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With the wide/rapid spread of distributed systems for information processing, such as cloud computing and social networking, not only transmission but also processing is done on the internet. Therefore, a lot of studies on secure, efficient…
Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. In this paper, we propose a data-driven CS framework that learns signal characteristics and individual…
Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic…
The laws of quantum physics endow superior performance and security for information processing: quantum sensing harnesses nonclassical resources to enable measurement precision unmatched by classical sensing, whereas quantum cryptography…
Lossless compression of dynamic 2D+t and 3D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in…
The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1…
Electrocardiogram (ECG), a technique for medical monitoring of cardiac activity, is an important method for identifying cardiovascular disease. However, analyzing the increasing quantity of ECG data consumes a lot of medical resources. This…
Electrocardiogram (ECG) signals, profiling the electrical activities of the heart, are used for a plethora of diagnostic applications. However, ECG systems require multiple leads or channels of signals to capture the complete view of the…
We present a framework for the design of coding mechanisms that allow remotely operating anomaly detectors in a privacy-preserving manner. We consider the following problem setup. A remote station seeks to identify anomalies based on system…
Cardiovascular disease is a major life-threatening condition that is commonly monitored using electrocardiogram (ECG) signals. However, these signals are often contaminated by various types of noise at different intensities, significantly…
In recent years, a large scale of various wireless sensor networks have been deployed for basic scientific works. Massive data loss is so common that there is a great demand for data recovery. While data recovery methods fulfil the…
A new grayscale-based block scrambling image encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems, which are used to securely transmit images through an untrusted channel provider. The proposed…
The Compressive Sensing framework maintains relevance even when the available measurements are subject to extreme quantization, as is exemplified by the so-called one-bit compressed sensing framework which aims to recover a signal from…
Method: In this study, a new method is introduced for distinguishing noise-free segments of ECG from noisy segments that use sample amplitude dispersion with an adoptive threshold for variance of samples amplitude and a method which uses…
In recent years, multiple sensor-based devices and systems have been deployed in smart agriculture, industrial automation, E-Health, etc. The diversity of sensor data types and the amount of data pose critical challenges for data…
Extracting information from the electrocardiography (ECG) signal is an essential step in the design of digital health technologies in cardiology. In recent years, several machine learning (ML) algorithms for automatic extraction of…
Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformer for…
Compressive imaging is an emerging application of compressed sensing, devoted to acquisition, encoding and reconstruction of images using random projections as measurements. In this paper we propose a novel method to provide a scalable…
Compressive sensing (CS) has been studied and applied in structural health monitoring for wireless data acquisition and transmission, structural modal identification, and spare damage identification. The key issue in CS is finding the…
In recent years, there has been a shift of interest towards the field of biometric authentication, which proves the identity of the user using their biological characteristics. We explore a novel biometric based on the electrical activity…