bluuebunny/crossref_metadata_2025 huggingface dataset:
Interference Mitigation Method for Millimeter-Wave Frequency-Modulation Continuous-Wave Radar Based on Outlier Detection and Variational Modal Decomposition
Authors: Zhou Wen , Hao Xinhong , Yang Jin , Duan Lefan , Yang Qiuyan , Wang Jianqiu
Published: July 2023

Aiming at the problem of mutual interference between millimeter-wave frequency-modulation continuous-wave (FMCW) radars, an interference mitigation method based on outlier detection and variational mode decomposition (VMD) is proposed in this paper. Firstly, by differential processing of the raw millimeter-wave FMCW radar data, combined with threshold detection, the interfered sample area is located. Adaptive amplitude limiting is applied to the interfered samples to achieve initial suppression of the interference. Then, based on the VMD algorithm, the processed data are adaptively decomposed to obtain multiple intrinsic mode functions (IMFs). The Pearson correlation coefficient between each IMF and the signal before decomposition is calculated, and the IMF with the maximum Pearson correlation coefficient is extracted as the signal component to achieve the separation of the target signal from the interference and noise. The proposed method was validated based on simulation and experimental data. The results show that the proposed method achieves the best performance in terms of signal-to-interference-plus-noise ratio (SINR), mean square error (MSE), and kurtosis in frequency (KF) compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition (CEEMD). Further comparison was made with two typical methods, and the Range–Doppler (RD) map and SINR results showed that the proposed method exhibited certain performance advantages.
Fast and Accurate Refocusing for Moving Ships in SAR Imagery Based on FrFT
Authors: Wang Jin , Leng Xiangguang , Sun Zhongzhen , Zhang Xi , Ji Kefeng
Published: July 2023

Synthetic aperture radar (SAR) is capable of monitoring the ocean all day, regardless of weather conditions. However, moving ships exhibit azimuth defocus in SAR images, which severely hampers ship recognition performance. Ships typically move in a linear motion at sea. For refocusing linear moving ships, existing SAR autofocus algorithms cannot accurately extract defocus information and require multiple iterations. To overcome the poor focusing quality and high computational complexity of existing refocusing algorithms, this paper proposes a fast and accurate refocusing scheme for moving ships in SAR imagery based on Fractional Fourier Transform (FrFT). Firstly, the azimuth line with the strongest energy in the SAR image is selected as the best azimuth line representing its motion property. Then, according to the entropy variation law of the azimuth line after FrFT, the azimuth line’s optimal rotation order is determined by the proposed minimum entropy search method, which can accurately and quickly obtain defocus information. In the final refocusing module, the scheme provides two ways, i.e., fast or fine refocusing approaches, to generate well-focused images. The fast refocusing approach performs FrFT on each azimuth line at the optimal rotation order of the best azimuth line. The fine refocusing approach takes the optimal rotation order of the best azimuth line as the initial value and further searches for the optimal rotation order of other azimuth lines. In order to verify the effectiveness of the proposed scheme, experiments are carried out on a number of Gaofen-3 SAR images in different acquisition modes. The experimental results show that the proposed fast refocusing approach can achieve the fastest speed, which is 2.1% of the traditional FrFT-based method’s processing time. Moreover, the proposed fine refocusing approach has the best focusing performance, achieving the lowest image entropy among existing methods.
Lightning Interferometry with the Long Wavelength Array
Authors: Stock Michael , Tilles Julia , Taylor Greg , Dowell Jayce , Liu Ningyu
Published: July 2023

The Long Wavelength Array is a radio telescope array located at the Sevilleta National Wildlife Refuge in La Joya, New Mexico, well suited and situated for the observation of lightning. The array consists of 256 high-sensitivity dual polarization antennas arranged in a 100 m diameter. This paper demonstrates some of the capabilities that the array brings to the study of lightning. Once 32 or more antennas are used to image lightning radio sources, virtually every integration period longer than the impulse response of the array includes at least one identifiable lightning emitter, independent of the integration period used. The use of many antennas also allows multiple simultaneous lightning radio sources to be imaged at sub-microsecond timescales; for the flash examined, 51% of the images contained more than one lightning source. Finally, by using many antennas to image lightning sources, the array is capable of locating sources fainter than the galactic background radio noise level, yielding possibly the most sensitive radio maps of lightning to date. This incredible sensitivity enables, for the first time, the emissions originating from the positive leader tips of natural in-cloud lightning to be detected and located. The tip emission is distinctly different from needle emission and is most likely due to positive breakdown.
Nickel Grade Inversion of Lateritic Nickel Ore Using WorldView-3 Data Incorporating Geospatial Location Information: A Case Study of North Konawe, Indonesia
Authors: Zhang Geng , Chen Qi , Zhao Zhifang , Zhang Xinle , Chao Jiangqin , Zhou Dingyi , Chai Wang , Yang Haiying , Lai Zhibin , He Yangyidan
Published: July 2023

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R2 of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method’s effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes.
Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data
Authors: Liu Qi , Zhang Shuangcheng , Li Weiqiang , Nan Yang , Peng Jilun , Ma Zhongmin , Zhou Xin
Published: July 2023

Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors.
Bilateral Adversarial Patch Generating Network for the Object Tracking Algorithm
Authors: Jia Erkenbieke , Xu Yuelei , Zhang Zhaoxiang , Tao Chengyang , Hui Tian
Published: July 2023

Deep learning-based algorithms for single object tracking (SOT) have shown impressive performance but remain susceptible to adversarial patch attacks. However, existing adversarial patch generation methods primarily focus on generating patches within the search region, neglecting the incorporation of template information, which limits their effectiveness in carrying out successful attacks. There is also a lack of evaluation metrics to assess the patch’s adversarial abilities. In this study, we propose a bilateral adversarial patch-generating network to address these limitations and advance the field of adversarial patch generation for SOT networks. Our network leverages a Focus structure that effectively integrates both template and search region information, generating separate adversarial patches for each branch. We also introduce the DeFocus structure to solve the size discrepancy between the template and search region of the tracking network. To effectively mislead the tracking network, we have designed adversarial object loss and adversarial regression loss functions tailored to the network’s output. Moreover, we propose a comprehensive evaluation metric that measures the patch’s adversarial ability by establishing a relationship between the relative patch size and attack performance. As UAV view data often constitute small objects requiring smaller patches, we evaluate our approach on the UAV123 and UAVDT datasets. Our evaluation encompasses not only the overall attack performance but also the effectiveness of our strategy and the transferability of the attacks. Experimental results demonstrate that our algorithm generates patches with higher attack efficiency compared to existing methods.
Automatic Monitoring of Maize Seedling Growth Using Unmanned Aerial Vehicle-Based RGB Imagery
Authors: Gao Min , Yang Fengbao , Wei Hong , Liu Xiaoxia
Published: July 2023

Accurate and rapid monitoring of maize seedling growth is critical in early breeding decision making, field management, and yield improvement. However, the number and uniformity of seedlings are conventionally determined by manual evaluation, which is inefficient and unreliable. In this study, we proposed an automatic assessment method of maize seedling growth using unmanned aerial vehicle (UAV) RGB imagery. Firstly, high-resolution images of maize at the early and late seedling stages (before and after the third leaf) were acquired using the UAV RGB system. Secondly, the maize seedling center detection index (MCDI) was constructed, resulting in a significant enhancement of the color contrast between young and old leaves, facilitating the segmentation of maize seedling centers. Furthermore, the weed noise was removed by morphological processing and a dual-threshold method. Then, maize seedlings were extracted using the connected component labeling algorithm. Finally, the emergence rate, canopy coverage, and seedling uniformity in the field at the seedling stage were calculated and analyzed in combination with the number of seedlings. The results revealed that our approach showed good performance for maize seedling count with an average R2 greater than 0.99 and an accuracy of F1 greater than 98.5%. The estimation accuracies at the third leaf stage (V3) for the mean emergence rate and the mean seedling uniformity were 66.98% and 15.89%, respectively. The estimation accuracies at the sixth leaf stage (V6) for the mean seedling canopy coverage and the mean seedling uniformity were 32.21% and 8.20%, respectively. Our approach provided the automatic monitoring of maize growth per plot during early growth stages and demonstrated promising performance for precision agriculture in seedling management.
Precision Aquaculture Drone Mapping of the Spatial Distribution of Kappaphycus alvarezii Biomass and Carrageenan
Authors: Nurdin Nurjannah , Alevizos Evangelos , Syamsuddin Rajuddin , Asis Hasni , Zainuddin Elmi Nurhaidah , Aris Agus , Oiry Simon , Brunier Guillaume , Komatsu Teruhisa , Barillé Laurent
Published: July 2023

The aquaculture of Kappaphycus alvarezii (Kappaphycus hereafter) seaweed has rapidly expanded among coastal communities in Indonesia due to its relatively simple farming process, low capital costs and short production cycles. This species is mainly cultivated for its carrageenan content used as a gelling agent in the food industry. To further assist producers in improving cultivation management and providing quantitative information about the yield, a novel approach involving remote sensing techniques was tested. In this study, multispectral images obtained from a drone (Unoccupied Aerial Vehicle, UAV) were processed to estimate the fresh and carrageenan weights of Kappaphycus at a cultivation site in South Sulawesi. The UAV imagery was geometrically and radiometrically corrected, and the resulting orthomosaics were used for detecting and classifying Kappaphycus using a random forest algorithm. The classification results were combined with in situ measurements of Kappaphycus fresh weight and carrageenan content using empirical relations between the area and weight of fresh seaweed/carrageenan. This approach allowed quantifying seaweed biometry and biochemistry at single cultivation lines and cultivation plot scales. Fresh seaweed and carrageenan weights were estimated for different dates within three distinct cultivation cycles, and the daily growth rate for each cycle was derived. Data were upscaled to a small family-scale farm and a large-scale leader farm and compared with previous estimations. To our knowledge, this study provides, for the first time, an estimation of yield at the scale of cultivation lines by exploiting the very high spatial resolution of drone data. Overall, the use of UAV remote sensing proved to be a promising approach for seaweed monitoring, opening the way to precision aquaculture of Kappaphycus.
Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models
Authors: Zhang Wenting , Hu Bin , Liu Yongzhi , Zhang Xingnan , Li Zhixuan
Published: July 2023

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.
A Thorough Evaluation of 127 Potential Evapotranspiration Models in Two Mediterranean Urban Green Sites
Authors: Proutsos Nikolaos , Tigkas Dimitris , Tsevreni Irida , Alexandris Stavros G. , Solomou Alexandra D. , Bourletsikas Athanassios , Stefanidis Stefanos , Nwokolo Samuel Chukwujindu
Published: July 2023

Potential evapotranspiration (PET) is a particularly important parameter for understanding water interactions and balance in ecosystems, while it is also crucial for assessing vegetation water requirements. The accurate estimation of PET is typically data demanding, while specific climatic, geographical and local factors may further complicate this task. Especially in city environments, where built-up structures may highly influence the micrometeorological conditions and urban green sites may occupy limited spaces, the selection of proper PET estimation approaches is critical, considering also data availability issues. In this study, a wide variety of empirical PET methods were evaluated against the FAO56 Penman–Monteith benchmark method in the environment of two Mediterranean urban green sites in Greece, aiming to investigate their accuracy and suitability under specific local conditions. The methods under evaluation cover all the range of empirical PET estimations: namely, mass transfer-based, temperature-based, radiation-based, and combination approaches, including 112 methods. Furthermore, 15 locally calibrated and adjusted models have been developed based on the general forms of the mass transfer, temperature, and radiation equations, improving the performance of the original models for local application. Among the 127 (112 original and 15 adjusted) evaluated methods, the radiation-based methods and adjusted models performed overall better than the temperature-based and the mass transfer methods, whereas the data-demanding combination methods received the highest ranking scores. The adjusted models seem to give accurate PET estimates for local use, while they might be applied in sites with similar conditions after proper validation.
Surface Displacement of Hurd Rock Glacier from 1956 to 2019 from Historical Aerial Frames and Satellite Imagery (Livingston Island, Antarctic Peninsula)
Authors: Prates Gonçalo , Vieira Gonçalo
Published: July 2023

In the second half of the 20th century, the western Antarctic Peninsula recorded the highest mean annual air temperature rise in the Antarctic. The South Shetland Islands are located about 100 km northwest of the Antarctic Peninsula. The mean annual air temperature at sea level in this Maritime Antarctic region is close to −2 °C and, therefore, very sensitive to permafrost degradation following atmospheric warming. Among geomorphological indicators of permafrost are rock glaciers found below steep slopes as a consequence of permafrost creep, but with surficial movement also generated by solifluction and shallow landslides of rock debris and finer sediments. Rock glacier surface velocity is a new essential climate variable parameter by the Global Climate Observing System, and its historical analysis allows insight into past permafrost behavior. Recovery of 1950s aerial image stereo-pairs and structure-from-motion processing, together with the analysis of QuickBird 2007 and Pleiades 2019 high-resolution satellite imagery, allowed inferring displacements of the Hurd rock glacier using compression ridge-and-furrow morphology analysis over 60 years. Displacements measured on the rock glacier surface from 1956 until 2019 were from 7.5 m to 22.5 m and surface velocity of 12 cm/year to 36 cm/year, measured on orthographic images, with combined deviation root-mean-square of 2.5 m and 2.4 m in easting and northing. The inferred surface velocity also provides a baseline reference to assess today’s displacements. The results show patterns of the Hurd rock glacier displacement velocity, which are analogous to those reported within the last decade, without being possible to assess any displacement acceleration.
Experimental Guesswork with Quantum Side Information Using Twisted Light
Authors: Katariya Vishal , Bhusal Narayan , You Chenglong
Published: July 2023

Guesswork is an information–theoretic quantity which can be seen as an alternate security criterion to entropy. Recent work has established the theoretical framework for guesswork in the presence of quantum side information, which we extend both theoretically and experimentally. We consider guesswork when the side information consists of the BB84 states and their higher-dimensional generalizations. With this side information, we compute the guesswork for two different scenarios for each dimension. We then performed a proof-of-principle experiment using Laguerre–Gauss modes to experimentally compute the guesswork for higher-dimensional generalizations of the BB84 states. We find that our experimental results agree closely with our theoretical predictions. This work shows that guesswork can be a viable security criterion in cryptographic tasks and is experimentally accessible in a number of optical setups.
Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision
Authors: Xie Shuang , Sun Hongwei
Published: July 2023

Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model’s ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model’s parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment.
Dual-Gate Organic Thin-Film Transistor and Multiplexer Chips for the Next Generation of Flexible EG-ISFET Sensor Chips
Authors: Rezaee Ashkan , Carrabina Jordi
Published: July 2023

Ion-sensitive field-effect transistors (ISFETs) are used as elementary devices to build many types of chemical sensors and biosensors. Organic thin-film transistor (OTFT) ISFETs use either small molecules or polymers as semiconductors together with an additive manufacturing process of much lower cost than standard silicon sensors and have the additional advantage of being environmentally friendly. OTFT ISFETs’ drawbacks include limited sensitivity and higher variability. In this paper, we propose a novel design technique for integrating extended-gate OTFT ISFETs (OTFT EG-ISFETs) together with dual-gate OTFT multiplexers (MUXs) made in the same process. The achieved results show that our OTFT ISFET sensors are of the state of the art of the literature. Our microsystem architecture enables switching between the different ISFETs implemented in the chip. In the case of sensors with the same gain, we have a fault-tolerant architecture since we are able to replace the faulty sensor with a fault-free one on the chip. For a chip including sensors with different gains, an external processor can select the sensor with the required sensitivity.
Comparing the Lower-Limb Muscle Activation Patterns of Simulated Walking Using an End-Effector-Type Robot with Real Level and Stair Walking in Children with Spastic Bilateral Cerebral Palsy
Authors: Ahn Yongjin , Hong Juntaek , Shim Dain , Choi Joong-on , Rha Dong-wook
Published: July 2023

Cerebral palsy is a neurologic disorder caused by lesions on an immature brain, often resulting in spasticity and gait abnormality. This study aimed to compare the muscle activation patterns of real level and stair walking with those of simulated walking using an end-effector-type robot in children with spastic cerebral palsy. The electromyographic activities of the vastus lateralis, biceps femoris, tibialis anterior and medial gastrocnemius of nine children with spastic bilateral cerebral palsy were measured during gait using a wireless surface EMG device. Morning walk was used for the simulated gait. Differences in the muscle activation patterns between the real and simulated gait conditions were analyzed. In the loading response, all four muscles showed reduced activity during two simulated conditions. In mid-stance, mGCM showed reduced activity during simulated conditions, whereas BFem showed greater activity during simulated level walking. In the swing phase, BFem and TAnt activity was reduced during the simulated conditions. The onset–offset of the VLat, BFem and TAnt activity was significantly delayed during simulated versus real level walking. No differences in activity onset–offset were observed between the simulated level and stair conditions. In conclusion, the robot-simulated gait showed differences in its muscle activation patterns compared with the real gait conditions, which must be considered for gait training using an end-effector-type robot.
End-to-End Bubble Size Distribution Detection Technique in Dense Bubbly Flows Based on You Only Look Once Architecture
Authors: Chen Mengchi , Zhang Cheng , Yang Wen , Zhang Suyi , Huang Wenjun
Published: July 2023

Accurate measurements of the bubble size distribution (BSD) are crucial for investigating gas–liquid mass transfer mechanisms and describing the characteristics of chemical production. However, measuring the BSD in high-density bubbly flows remains challenging due to limited image algorithms and high data densities. Therefore, an end-to-end BSD detection method in dense bubbly flows based on deep learning is proposed in this paper. The bubble detector locates the positions of dense bubbles utilizing objection detection networks and simultaneously performs ellipse parameter fitting to measure the size of the bubbles. Different You Only Look Once (YOLO) architectures are compared, and YOLOv7 is selected as the backbone network. The complete intersection over union calculation method is modified by the circumferential horizontal rectangle of bubbles, and the loss function is optimized by adding L2 constraints of ellipse size parameters. The experimental results show that the proposed technique surpasses existing methods in terms of precision, recall, and mean square error, achieving values of 0.9871, 0.8725, and 3.8299, respectively. The proposed technique demonstrates high efficiency and accuracy when measuring BSDs in high-density bubbly flows and has the potential for practical applications.
Wideband Array Signal Processing with Real-Time Adaptive Interference Mitigation
Authors: Whipple Adam , Ruzindana Mark W. , Burnett Mitchell C. , Kunzler Jakob W. , Lyman Kayla , Jeffs Brian D. , Warnick Karl F.
Published: July 2023

Wideband beamforming and interference cancellation for phased array antennas requires advances in signal processing algorithms, software, and specialized hardware platforms. A high-throughput array receiver has been developed that enables communication in radio frequency interference-rich environments with field programmable gate array (FPGA)-based frequency channelization and packetization. In this study, a real-time interference mitigation algorithm was implemented on graphics processing units (GPUs) contained in the data pipeline. The key contribution is a hardware and software pipeline for subchannelized wideband array signal processing with 150 MHz instantaneous bandwidth and interference cancellation with a heterogeneous, distributed, and scaleable digital signal processing (DSP) architecture that achieves 30 dB interferer cancellation null depth in real time with a moving interference source.
Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models
Authors: Horry Michael J. , Chakraborty Subrata , Pradhan Biswajeet , Paul Manoranjan , Zhu Jing , Loh Hui Wen , Barua Prabal Datta , Acharya U. Rajendra
Published: July 2023

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
Few-Shot Learning-Based Light-Weight WDCNN Model for Bearing Fault Diagnosis in Siamese Network
Authors: Lee Daehwan , Jeong Jongpil
Published: July 2023

In this study, bearing fault diagnosis is performed with a small amount of data through few-shot learning. Recently, a fault diagnosis method based on deep learning has achieved promising results. Most studies required numerous training samples for fault diagnosis. However, at manufacturing sites, it is impossible to have enough training samples to represent all fault types under all operating conditions. In addition, most studies consider only accuracy, and models are complex and computationally expensive. Research that only considers accuracy is inefficient since manufacturing sites change rapidly. Therefore, in this study, we propose a few-shot learning model that can effectively learn with small data. In addition, a Depthwise Separable Convolution layer that can effectively reduce parameters is used together. In order to find an efficient model, the optimal hyperparameters were found by adjusting the number of blocks and hyperparameters, and by using a Depthwise Separable Convolution layer for the optimal hyperparameters, it showed higher accuracy and fewer parameters than the existing model.
A Novel Method for Developing Thin Resin Scintillator Screens and Application in an X-ray CMOS Imaging Sensor
Authors: Linardatos Dionysios , Fountos George , Valais Ioannis , Michail Christos
Published: July 2023

Scintillating screens for X-ray imaging applications are prepared with various methods. Among them, the classic sedimentation method presents certain weak points. In this context, a novel fabrication process was developed that offers simplicity, economy of resources and time, while the screens exhibit adequate durability and image quality performance. The proposed technique involves a resin mixture that contains the phosphor in powder form (Gd2O2S:Tb in the present work) and graphite. The novel method was optimized and validated by coupling the screens to a complementary metal oxide semiconductor (CMOS) X-ray sensor. Indicatively, screens of two surface densities were examined; 34 mg/cm2 and 70 mg/cm2. Various established image quality metrics were calculated following the IEC 62220-1 international standard, including the detective quantum efficiency (DQE). Comparisons were carried out under the same conditions, with a sedimentation screen reported previously and a screen of wide commercial circulation (Carestream Min-R 2190). The novel screens exhibit has comparable or even better performance in image-quality metrics. The 34 mg/cm2 screen achieves a DQE 15–20% greater than its comparison counterpart, and its limiting resolution was 5.3 cycles/mm. The detector coupled to the 70 mg/cm2 screen achieved a DQE 10–24% greater than its own counterpart, and its limiting resolution was found to be 5.4 cycles/mm.
Authenticated Public Key Elliptic Curve Based on Deep Convolutional Neural Network for Cybersecurity Image Encryption Application
Authors: Hagras Esam A. A. , Aldosary Saad , Khaled Haitham , Hassan Tarek M.
Published: July 2023

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks.
Comparison of Microwave Hyperthermia Applicator Designs with Fora Dipole and Connected Array
Authors: Yildiz Gulsah , Farhat Iman , Farrugia Lourdes , Bonello Julian , Zarb-Adami Kristian , Sammut Charles V. , Yilmaz Tuba , Akduman Ibrahim
Published: July 2023

In microwave hyperthermia tumor therapy, electromagnetic waves focus energy on the tumor to elevate the temperature above its normal levels with minimal injury to the surrounding healthy tissue. Microwave hyperthermia applicator design is important for the effectiveness of the therapy and the feasibility of real-time application. In this study, the potential of using fractal octagonal ring antenna elements as a dipole antenna array and as a connected array at 2.45 GHz for breast tumor hyperthermia application was investigated. Microwave hyperthermia treatment models consisting of different fractal octagonal ring antenna array designs and a breast phantom are simulated in COMSOL Multiphysics to obtain the field distributions. The antenna excitation phases and magnitudes are optimized using the global particle swarm algorithm to selectively increase the specific absorption rate at the target region while minimizing hot spots in other regions within the breast. Specific absorption rate distributions, obtained inside the phantom, are analyzed for each proposed microwave hyperthermia applicator design. The dipole fractal octagonal ring antenna arrays are comparatively assessed for three different designs: circular, linear, and Cross—array. The 16-antenna dipole array performance was superior for all three 1-layer applicator designs, and no distinct difference was found between 16-antenna circular, linear, or cross arrays. Two-layer dipole arrays have better performance in the deep-tissue targets than one-layer arrays. The performance of the connected array with a higher number of layers exceeds the performance of the dipole arrays in the superficial regions, while they are comparable for deep regions of the breast. The 1-layer 12-antenna circular FORA dipole array feasibility as a microwave hyperthermia applicator was experimentally shown.
A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time
Authors: Al-Rubaii Mohammed , Al-Shargabi Mohammed , Aldahlawi Bayan , Al-Shehri Dhafer , Minaev Konstantin M.
Published: July 2023

When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECDeffc.m and MWeffc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software.
Use of Virtual Reality-Based Games to Improve Balance and Gait of Children and Adolescents with Sensorineural Hearing Loss: A Systematic Review and Meta-Analysis
Authors: Melo Renato S. , Lemos Andrea , Delgado Alexandre , Raposo Maria Cristina Falcão , Ferraz Karla Mônica , Belian Rosalie Barreto
Published: July 2023

Background: Children and adolescents with sensorineural hearing loss (SNHL) often experience motor skill disturbances, particularly in balance and gait, due to potential vestibular dysfunctions resulting from inner ear damage. Consequently, several studies have proposed the use of virtual reality-based games as a technological resource for therapeutic purposes, aiming to improve the balance and gait of this population. Objective: The objective of this systematic review is to evaluate the quality of evidence derived from randomized or quasi-randomized controlled trials that employed virtual reality-based games to enhance the balance and/or gait of children and adolescents with SNHL. Methods: A comprehensive search was conducted across nine databases, encompassing articles published in any language until 1 July 2023. The following inclusion criteria were applied: randomized or quasi-randomized controlled trials involving volunteers from both groups with a clinical diagnosis of bilateral SNHL, aged 6–19 years, devoid of physical, cognitive, or neurological deficits other than vestibular dysfunction, and utilizing virtual reality-based games as an intervention to improve balance and/or gait outcomes. Results: Initially, a total of 5984 articles were identified through the searches. Following the removal of duplicates and screening of titles and abstracts, eight studies remained for full reading, out of which three trials met the eligibility criteria for this systematic review. The included trials exhibited a very low quality of evidence concerning the balance outcome, and none of the trials evaluated gait. The meta-analysis did not reveal significant differences in balance improvement between the use of traditional balance exercises and virtual reality-based games for adolescents with SNHL (effect size: −0.48; [CI: −1.54 to 0.57]; p = 0.37; I2 = 0%). Conclusion: Virtual reality-based games show promise as a potential technology to be included among the therapeutic options for rehabilitating the balance of children and adolescents with SNHL. However, given the methodological limitations of the trials and the overall low quality of evidence currently available on this topic, caution should be exercised when interpreting the results of the trials analyzed in this systematic review.
GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems
Authors: Abboush Mohammad , Knieke Christoph , Rausch Andreas
Published: July 2023

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.
Learning Modality Complementary Features with Mixed Attention Mechanism for RGB-T Tracking
Authors: Luo Yang , Guo Xiqing , Dong Mingtao , Yu Jin
Published: July 2023

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention mechanism to achieve a complementary fusion of modalities (referred to as MACFT) is proposed in this paper. In the feature extraction stage, we utilize different transformer backbone branches to extract specific and shared information from different modalities. By performing mixed-attention operations in the backbone to enable information interaction and self-enhancement between the template and search images, a robust feature representation is constructed that better understands the high-level semantic features of the target. Then, in the feature fusion stage, a modality shared-specific feature interaction structure was designed based on a mixed-attention mechanism, effectively suppressing low-quality modality noise while enhancing the information from the dominant modality. Evaluation on multiple RGB-T public datasets demonstrates that our proposed tracker outperforms other RGB-T trackers on general evaluation metrics while also being able to adapt to long-term tracking scenarios.