In this research paper, threshold decomposition property (arising in the case of rank order order filters) is generalized to bipolar signals. By associating a square "threshold matrix" with a digital signal, its linear algebraic properties are investigated. Also, most general class of nonlinear filters for which the weak superposition property (based on threshold decomposition) holds (in the spirit of rank order filters) are identified. Partition theoretic interpretation of threshold decomposition is explored. It is reasoned that columnwise Boolean median filtering leads to faster implementation of median filter.

A document by Masood Khan . Click on the document to view its contents.

The next generation of shipping industry, namely Shipping 4.0 will integrate advanced automation and digitization technologies towards revolutionizing the maritime industry. As conventional maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL), a decentralized ML approach, enables collaborative model training across multiple distributed edge devices, such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More specifically, we present the main FL principles, the proposed workflow and then, we evaluate and compare various FL algorithms in three maritime use cases, i.e. regression to predict the naval propulsion gas turbine (GT) measures, classification to predict the ship engine condition, and time-series regression to predict ship fuel consumption. The efficiency of the proposed FL-based PdM highlights its ability to improve maintenance decision-making, reduce downtime in the shipping industry, and enhance the operational efficiency of shipping fleets. The findings of this study support the advancement of PdM methodologies in Shipping 4.0, providing valuable insights for maritime stakeholders to adopt FL, as a viable and privacy-preserving solution, facilitating model sharing in the shipping industry and fostering collaboration opportunities among them.

Goal: This paper introduces DISPEL, a Python DIgital Signal ProcEssing Library developed to standardize extraction of sensor-derived measures (SDMs) from wearables or smartphones data. Methods: DISPEL supports custom and third-party data formats and essential end-to-end processing steps from raw data to structured SDM datasets. DISPEL uses an object-oriented codebase for data import, data modelling and SDM extraction and export, with source-to-outcome traceability. Results: DISPEL is publicly available under MIT license. It is a flexible, modular framework with practical examples in extracting SDMs from structured tests and continuous monitoring scenarios (e.g. performance outcome assessments of cognition, manual dexterity, and mobility). Embedded data quality checks ensure robustness of SDMs for remotely collected data. The analysis of a smartphone-based balance and gait turn test illustrates the library's capabilities. Conclusion: DISPEL provides a highly standardized and robust analysis framework to support traceability and reproducibility in SDM development. We encourage contributions of new processing modules. Impact Statement-DISPEL offers a standardized way to extract sensor-derived measures from wearables and smartphone signals with traceability from source to outcome. Its open-source availability aims at supporting multidisciplinary collaborations to accelerate and scale development and adoption of digital biomarkers/endpoints.

Modeling optical coherence tomography (OCT) images is highly beneficial for various image processing applications as well as assisting ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (DL), play an important role in image modeling. Traditionally, DL transforms higher-order tensors into vectors and aggregates them into matrices, disregarding the multi-dimensional inherent structure of data. To overcome this problem, tensor-based DL approaches have been developed. In this study, we propose a tensor-based DL algorithm named CircWaveDL for OCT classification where both the training data and the dictionary are higher-order tensors. Instead of random initialization of the dictionary, we suggested initializing it with CircWave atoms, which has previously demonstrated its effectiveness in OCT classification. This algorithm employs CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into lower dimensions. Subsequently, we learn a sub-dictionary for each class using the training tensor of that class. A test tensor is reconstructed using each sub-dictionary individually and every test B-scan is assigned to the class with the minimal residual error. To assess the generalizability of the model, we have tested it on three different databases. Furthermore, we introduce a new heatmap generation approach based on averaging the most significant atoms of the learned sub-dictionaries, demonstrating that selecting an appropriate sub-dictionary for test B-scan restoration can lead to better reconstructions, emphasizing distinctive features of different classes. CircWaveDL demonstrates a high level of generalizability according to external validation conducted on three different databases and it outperforms previous classification methods designed for similar datasets.

Human emotion recognition from facial expressions depicted in images is an active area of research in computer vision and machine learning. Facial expressions depict emotions as a mix of basic emotions, each with a different intensity. Hence, measuring these intensities from facial expression images is a challenging task. Previous studies have solved this task by using label-distribution learning (LDL), which assigns a distribution of class intensities to an instance to describe the mix of emotions more explicitly. In this paper, we tackle this problem by proposing an LDL framework that predicts the intensities of six basic human emotions, i.e., happiness, sadness, anger, fear, surprise, and disgust, by using a novel convolutional (Conv) layer called the visibility convolutional layer (VCL). The VCL preserves the advantages of traditional Conv layers, in terms of using filters to extract features, while reducing the number of learnable parameters and allowing for the extraction of strong texture features. Our LDL framework, which we call VCNN-ELDL, uses the features extracted by the VCLs and those extracted by traditional Conv layers to predict a discrete distribution. We evaluate VCNN-ELDL on the widely-used s-JAFFE and s-BU-3DFE datasets. Our results show that our framework can effectively learn the distribution of emotions from face images by attaining a stronger performance than state-of-the-art LDL methods.

Multiuser gigantic-multiple-input multiple-output (MU-gMIMO) and nonorthogonal multiple access (NOMA) are jointly seen as important enabling technologies for sixth generation (6G) networks. They have many benefits, such as spatial multiplexing, spatial diversity, massive connectivity, and spectral efficiency (SE). However, gMIMO-NOMA suffers from many inherent challenges. In this paper, we propose an MU-gMIMO-hybrid multiple-access (HMA) heterogeneous network architecture to address the 'nearly same channel gain' issue. Then, an iterative minimal mean squared error (IMMSE) scheme is applied along with quadrature amplitude modulation (QAM) for maximal ratio transmission in the proposed transceiver design to address the 'residual error' caused by imperfect successive interference cancellation (ISIC). Finally, to assess the performance of the proposed architecture for the 'time offsets' issue, we investigate the asynchronous mode with an MMSE detector matrix following imperfect channel state information (ICSI) to provide a new analysis for HMA transmission and formulate an optimization problem for energy efficiency (EE).

We elaborate the effects of carrier heating on the mobility characteristics of MOSFET inversion layers in the range of interest for devices of practical technological import (transverse fields ET ≈ 0.1 to 1 MV/cm), insofar as they are underpinned and governed by energy and momentum conservation principles. Carrier heating relatively to their surrounding lattice begins when the transverse (gate) electric field ET approaches the saturation field of carriers' thermal velocity, a material property. Additional energy imparted to carriers by yet higher fields causes their mean velocity, hence their longitudinal velocity component, to fall as required by momentum conservation. Whereas energy is converted predominantly from potential to kinetic form at relatively low fields where the flow of carriers is quasi-laminar, potential energy is converted (lost) to thermal energy in the saturated velocity range. The degradation of mobility is thermally-mediated: it 'emerges' by virtue of nonlinear interactions between transverse field, saturation velocity, and carrier heating, and is constrained by overarching conservation laws. Our findings indicate that the rate of carrier heating is a function only of interface terrain 'roughness' amplitude and fundamental constants, hence that the influence of surface orientation is ancillary to the heating phenomenon. Our analytical results are surprisingly simple, intuitively appealing, and in uniform agreement with Takagi et al's extensive observations of long standing, when the latter are apprehended in light of the unifying percepts and order herein set forth. As such, they readily inform the modeling of silicon and silicon carbide integrated MOSFET technologies. 1 Which has ostensibly become all but synonymous with "universal" in the pertinent literature. " As levels of complexity mount, … new properties arise as results and interconnections emerging at each new level. A higher level cannot be fully explained by taking it apart into component elements and rendering their properties in the absence of these interactions." [1]

In order to overcome the limitations of phased array radar technology, the researchers proposed frequency diverse array technology to achieve accurate detection and tracking of target direction. In this paper, a method of distance and angle estimation based on DFT transform is proposed for frequency transform array. By cross-correlating the received data, a new received signal vector is established and solved by constructing an equation. Compared with other methods, this method overcomes the problem that FDA radar is prone to fuzzy estimation and insufficient resolution in target parameter estimation. It can not only directly estimate the distance and angle of the target, but also effectively improve the target resolution.

In this study, we explored the potential of seventeen quantitative ultrasound parameters (radiofrequency-based) in assessing the progressive loss of collagen and proteoglycans (mimicking an osteoarthritis condition) in ex-vivo bovine cartilage samples. The majority of the analyzed metrics showed significant changes as the degradation progressed due to trypsin and collagenase treatment. For the first time, we employed a combination of these ultrasound parameters to create machine learning models for the automated detection of a model of healthy and degraded cartilage samples. A logistic regression model exhibited a remarkable capability of distinguishing between healthy and collagenase-treated cartilage, achieving accuracy and an area under the curve values of 93% and 90%, respectively. When comparing healthy and trypsin-treated cartilage, an ensemble model yielded accuracy and an area under the curve values of 83% and 75%, respectively. Histological and mechanical analyses further confirmed the ultrasound findings, as collagenase had more pronounced impact on both mechanical and histological properties compared to trypsin. These metrics were obtained using an ultrasound probe, with a transmission frequency of 15 MHz, typically used for the diagnosis of musculoskeletal diseases. As a perspective, the proposed quantitative ultrasound assessment could become a new standard for monitoring cartilage health, aiding in the early detection of cartilage pathologies and enabling prompt interventions.

A Magnetic field Aided Inertial Navigation System (MAINS) for indoor navigation is proposed in this paper. MAINS leverages an array of magnetometers to measure spatial variations in the magnetic field, which are then used to estimate the displacement and orientation changes of the system, thereby aiding the inertial navigation system (INS). Experiments show that MAINS significantly outperforms the stand-alone INS, demonstrating a remarkable two orders of magnitude reduction in position error. Furthermore, when compared to the state-of-the-art magnetic-field-aided navigation approach, the proposed method exhibits slightly improved horizontal position accuracy. On the other hand, it has noticeably larger vertical error on datasets with large magnetic field variations. However, one of the main advantages of MAINS compared to the state-of-the-art is that it enables flexible sensor configurations. The experimental results show that the position error after 2 minutes of navigation in most cases is less than 3 meters when using an array of 30 magnetometers. Thus, the proposed navigation solution has the potential to solve one of the key challenges faced with current magnetic-field simultaneous localization and mapping (SLAM) solutions â\euro” the very limited allowable length of the exploration phase during which unvisited areas are mapped.

Ray-tracing is a promising alternative for Radio Frequency Planning particularly in urban areas. There are two fundamental techniques used for ray-tracing namely Shooting and Bouncing Rays and Method of Images. In this paper, we focus on the former and present simulation results for an urban scenario in the city of Helsinki. We also give an insight into how the Shooting and Bouncing Ray method can be implemented using basic linear algebra techniques. We show that ray-tracing can be used to evaluate the performance improvement attained through electromagnetic reflectors. Finally, we close the discussion by outlining the existing challenges and the way forward.

This research delves into sophisticated neural network frameworks like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for improved analysis of ECG signals via Field Programmable Gate Arrays (FPGAs).  The main purpose of this study is to see the advantage of FPGA in biomedical computation. Â

This paper investigates the pivotal role of artificial languages, with a specific focus on BILA, in addressing challenges in Human-Robot Interaction (HRI). By delving into the complexities of speech recognition and technological constraints, the study introduces BILA as a groundbreaking solution. The research encompasses the design, implementation, and evaluation phases, emphasizing its transformative potential in enhancing user-robot interactions.

The increasing number of actions in the real world makes it difficult for traditional deep-learning models to recognize unseen actions. Recently, pretrained contrastive image-based visual-language (I-VL) models have been adapted for efficient â\euroœzero-shotâ\euro? scene understanding, with transformers for temporal modeling. However, the significance of modeling the local spatial context of objects and action environments remains unexplored. In this work, we propose a framework called LoCATe-GAT, comprising a novel Local Context-Aggregating Temporal transformer (LoCATe) and a Graph Attention Network (GAT) that take image and text encodings from a pretrained I-VL model as inputs. Motivated by the observation that object-centric and environmental contexts drive both distinguishability and functional similarity between actions, LoCATe captures multiscale local context using dilated convolutional layers during temporal modeling. Furthermore, the proposed GAT models semantic relationships between classes and achieves a strong synergy with the video embeddings produced by LoCATe. Extensive experiments on two widely-used benchmarks â\euro“ UCF101 and HMDB51 â\euro“ show we achieve state-of-the-art results. Specifically, we obtain absolute gains of 2.8% and 2.3% on these datasets in conventional and 8.6% on UCF101 in generalized zero-shot action recognition settings. Additionally, we gain 18.6% and 5.8% on UCF101 and HMDB51 as per the recent â\euroœTruZeâ\euro? evaluation protocol.Â

This letter proposes and analyzes three different methods to achieve robust consensus control in a leaderâ\euro“follower multiagent system framework prone to bounded disturbance in finite time. In the first method, a sliding mode surface is proposed using the basic definition of consensus. An upper bound on noise is considered while designing the surface. Then a Lyapunov stability analysis is used to determine the control law. Second method deals with using a high-gain disturbance observer to dynamically estimate the noise. A sliding mode surface was then developed using the estimates. Further, a Lyapunov analysis was done to show the stability of the system. Lastly, a new sliding surface based on High Gain DIsturbance Observer is proposed which alleviates the problem of mismatched uncertainties. A Lyapunov analysis was then performed to ensure convergence of the system in finite time. The robustness of the proposed approach is validated through simulations.

A document by Anton Krynkin . Click on the document to view its contents.

 X is a discrete memoryless binary source , Sequence X undergoes lossless transformation to satisfy the requirement â\euroœEach 1 is separated by one or more zeros“, ”Each 0 is separated by one or two 1s” or similar condition , These conditions are the decision conditions for error detection in channel transmission. A new channel coding method is proposed based on a weighted probability model for lossless coding , The encoding rate and encoding and decoding steps of different conversion methods are different , It is proved that the decoding error probability can reach 0 when the code length is long enough. In the simulation experiment of BPSK signal in AWGN channel, At 0.5 bit rate, the proposed method improves 1.1dB over Polar code when FER is 0.001, and 1.4dB over Polar code when FER is 0.0001.Â

In this paper, we introduce a novel resource allocation approach for integrated sensing-communication (ISAC) using the Kullbackâ\euro“Leibler divergence (KLD) metric. Specifically, we consider a base-station with limited power and antenna resources serving a number of communication users and detecting multiple targets simultaneously. First, we analyze the KLD for two possible antenna deployments, which are the separated and shared deployments, then use the results to optimize the resources of the base-station through minimising the average KLD for the network while satisfying a minimum predefined KLD requirement for each user equipment (UE) and target. To this end, the optimisation is formulated and presented as a mixed integer nonlinear programming (MINLP) problem and then solved using two approaches. In the first approach, we employ a genetic algorithm, which offers remarkable performance but demands substantial computational resources; and in the second approach, we propose a rounding-based interior-point method (RIPM) that provides a more computationally-efficient alternative solution at a negligible performance loss. The results demonstrate that the KLD metric can be an effective means for optimising ISAC networks, and that both optimisation solutions presented offer superior performance compared to uniform power and antenna allocation.Â

Electrocardiographic (ECG) signals that monitor heart activity can help identifying disease-related anomalies. Reliable automatic anomaly detection has been shown to be useful in supporting physicians in reading ECG signals. Decision support systems may be useful in such cases but their reliability can be guaranteed. Autoencoders (AEs) have been extensively used to analyse signals in many fields. Convolutional Autoencoders (CAE) are a particular class of AE showing optimal performances in detecting signal anomalies. Thus, CAEs can be used to support and automatise the task of anomaly detection. We design and use a CAE-based system to detect anomalies in ECG signals to support cardiologists in identifying anomalies related to possible diseases. Our tool outperforms other state-of-the-art ECG anomaly detection approaches tested on a real dataset. In the task of anomaly detection, our CAE obtains a ROC AUC of 97.82% with a simulated test set and a ROC AUC of 99.75% using on a real test set. The tool and the source code are available at https://github.com/UgoLomoio/EG_DSS_CAE.

Recent advancements in ultrasound technologies, such as ultrasound localization microscopy and ultrafast ultrasound Doppler, have enabled high-definition imaging of microvasculature. However, detecting weak microbubble or blood flow signals amid strong background noise remains a challenge, particularly in deep tissues. This study aims to enhance the signal contrast of microbubble and blood flow by leveraging their distinct spatial-temporal coherence in comparison to undesired noise for robust microbubble detection and microvascular imaging. We propose to quantify the signal coherence based on similarity analysis of beamformed ultrasound microbubble/blood flow data within the plane wave imaging framework. A spatial pixel is considered more likely to be a true microbubble/blood flow signal with a higher level of similarity, which can be measured by either of the following methods: 1) spatially block-wise normalized cross-correlation between two compounded frames; 2) temporally normalized autocorrelation across multiple compounded frames; 3) normalized cross-correlation between two subsets of post-compounded frames; 4) normalized autocorrelation of the pre-compounded data across angular direction. The original microbubble/blood flow signal is then weighted by the similarity measurement on a pixel-by-pixel basis to generate images with an improved signal contrast. The robustness of the proposed methods was first demonstrated in both phantom experiments and in vivo microbubble data from kidney transplant. We further validated their feasibility in blood flow imaging without the use of microbubbles based on in vivo data of human liver and kidney. Significant contrast improvement was observed, facilitating better visualization and detection of both microbubble and noncontrast microflow signals, which indicates a great potential of the methods for improved microvascular imaging and widespread clinical translation.

This work addresses symbol detection in timevarying Massive Multiple-Input Multiple-Output (M-MIMO) systems. While conventional symbol detection techniques often exhibit subpar performance or impose significant computational burdens in such systems, learning-based methods have shown potential in stationary scenarios but often struggle to adapt to nonstationary conditions. To address these challenges, we introduce a hierarchy of extensions to the Learned Conjugate Gradient Network (LcgNet) M-MIMO detector. Firstly, we present Preconditioned LcgNet (PrLcgNet), which incorporates a preconditioner during training to enhance the uplink M-MIMO detectorâ\euro™s filter matrix. This enhancement enables the detector to achieve faster convergence with fewer layers compared to the original, nonpreconditioned approach. Secondly, we introduce an extension of PrLcgNet, known as the Dynamic Conjugate Gradient Network (DyCoGNet), specifically designed for time-varying environments. DyCoGNet leverages self-supervised learning with forward error correction, enabling autonomous adaptation without the need for explicit labeled data during training. It also employs metalearning, facilitating rapid adaptation to unforeseen channel conditions. Our simulation results demonstrate that PrLcgNet achieves faster convergence, lower residual error, and comparable symbol error rate (SER) performance to LcgNet in stationary scenarios. Furthermore, in the time-varying context, DyCoGNet exhibits swift and efficient adaptation, achieving significant SER performance gains compared to baseline cases without metalearning and online self-supervised learning.

Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time. Early internal abnormalities in the DPS may develop into uncontrollable thermal failures, causing serious safety incidents. In this study, the multiscale information fusion is proposed for internal abnormality detection and localization of DPSs under different scenarios. We introduce the dissimilarity statistic as a means to identify anomalies for lumped variables, whereas spatial and temporal statistic measures are presented for the anomaly detection for distributed variables. Through appropriate parameter optimization, these statistic functions are integrated into the comprehensive multiscale detection index, which outperforms traditional single-scale detection methods. The proposed multiscale statistic has good physical interpretability from the system disorder degree. Experiments on the internal short circuit (ISC) of a battery system have demonstrated that our proposed method can swiftly identify ISC abnormalities and accurately pinpoint problematic battery cells under various working conditions.Â

This paper describes the historical trajectory of video conferencing systems, spanning from their earlier mechanical and analog origins in the 1920s to the sophisticated IP-based services delivered from the cloud in the 2020s. Each technological age is examined, highlighting the technical and functional aspects that characterized its evolution. Commercial landmarks of each age are presented providing a comprehensive overview of the most prominent offerings at pivotal moments in the timeline. By examining the past and speculating on the future, this paper aims to provide a holistic understanding of the development, current state, and forthcoming trends in video conferencing technology.