The quality assessment of biomaterials in pathological anatomy is crucial for the optimal diagnosis and treatment of conditions like cancer. This is exemplified in the immunohistochemistry profiling of the human epidermal growth factor receptor 2 (HER2) in breast cancer. Therefore, it is relevant to understand how preanalytical processes, such as post-surgery handling and fixation quality, impact biomaterial quality and diagnostic accuracy. This study investigates first the influence of fixation steps on the performance of HER2 diagnosis. Then a quantitative and automated approach is proposed to correct these biases. This approach is derived from a previous supervised Machine Learning model. The method, which employs a high-performance logistic model, has been further enhanced with a compensation strategy based on tissue quality. This enhancement utilizes a correction derived from a Tissue Quality Index (TQI) to fine-tune the input parameters of the classification model (referred to as TQI-enhancer). Results, obtained from 60 quality control samples with Vimentin and 75 HER2 classification samples, first demonstrate that cold ischemia and fixation times lead to significant changes in immunoreactivity within a short period. Second, adjusting specific parameters quantified in HER2 samples through automated image analysis based on the TQI-Enhancer equation exhibits an improved correlation with the reference diagnosis. This adjustment significantly enhances the classification performance of the logistic classifier in ML-based diagnosis compared to uncompensated data with improved AUC values from 0.84 to 0.93. We anticipate that implementing similar strategies will enhance the performance of digital pathology techniques, ultimately leading to the development of robust diagnostic classifiers for cases of aggressive breast cancer. By analyzing the association between biomarkers like HER2 with patients' clinical outcomes, these classifiers are expected to provide invaluable insights.
As biological wide-field visual neurons in locusts, lobula giant motion detectors (LGMDs) can effectively predict collisions and trigger avoidance before the collision occurs. This capability has extensive potential applications in the field of autonomous driving, unmanned aerial vehicles, and more. Currently, describing the LGMD characteristics is divided into two viewpoints, one emphasizing the presynaptic visual pathway and the other emphasizing the postsynaptic LGMDs neuron. Indeed, both have their research support leading to the emergence of two computational models, but both lack a biophysical description of the behavior in the individual LGMD neuron. This paper aims to mimic and explain LGMD's individual behavior based on fractional spiking neurons and construct a biomimetic visual model for the LGMD compatible with these two characteristics. Methods: We implement the visual model in the form of spikes by choosing an event camera rather than a conventional CMOS camera to simulate the photoreceptors and follow the topology of the ON/OFF visual pathway, enabling it to incorporate the lateral inhibition to mimic the LGMD's system from the bottom up. Second, most computational models of motion perception use only the dendrites within the LGMD neurons as the ideal pathway for linear summation, ignoring dendritic effects inducing neuronal properties. Thus, we introduced fractional spiking neuron (FSN) circuits into the model by altering dendritic morphological parameters to simulate multiscale spike frequency adaptation (SFA) observed in LGMDs. In addition, we have attempted to add one more circuit of dendritic trees into fractional spiking neurons to be compatible with the postsynaptic FFI in LGMDs and provide a novel explanatory approach and a predictive model for studying LGMD neurons. Results: Finally, we test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects.
Electronic parts in space inevitably subject to radiation effects leading to the degradation of electronic performance or even failure, so radiation performance of an electronic part must be assessed to ensure it work normally in space. At present, to assess the ion radiation effects on a semiconductor device is directly through irradiation tests. However, due to the scarcity of cyclotron resources, the test time is difficult to appoint and the cost is huge. Due to schedule and budget constraints, it is also impossible to conduct irradiation tests on all semiconductor devices in actual space missions. Therefore, assessment of the radiation effects on semiconductor devices through irradiation tests has caused difficulties. Radiation susceptibility of semiconductor device is determined by the design topology and fabrication technology, and the irradiation test data shows that similar semiconductor devices has similar radiation susceptibility, so a method to assess the radiation effects on semiconductor devices base on similarity theory is proposed at first time in this paper. This assessing method does not require irradiation testing and does not require separate sampling. It has the virtues of easy implementation, quick response and low cost, providing an efficient method of assessing radiation effects on semiconductor devices.
In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).
Generating videos from text has long been recognized as a significant challenge in the realm of computer science. This study aims to evaluate advancements in this field, which have gained considerable traction with contemporary technological developments. Specifically, we will examine the contributions of natural language processing, large language models, and generative artificial intelligence approaches to text-to-video synthesis. We will explore OpenAI Sora, Stable Diffusion, and Lumiere; their architecture and models. We will compare OpenAI Sora to the computing platforms and provide a critical analysis of its strengths and weaknesses. Text-to-video has the potential to revolutionize various creative sectors, including filmmaking, advertising, graphic design, and game development, alongside industries like social media, influencer marketing, and educational technology. Its potential and future are discussed.
The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results.
Switched-capacitor converters (SCCs) are subset of switched-mode converters which can be designed to buck/boost the input voltage. However, synthesizing SCC has been a challenging task due to the large number of possible circuit realizations. This work proposes a synthesis method for constructing an efficient reconfigurable SCC that adheres to the Fibonacci canonical structure. The optimization is achieved by operating the converter with the minimum number of capacitors required to achieve a certain voltage conversion ratio (VCR). Indeed, decreasing the number of flying capacitors minimizes the equivalent output resistance of the converter which improves the converter performance, like the settling time and the power conversion efficiency (PCE). Moreover, by analyzing the differences between each VCR terminal, the number of switches required to create a unique VCR is also decreased. The performance of the proposed synthesis tool is verified using SPICE simulations for 4-stage reconfigurable SCC. The simulated converter efficiency ranges from 85% to 95% for a 50mA load tested at 1 MHz switching frequency, compared to 43.5% to 86% using the conventional method. The performance of the converter is compared with recent reconfigruable SCCs in the literature.
We report herein the observed null or negative results in measuring any known finite light speed in air, of the one way near-field signal and information velocity, of the field displacement current between the poles of a total 1.5m separated poles spherical air capacitor, caused by its impulse spark discharge.
Earth Observation spacecraft play a pivotal role in various critical applications impacting life on Earth. Historically, these systems have adhered to conventional operational paradigms, namely the "mow-the-lawn" and "bent pipe" approaches. In these paradigms, operational schedules are formulated on the ground and subsequently uploaded to the spacecraft for execution. Execution involves either systematically acquiring vast amounts of data (mow-the-lawn) or targeting specific areas of interest as defined by end users or operators. We aim to depart from these traditional methodologies by integrating onboard Artificial Intelligence, real-time communication, and new observing strategies in one system called CogniSAT-6. These transformative innovations will amplify the amount, speed, and quality of the information yielded by such a system by up to an order of magnitude. Consequently, these advancements are poised to revolutionize conventional Earth Observation systems from static entities into dynamic, intelligent, and interconnected instruments for highly efficient information gathering. This paper provides an overview of the current state of the art in autonomous Earth Observation spacecraft and the application of onboard processing in Earth Observation spacecraft. An overview is given of the CogniSAT-6 mission, its concept of operations, system architecture, and data processing design. Since we believe that the technology presented here will have a significant impact on society, an ethical framework for such systems is presented. Finally, the benefits of the technology and implications for EO systems going forward are discussed.
This paper introduces a new spatial domain-based self-interference cancellation (SIC) precoding method named constrained minimum mean square error (C-MMSE) for an asymmetric massive multiple-input multiple-output (mMIMO) full-duplex (FD) system. The main idea is to translate the commonly used singular value decomposition (SVD)-based null-space projection approach, which is unfeasible in our considered system model, into an optimization problem under MMSE criterion, where additional constraints are implemented to perform SIC. Theoretical derivation of the C-MMSE precoder is presented, followed by performance comparison with conventional MMSE precoding, where no constraints are added for SIC. We theoretically show that the C-MMSE scheme outperforms the conventional one in terms of SIC, and allows the FD system to work under an almost interference-free environment. Additionally, we also assess the performance of the proposed method under imperfect channel state information (CSI), to further evaluate the robustness of our spatial precoder in more realistic conditions. We show that the C-MMSE precoder outperforms MMSE in terms of interference suppression ratio (ISR), even in CSI imperfection. Additionally, the C-MMSE achieves the same spectral efficiency (SE) as an hypothetical perfect SIC in a wide SNR range, whereas the MMSE is upper bounded in large SNR range.
This work emerges from the intensifying need to understand and address security issues in rapidly advancing technologies such as 5G and beyond, including open radio access network (O-RAN). The current paper provides an in-depth examination of the security aspects of the E2 interface within the O-RAN context. The research underscores the diverse roles that the E2 interface assumes in enabling communication between the RAN Intelligent Controller (RIC) and the E2 node. It critically examines the various vulnerabilities and potential security threats of this interface. This work subsequently reviews the security mechanisms and methodologies proposed by the O-RAN Alliance to secure the E2 interface. This work aims to highlight the crucial role that the E2 interface undertakes in the network's overall communication and the indispensable security questions, given the stakes of these networks. The findings from this work could serve as a valuable addition to existing resources and provide insightful perspectives for future research in this field. The paper concludes with a discussion of potential directions for future work on the security of the E2 interface.
We introduce SIGNOVA, a new semi-supervised framework for detecting anomalies in streamed data. While our initial examples focus on detecting radio-frequency interference (RFI) in digitized signals within the field of radio astronomy, it is important to note that SIGNOVA's applicability extends to any type of streamed data. The framework comprises three primary components. Firstly, we use the signature transform to extract a canonical collection of summary statistics from observational sequences. This allows us to represent variable-length visibility samples as finite-dimensional feature vectors. Secondly, each feature vector is assigned a novelty score, calculated as the Mahalanobis distance to its nearest neighbor in an RFI-free training set. By thresholding these scores we identify observation ranges that deviate from the expected behavior of RFI-free visibility samples without relying on stringent distributional assumptions. Thirdly, we integrate this anomaly detector with Pysegments, a segmentation algorithm, to localize consecutive observations contaminated with RFI, if any. This approach provides a compelling alternative to classical windowing techniques commonly used for RFI detection. Importantly, the complexity of our algorithm depends on the RFI pattern rather than on the size of the observation window. We demonstrate how SIGNOVA improves the detection of various types of RFI (e.g., broadband and narrowband) in time-frequency visibility data. We validate our framework on the Murchison Widefield Array (MWA) telescope and simulated data and the Hydrogen Epoch of Reionization Array (HERA).
The Kanta Patient Data Repository contains healthcare data from the population of Finland for more than a decade. The repository is a continuously expanding real world dataset produced by many information systems and healthcare service providers. Kanta data has been accessible for secondary uses such as scientific research since 2019. The data can be requested from the Finnish authority Findata. However, before a request has been accepted, it is difficult to assess if the accumulated data allows answering a specific research question. Publicly available descriptions of data structures in Kanta do not tell how much they are used in practice. This publication enables future data use cases by providing a view on the overall availability of types of structured health data in the Kanta patient data repository based on a sample of 96 200 medical histories of over 18-year-old patients. We conclude that Kanta PDR is a promising source of real world data for development and evaluation of medical risk calculators within the Finnish population. The wide coverage of the Finnish population and timeliness of the data are its strengths as a source of research data also outside of Finnish context. However, the limitations on data availability in variable level need to be considered on a case-by-case basis. Main challenges in the use of Kanta data are multiple code systems for laboratory results, short durations of recorded data for specific data types, and missing or very rarely used structured format e.g., in cases of tobacco and alcohol use.
This work delves into the exploration of optimizing Multilayer Perceptrons (MLP) or the dense layers of other sorts of Deep Neural Networks when they are aimed at edge computing applications such as Internet of Things (IoT) devices, very limited in resources at the edge. The proposed optimization approach consists of generating a pruning mask for the hidden dense layers of the original neural network by using auxiliary dense Morphological Neural Networks (MNN). These MNN have shown a notable efficiency when it comes to the process of pruning, resulting in a significant decrease in the overall number of connections and a low cost in terms of accuracy degradation. The effectiveness of this new pruning methodology has been explained in detail and validated for two widely used datasets as MNIST and Fashion MNIST and two very well-known neural networks such as LeNet-5 and LeNet-300-100. Subsequently, the performance of these pruned neural networks has been assessed using an IoT hardware platform. The experimental results have outperformed other contemporary state-of-the-art pruning techniques, in terms of power efficiency and processing speed for a similar percentage of weight reduction, all while maintaining minimal impact on overall accuracy. In addition, a custom software tool has been developed to generate a C code designed to optimize the inference of these pruned networks on IoT edge devices. These findings hold important implications for advancing the development of efficient and scalable deep learning models that are specifically tailored to meet the demands of edge computing applications.
In recent years, RIS has made significant progress in engineering application research and industrialization, as well as in academic research. However, the engineering application research field of RIS still faces several challenges. This article analyzes and discusses the two deployment modes of RIS-Assisted wireless networks, namely Network Controlled Mode and Standalone mode. It also presents three typical collaboration scenarios of RIS networks, including multi-RIS collaboration, multiuser access, and multi-cell coordination, which reflect the differences between the two deployment modes of RIS. The article proposes collaborative regulation mechanisms for RIS and analyzes their applications in the two network deployment modes in-depth. Furthermore, the article establishes simulation models of three scenarios and provides rich numerical simulation results. An actual field test environment is also built, where a specially designed and processed RIS prototype was used for preliminary field test and verification. Finally, this article puts forward future trends and challenges.
We are designing and implementing a solar inverter system that generates green power from solar energy and reduces air pollution and other environmental impacts. Our system uses a pure sine wave inverter that produces a sine wave virtually identical to the utility grid. The IoT-based MPPT solar charge controller ensures that the maximum amount of power is transferred from the solar panels to the battery bank and monitors the system in real-time. We also use a solar tracker with a single-axis rotation that orients the panels toward the sun in two directions. Our solar inverter system can handle a maximum load of 300 watts.
In order to effectively manage agriculture and promote sustainable land use in Africa, accurate mapping of irrigated croplands is imperative. This paper pioneers a novel Hybrid Vegetation Index (HVI) and integrated an approach harnessing the synergies between Radar imagery from Sentinel-1 and optical data from Sentinel-2, key vegetation indices (HVI, MSAVI), and Principal Component Analysis (PCA) to significantly enhance mapping accuracy across diverse African landscapes. Employing Convolutional Neural Network (CNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers, the combined potential of these satellite datasets is thoroughly Assessed. The integrated utilization of both Sentinel-1 and Sentinel-2 demonstrates a substantial enhancement in overall accuracy, elevating classification results by approximately 5-6% when compared to individual sensor applications. Specifically, in Kenya, Egypt, and Nigeria, the amalgamation of Sentinel-1 and Sentinel-2 enables CNN to achieve remarkable accuracies of 97.5%, 98.01%, and 98%, respectively. These findings underscore the superior performance of the fused Sentinel data and highlighted the pivotal role of their integration in advancing precise agricultural mapping. This research emphasizes the promising impact of HVI and Sentinel satellite missions in empowering informed decision-making and promoting sustainable land management practices across vital agricultural regions in Africa.
In this study, we present an analytical model for predicting the magnetic properties and optimization of thermomagnetic devices using mathematical models. The 3D analytical magnetic model is firstly validated by the dipole model and confirmed through experimentation, enabling to accurately estimate the magnetization of the used magnet. The stray field induced by the permanent magnet over the lateral surface of the rotor is computed. Then, the resultant force and torque are derived allowing to estimate the exact number of ferromagnetic active material required and their angular gap.