National Journal of Signal and Image Processing
National Journal of Signal and Image Processing (ISSN: 3107-5231) aims to provide a high-quality platform for researchers, engineers, and academicians to publish innovative research in the fields of signal and image processing. The journal fosters interdisciplinary studies that bridge the gap between theory, implementation, and real-world applications. Scope Includes (but is not limited to): Digital signal processing (DSP) techniques and algorithms Image acquisition, enhancement, restoration, and segmentation Feature extraction, object recognition, and computer vision Medical image processing and diagnostics Remote sensing and satellite image analysis Multidimensional signal and image modeling Real-time image and signal processing in embedded systems Deep learning and AI applications in image/signal domains Signal processing for sensor networks and IoT applications Compressive sensing and sparse representations Frequency of publication - Quarterly Language - English Subject - Engineering Year of Starting - 2025 format of publication - Online Only ISSN - 3107-5231
SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
Spatiotemporal Transformer Networks for Real-Time Video-Based Anomaly Detection in Smart City Surveillance
The quick growth in the infrastructure of smart cities has led to the widespread and unending video-based data flow of highly dense surveillance systems, establishing the utmost vitality of effective anomaly detection in real-time. A new Spatiotemporal Transformer Network (STTN) is developed in this work that incorporates modern signal and image processing approaches and connects them to transformer-based deep learning to increase the efficiency of urban safety monitoring. The offered architecture leverages spatial time embedded patch, multi-head self-attention, and hybrid scheme of supervised learning and self-training to account for intricate inter-frame dependency and do it with low latency. In signal processing terms, the model includes patch-wise feature collection, sequence modeling of a temporal sequence and slide-inference over continuous video streams. State-of-the-art accuracy (AUC: 96.7% UCSD Pedestrian, 90.3% Shanghai Tech Campus and a proprietary SmartCitySurv dataset), lower rates of false alarms, and inference velocity appropriate to edge implementation are found using experimental validation using UCSD Pedestrian, ShanghaiTech Campus, and a proprietary SmartCitySurv dataset. Attention-based visualizations also increase understandability and can be used to facilitate human-in-the-loop decision-making. This work brings sophisticated deep architecture to fundamental video signal processing, realizing a scalable, interpretable, and real-time means to next-generation smart city-level video surveillance.
Video Signal Processing, Spatiotemporal Transformer, Real-Time Anomaly Detection, Smart City Surveillance, Edge Computing, Self-Attention, Deep Learning, Video Analytics
Deep Residual U-Net for Accurate Medical Image Segmentation in Limited Data Scenarios
Proper and effective segmentation of medical images is of central importance in the diagnosis in the clinics, preoperative investigation of the disease, and postoperative follow-up of the process. Even in spite of the progress in deep learning, traditional convolutional neural network (CNN) models including U-Net still suffer challenges when trained on small-scale datasets annotated ones that are normally the case in medical imaging since there are privacy concerns and also the annotation cost and limited data. The limitation can be addressed by the novel type of Deep Residual U-Net (DRU-Net) architecture incorporating residual learning methods into the classic encoder-decoder block system of the original U-Net. The proposed model utilizes the residual connections on multiple levels, which helps to use features more productively, smooth the gradient flow of deeper layers, and reduce the vanishing gradient problem, which promotes the effectiveness of training and the quality of segmentation results. To further optimize on the situation of limited data, DRU-Net also uses batch normalization and dropout regularization, as well as a hybrid loss composed of binary cross-entropy and Dice loss to solve the class imbalance problem. Severe experiments were done in two publicly released benchmark objects, ISIC 2018 and DRIVE, used in skin lesion segmentation and retinal vessel segmentation correspondingly. The findings reveal that DRU-Net surpasses the original U-Net, ResUNet and other newer architectures, in measures of Dice coefficient, Intersection-over-Union (IoU) sensitivity and accuracy of the boundaries (Hausdorff distance). DRU-Net showed an improvement in the Dice score of up to 6 percent as compared to baseline models, which shows that DRU-Net does not require so much supervision to extract fine-grained anatomy features. Qualitative visualization additionally answers that DRU-Net has the capability to maintain the anatomical boundaries and more accurately defining ambiguous areas. The proposed architecture is a scalable and computationally efficient medical image segmentation solution that can guarantee detection with a satisfactory result in resource-based or privacy-related clinical settings. This is the first step toward the practical application of data-efficient segmentation models in real-world clinical practice, and leaves room to optimize the models even further with attention, transfer learning, and federated training paradigms to achieve closer integration with clinical practice.
Medical image segmentation, U-Net, residual learning, deep learning, limited data, Dice coefficient, convolutional neural network (CNN), ISIC, DRIVE, DRU-Net.
Lightweight GAN-Based Image Super-Resolution for Satellite Imagery on Low-Power Embedded Hardware
With the emerging trend of satellite-based earth observation systems and edge-enabled UAV platforms, there is increasing pressure to reconstruct image at a high resolution but also limited available computational or energy resources. In this paper, the authors would like to propose a lightweight GAN-based image super-resolution (ISR) pipeline that is targeted towards a real-time use case on low-power embedded systems. The framework combines depth-wise separable convolutions, structured channel pruning and quantization aware training to achieve very large model compression with little loss in fidelity of reconstruction of images. An architecture was benchmarked against publicly available satellite dataset and demonstrated up to 3.5 dB improvement in Peak Signal-to-Noise Ratio (PSNR) and 28.7 percent better Structural Similarity Index (SSIM) over regularly used interpolation method of bicubic interpolation. In comparison with the conventional SRGAN and ESRGAN alike, our model was, on the one hand, much smaller, as it reduced the size by 45% and, on the other, its performance was up to 60 percent faster to operate on NVIDIA Jetson Nano (or ARM Cortex-A based devices) with the given power consumption limit of 10W. These findings demonstrate the framework as capable of providing efficient ISR through high-quality operations done directly on the edge hardware without depending on a cloud-based AI computing facility. This makes the technique scalable and viable to practical applications as techniques in real time remote sensing in power limited environs like satellites onboard and Unmanned Air Vehicles (UAVs).
Generative Adversarial Networks (GANs), Image Super-Resolution (ISR), Lightweight Deep Learning Models, Embedded AI, Low-Power Hardware, Satellite Imagery Enhancement, Quantization-Aware Training, Edge Inference, Remote Sensing, Onboard Image Processing, Real-Time ISR, UAV-Based Imaging, Energy-Efficient AI, Model Compression, SpaceNet Dataset.
Structure-Preserving Denoising of Large-Scale Satellite Imagery Using Graph Signal Processing
Satellite imagery is an important part of many activities, which include environmental monitoring, land use theme, urban planning, and disaster relief. Nevertheless, such imagery quality is often interfered with by several forms of noise that may be involved when acquiring, transmitting, or through atmospheric perturbations. Denoising is thus needed, however with most traditional approaches, the noise will be suppressed and also it will blur the important structural information found in the image, including edges and fine details. The following paper proposes a structure -preserving graph signal processing (GSP ) based novel framework to complete denoising of large-scale satellite imagery. Under the proposed method, both local and non-local structural relations will be captured as every image is represented as an adaptive graph structured such that the nodes of the graph are the pixels or the superpixels with the edge-links identifying both the spatial and the radiometric similarity, hence, the spatial and radiometric neighbourhood of the graph. Denoising is performed with the help of graph Laplacian regularization, which enables a local refinement of the signal taking into account only the important geometric characteristic and discontinuities. Thorough testing on standard satellite datasets together with a wide range of synthetic and realistic noise settings confirms higher performance of the proposed GSP-based approach in comparison to current benchmark denoising algorithms, as expressed by peak signalto-noise ratio (PSNR), structural similarity index (SSIM), and edge preservation indices. Qualitative outcomes additionally support the idea that our technique is successful in preserving key structures and textures necessary to downstream investigation. Such results demonstrate the potential of GSP-based denoising as a way of enhancing the quality and usefulness of satellite imagery in higher order remote sensing services.
Graph Signal Processing (GSP), Structure-Preserving Denoising, Satellite Imagery, Remote Sensing, Edge Preservation, Image Restoration, Adaptive Graph Construction, Image Enhancement, Noise Suppression, Large-Scale Image Processing
Wavelet-Based Biomedical Signal Denoising for Remote Patient Monitoring Applications
Remote patient monitoring (RPM) is nowadays viewed as an indispensable constituent of modern healthcare infrastructure, thanks to which it is now possible in a non-clinical environment to monitor physiological parameters in real-time and continuously using such devices wearable and embedded sensors. Nonetheless, one tends to get polluted biomedical signals in such an environment as electrocardiograms (ECG) and photoplethysmograms (PPG) with biomedical applications are usually affected by noise of different forms, including baseline wandering, power-line noise, and motion artifacts, which may severely hamper the efficiency of diagnostic. In the given paper, a strong signal denoising model, which is related with the discrete wavelet transform (DWT), is shown with reference to biomedical signal improvement under RPM responses. Multi-resolution analysis breaks the signal into different frequencies and after the soft thresholding, the inverse reconstruction is used to suppress the noise without interfering with the morphological portion of the original waveform. To evaluate the performance of the proposed DWT-based approach compared to standard FIR/IIR filters and empirical mode decomposition (EMD), we attain some benchmarks datasets that are relevant and relevant so as to make a comparative study between both of them. The quality of performance is evaluated on the basis of signal-to-noise ratio (SNR), mean squared error (MSE) and percentage root mean square difference (PRD) which illustrate that the wavelet-based method focuses on achieving a high performance level in terms of denoising. Also, the real-time capability is confirmed by running on ARM Cortex-M4 microcontroller, where the time latency of each signal segment is less than 10 ms, hence attesting the suitability in an embedded healthcare system. The results support the effectiveness of wavelet-based denoising in the reliability of interpreting signals in anomaly detection, HRV analysis, and various end-use in RPM. In this work, the energy-efficient and precise signal processing algorithms will be developed towards making the next generation wearable health monitoring platform energy-efficient, which can ultimately deliver proactive healthcare services and enhance patients outcomes by analyzing physiological data at the right time and in a noise-free manner.
Wavelet Transform, Biomedical Signal Denoising, Remote Patient Monitoring (RPM), ECG, PPG, Discrete Wavelet Transform (DWT), Motion Artifacts, Time-Frequency Analysis, Real-Time Signal Processing, Embedded Healthcare Systems
AI-Augmented Adaptive Filtering for Real-Time Noise Suppression and Feature Enhancement in Biomedical Signals
Electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG) are examples of biomedical signals that are frequently compromised by motion artifacts, the power-line interference, and physiological noise interferences, thus, limiting the accuracy of their analysis. Traditional adaptive filters that are in use such as the Least Mean Squares (LMS) filter work reasonably well in stationary situations but fail in non-stationary ones found in reality. The proposed paper introduces a new concept of AI-enhanced adaptive filtering framework which integrates conventional adaptive filtering algorithm with data-driven noise prediction model to improve real-time signal quality. The proposed technique incorporates the 1D CNN lightweight architecture with the classic LMS filter, making it possible to adjust filter weighting to contextual patterns of signals, in order to better detect and neutralize time-varying noise by the system. This architecture is justified using some benchmark biomedical datasets such as the MIT-BIH Arrhythmia and PhysioNet EEG Motor Movement set. Quantitative tests based on measures Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) demonstrate the high effectiveness of the suggested method in comparison with standalone LMS, RLS, and CNN-based filters. Notably, the design reaches real-time on embedded processors and would support wearable and edge-AI healthcare. The obtained results indicate that both noise suppression and preservation of diagnostic features improve significantly, providing a basis to further develop intelligent biomedical signal processing systems.
Biomedical Signal Processing, Adaptive Filtering, AI-Augmented DSP, Real-Time Signal Enhancement, Noise Suppression, 1D Convolutional Neural Networks, ECG Denoising, Edge AI, Feature Preservation, Wearable Health Monitoring.
Privacy-Preserving Federated Learning for EEG Signal Classification in Remote Brain–Computer Interfaces
The development of Brain Computer Interfaces (BCIs) has led breakthrough areas in distant care, neurorehabilitation and assistive technology. Nonetheless, remote implementation of BCI, especially those based on electroencephalogram (EEG) signal, creates serious privacy and security issues. Processing of the EEG waveforms entails multiple operations that imply bandpass filtering, noise rejection, spatial feature enhancement, and modeling, temporal patterns, which is why maintaining fidelity as well as privacy is of the essence. A privacy preserving federated learning (FL) framework of EEG signal classification is suggested that combines neural signal decoding based on the hybrid CNN-LSTM convolutional neural network-long short term memory architecture with smart spatio temporal signal processing. The model is able to capture both cross-channel spatial synchrony as well as temporal time-varying patterns in neural activity and is motivated by time frequency domain preprocessing to maximize feature discriminability. It gives some privacy through using differential privacy and secure aggregation and minimizes communication overhead by compressing the model. On benchmark EEG datasets, the framework shows high classification accuracy 92.6% with little accuracy degradation as compared to centralized training, further proving secure, real-time execution as applied in bandwidth-limited, embedded neurotechnology systems.
EEG Signal Processing,Brain–Computer Interface (BCI), Privacy PreservationFederated Learning,Neural Signal Decoding, Secure Aggregation, Differential Privacy, Remote Healthcare, CNN–LSTM Architecture, Spatio–Temporal Feature Extraction, Wearable Neurotechnology
Deep Cross-Attention-Based Dual-Modal Fusion of LiDAR Signals and Visual Imagery for Robust Autonomous Navigation
This paper introduces a new deep learning architecture where a dual-modal fusion mechanism is based on cross-attention scheme to achieve resilient autonomous navigation. The architecture combines LiDAR point cloud data and visual image in a manner such that transformer-based cross-attention modules learn about fine-grained spatial-temporal cross-modal dependence. The system described overcomes the limitations of unimodal and early fusion approaches in recognizing inter-modal features through an instance-based balancing weighting scheme where the system balances these limitations under difficult circumstances when the light source is low, occlusion, and white noise. The dataset that the model is trained and tested on are benchmark autonomous driving datasets such as KITTI and nuScenes. Quantitative findings show significant improvements in accuracy levels of object detection (IoU+6.2), trajectory prediction (ADE/FDE improvements) and collision-free path planning in dynamic settings. Ablation experiments prove that cross-attention fusion is better than standard concatenation and late fusion networks. In further addition, the framework enables real-time inference to take place on embedded systems thereby, it can be deployed very easily on resource limited autonomous vehicles and mobile robots. The given proposed method is just not only useful to improve perception robustness but also in decision-making in safety-critical settings to form a scalable and adaptive solution to next-generation autonomous navigation systems.
Cross-Attention Mechanism, Dual-Modal Sensor Fusion, LiDAR-Visual Integration, Autonomous Navigation, Multimodal Deep Learning, Transformer Architecture, Object Detection, Trajectory Estimation, Embedded Edge AI, Real-Time Perception Systems.
Embedded Implementation of Speech-to-Text Translation Using Compressed Deep Neural Networks
Speech-to-text (STT) translation in real-time has now become a key component of voice-controlled embedded applications which now apply to everything smart and connected (IoT devices), wearable medical devices and voice-enabled industrial and digital control systems. Nonetheless, integrating correct and responsive STTs on-board systems are hampered by extreme processing power, memory and power limitations. The proposed compressed deep neural network (DNN) architecture in this paper is a low-powered embedded model that is best-suited to realize the efficient and large-scale STT translation. The peculiarity of our solution is based on combining three mutually complementary components of model compression speeding up: magnitude-based pruning with the aim of discarding redundant weights, post-training quantization to cut the computational accuracy and memory usage, and knowledge distillation as a strategy to port the performance of a large model to its smaller, lightweight, counterpart. These strategies have been integrated into one that has a modular STT pipeline that consists of an MFCCs based feature extractor, compact acoustic model (CNN-RNN-based or Transformer-based), quantized language model, and a decoder that can be efficiently optimized to run on fixed-point operations. The deployment targets include popular embedded processors such as the ARM Cortex-M7-based STM32F746 and RISC-V-based Kendryte K210 that can also be easily deployed using toolchains like TensorFlow Lite Micro, CMSIS-NN, and the Kendryte SDK. Experimental analysis on benchmarking data LibriSpeech and Mozilla Common Voice revealed that our optimized models can reduce memory size by up to 4.2x and save more than 35 percent energy with less than 2 percent drop in word error rate (WER) against baseline models that use full-precision training. It is typical that, where transcription is emulated using cloud processing capabilities, it will be faster on edge devices with an average inference latency of less than 100 milliseconds. This means that it can be emulated close to real-time. The present work shows that low-latency, energy-efficient, and multilingual STT translation systems can be deployed to embedded targets where voice-only, privacy-preserving, offline voice interfaces are expected to become a reality in next-generation smart devices.
Embedded Systems, Speech-to-Text Translation, Model Compression, Deep Neural Networks, Quantization, Pruning, Knowledge Distillation, TinyML, Low-Power AI, Edge Inference.
Noise-Robust Image Restoration Using Attention-Guided Transformer Networks for Real-Time Visual Enhancement
Restoring images corrupted by noise is an everlasting process in computer vision, particularly in real-time instances, including the self-governing cars, surveillance cameras, and mobile photo capturing. The proposed Attention-Guided Transformer Network (AGTN) is a novel approach to real-time and noise-resistant image restoration that will be introduced in this paper. The proclaimed architecture has taken hybrid encoder-decoder form, revealing some convolutional layers in local texture decryption with transformer blocks that estimate global dependencies through self-attention in the multifaceted sense. The Attention-Guided Denoising Module (AGDM) learns to be adaptive to image artifacts caused by noise to be coherent to structural details in various levels of noise and distributions at a fine granularity. These experiments train and test the model on common components of image restoration benchmarks: BSD68, Set12, and Urban100, and the result shows a consistent improvement with the state-of-the-art with CNN-based and transformer-based methods. The quantitative performance indicates as much as 31.16 dB PSNR and 0.851 SSIM on Gaussian noise (sigma=25) and major improvements are also found in real-world noise datasets. Further, the model runs with 27 FPS of inference speed on NVIDIA Jetson Nano and the speed was proven to be usable in embedded vision tasks with the 256256 bit image resolution. These findings indicate the promised attentional transformer architectures to be useful in removing noise, distorting structures and use in real-time implementation. Future experiments are planned on the lightweight model derivations, eigen-noises domain adaptation to novel type of noise, video and multimodal restoration.
Image Restoration, Transformer Networks, Attention Mechanisms, Real-Time Denoising, Embedded Vision, Noise Suppression, AGTN
Multimodal Fusion Techniques for Emotion Recognition Using Audio, Visual, and Physiological Signals
Sensitive emotion recognition information depends on high-level signal and image processing technologies to acquire, synchronize, and merge multimodal data involving heterogeneous information. One-modal methods are usually sensitive to isolated noise, occlusions or information losses. This paper examines multimodal signal processing techniques which combine audio, visual and physiological information using new feature extraction pipelines and adaptive fusion algorithms. The suggestion of fusion using a hybrid deep learning focus refers to the following presentation of integrating time and space representations by achieving a fusion of synchronized time frequency features, statistical descriptors, and deep embeddings, combined with adaptive weighting operations that help to overcome domain-specific noise. Test on benchmark datasets shows that the framework is more accurate, F1-score, and robust under realistic conditions when compared with unimodal and conventional fusion methods. This work helps in achieving scalable, real-time and noise-invariant multimodal systems that can be applied to healthcare, adaptive interfaces and affective computing through increased signal preprocessing, feature-level integration and classifier decision fusion.
Multimodal Signal Processing, Time–Frequency Analysis, Feature Fusion, Adaptive Filtering, Emotion Recognition, Physiological Signal Processing, Audio-Visual Processing, Deep Learning, Affective Computing, Robust Classification
Real-Time Gesture Recognition Using mmWave Radar Signal Processing and CNN Models
The current paper presents a model of real-time gesture recognition, where the usage of millimeter-wave (mmWave) radar signal processing is combined with convolutional neural networks (CNN) in order to effectively and precisely interact with machines as a human being. The system employs a 60 GHz frequency-modulated continuous-wave (FMCW) radar sensor to capture reflected signals of dynamic hand gestures to identify them through spatial and temporal signs of the motion. These raw appreciative intermediate frequency (IF) signals are pre-processed using an uncover of DNA Doppler-range transformation that entails Fast Fourier Transform (FFT)-based profiling of range and velocity, clutter repression and considering spectrograms to deliver range-Doppler maps (RDMs). The RDMs are used as powerful input features to a compact and optimized CNN model that is pretrained to classify ten different gesture patterns including, swipe, push, pull, and rotate. CNN architecture should have low computation complexity, which is possible to implement on the resource-limited edge devices like Raspberry Pi 4 and NVIDIA Jetson Nano. We used 20 subjects across all the gestures at different orientations and distances to collect a huge dataset using this technique to make it robust. Data augmentation and quantization-aware optimization techniques have been used to train the model and strike the right compromise between real time and accuracy performance. According to experimental analysis, the suggested system obtains a gesture classification precision above 95 percent, and the inference latency is averagely less than 100 milliseconds with little power demands, thus richly appliable to embedded systems. In addition, the environment is highly robust where the framework is economical working in changing lighting conditions, occlusion, and environmental noise as compared to conventional vision-based systems in areas of privacy and low-light performance. Extendable and no-contact smart environment The suggested solution would allow a touchless interface to be used in smart environments, such as home automation, industrial control panels, and wearable assistive solutions. To improve upon cross-user generalization in terms of domain adaptation, further research will be based on incorporating attention-based deep learning models. On the whole, this study presents an effective and power-efficient approach to realizing a gesture recognition system based on radar with deep-learning in real-time on embedded systems.
mmWave radar, gesture recognition, FMCW radar, Doppler-range processing, convolutional neural networks (CNN), real-time classification, edge computing, signal processing, human-computer interaction, embedded systems.
Wavelet-Based Multiresolution Analysis for Efficient Compression of Non-Stationary Signals in Resource-Constrained IoT Devices
The challenge with non-stationary signals is to process it efficiently in edge-based Resources of the Internet of Things (IoT) which already face constraints in memory and compute bottlenecks, and even stricter energy budgets. In this paper, we present a lightweight, wavelet-based multiresolution analysis (MRA) framework to provide real-time lossy compression of dynamic signal encountered in IoT tasks such as biomedical, acoustic, and environment related data streams. The worn-out approach includes discrete wavelet transform (DWT) to decompose the signal using the chosen orthogonal and parabolic sets of bases and then using adaptive thresholding and quantisation to obtain high compression ratios without losing important elements of the signal. Benchmark datasets (ECG (MIT-BIH) and vibration signals) were employed to make the approach valid. The proposed scheme had an average percent root mean square distortion PRD, of less than 3 percent, complying with a high fidelity reconstruction and the maximum compression ratio was 12:1. Besides, its realization on an ARM Cortex-M4 microcontroller showed energy savings of 58% and more than 40 percent in memory reduction relative to conventional Fourier-based methods, making it appropriate to be applied in real-time embedded systems. This architecture delivers scalable and low-power signal processing in edge-based IoT systems with a feasible course of action in tasks that require a lot of data like health wearables, in-structure health sensors and remote loggers of environmental data. These findings support that multiresolution wavelet analysis is effective in solving the twin problems of data compression and energy utilization in an IoT setting that has limited resources.
Wavelet Transform, Signal Compression, IoT, Non-Stationary Signals, Embedded Systems, DWT, Multiresolution Analysis, Low Power, Edge Computing
An Energy-Efficient DSP Pipeline for Real-Time Audio and Image Processing in Battery-Constrained Embedded Systems
Embedded systems that operate on batteries are becoming more involved with executing advanced tasks related to processing audio and image information within strict power limitations. To deal with this problem, this paper presents a new energy-conservative digital signal processing (DSP) pipeline to be implemented on battery-constrained platforms to support real-time multimedia inference. Its architecture relies upon the algorithmic approximations, data reuse optimization, and dynamic voltage-frequency scaling (DVFS) to reduce the degree of computational overhead to a significant degree. The pipeline contains a hybrid of a fixed-point arithmetic engine that is used to provide high-volume, low-power operation without affecting the fidelity of the signal. The described DSP pipeline isentedal and tested on a System-on-Chip (SoCostrained RISC-V-based fabricated in 65M cmos node. Experimental results on real-word data-sets show up to 42 per cent reduction in power envelope along with 33 per cent improvement in throughput per watt compared to the state-of-the-art embedded processors in use. It further advances the accuracy of classification and quality of images further under sub-50 mW power budgets. The findings make the proposed automatic pipeline a resilient and scalable approach in solving energy-constrained embedded systems that are deployed to perform Internet of Things (IoT), wearable computing, and autonomous sensing solutions. Future directions will involve the ability to further exploit CNN-DSP hybrid acceleration, not to mention adaptive task-aware pipeline reconfiguration.
DSP, Low-Power Embedded Systems, Fixed-Point Arithmetic, Real-Time Processing, Audio Classification, Image Enhancement, RISC-V SoC, Energy Efficiency, Battery-Constrained Devices
Multi-Task Deep Neural Network for Simultaneous Audio Event Detection and Localization in Smart Surveillance Systems
Smart surveillance systems with audio intelligence embedded provide substantial situational awareness in designs where it may be inconvenient to observe the situation visually because of a blocked view, low-light situations, or privacy requirements. It is the purpose of this paper to propose a new Multi-Task Deep Neural Network (MT-DNN) architecture that aims to conduct multiple tasks, Audio Event Detection (AED) and Sound Source Localization (SSL) in multi-channel audio recordings. In contrast to traditional single-task models where different pipelines are involved in the detection and localization processes, the present architecture follows with shared spectral-spatial encoder and task-specific attention-enhanced heads, which allows to reuse features efficiently and contributes to better cross-task generalization. The encoder exploits convolutional layers in extracting local spectral patterns, bidirectional recurrent units in the modeling of the temporal context, and high-seen in attention mechanisms to discriminately focus features. AED is treated as a multi-classification task where SSL is solved as a regression task of predicting source azimuth and elevation angles jointly optimized with a weighted composite loss. Thorough evaluation of the UrbanSound8K data on AED and the TAU Spatial Sound Events 2021 data on SSL shows that the MT-DNN has an AED accuracy of 93.1 percent and an SSL mean angular error of 4.2, equivalent to 6 percent and 12 percent, respectively, improvement over comparable single-task baselines. Furthermore, the model has the parameter reduction of 25 percent and reduced inference latency and it is useful in real-time edge implementation on embedded surveillance devices. Such results highlight the opportunities of multi-task learning to develop resource-sparing multimodal surveillance systems and leave room to future realizations of the integration with vision-based analytics to better understand events.
Multi-task deep neural network (MT-DNN), audio event detection (AED), sound source localization (SSL), smart surveillance systems, spectral–temporal feature extraction, attention mechanisms in audio processing.
Fusion of Multispectral and Panchromatic Images for Enhanced Remote Sensing Resolution
This combination of multispectral (MS) and panchromatic (PAN) data commonly known as pansharpening has become a ubiquitous operation in all remotely determined research and development and it enables the creation of images that produce the saturation of the multispectral datasets combined with the fine-resolution capacity of the panchromatic data. This paper gives an extensive comparative study as well as practical use of new high-quality pansharpening methods, with a view to overcome the current insatiable needs in high-quality remote sensing information as well as rigorously reserving the spectral fidelity. Such state-of-the-art methods as convolutional neural networks (CNNs), Vision Transformer and deep learning-based methods which are Component Substitution (CS) and Multiresolution Analysis (MRA) are also included. Techniques are then tested on quantifiable scales with firm measures like Spectral Angle Mapper (SAM), Correlation Coefficient (CC), Error Relative Globules Adimensionnelle de Synthaje (ERGAS) and Quality Index (Q-index) so that there is the multi-faceted analysis of respective spectral and spatial work. Experimental validations were conducted with high-resolution WorldView-3 satellite images of large variety of types of land cover and scene complexities. The results reveal that deep-learning-based-fusion techniques, mainly the Transformer-architectures-based, have achieved significant bettering over conventional approaches in terms of providing enhanced spatial sharpness and superior spectral preservation with enhancements in all forms of assessment measures. The results illustrate the extent of the Transformer models in learning the complex cross-modality relationships and present the possibility of usage in real-time, large-scale, application-specific remote sensing applications like land cover classification, urban mapping, and precision agriculture. Not only has this work created a critical benchmarking of both present and developing fusion methods but it has also suggested practical lessons on possible future research on unsupervised fusion, model generalizability, and finally edge deployment in the service of in-situ applications of remote sensing.
Remote Sensing, Pansharpening, Multispectral Image, Panchromatic Image, Image Fusion, Spatial Resolution Enhancement, Spectral Preservation, Wavelet Transform, Principal Component Analysis (PCA), Vision Transformer, Deep Learning, CNN-Based Fusion, Quality Metrics, SAM, ERGAS, Q-index
Residual Attention-Enhanced Deep U-Net for High-Resolution Medical Image Segmentation
Precise and automatic segmentation of high resolution medical images is a central step in computer assisted diagnosis, treatment planning, surgical navigation. Nonetheless, standard segmentation networks have poor capabilities of detecting boundaries, typically in the heat areas and non-homogenous parts. This paper proposes a Residual Attention-Enhanced Deep U-Net (RA-U-Net) which combines the concepts of residuals learning and attention gating into a traditional backbone U-Net. Residual blocks enhance the gradient flow and allow greater feature learning and attention gates allow the model to prioritize focusing on important anatomical structures during decoding. Two benchmark datasets used to test the proposed RA-U-Net include BraTS 2021 and ISIC 2018 that deal with the brain tumor-related data segmentation in MRI and dermoscopic skin lesion segmentation, respectively. Based on the outcome of experiments, it is evident that the use of standard U-Net and Attention U-Net does not match RA-U-Net, which has Dice scores of 91.4%, in terms of accuracy in segmenting the object. Moreover, the model works well in terms of preservation of boundaries and denoising, particularly in high resolution. Such results show that residual connection combined with spatial attention is to increase the feature representation and the precise localization. Proposed framework is computationally efficient, has a good transfer across modalities thus it is very ideal to be used in clinical settings on radiology workflow and telemedicine where there is the need to produce a high-fidelity segmentation in real-time.
Medical Image Segmentation, Deep U-Net, Residual Learning, Attention Mechanism, High-Resolution Imaging, Brain Tumor Segmentation, Skin Lesion Detection, Deep Learning in Healthcare, Dice Similarity, Biomedical Image Analysis
Zero-Shot Learning for Remote Sensing Image Segmentation Using Cross-Domain Transfer and Self-Training
Segmentation of remote sensing (RS) images plays a pivotal role in signal and image processing as it helps in performing pixel-level interpretation of these images to help land cover mapping, environmental monitoring, studying urban infrastructure, and disaster assessment, etc. Although distilled deep learning architectures (e.g., U-Net, DeepLab, SegFormer) have produced good outcomes, their usage of high annotations (particularly with pixel-level) large-scale datasets conditions them to lack generalizability. The work presents a Zero-Shot Learning (ZSL) structure-the Cross-Domain Transfer and Self-Training (CDT-ST) model-to perform RS image segmentation without target-domain labelled information. It combines a domain-invariant feature extraction module based on signal processing, with cross-domain class-mapping based on semantic embedding, and a sequence of refinements to pseudo-labels, namely confidence thresholding, spatial consistency filtering, and Conditional Random Fields (CRFs). The combination of these techniques makes such adaptation strong when dealing with extreme changes in the domain of spatial resolution, illumination, and scene structure. The experiments include evaluating on SpaceNet, DeepGlobe, and LoveDA datasets where the mean Intersection over Union (mIoU) was found to be 87.2% with no target-domain labels, only 233 fewer than fully supervised counterparts. Combining transfer learning, semantic mapping and primitive signal/image processing methods, CDT-ST proposes a scalable, no annotation required, high-accuracy system with application of large-scale, heterogeneous RS segmentation.
Zero-shot learning, remote sensing imagery, semantic segmentation, cross-domain transfer learning, self-training, unsupervised domain adaptation, pseudo-label refinement.
IoT-Integrated Deep Learning Framework for Real-Time Image-Based Plant Disease Diagnosis
The plant diseases are a major threat to food security globally since they result in production loss through low crop yields in addition to compromising the yield quality and quantity. Manual inspection is a primary application in traditional disease detection techniques as it requires labour, prolongs diagnosis time, and has a very low rate of error occurrence as well as it is not applicable across large landscapes of farming. In order to overcome these restrictive factors, this study develops an Internet of Things (IoT)-enhanced deep learning model of real-time plant disease diagnosis based on images. The system is a combination of the most recent advances in computer vision through convolutional neural networks (CNNs) and edge computing to allow detecting and classifying plant diseases early in real-world farms. The selected model consists of a curated dataset of 50,000 leaves of various crops, both healthy and diseased, used to feed a lightweight CNN, which is a modified MobileNetV2 that is optimized to run on the resource-constrained edge device, namely Raspberry Pi 4B. The model is optimized to TensorFlow Lite to achieve fast inference (in real-time) and is deployed down at the field level, an IoT-based camera takes image 2-4 times in a minute of the leaves. The images that would be captured are processed locally on the edge node whereby the disease classification has to be done with high accuracy and low latency. The system performs classification with 93.23 percent accuracy with an average of 0.596 seconds inference time, making it feasible in field conditions and an activity that is real time. In addition, the system offers low power drain, wireless interconnection and horizontal expansion using MQTT enabled communication with remote cloud dashboard which stores long term data and provides analytics. The given proposed solution presents the cost-efficient, mobile-friendly, scalable tool, which might help farmers to get timely and actionable information, which will help farmers cue precision agriculture approaches and reduce the transmission of the plant diseases. By filling in the gap between deep learning and the technology of field-deployable IoTs, this framework will present substantial possibilities of shifting the traditional agricultural monitoring systems toward smart, centralized farming monitoring systems.
IoT, Deep Learning, Plant Disease Detection, MobileNetV2, Edge AI, Image Classification, Precision Agriculture, TensorFlow Lite, Smart Farming
Explainable AI Models for Medical Signal and Image Interpretation in Healthcare Monitoring Systems
Artificial Intelligence (AI) has transformed the healthcare monitoring landscape and empowered real-time, real-time, automated interpretation of physiological signals and imagery. Nonetheless, AI has not yet been widely endorsed in healthcare and clinical practice as there is a strict concern over the model of deep learning being deemed as opaque to the extent of it being described as a black box. This is non transparent which is highly questionable in terms of trust, accountability and explainability which are very crucial in clinical decision support. This paper describes a generational framework that incorporates Explainable Artificial Intelligence (XAI) procedures into the processing methods of electrocardiogram (ECG) and electroencephalogram (EEG) and magnetic resonance imaging (MRI) data. SHAP, LIME, attention maps, GRAD-CAM, and TCAV are all involved in the combination of our approach that will deliver interpretable information about model predictions. These methods allow clinicians to visualize and understand how particular aspects or pieces of signals affect the diagnostic judgment. We test our framework on three existing benchmark datasets that we obtained publicly MIT-BIH Arrhythmia to test on ECG, PhysioNet EEG to test on neural activity, and BraTS-2021 to test on brain tumor segmentation and find that the proposed models achieve high performance at diagnosing the input and also provides explanations that can be easily understood and are of clinical relevance. The results demonstrate that our explainable models match or outperform baseline classification and segmentation performance, as well as provide visualization of key features that can be used in diagnostics that yields trust in the diagnosis by health care professionals. This will assist in bridging the trade-off between the accuracy and explainability of models, and assist in the development of AI-supported medical systems that are not only good but are accountable. The suggested XAI-powered system improves the understandability of automated healthcare analytics, which makes it applicable to early disease detection, constant patient monitoring, risk stratification, etc. In the long term, the study contributes to the implementation of reliable AI-based technologies in a practical clinical setting, backloading technological processes to the principles of ethics and lawfulness.
Explainable Artificial Intelligence (XAI); Medical Imaging; Biomedical Signal Processing; Healthcare Monitoring Systems; Deep Learning Interpretability; Saliency Maps; Grad-CAM; Clinical Decision Support
Low-Light Image Restoration Using Transformer-Based Enhancement Networks
Restoration of low-light images is a classic amongst the challenges of computer vision, relevant to most image formation processes in the real world, including surveillance at night time, automated driving, medical diagnosis, and low-light imaging. Photos taken in dim light usually undergo severe degradation in the form of noise, loss of contrast, color distortion and detailing which makes it very difficult on the viewer to interpret as well as perform automated vision systems. Although the current image enhancement algorithms and convolutional neural network (CNN)-based models partially solve this issue, they remain limited by small receptive fields and unsuccessful modeling of global dependencies, therefore resulting in poor enhancement and artifact creation. To overcome these drawbacks, in this paper, a new low-light enhancement transformer model, LightFormer, inspired by the application of self-attention mechanisms, is introduced, which has many local textures and long-range contextual relationships in a unified framework. LightFormer has a two-branch encoder-decoder style along with Transformer bottleneck, and a new Illumination-Aware Attention Module (IAAM) that adaptively adjusts dark areas depending on what the model has learned about the illumination distributions. This better model is trained on modified loss involving the pixel wise reconstruction loss, the perceptual loss and illumination loss with the view to ensuring the quantitative accuracy and the visual plausibility. An immense number of experiments on published datasets like LOL and SID has shown that LightFormer is significantly better than the state of the art in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual measures like NIQE and LPIPS. Along with that, the high degree of generalization in numerous lighting conditions with performance output in real-time on embedded hardware (NVIDIA Jetson AGX Xavier) means the proposed model can be deployed in settings with limited resources. This paper demonstrates that LightFormer is a stable and scalable model to enhance low-light with a tradeoff between high-quality restoration and inference time requirements in current computer vision.
Low-Light Image Restoration, Vision Transformer, Image Enhancement, Illumination-Aware Attention, Real-Time Inference, Self-Attention, Deep Learning, Image-to-Image Translation
Enhanced Time-Frequency Analysis of Seismic Signals Using Modified S-Transform and Deep Autoencoder Networks
Proper decoding of seismic signals is used during earthquake detection, geophysical exploration as well as during the monitoring of a structure. Some traditional approaches to time-frequency (T-F) analysis like Short-Time Fourier Transform (STFT) and Wavelet Transform (WT) are burdened by trade-offs between time and frequency resolutions and cannot be used easily to analyze nonstationary seismic events. This paper introduces an improved time-frequency analysis scheme that uses Deep Autoencoder Networks to improve resolution and features of existing time-frequency analysis schemes that incorporate Modified S-Transform (MST). The MST generalizes the S-Transform to include an adaptive Gaussian window that is rescaled depending on instantaneous frequency components in order to better localize in time and frequency. At the same time, a deep autoencoder is used to train noise-tolerant feature representations in terms of compressed features on the spectrograms generated by MST in unsupervised learning. Both the synthetic and real seismic data are used in the validation of the hybrid approach through use of events occurring in IRIS seismic network. Findings indicate that spectral clarity, classification accuracy (94.2 per cent), and performance in low SNR regimes has greater improvement as compared to conventional STFT and WT methods. The evaluated method manages to capture minute changes more accurately in waveforms and becomes interpretable under noisy situations. This framework allows even more complex task of seismic signal analysis that can be used in earthquake early warning systems, microseismic monitoring, and exploration geophysics thanks to powerful and scalable computation framework.
Modified S-Transform, Deep Autoencoder, Seismic Signal Processing, Time-Frequency Analysis, Feature Extraction, Noise Reduction, Earthquake Detection, Signal Decomposition, Unsupervised Learning, Time-Frequency Representation (TFR).
Hybrid Quantum-Inspired Signal Processing Algorithms for Ultra-Low-Power Embedded IoT Applications
The fastest growing Internet of Things (IoT) application field necessitates efficient signal processing frameworks with high computational portions that have an incomparably low power consumption, especially among embedded devices subject to very limited resources. In this paper, we present a hybrid quantum-inspired signal processing paradigm combining both theoretical aspects of a quantum-inspired computation we have used along our theoretical aspects of classical DSP, e.g., moving between amplitude encoding, quantum superposition, quantum optimization of rotation gates, adaptive FIR/IIR filtering and discrete wavelet transforms, and fixed-point optimisation. Such theoretical combination solves some of the most important signal processing issues that involve lowered computational complexity, enhanced convergence characteristics of adaptive filters, and heightened noise resilience in non-stationary settings. The hardware-algorithm co-design in the framework uses a combination of fixed-point arithmetic, loop unrolling and memory blocking to facilitate successful real-time deployment on an ARM Cortex-M4 microcontroller. Up to 43 percent reduction of energy, 31 percent improvement on execution speed and 25 percent better in the classification rate compared to baseline DSP methods are also shown through experimental validation. Demonstration of the generalizability of the strategy by utilizing it in the area of biomedical signal denoising, environmental sound analysis, and structural vibration monitoring creates a scalable methodology of realizing elegant signal processing theory under resource-limited embedded IoT devices.
Quantum-inspired algorithms, ultra-low-power DSP, embedded IoT, evolutionary optimization, adaptive filtering, hardware–algorithm co-design, amplitude encoding, energy-efficient algorithms, IoT sensor analytics.
A Sparse Representation and Dictionary Learning Framework for Real-Time EEG Artifact Removal in Mobile Brain Imaging
MoBI Technologies offer the opportunity to record electroencephalography (EEG) in naturalistic, unconstrained conditions; still, the application of these technologies is limited by extreme signal contamination due to ocular, muscular and motion artifacts and environmental noise. These artifacts are superimposed on areas of neural frequencies and hence the removal of these frequencies without altering brain signals is an ongoing challenge especially in mobile platforms with limited resources during real-time processing. The paper will describe a new latency-tradeoff and signal processing framework, based on sparse representation, adaptive learned dictionaries and statistical artifacts classification, that can perform efficient, real-time artifact removal in MoBI applications. The method is a union between high performance adaptive filtering and sparse coding with system optimization to operate as an embedded system. Through the proposed method, there is segmentation and decomposition of incoming EEG streams into overlapping windows into a combination of sparse coefficient vectors based on the Orthogonal Matching Pursuit (OMP) performed in adaptively trained overcomplete dictionary. The dictionary is online-subsequently updated utilizing a low computational burden K-SVD variant ensuring renewing EEG structures and atom specializing to artifacts founded on the statistical properties e.g. kurtosis, variance, and correlation with reference EOG/EMG channels. There is selective attenuation of artifact coefficients which can reconstruct the clean EEG signals with minimal distortion. It was examined with publicly available Mobile Brain/Body Imaging data dealing with walking, cycling, and virtual reality trials. Compared to Independent Component Analysis (ICA), wavelet thresholding and regression-based methods, the proposed approach provided an average SNR gain of 6.8 dB, 94.5 percent ERP peak retention and 47 ms latency per 500 ms window runtime on an ARM Cortex-A53 embedded platform with less than 40 percent CPU resource utilisation. These findings validate the technique as applicable to non-stop, real-on-device EEG artifact suppression, in real-time removing the need to use brain computing interface (BCI) and neurofeedback in real, portable conditions. The suggested framework falls in between the high-fidelity EEG denoising and embedded mobility and provides a scalable solution to next-generation mobile neurotechnology.
EEG artifact removal, sparse representation, dictionary learning, adaptive filtering, real-time signal processing, embedded systems, mobile brain imaging, brain–computer interface.
Sparse Signal Recovery via Reinforcement-Learned Basis Selection in Wireless Sensor Networks
Efficient and precise recovery of signals continues to be an important challenge in wireless sensor networks (WSNs) due to the limitations experienced in terms of availability of energy, bandwidth and computation from devices. Compressive sensing (CS) has become a game changer to circumvent these limitations due to the sparsity of signal with sub-Nyquist sampling that minimizes transmission overhead. Nevertheless, the performance of CS-based recovery is extremely dependent upon the choice of sparse basis in which the signal exists. Basically, conventional methods use fixed bases; these include Discrete Cosine Transform (DCT), wavelets, or Fourier transforms among others; these fail to adapt to dynamic environmental and signal conditions that are common in practical WSN systems. In an attempt to resolve this gap, this paper introduces a new reinforcement learning (RL) based framework that autonomously chooses the best possible basis function from a preselected plurality according to observed signal attributes. The system uses a policy gradient algorithm to train a lightweight decision-making agent that maximizes reconstruction accuracy at a minimum computational complexity. Proposed RL-based basis selection method has superior performance in the context of MSE, SSIM and runtime efficiency over traditional CS approaches as shown by extensive simulations running on both synthetic and real world datasets. Using this adaptive framework, not only the signal reconstruction fidelity can be improved but also the capability of scaling up and real-time use are possible in heterogeneous WSN cases, which makes this an attractive direction for future intelligent sensor networks as well as edge computing spaces.
Unsupervised Feature Learning for Object Detection in Low-Light Surveillance Footage
Detecting an object in lacking light environment is a challenge because of low quality images, eg. lacking visibility, low contrast, high amount of sensor noise, and loss of object texture and its boundaries. Degradation of input data such as lighting and camera results in severe performance degradation of the traditional object detection models, especially those that depend on large scale annotated data that was mostly acquired under ideal lighting conditions. Further, manual annotation of nighttime surveillance data is laborious, and results in uneven training data for most supervised models. In this thesis we propose a new unsupervised feature learning framework for low light surveillance videos for object detection. Illumination invariant and semantically rich features are extracted from unlabeled images by an approach that integrates contrastive self supervised learning with domain specific data augmentation. To improve feature discrimination, we use a dual branch encode_decode architecture, and to guide a detection head to learn to localize objects without explicit supervision, we propose to use a clustering based pseudo labelling strategy. A self distillation process further refines the entire framework by penalizing inconsistency with predictions and generalizing better. On top of that, the proposed method achieves better mean average precision and F1 score than conventional supervised and semi-supervised baselines based on experiments on publicly available low light datasets including ExDark and LLVIP, and is also proven to be robust to occlusion, noise, and extreme darkness. The results herein demonstrate that the proposed unsupervised framework is effective for annotaion free object detection on a large scale in real life low light surveillance purposes.
Adaptive Filtering Techniques for Real-Time Audio Signal Enhancement in Noisy Environments
Real-time audio processing systems in real world environments must contend with the fact that they will often encounter non-stationary noise sources that degrade intelligibility of the speech and overall audio quality. Noise conditions are common in mobile communication, hearing aids, teleconferencing, and human-machine interfaces, but since noise dynamics are rapidly varying, traditional fixed filtering techniques fail to adapt to the changing noise conditions. This paper carries out a systematic investigation of the performance of the Least Mean Squares (LMS), Normalized LMS (NLMS), Recursive Least Squares (RLS), and Affine Projection Algorithms (APA) for real time audio signal enhancement. The convergence behavior, noise suppression effectiveness, computational overhead and the embedded feasibility of these algorithms are analyzed. Through experiments with rigorous noisy speech data experiments, we show that while RLS has the least amount of noise and most signal fidelity, its computational complexity may constrain its use in resource constrained systems. On the other hand, NLMS comes out with a good compromise, allowing for reliable performance, with a relatively low latency and power requirements. In addition, we design a hybrid adaptive filtering scheme combining NLMS and spectral subtraction algorithms to cope with instantaneous acoustic noises in real time. The approach is validated on embedded hardware, the Raspberry Pi and Jetson Nano, with low inference time yet achieving tremendous gains in SNR and perceptual speech quality metrics. Adaptive filtering and its hybridisation with spectral analysis is confirmed to be a promising direction of achieving near real time audio enhancement in a noisy environment across a broad spectrum of industrial and consumer applications.
An Edge-Aware Signal Processing Framework for Structural Health Monitoring in IoT Sensor Networks
In the context of ever growing urbanization and aging structures, safety and longevity aside, Structural Health Monitoring (SHM) have played a very important role in assuring the safety and continued life of civil infrastructure. Continuous monitoring using dense sensor networks is now feasible with the coming of Internet of Things (IoT). But yet, the challenges such as high communication overhead, latency, and power consumption to hinder real time responsiveness and scalability. In this paper, we present a novel edge-aware signal processing framework that addresses the aforementioned limitations by utilizing an edge computing capability available within the IoT sensor nodes. The proposed framework integrates a lightweight, yet efficacious signal processing pipeline consisting of real time wavelet based noise reduction, feature extraction and computation of important vibration and strain parameters, and localized anomaly detection through compressed machine learning models designed for the edge. We introduce a hierarchy, in which raw sensor data is processed on the edge, thus reducing the need to send high bandwidth transmissions to the cloud. To prove the merit of the framework, we developed a simulated SHM testbed of bridge infrastructure, and performance improvements were experimentally demonstrated as a result. It was demonstrated that the system achieved anomaly detection accuracy of 96.4%, reduced the data transmission bandwidth by 42% while still providing real time operation with decision latency minimal than 120 ms. The results illustrate the framework’s potential as a scalable, energy efficient, intelligent, and enable standalone or precursory systems to populate smart cities and industrial environments of the next generation.
Wavelet-Based Denoising and Classification of ECG Signals Using Hybrid LSTM-CNN Models
Electrocardiogram (ECG) signals are essential as means to diagnose and monitor cardiovascular diseases, in particular arrhythmias. Nevertheless different noise sources like baseline wander, powerline interference and muscle artifacts make an accurate interpretation of ECG signals difficult as these noise components tend to cover up morphological features needed for diagnosis. To overcome these limitations, this work proposes an integrated signal processing and deep learning framework combining denoising using Discrete Wavelet Transform (DWT) and robust classification using a hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) architecture. The ECG signals are effectively decomposed into multi resolution components within the DWT and the selective suppression of noise can be achieved without loss of clinically relevant features like QRS complexes, P waves and T waves. After denoising, a hybrid LSTM CNN model is used, in which convolutional layers get spatial patterns and the LSTM layers capture temporal dependencies embedded in sequential ECG data. We train and validate the model on the MIT-BIH Arrhythmia Database, a well known benchmark for ECG analysis. Experimental results show that proposed framework substantially outperforms existing methods (entropy based selection of prevalent methods), achieving its accuracy 98.6%, sensitivity 98.2%, and specificity 99.1%. As such, these results verify the effectiveness of using wavelet based signal enhancement coupled with deep hybrid modeling to perform ECG interpretation more accurately and in real time for use in clinical and wearable health care applications.
Lightweight CNN Architecture for Real-Time Image Super-Resolution in Edge Devices
Image super resolution (SR) is a fundamental problem in computer vision that has a wide range of applications in both medical imaging, video surveillance and mobile photography. While high-accuracy SR models can be deployed to end devices, the lack of computational power, memory and energy consumption constraints prevents them from being deployed in edge devices. This work proposes a novel lightweight convolutional neural network (CNN) architecture, EdgeSRNet, for real time image super resolution on low power embedded platforms. To achieve this, depthwise separable convolutions, residual efficient building blocks, along with sub pixel convolutional layers are incorporated to significantly reduce model complexity while maintaining high reconstruction fidelity. We optimize the architecture for low latency inference, with real time inference achievable without GPU acceleration. Extensive experiments have been conducted to evaluate its effectiveness on publicly available benchmark of Set5, Set14, and BSD100. Results show that EdgeSRNet provides competitive PSNR/SSIM performance with fewer than 500K parameters and under 1.5 GFLOPs per forward pass. Further, we compare our EdgeSRNet with several existing lightweight SR models on edge devices, such as Raspberry Pi 4 and NVIDIA Jetson Nano, and show that the EdgeSRNet achieves better visual quality and high computational efficiency than existing lightweight models on edge devices. With these attributes, EdgeSRNet shows great potential for edge real time image enhancement in resource constrained scenarios, for instance IoT devices, smart cameras, autonomous systems, and mobile platforms.