bluuebunny/crossref_metadata_2025 huggingface dataset:
Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children?
Authors: Cinteza Eliza , Vasile Corina Maria , Busnatu Stefan , Armat Ionel , Spinu Arsenie Dan , Vatasescu Radu , Duica Gabriela , Nicolescu Alin
Published: January 2024

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike.
Coexistence of Left Atrial Tumor and Lung Cancer—The Key Role of an Individualized Approach
Authors: Hawryszko Maja , Sławiński Grzegorz , Jankowska Hanna , Dorniak Karolina , Kochańska Anna , Daniłowicz-Szymanowicz Ludmiła , Lewicka Ewa
Published: January 2024

During the diagnostic work-up in oncology, it is exceedingly rare to assume a concomitant presence of two cancers, a benign one and a malignant one, in a single patient. A 61-year-old man was admitted to the cardiology department for cardiac evaluation prior to planned radical treatment of non-small cell (NSCLC) left lung cancer (cT3N1M0). Echocardiography revealed a prominent, unpedunculated structure, measuring 17 × 14 mm, located in the left atrium (LA) near the fossa ovalis. The tumor was confirmed via cardiac magnetic resonance (CMR) imaging, which showed the radiological features of an atrial myxoma. The patient consulted with the Cardiac Surgery Department and was deemed ineligible for surgical treatment of a lesion with mucinous features; thus, no definitive histopathologic confirmation of the tumor present was possible. He was then successfully treated with radical radiochemotherapy and immunotherapy. During the 2-year follow-up, regular echocardiography and CMR were performed, which documented a stable LA tumor size.
Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation
Authors: Lin Pao-Chun , Chang Wei-Shan , Hsiao Kai-Yuan , Liu Hon-Man , Shia Ben-Chang , Chen Ming-Chih , Hsieh Po-Yu , Lai Tseng-Wei , Lin Feng-Huei , Chang Che-Cheng
Published: January 2024

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.
Staging Computed Tomography Parameters Predict the Need for Vein Resection during Pancreaticoduodenectomy in Resectable Pancreatic Ductal Adenocarcinoma
Authors: Pande Rupaly , Liu Wingyan , Raza Syed S. , Papamichail Michail , Suthananthan Arul E. , Bartlett David C. , Marudanayagam Ravi , Dasari Bobby V. M. , Sutcliffe Robert P. , Roberts Keith J. , Wadhwani Sharan , Chatzizacharias Nikolaos
Published: January 2024

Background: Surgery-first approach is the current standard of care for resectable pancreatic ductal adenocarcinoma (PDAC), and a proportion of these cases will require venous resection. This study aimed to identify parameters on staging computed tomography (CT) that predict the need for venous resection during pancreaticoduodenectomy (PD) for resectable PDAC. Methods: We conducted a retrospective analysis of prospectively collected data on patients who underwent PD for resectable staged PDAC (as per NCCN criteria) between 2011 and 2020. Staging CTs were independently reviewed by two specialist radiologists blinded to the clinical outcomes. Univariate and multivariate risk analyses were performed. Results: In total, 296 PDs were included. Venous resection was performed in 62 (21%) cases. There was a higher rate of resection margin positivity in the vein resection group (72.6% vs. 48.7%, p = 0.001). Tumour at the neck of the pancreas, superior mesenteric vein involvement of ≥10 mm and pancreatic duct dilatation were identified as independent predictors for venous resection. Discussion: Staging CT parameters can predict the need for venous resection during PD for resectable cases of PDAC. This may assist in surgical planning, patient selection and counselling. Future efforts should concentrate on validating these results or identifying additional predictors in a multicentre and prospective setting.
Contemporary Evidence and Practice on Right Heart Catheterization in Patients with Acute or Chronic Heart Failure
Authors: Manzi Lina , Sperandeo Luca , Forzano Imma , Castiello Domenico Simone , Florimonte Domenico , Paolillo Roberta , Santoro Ciro , Mancusi Costantino , Di Serafino Luigi , Esposito Giovanni , Gargiulo Giuseppe
Published: January 2024

Heart failure (HF) has a global prevalence of 1–2%, and the incidence around the world is growing. The prevalence increases with age, from around 1% for those aged <55 years to >10% for those aged 70 years or over. Based on studies in hospitalized patients, about 50% of patients have heart failure with reduced ejection fraction (HFrEF), and 50% have heart failure with preserved ejection fraction (HFpEF). HF is associated with high morbidity and mortality, and HF-related hospitalizations are common, costly, and impact both quality of life and prognosis. More than 5–10% of patients deteriorate into advanced HF (AdHF) with worse outcomes, up to cardiogenic shock (CS) condition. Right heart catheterization (RHC) is essential to assess hemodynamics in the diagnosis and care of patients with HF. The aim of this article is to review the evidence on RHC in various clinical scenarios of patients with HF.
Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences
Authors: Singh Rahul Kumar , Nayak Nirlipta Priyadarshini , Behl Tapan , Arora Rashmi , Anwer Md. Khalid , Gulati Monica , Bungau Simona Gabriela , Brisc Mihaela Cristina
Published: January 2024

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth’s subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.
Comparison of Two Strategies for Hypercholesterolemia Detection through Point-of-Care Testing
Authors: Arriaga-Cázares Héctor Eliud , Vega-Morales David , Moreno-Treviño Carlos Alberto , Juarez-Juarez Juana Lorena , Pérez-Arizmendi Carlos Azael , Martagón-Rosado Alexandro J.
Published: January 2024

Background: Childhood dyslipidemia is a common condition that can lead to atherosclerotic cardiovascular disease in adulthood. It is usually multifactorial. Screening for cholesterol disorders in children varies based on risk factors, with some guidelines recommending cascade screening for children with a clear family history of familial hypercholesterolemia, targeted screening for those with specific risk factors, and universal screening. Point-of-care testing (POCT) cholesterol tests offer potential advantages, including ease of use, portability, increased patient access, low cost, fewer medical or laboratory visits, and instant results. This study aimed to evaluate the effect of POCT cholesterol screening on the diagnosis of hypercholesterolemia in children in a family practice setting. Methods: We used a POCT cholesterol analyzer to perform two different (universal and targeted) screening approaches for dyslipidemia in children. We used the NCEP guidelines for the classification of the results. Results: We screened 183 children, 105 in the universal screening group and 78 in the targeted screening group. Eight patients in the targeted screening group had elevated cholesterol levels (p = 0.02). Conclusions: All participants received instant feedback and recommendations. Using a targeted screening approach, POCT could be a practical and effective tool for identifying at-risk children with hypercholesterolemia.
Positive Echocardiographic Association between Carotid Artery and Coronary Artery Diameter and Z-Score in a Mouse Model of Kawasaki Disease
Authors: Shih Wen-Ling , Yeh Tsung-Ming , Chen Kuang-Den , Leu Steve , Kuo Ho-Chang
Published: January 2024

Kawasaki disease (KD) occurs in young children, has an unknown etiology, and can cause such life-threatening complications as coronary artery aneurysm. A mouse model using Lactobacillus casei cell wall extract (LCWE) with intraperitoneal injection was established for KD years ago. Histological examination of coronary artery lesions indicated features similar to those of vascular lesions of patients with KD. Since animals must be sacrificed during histological examination, the longitudinal survey of coronary artery lesions (CALs) is difficult. The aim of this study was to survey the vasculitis status of the coronary artery and the carotid artery in a KD mouse model. Method: LCWE was intraperitoneally injected into 5-week-old male C57BL/6 mice to induce CALs. We studied the longitudinal status of the carotid and coronary arteries and analyzed the Z-score of coronary artery diameter. Results: Carotid artery wall thickness (day 7) and diameter (day 14) significantly increased in the LCWE group with a dose-dependent effect (p < 0.05). Aortic diameter and wall thickness demonstrated significant increases on day 28 and day 7, respectively (p < 0.05). Carotid artery outer diameter and wall thickness were positively associated with coronary artery diameter on day 28 (p < 0.01). Coronary artery diameter significantly increased in the LCWE group after day 7 (p < 0.05). The percentage of Z > 3.0 indicated was more than 80% in the high-dose LCWE group and 0% in the control group. Conclusions: This report is the first to use coronary artery Z-score in a mouse model of KD by echocardiography and to find a positive association between carotid artery and coronary artery diameter.
Epidemiology of Bronchiolitis and Respiratory Syncytial Virus and Analysis of Length of Stay from 2015 to 2022: Retrospective Observational Study of Hospital Discharge Records from an Italian Southern Province before and during the COVID-19 Pandemic
Authors: Cedrone Fabrizio , Montagna Vincenzo , Del Duca Livio , Camplone Laura , Mazzocca Riccardo , Carfagnini Federica , Ancona Angela , Santangelo Omar Enzo , Fortunato Valterio , Di Martino Giuseppe
Published: January 2024

Background: Severe respiratory infections, including pneumonia or bronchiolitis, caused by RSV can range from mild upper respiratory tract infections to those leading to hospitalization and serious complications such as respiratory failure in children. High-risk groups, such as premature infants and infants with underlying medical conditions, have a higher susceptibility to severe RSV disease. We conducted a retrospective study from years 2015 to 2022 in the Local Health Authority (LHA) of Pescara that counts about 320,000 inhabitants, with the aim to evaluate the burden of RSV infection, focusing on the incidence, hospitalization, and characteristics that may prolong hospital stays. Methods: All hospitalizations from 2015 to 2022 were extracted from the hospital discharge record. The monthly hospitalization rates were calculated and standardized by gender and age for the population resident in the Province of Pescara on 1 January 2015. Results: During the study period, 31,837 admissions were reported among patients aged less than 6 years. Of those, 520 hospitalizations were referred for bronchiolitis. Monthly admission rates highlighted the seasonality of bronchiolitis admissions, with higher rates in the months from December to March in all study years included. The winter seasons of years 2021 and 2022 reported a surge in bronchiolitis incidence, with a rate of 4.0/1000 (95% CI 2.964–5.146) in December 2021 and 4.0 (95% CI 2.891–5.020) in December 2022. Conclusions: Bronchiolitis represents an important cause of hospitalization among patients aged less than 6 years. The incidence was particularly increased during the winter seasons in years 2021 and 2022.
Efficacy and Safety of Propolis for Treating Recurrent Aphthous Stomatitis (RAS): A Systematic Review and Meta-Analysis
Authors: Roberts Tina , Kallon Idriss Ibrahim , Schoonees Anel
Published: January 2024

The systematic review assessed the efficacy and safety of propolis for treating recurrent aphthous stomatitis (RAS). The review adopted the PICO framework to examine the effects of topical and systemic propolis on RAS while also comparing it to established treatments, placebos, or no treatment. The main focus was on the healing time, pain levels, adverse effects, the likelihood of ulcer recurrence, and accompanying symptoms such as redness. The team included randomised controlled trials (RCTs) and quasi-randomised trials, excluding case reports and studies on oral ulcers other than RAS. In May 2022, the review team comprehensively searched nine databases and trial registries following the PRISMA guidelines. The protocol was registered in the PROSPERO database under the registration number CRD42022327123. Two review authors conducted a comprehensive and autonomous search for pertinent papers and extracted essential data. Where data permitted, the team utilised Review Manager 5 to conduct a random-effects meta-analysis, assessing the risk of bias and heterogeneity of the included studies. Where possible, the GRADE Pro programme was used to assess the certainty of the evidence for all the outcomes. This review included 10 RCTs, comprising 825 participants aged between 18 and 69 years. Seven studies evaluated the efficacy and safety of propolis when applied topically, all of which used different formulations, concentrations, and carriers. The remaining three studies assessed systemic administration in tablet form. The duration of investigations ranged from 5 days to 3 years. The review team classified two studies as having an overall ‘high risk’ of bias, while the remaining studies were categorised as having an overall ‘uncertain risk’. The overall certainty of the evidence was ‘very low’. The results indicate that topical and systemic propolis may decrease the duration of healing, alleviate pain, and reduce redness in patients with RAS compared to a placebo. However, the certainty of the evidence is very low. These may be due to the high risk of bias, substantial heterogeneity, and limited sample sizes in the included studies. For these reasons, the results of this review should be interpreted with caution. Nevertheless, the limited number of adverse effects observed suggests that propolis may have a favourable safety profile when used for a short period in treating RAS.
Bilayer LDPC Codes Combined with Perturbed Decoding for MLC NAND Flash Memory
Authors: Kong Lingjun , Liu Haiyang , Hou Wentao , Meng Chao
Published: January 2024

This paper presents a coding scheme based on bilayer low-density parity-check (LDPC) codes for multi-level cell (MLC) NAND flash memory. The main feature of the proposed scheme is that it exploits the asymmetric properties of an MLC flash channel and stores the extra parity-check bits in the lower page, which are activated only after the decoding failure of the upper page. To further improve the performance of the error correction, a perturbation process based on the genetic algorithm (GA) is incorporated into the decoding process of the proposed coding scheme, which can convert uncorrectable read sequences into error-correctable regions of the corresponding decoding space by introducing GA-trained noises. The perturbation decoding process is particularly efficient at low program-and-erase (P/E) cycle regions. The simulation results suggest that the proposed bilayer LDPC coding scheme can extend the lifetime of MLC NAND flash memory up to 10,000 P/E cycles. The proposed scheme can achieve a better balance between performance and complexity than traditional single LDPC coding schemes. All of these findings indicate that the proposed coding scheme is suitable for practical purposes in MLC NAND flash memory.
(Dis)connection between Multiculturalism, Higher Education and Health: Experiences of International Students in Portugal during the Covid-19 Pandemic
Authors: Iorio Juliana Chatti , Silva Katielle
Published: January 2024

Multiculturalism may point to the idea of ‘cohesion in diversity’, but if differentiated institutional policies and practices do not accompany it, it may contribute to promoting or deepening inequalities between different groups/cultures. Given the increase in students’ international mobility, multicultural awareness in Higher Education Institutions (HEIs) must be considered. Based on this idea, this article aims to answer: (i) what were the main health problems (physical and mental) that international students (IS) in Portugal experienced during the COVID-19 pandemic; and (ii) how they perceived their health rights in Portugal. To this end, we used data from an online survey, which was made available to all IS enrolled in a Portuguese HEI between April and May 2020, and from 22 online interviews conducted between September 2020 and January 2021 with some of these students. The results showed that the pandemic impacted the mental health of IS; fear, anxiety and stress were some of the symptoms experienced. Portuguese public health services have been perceived as untrustworthy by IS, and some students’ experience reinforces the untrustworthiness. The research shows the (dis)connection between multiculturalism, health, and HEIs. While HEIs promote multiculturalism, they have failed to provide answers in the field of health that take into account the specificities of their different student groups.
Shaping Entrepreneurial Attitudes among Young Children on the Basis of the “Entrepreneurial Kids” International Project
Authors: Chojak Małgorzata
Published: January 2024

Entrepreneurship is one of the key competencies that should be developed in the educational process. However, in the form of a specific subject with particular objectives and outcomes, it only appears in secondary school. Younger children are involved in direct or indirect entrepreneurial activities in a variety of activities. However, these are not planned and not evaluated. Preschool and early primary school children also cannot benefit from the offer of projects or competitions outside of school due to the limited number of opportunities and because they often focus on economic aspects rather than the development of soft skills, which in children aged 3–10 seem to be more important for their overall development. This article presents an example of an initiative that can fill these gaps, i.e., the “Entrepreneurial Kids” project. The theoretical foundations of this project, its course, and its results to date are discussed.
Three Approaches to Using Mixed Reality Simulations for Teacher Preparation and Recruitment of Future Teachers
Authors: Budin Shannon
Published: January 2024

Mixed reality simulations have versatility in both the preparation as well as the recruitment of future teachers. We describe three approaches that have led to successful outcomes for general and special education preservice teachers, as well as in the recruitment of future teacher candidates from the high school level. First, we explored the use of TeachLivE™ to enhance special education teacher candidates’ behavior observation skills when conducting functional behavior assessments. Second, we studied the impact of different simulation methods on early childhood candidates’ preparedness to communicate with parents and caregivers about sensitive topics. Finally, we utilized TeachLivE™ as a recruitment tool to engage current high-achieving high school students in the teaching and reflection process as part of their on-campus dual credit program. In all cases, candidates and students valued the simulation experiences, recognizing the opportunity to deliver instruction in supportive environments with immediate feedback, coaching, and practice opportunities.
Camera–LiDAR Calibration Using Iterative Random Sampling and Intersection Line-Based Quality Evaluation
Authors: Yoo Ju Hee , Jung Gu Beom , Jung Ho Gi , Suhr Jae Kyu
Published: January 2024

This paper proposes a novel camera–LiDAR calibration method that utilizes an iterative random sampling and intersection line-based quality evaluation using a foldable plane pair. Firstly, this paper suggests using a calibration object consisting of two small planes with ChArUco patterns, which is easy to make and convenient to carry. Secondly, the proposed method adopts an iterative random sampling to make the calibration procedure robust against sensor data noise and incorrect object recognition. Lastly, this paper proposes a novel quality evaluation method based on the dissimilarity between two intersection lines of the plane pairs from the two sensors. Thus, the proposed method repeats random sampling of sensor data, extrinsic parameter estimation, and quality evaluation of the estimation result in order to determine the most appropriate calibration result. Furthermore, this method can also be used for the LiDAR–LiDAR calibration with a slight modification. In experiments, the proposed method was quantitively evaluated using simulation data and qualitatively assessed using real-world data. The experimental results show that the proposed method can successfully perform both camera–LiDAR and LiDAR–LiDAR calibrations while outperforming the previous approaches.
Edge-Computing-Enabled Abnormal Activity Recognition for Visual Surveillance
Authors: Ali Musrrat , Goyal Lakshay , Sharma Chandra Mani , Kumar Sanoj
Published: January 2024

Due to the ever increasing number of closed circuit television (CCTV) cameras worldwide, it is the need of the hour to automate the screening of video content. Still, the majority of video content is manually screened to detect some anomalous incidence or activity. Automatic abnormal event detection such as theft, burglary, or accidents may be helpful in many situations. However, there are significant difficulties in processing video data acquired by several cameras at a central location, such as bandwidth, latency, large computing resource needs, and so on. To address this issue, an edge-based visual surveillance technique has been implemented, in which video analytics are performed on the edge nodes to detect aberrant incidents in the video stream. Various deep learning models were trained to distinguish 13 different categories of aberrant incidences in video. A customized Bi-LSTM model outperforms existing cutting-edge approaches. This approach is used on edge nodes to process video locally. The user can receive analytics reports and notifications. The experimental findings suggest that the proposed system is appropriate for visual surveillance with increased accuracy and lower cost and processing resources.
An Optimized Inversion Method for Hyperspectral Image Fusion Based on a Hue–Intensity–Saturation, Wavelet, and Trust-Region Conjugate Gradient Method
Authors: Wu Jiangbo , Ge Aiming , Liu Shuo , Wang Qiuyang , Zhu Dongsheng , Chen Xindi
Published: January 2024

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods to hyperspectral image fusion and present an innovative approach for hyperspectral image fusion which combines the Hue–Intensity–Saturation (HIS) transform, the wavelet transform, and the Trust-Region Conjugate Gradient technique. This amalgamation not only refines spatial precision but also augments spectral faithfulness, which is a pivotal aspect for applications like precise object detection and classification. In the context of our investigation, we conducted a thorough validation of our proposed HIS, Wavelet, and Trust-Region Conjugate Gradient (TRCG-HW) method for image fusion using a comprehensive suite of evaluation metrics. These metrics encompassed the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), and Error Relative Global Accuracy Score (ERGAS). The findings incontrovertibly establish TRCG-HW as the preeminent method among those considered. Our study effectively tackles the pressing predicament of low spatial resolution encountered in hyperspectral imaging. This innovative paradigm harbors the potential to revolutionize high-resolution hyperspectral data acquisition, propelling the field of hyperspectral remote sensing forward and efficiently catering to crucial application.
Research on the Construction of an Efficient and Lightweight Online Detection Method for Tiny Surface Defects through Model Compression and Knowledge Distillation
Authors: Chen Qipeng , Xiong Qiaoqiao , Huang Haisong , Tang Saihong , Liu Zhenghong
Published: January 2024

In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression and knowledge distillation is proposed. Firstly, data augmentation is employed in the preprocessing stage to increase the diversity of training samples, thereby improving the model’s robustness and generalization capability. The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. Secondly, the cross stage partial (CSP) Darknet53 network and spatial pyramid pooling (SPP) module extract features from the input raw images, enhancing the accuracy of defect location detection and recognition in YOLO. Finally, the concept of model compression is integrated, utilizing scaling factors in the batch normalization (BN) layer, and introducing sparse factors to perform sparse training on the network. Channel pruning and layer pruning are applied to the sparse model, and post-processing methods using knowledge distillation are used to effectively reduce the model size and forward inference time while maintaining model accuracy. The improved model size decreases from 244 M to 4.19 M, the detection speed increases from 32.8 f/s to 68 f/s, and mAP reaches 97.41. Experimental results demonstrate that this method is conducive to deploying network models on embedded devices with limited GPU computing and storage resources. It can be applied in distributed service architectures for edge computing, providing new technological references for deploying deep learning models in the industrial sector.
A Survey of Physical Layer Secret Key Generation Enhanced by Intelligent Reflecting Surface
Authors: Xia Enjun , Hu Bin-Jie , Shen Qiaoqiao
Published: January 2024

As wireless communication scenarios grow more complicated, security issues are becoming increasingly prominent and severe. In the Internet of Things and vehicle-to-everything scenarios, conventional cryptographic technology faces numerous challenges. These include difficulties in secret key distribution and management, low update rates of secret keys, and vulnerability to quantum attacks. Physical layer secret key generation is considered a promising solution to security issues. The perfect secrecy proposed by Shannon can be achieved by combining secret key generation and the one-time pad when the length of secret keys is equal to that of plaintext. Hence, it is important to increase secret key generation rates. Intelligent reflecting surfaces demonstrate great advantages in improving the secret key generation performance. This paper provides a comprehensive review of current research efforts related to secret key generation assisted by intelligent reflecting surfaces, which is divided into three main categories: introducing the randomness of intelligent reflecting surfaces, optimizing the reflecting coefficients, and designing probing protocols. Comparative results of existing optimization approaches are provided and discussed. Furthermore, we emphasize the significance of selecting a random source of secret key generation from the perspective of information theory. Finally, two significant application scenarios, the Industrial Internet of Things and vehicle-to-everything, are discussed, and some challenges and opportunities are presented.
Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep Learning
Authors: Nie Mingxing , Zou Liwei , Cui Hao , Zhou Xinhui , Wan Yaping
Published: January 2024

This paper introduces a novel approach for enhancing human activity recognition through the integration of LoRa wireless RF signal preprocessing and deep learning. We tackle the challenge of extracting features from intricate LoRa signals by scrutinizing the unique propagation process of linearly modulated LoRa signals—a critical aspect for effective feature extraction. Our preprocessing technique involves converting intricate data into real numbers, utilizing Short-Time Fourier Transform (STFT) to generate spectrograms, and incorporating differential signal processing (DSP) techniques to augment activity recognition accuracy. Additionally, we employ frequency-to-image conversion for the purpose of intuitive interpretation. In comprehensive experiments covering activity classification, identity recognition, room identification, and presence detection, our carefully selected deep learning models exhibit outstanding accuracy. Notably, ConvNext attains 96.7% accuracy in activity classification, 97.9% in identity recognition, and 97.3% in room identification. The Vision TF model excels with 98.5% accuracy in presence detection. Through leveraging LoRa signal characteristics and sophisticated preprocessing techniques, our transformative approach significantly enhances feature extraction, ensuring heightened accuracy and reliability in human activity recognition.
IRS Backscatter-Based Secrecy Enhancement against Active Eavesdropping
Authors: Miao Yuanyuan , Shao Yu , Zhang Jie
Published: January 2024

This paper proposes to combat active eavesdropping using intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits a jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS, and the allowable maximum the SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme. The proposed scheme is effective in combating active eavesdropping. Furthermore, the deployment of large-scale IRS significantly enhances the secrecy rate.
Efficient FPGA Binary Neural Network Architecture for Image Super-Resolution
Authors: Su Yuanxin , Seng Kah Phooi , Smith Jeremy , Ang Li Minn
Published: January 2024

Super-resolution systems refer to computer-based systems designed to enhance the quality of images or video by producing high-resolution renditions from low-resolution counterparts using computational algorithms and technologies. Various methods and techniques have been used in development of super-resolution systems. The development of Convolution Neural Networks (CNNs) and the Deep Learning (DL) methods have outperformed traditional methods. However, as models become increasingly deeper with wider receptive fields, the number of parameters significantly increases. While this often results in better performance, it renders these models impractical for real-life scenarios such as smartphones or other mobile systems. Currently, most proposed methods with higher perceptual quality demand a substantial amount of time to process a single image, even on powerful hardware like NVIDIA GPUs. Such computationally expensive models are not cost-effective for real-world application scenarios. Optimization is needed to reduce the computational costs and memory requirements to enhance their suitability for less powerful hardware configurations. In this work, we propose an efficient binary neural network architecture, ResBinESPCN, designed for image super-resolution. In our design, we improved the energy efficiency of the architecture through algorithmic and hardware-level optimizations. These optimizations not only enhance computational efficiency and reduce memory consumption but also achieve effective image super-resolution in resource-constrained environments. Our experimental validation highlights the effectiveness of this network structure and includes ablation studies on models with varying data bit widths. Hardware analysis substantiates the efficiency and real-time capabilities of this model. Additionally, deploying the model on FPGA using FINN demonstrates its low hardware resource usage and low power consumption.
Circuit and Signal Processing Section of Electronics: Editorial 2023
Authors: Psychalinos Costas
Published: January 2024

What follows is a brief history of the Circuit and Signal Processing Section of Electronics: In 2018, this Section was divided from Electronics [...]
Key Technologies for 6G-Enabled Smart Sustainable City
Authors: Kim Nahyun , Kim Gayeong , Shim Sunghoon , Jang Sukbin , Song Jiho , Lee Byungju
Published: January 2024

With the advancement of information and communication technologies (ICTs), the way we live and communicate with each other is changing rapidly. As urban environments continue to evolve, the smart sustainable city (SSC) has sparked considerable attention. We are hoping for a new era in which numerous devices and machines including vehicles, sensors, and robots are all connected to communicate, respond, and operate in real time. The next-generation communication system, the sixth generation (6G), is expected to play a crucial role in improving the efficiency of urban operations and services. In this paper, we first provide the recent trends and key features of standardization in the SSC. To make the future SSC, we highlight key candidate technologies of 6G such as non-terrestrial networks, advanced mobile edge computing, vision-aided wireless communication, artificial intelligence (AI)-based wireless communication, and integrated sensing and communication. We put forth the main technical challenges given to each prime technology along with the potential benefits to pave the way for 6G-enabled SSC. We further address how the potential benefits of prime technologies enable various urban practice cases for 6G-enabled SSC.
A Real-Time Image Stitching and Fusion Algorithm Circuit Design Based on FPGA
Authors: Jia Yu , Wang Ruibo , Jiang Xianyang
Published: January 2024

In the widely used field of panoramic image stitching, the key technologies mainly cover two parts, i.e., image registration and image fusion. In order to achieve low-cost and real-time processing, researchers often design dedicated circuits for various image stitching algorithms. Many studies focus on image registration algorithms and ignore image fusion algorithms, let alone dedicated circuit design. In addition, to reduce the ghosting effect and deformation caused by seams in stitching, finding the best seam line based on the overlapping area of the stitching image is crucial, which directly affects the quality of image stitching and fusion. In order to solve the above problems and achieve the efficient fusion of registered images, an image stitching and fusion algorithm circuit based on a dynamic programming algorithm to search for seam lines was proposed. Comprehensive experimental results and a theoretical analysis based on Cyclone IV FPGA devices show that, with a clock frequency of 100 MHz, the proposed circuit takes about 7.04 ms to carry out the fusion processing of two 486 × 643 images, and the corresponding frame rate is approximately 142 FPS, achieving a perfect real-time stitching effect and meeting the demand for real-time image processing. After a theoretical derivation and comparison with other similar works, its processing speed is better than four state-of-the-art implementations.
Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine
Authors: Rojek Izabela , Kotlarz Piotr , Kozielski Mirosław , Jagodziński Mieczysław , Królikowski Zbyszko
Published: January 2024

The future paradigm of early cardiac diagnostics is shifting the focus towards heart attack preventive medicine based on non-invasive medical imaging with the support of artificial intelligence. It is necessary to preventively detect its increased risk early and respond with preventive drugs before moving on to more effective, but also more invasive, forms of therapy. The main motivation of our study was to improve existing and develop new AI-based solutions for cardiac preventive medicine, with particular emphasis on the prevention of heart attacks. This is due to the fact that the epidemic of lifestyle diseases (including cardiologic ones) has been stopped but not reversed; hence, automatically supervised prevention using AI seems to be a key opportunity to introduce progress in the above-mentioned areas. This can have major effects not only scientific and clinical in nature, but also economic and social. The aim of this article is to develop and test an AI-based tool designed to predict the occurrence of a heart attack for the purposes of preventive medicine. It used the combination and comparison of multiple AI methods and techniques to determine a personalized heart attack probability based on a wide range of patient characteristics and, from a computational point of view, determine the minimum set of characteristics necessary to do so. When applied to a specific patient, this represents progress in this field of research, resulting in improvements in preclinical care and diagnostics, as well as predictive accuracy in preventive medicine. After an initial selection based on the authors’ knowledge and experience, four solutions turned out to be the best: linear support vector machine (Linear SVC), logistic regression, k-nearest neighbors algorithm (KNN, k-NN), and random forest. A comparison of the models developed in the study shows that models based on logistic regression proved to be the most accurate, although their predictive value is moderate, but sufficient for the initial screening diagnosis—selecting patients who require further, more accurate testing. In addition, this can be performed based on a reduced set of parameters, particularly heart rate, age, BMI, and cholesterol. This allows the development of a prevention strategy based on modifiable factors (e.g., in the form of diet, activity modification, or a hybrid combining different factors) combined with the monitoring of heart attack risk by the proposed system. The novelty and contribution of the described system lies in the use of AI for a widely available, cheap, and quick predictive analysis of cardiovascular functions in a group of patients classified as at risk, and over time in all patients as a standard periodic examination qualifying them for further, more advanced diagnosis of heart diseases.
Zero-Trust Marine Cyberdefense for IoT-Based Communications: An Explainable Approach
Authors: Nkoro Ebuka Chinaechetam , Njoku Judith Nkechinyere , Nwakanma Cosmas Ifeanyi , Lee Jae-Min , Kim Dong-Seong
Published: January 2024

Integrating Explainable Artificial Intelligence (XAI) into marine cyberdefense systems can address the lack of trustworthiness and low interpretability inherent in complex black-box Network Intrusion Detection Systems (NIDS) models. XAI has emerged as a pivotal focus in achieving a zero-trust cybersecurity strategy within marine communication networks. This article presents the development of a zero-trust NIDS framework designed to detect contemporary marine cyberattacks, utilizing two modern datasets (2023 Edge-IIoTset and 2023 CICIoT). The zero-trust NIDS model achieves an optimal Matthews Correlation Coefficient (MCC) score of 97.33% and an F1-score of 99% in a multi-class experiment. The XAI approach leverages visual and quantitative XAI methods, specifically SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-agnostic Explanations (LIME) algorithms, to enhance explainability and interpretability. The research results indicate that current black-box NIDS models deployed for marine cyberdefense can be made more reliable and interpretable, thereby improving the overall cybersecurity posture of marine organizations.
EEUR-Net: End-to-End Optimization of Under-Sampling and Reconstruction Network for 3D Magnetic Resonance Imaging
Authors: Dong Quan , Liu Yiming , Xiao Jing , Pang Yanwei
Published: January 2024

It is time-consuming to acquire complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Under-sampling in the 3D k-space is promising in accelerating such 3D MRI process. Although 3D under-sampling can be conducted according to predefined probability density, the density-based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D under-sampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific under-sampling patterns for the fully sampled 3D k-space data. Innovatively, our under-sampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of under-sampled data. Through end-to-end training, we obtain an optimized 3D under-sampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrate that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian under-sampling schemes, our end-to-end learned under-sampling pattern considerably improves the reconstruction quality of under-sampled MRI images.
Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity
Authors: Li Linxi , Dou Xun , Yang Hanyu , Fu Yadie , Yu Jiancheng , Huo Xianxu , Pang Chao
Published: January 2024

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system.