Measurement Using a Fiber Optic Current Sensor in ITER
Authors: Kim Sung-Moon , Dandu Prasadaraju , Gusarov Andrei , Danisi Alessandro , Vayakis George , Wuilpart Marc
Published: January 2023

In this paper, we assess the effect of cryostat bridge vibrations on the plasma current measurement accuracy when using a fiber optic current sensor (FOCS) in ITER. The impact of vibrations on the light polarization state was first experimentally investigated using a miniaturized mock-up which represented a relevant part of the ITER FOCS structure. The set-up was then numerically simulated using the Jones matrix approach. Equivalent vibration matrices obtained from the experiment were used in the simulations to determine the effect of the vibrations on the FOCS accuracy. It is demonstrated that although the vibrations imply some changes in the polarization state, this effect can be strongly reduced when a proper low-birefringent spun optical fiber is used. The ITER requirement regarding the plasma current measurement accuracy can therefore be fulfilled.
Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security
Authors: Raivi Asif Mahmud , Huda S. M. Asiful , Alam Muhammad Morshed , Moh Sangman
Published: January 2023

Recently, owing to the high mobility and low cost of drones, drone-based delivery systems have shown considerable potential for ensuring flexible and reliable parcel delivery. Several crucial design issues must be considered to design such systems, including route planning, payload weight consideration, distance measurement, and customer location. In this paper, we present a survey of emerging drone routing algorithms for drone-based delivery systems, emphasizing three major drone routing aspects: trajectory planning, charging, and security. We focus on practical design considerations to ensure efficient, flexible, and reliable parcel delivery. We first discuss the potential issues arising when designing such systems. Next, we present a novel taxonomy based on the above-mentioned three aspects. We extensively review each algorithm for drone routing in terms of key features and operational characteristics. Furthermore, we compare the algorithms in terms of their main idea, advantages, limitations, and performance aspects. Finally, we present open research challenges to motivate further research in this field. In particular, we focus on the major aspects that researchers and engineers need to consider in order to design effective and reliable drone routing algorithms for drone-based delivery systems.
An Insight of Deep Learning Based Demand Forecasting in Smart Grids
Authors: Aguiar-Pérez Javier Manuel , Pérez-Juárez María Ángeles
Published: January 2023

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.
Characterization and Validation of Flexible Dry Electrodes for Wearable Integration
Authors: Nunes Tiago , da Silva Hugo Plácido
Published: January 2023

When long-term biosignal monitoring is required via surface electrodes, the use of conventional silver/silver chloride (Ag/AgCl) gelled electrodes may not be the best solution, as the gel in the electrodes tends to dry out over time. In this work, the electrical behaviour and performance of dry electrodes for biopotential monitoring was assessed. Three materials were investigated and compared against the gold-standard Ag/AgCl gelled electrodes. To characterize their electrical behaviour, the impedance response over the frequency was evaluated, as well as its signal to noise ratio. The electrodes’ performance was evaluated by integrating them in a proven electrocardiogram (ECG) acquisition setup where an ECG signal was acquired simultaneously with a set of dry electrodes and a set of standard Ag/AgCl gelled electrodes as reference. The obtained results were morphologically compared using the Normalised Root Mean Squared Error (nRMSE) and the Cosine Similarity (CS). The findings of this work suggest that the use of dry electrodes for biopotential monitoring is a suitable replacement for the conventional Ag/AgCl gelled electrodes. The signal obtained with dry electrodes is comparable to the one obtained with the gold standard, with the advantage that these do not require the use of gel and can be easily integrated into fabric to facilitate their use in long-term monitoring scenarios.
IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning
Authors: Khan Mudasir , Shah Pir Masoom , Khan Izaz Ahmad , Islam Saif ul , Ahmad Zahoor , Khan Faheem , Lee Youngmoon
Published: January 2023

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.
Illumination Temporal Fluctuation Suppression for Single-Pixel Imaging
Authors: Wang Han , Sun Mingjie , Song Lailiang
Published: January 2023

Single-pixel cameras offer improved performance in non-visible imaging compared with modern digital cameras which capture images with an array of detector pixels. However, the quality of the images reconstructed by single-pixel imaging technology fails to match traditional cameras. Since it requires a sequence of measurements to retrieve a single image, the temporal fluctuation of illumination intensity during the measuring will cause inconsistence for consecutive measurements and thus noise in reconstructed images. In this paper, a normalization protocol utilizing the differential measurements in single-pixel imaging is proposed to reduce such inconsistence with no additional hardware required. Numerical and practical experiments are performed to investigate the influences of temporal fluctuation of different degrees on image quality and to demonstrate the feasibility of the proposed normalization protocol. Experimental results show that our normalization protocol can match the performance of the system with the reference arm. The proposed normalization protocol is straightforward with the potential to be easily applied in any temporal-sequence imaging strategy.
NetAP-ML: Machine Learning-Assisted Adaptive Polling Technique for Virtualized IoT Devices
Authors: Park Hyunchan , Go Younghun , Lee Kyungwoon , Hong Cheol-Ho
Published: January 2023

To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine learning technique to shrink the search space for finding an optimal polling interval. NetAP-ML is able to minimize the performance degradation in the search process and find a more accurate polling interval with the random forest regression algorithm. We implement and evaluate NetAP-ML in a Linux system. Our experimental setup consists of a various number of virtual machines (2–4) and threads (1–5). We demonstrate that NetAP-ML provides up to 23% higher bandwidth than the state-of-the-art technique.
Realization and Tests of Prototype Fluxgate Magnetic Sensors for the ITER Neutral Beam Injectors
Authors: Chitarin Giuseppe , Marconato Nicolò , Mayer Stefan
Published: January 2023

In the ITER neutral beam injectors (NBI), the presence of an external variable magnetic field generated by the ITER tokamak itself, could deflect the ion beam during acceleration and cause a loss of beam focusing. For this reason, the ion source, the accelerator and the neutralizer will be shielded from external magnetic field by means of a passive magnetic shield and a system of active correction and compensation coils (ACCC). The ACCC will operate in a feedback control loop and thus require the measurement of magnetic field inside the NBI vessel. Magnetic sensors for this application must be capable of measuring DC and slow variable magnetic fields, and be vacuum-compatible, radiation-hard and robust, since they will be subjected to neutron flux produced by fusion reactions in the tokamak and inaccessible for maintenance. This paper describes the realization and tests of fluxgate magnetic sensors prototypes specifically designed for this purpose before the installation in MITICA and ITER.
Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module
Authors: Jia Yinjiang , Shi Yaoyao , Luo Jiaqi , Sun Hongmin
Published: January 2023

Corn diseases are one of the significant constraints to high–quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three–dimensional–two–dimensional (3D–2D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial–spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D–2D hybrid CNN, resulting in a Y–shaped architecture named Y–Net. The results show that the spectral bands selected by the band selection module of Y–Net achieve more reliable classification performance than traditional feature selection methods. Y–Net obtained the best classification accuracy compared to support vector machines, one–dimensional (1D) CNNs, and two–dimensional (2D) CNNs. After the network pruned the trained Y–Net, the model size was reduced to one–third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops.
FDSS-Based DFT-s-OFDM for 6G Wireless Sensing
Authors: Chen Lu , Pan Jianxiong , Zhang Jing , Cheng Junfeng , Xu Luyan , Ye Neng
Published: January 2023

Integrated sensing and communications (ISAC) is emerging as a key technology of 6G. Owing to the low peak-to-average power ratio (PAPR) property, discrete Fourier transform spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is helpful to improve the sensing range and suitable for high-frequency transmission. However, compared to orthogonal frequency-division multiplexing (OFDM), the sensing accuracy of DFT-s-OFDM is relatively poor. In this paper, frequency-domain spectral shaping (FDSS) is adopted to enhance the performances of DFT-s-OFDM including sensing accuracy and PAPR by adjusting the correlation of signals. Specifically, we first establish a signal model for the ISAC system, followed by the description of performance indicators. Then, we analyze the influence of amplitude fluctuation of frequency domain signals on sensing performance, which shows the design idea of FDSS-enhanced DFT-s-OFDM. Further, a FDSS-enhanced DFT-s-OFDM framework is introduced for ISAC, where two types of FDSS filters including a pre-equalization filter and an isotropic orthogonal transform algorithm (IOTA) filter are designed. The simulation results show that the proposed scheme can obtain about 4 dB performance gain in terms of sensing accuracy over DFT-s-OFDM. In addition, FDSS-enhanced DFT-s-OFDM can significantly reduce PAPR and improve the power amplifier efficiency.
Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System
Authors: Naf’an Emil , Sulaiman Riza , Ali Nazlena Mohamad
Published: January 2023

This study aims to optimize the object identification process, especially identifying trash in the house compound. Most object identification methods cannot distinguish whether the object is a real image (3D) or a photographic image on paper (2D). This is a problem if the detected object is moved from one place to another. If the object is 2D, the robot gripper only clamps empty objects. In this study, the Sequential_Camera_LiDAR (SCL) method is proposed. This method combines a Convolutional Neural Network (CNN) with LiDAR (Light Detection and Ranging), with an accuracy of ±2 mm. After testing 11 types of trash on four CNN architectures (AlexNet, VGG16, GoogleNet, and ResNet18), the accuracy results are 80.5%, 95.6%, 98.3%, and 97.5%. This result is perfect for object identification. However, it needs to be optimized using a LiDAR sensor to determine the object in 3D or 2D. Trash will be ignored if the fast scanning process with the LiDAR sensor detects non-real (2D) trash. If Real (3D), the trash object will be scanned in detail to determine the robot gripper position in lifting the trash object. The time efficiency generated by fast scanning is between 13.33% to 59.26% depending on the object’s size. The larger the object, the greater the time efficiency. In conclusion, optimization using the combination of a CNN and a LiDAR sensor can identify trash objects correctly and determine whether the object is real (3D) or not (2D), so a decision may be made to move the trash object from the detection location.
Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference
Authors: Na Jun , Zhang Handuo , Lian Jiaxin , Zhang Bin
Published: January 2023

In order to effectively apply BranchyNet, a DNN with multiple early-exit branches, in edge intelligent applications, one way is to divide and distribute the inference task of a BranchyNet into a group of robots, drones, vehicles, and other intelligent edge devices. Unlike most existing works trying to select a particular branch to partition and deploy, this paper proposes a genetic algorithm (GA)-based online partitioning approach that splits the whole BranchyNet with all its branches. For this purpose, it establishes a new calculation approach based on the weighted average for estimating total execution time of a given BranchyNet and a two-layer chromosome GA by distinguishing partitioning and deployment during the evolution in GA. The experiment results show that the proposed algorithm can not only result in shorter execution time and lower device-average energy cost but also needs less time to obtain an optimal deployment plan. Such short running time enables the proposed algorithm to generate an optimal deployment plan online, which dynamically meets the actual requirements in deploying an intelligent application in the edge.
Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling
Authors: Abbas-Turki Abdeljalil , Mualla Yazan , Gaud Nicolas , Calvaresi Davide , Du Wendan , Lombard Alexandre , Dridi Mahjoub , Koukam Abder
Published: January 2023

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights’ phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles’ speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.
Blind Video Quality Assessment for Ultra-High-Definition Video Based on Super-Resolution and Deep Reinforcement Learning
Authors: Ying Zefeng , Pan Da , Shi Ping
Published: January 2023

Ultra-high-definition (UHD) video has brought new challenges to objective video quality assessment (VQA) due to its high resolution and high frame rate. Most existing VQA methods are designed for non-UHD videos—when they are employed to deal with UHD videos, the processing speed will be slow and the global spatial features cannot be fully extracted. In addition, these VQA methods usually segment the video into multiple segments, predict the quality score of each segment, and then average the quality score of each segment to obtain the quality score of the whole video. This breaks the temporal correlation of the video sequences and is inconsistent with the characteristics of human visual perception. In this paper, we present a no-reference VQA method, aiming to effectively and efficiently predict quality scores for UHD videos. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet), which can quickly extract the global spatial distortion features of UHD videos. Then, to aggregate the spatial distortion features of each UHD frame, we propose a time fusion network based on a reinforcement learning model (RL-TFNet), in which the actor network continuously combines multiple frame features extracted by SR-SDFNet and outputs an action to adjust the current quality score to approximate the subjective score, and the critic network outputs action values to optimize the quality perception of the actor network. Finally, we conduct large-scale experiments on UHD VQA databases and the results reveal that, compared to other state-of-the-art VQA methods, our method achieves competitive quality prediction performance with a shorter runtime and fewer model parameters.
Real-Time Risk Assessment Detection for Weak People by Parallel Training Logical Execution of a Supervised Learning System Based on an IoT Wearable MEMS Accelerometer
Authors: Hoang Minh Long , Nkembi Armel Asongu , Pham Phuong Ly
Published: January 2023

Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal activities. When there are many categories, the ML prediction can be confused between these activities with each other. The PTLE system trains several models in parallel with more specific activity classes in each dataset. The shared tasks between parallel models relieve the complexity for a single one. There are six additional parameters for accelerometer characteristics, which were calculated from three axes accelerations as input features to improve the ML’s consciousness. Once all models were trained, the system was ready to receive the input accelerations and activated the logical flow to manage link operation between these ML models for output predictions. Random Forest (RF) had the highest potential among the ML classification algorithms after the validation. In the experiment, the comparison between the PTLE model and the regular ML model were carried out with real-time data from an M5stickC wearable device on the user’s chest to the trained models on PC. The result showed the advancement of the proposed method in term of precision, recall, F1-score with an overall accuracy of 98% in the real-time test. The accelerations from the wearable device were sent to ML models via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker, and the activity predictions were transferred to the cloud for the family members or doctor care based on Internet of Things (IoT) communication.
Humidity Effect on Low-Temperature NH3 Sensing Behavior of In2O3/rGO Composites under UV Activation
Authors: Nasriddinov Abulkosim , Shatalova Tatiana , Maksimov Sergey , Li Xiaogan , Rumyantseva Marina
Published: January 2023

The nature of the constituent components of composite materials can significantly affect the character of their interaction with the gas phase. In this work, nanocrystalline In2O3 was synthesized by the chemical precipitation method and was modified using reduced graphene oxide (rGO). The obtained composites were characterized by several analysis techniques—XRD, TEM, SEM, FTIR and Raman spectroscopy, XPS, TGA, and DRIFTS. The XPS and FTIR and Raman spectroscopy results suggested the formation of interfacial contact between In2O3 and rGO. The results of the gas sensor’s properties showed that additional UV illumination led to a decrease in resistance and an increase in sensor response at room temperature. However, the presence of humidity at room temperature led to the disappearance of the response for pure In2O3, while for the composites, an inversion of the sensor response toward ammonia was observed. The main reason may have been the formation of NH4NO3 intermediates with further hydrolysis and decomposition under light illumination with the formation of nitrite and nitrate species. The presence of these species was verified by in situ DRIFT spectroscopy. Their strong electron-accepting properties lead to an increase in resistance, which possibly affected the sensor signal’s inversion.