Design of Low-Power SPIHT Decoder Based ECG Compression and Reconstruction System

This article proposes a 1D biomedical signal encoding scheme to allow embedding of metadata and to protect privacy. The compression of ECG signal and its reconstruction is implemented. The design concentrates on an overview of the criteria for safe and effective m-health storage, transmission, and access to medical tests. However, existing architectures for encoding SPIHT are designed to process images/videos. Significant memory and complex sorting algorithms are required for both architectures, and they all require time-consuming tasks that do not apply to mobile ECG applications. On the basis of our previously updated SPIHT coding research, we used flags and bit controls to reduce memory needs and code complexity through a combination of three search processes in one phase. The goal of real-time architecture for mobile ECG applications is therefore to be accomplished. In order first, to solve the disadvantages of the low-encryption speed of coded and complex hardware architectures that characterize previous SPIHT algorithms, we propose a SPIHT coding algorithm that uses several types of state registry files because of its need for dynastic c to attain real-time and performance design objectives. Secondly, a highly piped and efficient VLSI architecture is used to implement a high-efficiency and low-power SPIHT design based on the proposed algorithm.

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Biologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health.

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health.

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding

In this work we propose a method of in situ clutter deconvolution and modeling using experimentally obtained UWB radar data. The obtained clutter models are then used for random sequence encoding of radar-communication (radarcom) signals to achieve clutter-masked transmissions and improve communication security. We present the results of clutter modeling from the laboratory data obtained with the software-defined radar system. We then show that such clutter-masked radarcom signals generated using the local clutter model are highly likely to be interpreted as just clutter returns by an unauthorized interceptor. We also present the results of communication and radar performance of these radarcom signals and contrast them with those obtained using a linear frequency modulated waveform. It is shown that the proposed radarcom design method has high potential to achieve secure communications in adversarial conditions, while simultaneously addressing radar sensing needs.

Research on Mud Pulse MWD Device for Mines

As the core accoutrement of directional drilling construction, the measurement while drilling (MWD) device can be divided into three types due to the different data transmission methods: wired, mud pulse and electromagnetic wave. This paper used the mud pulse method to develop a mud pulse MWD device for mines, and the working principle of the mud pulse signal transmission, the signal encoding method and the structure of the device were described. Experimental research showed that the mud pulse wireless MWD device had the advantages of long transmission distance and strong working stability. At the same time, the device was not restricted by the drill pipe during operation, which could be combined with sliding orientation and rotary feed, and had great promotion and application value.