Linear systems involved in engineering and scientific calculations can be analyzed more easily using the mathematical tool of similarity transformation. However, understanding the numerous abstract linear algebra theorems associated with this transformation can be quite challenging. In this paper, we present computational methods for systematizing these theorems using SymPy (a symbolic mathematics library) in the Python programming language. The paper covers numerous theorems, including the basis of vector space, range space, null space, rank and nullity, eigenvalues, generalized eigenvectors, eigenspace, and diagonal-Jordan canonical form transformations. These theorems are transformed into abstract data models, which are then represented as programmatic objects using Object-Oriented Programming (OOP) for processing input and output data. The aim of this paper is to provide a more efficient and effective means of displaying and applying these theorems through the use of computers. A Python programming language module was eventually created from the developed programmatic objects. It can be used for programming both offline on PC and online through the web application “SymPy Live,” producing satisfactory computational results compared to MATLAB, even when used on mobile devices.

 Vision-threatening conditions, such as age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR), arise from dysfunctions in the highly vascular choroid layer in the eyeâ\euro™s posterior segment. Optical coherence tomography (OCT) images play a crucial role in diagnosing choroidal structural changes in clinical practice. This review emphasizes the significant efforts in developing precise detection, quantification, and automated disease classification of choroidal biomarkers. The rapid progress of artificial intelligence (AI) has triggered transformative breakthroughs across sectors including medical image analysis. Recently, the integration of AI within the diagnosis and treatment of choroidal diseases has captured significant attention. Multiple studies highlight AIâ\euro™s potential to enhance diagnostic precision and optimize clinical outcomes in this context. The review provides an extensive overview of AIâ\euro™s current applications in choroidal analysis using OCT imaging. It encompasses a diverse array of algorithms and techniques employed for biomarker detection, such as thickness and vascularity index, and for identifying diseases like AMD and CSCR. The overarching goal of this review is to provide an updated and comprehensive exploration of AIâ\euro™s impact on the choroid, highlighting its potential, challenges, and role in driving innovation in the field. Â

Enhancing Accuracy of Low-Cost Floor Sensor Data for Human Localization using the Human SLIP Model

Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing. However, it is predominantly confined to an individual platform with a limited observation range as well as susceptibility to interference, resulting in limited accuracy. Considering the current state of multi-platform collaborative observation, this article innovatively presents a distributed collaborative perception network called DCP-Net. Firstly, the proposed DCP-Net helps members to enhance perception performance by integrating features from other platforms. Secondly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners, prioritizing critical collaborative features and reducing redundant transmission cost. Thirdly, a related feature fusion module is designed to address the misalignment between local and collaborative features, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, including Potsdam, iSAID and DFC23. The results demonstrate that DCP-Net outperforms the existing methods comprehensively, improving mIoU by 2.61%~16.89% at the highest collaboration efficiency, which promotes the performance to a state-of-the-art level.

Early diagnosis of Alzheimer’s disease plays a crucial role in treatment planning that might slow down the disease’s progression. This problem is commonly posed as a classification task performed by machine learning and deep learning techniques. Although data-driven techniques set the state-of-the-art in many domains, the scale of the available datasets in Alzheimer’s research is not sufficient to learn complex models from patient data. This study proposes a simple yet promising framework to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD). The proposed framework comprises a shallow neural network for binary classification and a single-step gradient-based adversarial attack to find an adversarial progression direction in the input space. The step size required for the adversarial attack to change a patient’s diagnosis from MCI to AD indicates the distance to the decision boundary. The patient’s diagnosis at the next visit is predicted by employing this notion of distance to the decision boundary. We also present a potential application of the proposed framework to patient subtyping. Experiments with two publicly available datasets for Alzheimer’s disease research provide evidence that the proposed framework can address predicting MCI-to-AD conversion and subtyping by only training a shallow neural network.

This letter focuses on the description of the divergence factor of a wave in terms of differential geometry, called Vectorial Complex Ray Model (VCRM), and its application in the scattering of a large nonspherical dielectric particle. The VCRM permits to calculate the amplitude and the phase of electric field rigorously in terms of ray model, but it fails to predict correctly the field near caustics (in rainbow angles). This problem is solved by combining the field calculated with VCRM and the physical optics. This hybrid method is then applied, as example, to the scattering of an infinite cylinder and an ellipsoid. The comparison with the rigorous Debye theory shows that our method can predict very precisely the scattered field in all directions including near the rainbow angles. The hybrid method removes the two approximations in Airy theory, so the results are much better. Because of its flexibility, this method can be applied directly to the scattering of a large nonspherical object of smooth surface.

In complex environments such as night, fog and battle? field camouflage, a single camera sensor is not sufficient to reflect scene information, and multi-source sensor can raise environmental awareness. Fortunately, the combination of infrared sensors and visible cameras can be a perfect solution to the problem of sensing complex environments. However, there are huge differences in the inputs from different sensors, and how to fuse the information from two sensors and apply it to a specific task is a problem that needs to be solved. To address this problem, we propose a multi-source input detection algorithm with the combination of infrared sensors and visible cameras. Its purpose is to solve the problem of low detection accuracy of a single sensor in complex and changing environments. First, we design a differential feature enhancement(DFE) module to enhance features that are constantly degraded during network transmission. Second, we design a cross-modal fusion module(CF) to fuse features from multiple sources. Finally, we embed the designed module into a generic two-stream network. Experiments on the publicly available FLIR and LLVIP datasets show that the algorithm in this paper improves mAP75 by 8.3/4.3 compared to a single-source detector. In some special environments, our modules and even libraries occupy 0.1MB of space for a 1.7 mAP boost! Extensive ablation experiments demonstrate that the module pro? posed in this paper is lightweight, efficient, plug and play.

Research on the magnetics design of inductive power transfer using nanocrystalline flake ribbon cores. We have proposed a design methodology to ensure a high power density as well as a superior efficiency than traditional MnZn ferrite.

M-ary Aggregate Spread Pulse Modulation (M-ASPM) is a recently introduced physical layer (PHY) modulation technique that is well suited for use in low-power wide-area networks (LPWANs). Notably, M-ASPM combines high energy-per-bit efficiency, robustness, resistance to interference, and a number of other favorable technical characteristics, with the spread-spectrum ability to maintain the capacity of an uplink-focused network while extending its range. However, while the essential tools for detection and synchronization of pulsed spread-spectrum waveforms in general, and the M-ASPM signals in particular, have been previously provided, a practical framework for combining the detection, synchronization, and decoding of an M-ASPM packet has not yet been suggested. In this paper, we outline such a framework, and describe a prototype algorithm for its implementation. This implementation can be subsequently adapted, under given technical constraints, to specific practical complications such as, for example, significant delay spreads, external technogenic interference, or co-channel and inter-channel collisions. In addition to low latency and computational complexity, the main requirement for this prototype algorithm is that the signal quality remains effectively invariant, for a given path loss, and for a wide range of the data rates, payload sizes, lengths of pulse shaping filters (PSFs), and pulse duty cycles, for a relatively large mismatch in the frequency of the local oscillators (LOs) in the transmitter (TX) and the receiver (RX), and for the TX and RX motions at relatively high speeds. Further, this needs to be achieved without any feedback communications between the TX and RX, any hardware or software changes in the TX, and any hardware adjustments in the RX (e.g., in the LO~frequency or sampling time offsets).

The field of the Internet of Things (IoT) is expanding rapidly and shows the potential to completely transform the healthcare sector. The integration of machine learning algorithms in Internet of Medical Things (IoMT) systems has shown the potential to revolutionize healthcare by enabling efficient, accurate, and privacy-preserving services. However, the limitations of existing IoMT models pose significant challenges that need to be addressed to achieve sustainability in their adoption. Some of these challenges include energy consumption, complex data scheduling, low-latency models, privacy, joint offloading, limited data, and data security. There is a need to explore various novel architectures, develop new training and optimization techniques such as generative adversarial networks and stochastic gradient descent variants, and developed interpretative models to help in the decision-making process. Additionally, designing IoMT models that are suitable for low-resource environments and addressing ethical and fairness issues are crucial to preventing bias and discrimination, particularly in healthcare domains. In general, this paper reviews the current state of the art of IoMT and existing models and suggests various areas for further improvement to address the limitations of IoMT systems and enable the development of more efficient, accurate, and privacy-preserving healthcare services.

Abstract— Objective: High physical function is a major achievement of healthy aging. Many older adults have low levels of exercise and physical activity, which may be due to a lack of motivation or access to proper exercise guidance tools. Providing a proper and enjoyable exercise guidance tool to older adults can be a good alternative to simply providing them conventional materials such as exercise training videos and encourage them to exercise regularly. Here, we demonstrate a novel home-based exercise guidance system named “Pei-Wo Drone” developed using a drone, a real, flyable three-dimensional (3D) object, to guide physical exercise or movement. The system provides real-time continuous sound feedback to notify the user if they are not following the drone correctly. Methods: The Pei-Wo Drone was developed and tested to ensure accuracy and precision in guiding two selective movements: “lateral arm reach” and “arm up and down” movements. Fifteen subjects (12 females, 3 males; aged 67.40±5.85 years) were recruited to evaluate their performance in following the guidance of the drone with interactive feedback. Results: The accuracy and precision of the system were impressive. Additionally, the results of this study showed that the subjects were able to follow the drone well. Conclusion: The Pei-Wo Drone, a newly developed drone guidance system, could encourage and guide older adults in proper exercise. Significance: Using a drone with real-time feedback to guide proper physical exercise or movement in older adults has a potential to enhance their physical well-being, while simultaneously providing a realistic and joyful experience reminiscent of playing with a toy at home.

This paper forms the basis for wireless constellation detection in seamless digital wireless-photonic receivers. The two intermediary stages in the proposed receiver model are discussed: Wireless-to-Optical (W-O) and Optical-to-Electrical (O-E) symbol mapping stages. The inherent barrier of erroneous constellation detection due to symbol degeneracy in the W-O and O-E mapping stages is reported. A receiver architecture with optical phase diversity is subsequently proposed to overcome the barrier of degeneracy. Breaking degeneracy not only lays the foundation for reliable detection of wireless constellations in seamless digital wireless-photonic receivers, but also ensures their compatibility with practical modulation schemes such as M-PSK and M-QAM.

Opioid Use Disorder (OUD) is a major public health crisis in the US affecting over 2.4 million Americans across all age groups and backgrounds. Previous studies have developed machine learning (ML) and deep learning (DL) models with strong predictive performance for OUD; however, bias related to sociodemographic features remains unaddressed. In this study, we propose a bias mitigation algorithm, based on equality of odds (EO), to mitigate ML model bias in OUD prediction. Our algorithm adjusts the classification threshold to minimize the difference between specificity and recall across sociodemographic groups. We develop a neural network (NN) for OUD prediction and analyze bias by comparing the area under the curve (AUC) values. We implement our algorithm to mitigate bias related to gender, marital status, working condition, race, and income. The proposed algorithm achieves significant improvements in bias reduction: 16.96% for gender, 8.87% for marital status, 8.45% for working condition, 41.62% for race, and 0.20% for income. We also propose a fairness-aware weighted majority voting (WMV) classifier, achieving a recall of 92.68% and an accuracy of 90.21%. Furthermore, we evaluate the proposed methods using logistic regression and support vector machine classifiers with linear and radial basis function kernels, confirming the significant bias reduction and high performance of the WMV classifier for OUD prediction

There has been a rapid advancement in developing computational models originating from the governing principles of quantum mechanics. Quantum computing approaches such as variational quantum algorithms (VQAs) and quantum-inspired algorithms have shown advantages across various applications. VQAs have been widely used as hybrid classical-quantum computational frameworks that are well-suited for NISQ advantages. Alternatively, the quantum-inspired evolutionary algorithm (QiEA) provides an efficient search space and parameter optimization solution, useful in handling problems that consist of large parameters. Following the recent trends, quantum-inspired technique, developments, limitations, and future scope are highlighted. Also, an approach towards advancing neuromorphic computation is discussed. Neuromorphic computation and systems are a fast developing domain inherited from fast and massively parallel processing, low energy consumption, high density of millions of neurons in a chip, and have found successful applications across spatio-temporal domain areas including brain data modelling. However, they require a fast and efficient parameter optimization process in large parameter space. Finally, future directions for the development of quantum-enhanced spiking neural networks and neuromorphic systems are outlined.

Master Data Management (MDM) stands as a pivotal discipline in the realm of modern data management, owing to its profound significance in upholding data accuracy and completeness across organizational landscapes. In an era driven by data-driven decision-making, MDM emerges as a strategic framework that orchestrates the harmonious synchronization, governance, and dissemination of critical data. At its core, MDM entails the establishment of a centralized repository, encapsulating vital business entities like customers, products, and locations. This repository serves as the single source of truth, ensuring consistent, accurate, and reliable data dissemination across various applications and departments. By enforcing stringent data quality standards, MDM not only curbs discrepancies and redundancies but also cultivates a foundation of trust in the data’s integrity. The ramifications of MDM reverberate across multiple dimensions. Enhanced data accuracy leads to improved analytics, yielding sharper insights and informed decision-making. Moreover, MDM enables comprehensive data lineage and traceability, fostering compliance with regulatory mandates. Furthermore, its impact spans customer satisfaction, as consistent, accurate data fortifies customer interactions and loyalty. In conclusion, Master Data Management stands as the linchpin for sustaining data accuracy and completeness within enterprises. Its role transcends mere data management, evolving into a strategic asset that empowers organizations to navigate the complexities of the data-driven landscape with confidence and precision.

This paper describes a simulation-based approach for automated risk assessment of complex cyber-physical systems to support implementers of ISO 27005. The approach is based on systematic causeand-effect modelling of threats, their causes and effects, and the ways in which the effects of one threat can lead to other threats. In this way, the approach deals with inter-dependencies within the target system, automatically finding attack paths and secondary effect cascades, which generally are very complex and the source of many challenges for implementation of ISO 27005. The approach uses a knowledgebase describing classes of system assets and their possible relationships, along with the associated threats, causes and effects in a generic context. A target system can then be modelled in terms of related assets, describing the intended system structure and purpose (in the absence of any deviations). The knowledgebase is then used to identify which threats are relevant and create a cause-and-effect simulation of those threats. This allows threat likelihoods and risk levels to be found based on input concerning trust assumptions and the presence of controls in the system. The approach has been implemented by the open source Spyderisk project and validated by modelling a published case study of an attack on a steel mill. Given reasonable assumptions about security controls in place, the shortest, highest likelihood attack path found coincides with the published analysis. The case study demonstrates the strengths of the approach: transparency, reproducibility, and performance.

Refat Mohamed Abdullah Eshaq declares and claims the following: 1) We are the first to explore this phenomenon. 2) We are the first to invent this algorithm (Max Planck algorithm) for image segmentation, which is a completely new design in both the digital image processing and medicine fields. 3) Although the methodology appears simple, this work took a lot of research into and imagination of physical theories, especially the quantum theory of scientist Max Planck. We are the first to bring physical theory to digital image processing. 4) If this work is simple, why did computer scientists and medical scientists not discover and apply it before? 5) No one can use and develop this algorithm or merge it with any other algorithm under any circumstances without explicit permission from the authors of this paper, because we are the first to discover this phenomenon. If you redistribute the adjusted materials, the authors have the right to prosecute individuals, journals, institutions, and even governments. 6) These results are just part of our future work to merge Max-Planck theory and PID-NN (MinerNet) to create the optimal classification system.

This study introduces a groundbreaking idea: using swallowable sensor capsules with augmented reality (AR) to conduct real-time, high-resolution examinations of the human body. These capsules, ingested like pills, transmit data as they pass through the body. Physicians can then use AR headsets to view this data as a 3D model of the organ or tissue being examined, along with sensor readings and a miniature endoscopic view. While there are technical challenges to overcome, this innovation has the potential to revolutionize non-invasive examinations of the digestive tract and internal cavities, improving the diagnosis of conditions like inflammation, infections, and cancers. With further technological development, swallowable sensor capsules with AR integration could become a valuable complement to traditional medical imaging methods, offering dynamic 3D visualizations of the body’s inner workings.