Cardiovascular Signal Processing: State of the Art and Algorithms

2021 ◽  
pp. 113-127
Author(s):  
Hiwot Birhanu ◽  
Amare Kassaw
Keyword(s):  
Signal Processing ◽  
State Of The Art

Digital Watermarking Techniques for Audio and Speech Signals

2011 ◽  
pp. 132-160
Author(s):  
Aparna Gurijala ◽  
John R. Deller Jr.
Keyword(s):  
Signal Processing ◽  
Performance Evaluation ◽  
Digital Watermarking ◽  
State Of The Art ◽  
Audio Watermarking ◽  
Speech Signals ◽  
Advantages And Disadvantages ◽  
Signal Features

The main objective of this chapter is to provide an overview of existing speech and audio watermarking technology and to demonstrate the importance of signal processing for the design and evaluation of watermarking algorithms. This chapter describes the factors to be considered while designing speech and audio watermarking algorithms, including the choice of the domain and signal features for watermarking, watermarked signal fidelity, watermark robustness, data payload, security, and watermarking applications. The chapter presents several state-of-the-art speech and audio watermarking algorithms and discusses their advantages and disadvantages. The various applications of watermarking and developments in performance evaluation of watermarking algorithms are also described.

Masking Models and Watermarking

2011 ◽  
pp. 93-116
Author(s):  
Mirko Luca Lobina ◽  
Luigi Atzori ◽  
Davide Mula
Keyword(s):  
Signal Processing ◽  
Intellectual Property ◽  
State Of The Art ◽  
The State ◽  
Audio Watermarking ◽  
Digital Right Management ◽  
The Future ◽  
Psychological Models ◽  
High Level ◽  
The Relationship

Many audio watermarking techniques presented in the last years make use of masking and psychological models derived from signal processing. Such a basic idea is winning because it guarantees a high level of robustness and bandwidth of the watermark as well as fidelity of the watermarked signal. This chapter first describes the relationship between digital right management, intellectual property, and use of watermarking techniques. Then, the crossing use of watermarking and masking models is detailed, providing schemes, examples, and references. Finally, the authors present two strategies that make use of a masking model, applied to a classic watermarking technique. The joint use of classic frameworks and masking models seems to be one of the trends for the future of research in watermarking. Several tests on the proposed strategies with the state of the art are also offered to give an idea of how to assess the effectiveness of a watermarking technique.

Simulation of frequency tracking loops with complex targets

SIMULATION ◽  
1967 ◽  
Vol 8 (2) ◽  
pp. 111-115
Author(s):  
J.O. Engle ◽  
C.J. Nisson
Keyword(s):  
Signal Processing ◽  
State Of The Art ◽  
Simulation Models ◽  
Council Meeting ◽  
Frequency Tracking ◽  
Modern Equipment ◽  
Tracking Loops ◽  
Simple Picture

This paper was written for the analog user to relate some of our experience in the area of signal processing (in this case signal processing by a radar receiver) to fit the theme of the Eastern Simulation Council meeting at which it was presented. "In the paper we attempted to give a complete but simple picture; by describing the signals and system, de veloping the simulation models, instrumenting the models with available computing equipment and finally by dis cussing simulation difficulties. (We wish that we had more modern equipment so that the instrumentation, particu larly of the dc models, could be more significant relative to state-of-the-art.)"

scholarly journals Signal Processing in High-End Hearing Aids: State of the Art, Challenges, and Future Trends

2005 ◽  
Vol 2005 (18) ◽  
Author(s):  
V. Hamacher ◽  
J. Chalupper ◽  
J. Eggers ◽  
E. Fischer ◽  
U. Kornagel ◽  
...  
Keyword(s):  
Signal Processing ◽  
Hearing Aids ◽  
State Of The Art ◽  
Future Trends

scholarly journals Identity Recognition Using Biological Electroencephalogram Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Liang ◽  
Liang Cheng ◽  
Mingdong Tang
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Human Brain ◽  
State Of The Art ◽  
Brain Wave ◽  
Identity Recognition ◽  
Wave Signal ◽  
Machine Learning Methods ◽  
Characteristic Extraction ◽  
Critical Issues

Brain wave signal is a bioelectric phenomenon reflecting activities in human brain. In this paper, we firstly introduce brain wave-based identity recognition techniques and the state-of-the-art work. We then analyze important features of brain wave and present challenges confronted by its applications. Further, we evaluate the security and practicality of using brain wave in identity recognition and anticounterfeiting authentication and describe use cases of several machine learning methods in brain wave signal processing. Afterwards, we survey the critical issues of characteristic extraction, classification, and selection involved in brain wave signal processing. Finally, we propose several brain wave-based identity recognition techniques for further studies and conclude this paper.

scholarly journals A Hybrid Vision Processing Unit with a Pipelined Workflow for Convolutional Neural Network Accelerating and Image Signal Processing

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2989
Author(s):  
Peng Liu ◽  
Yan Song
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
High Speed ◽  
High Efficiency ◽  
State Of The Art ◽  
Vision System ◽  
Processing Unit ◽  
Processing Elements ◽  
Vision Processing ◽  
Field Programmable

Vision processing chips have been widely used in image processing and recognition tasks. They are conventionally designed based on the image signal processing (ISP) units directly connected with the sensors. In recent years, convolutional neural networks (CNNs) have become the dominant tools for many state-of-the-art vision processing tasks. However, CNNs cannot be processed by a conventional vision processing unit (VPU) with a high speed. On the other side, the CNN processing units cannot process the RAW images from the sensors directly and an ISP unit is required. This makes a vision system inefficient with a lot of data transmission and redundant hardware resources. Additionally, many CNN processing units suffer from a low flexibility for various CNN operations. To solve this problem, this paper proposed an efficient vision processing unit based on a hybrid processing elements array for both CNN accelerating and ISP. Resources are highly shared in this VPU, and a pipelined workflow is introduced to accelerate the vision tasks. We implement the proposed VPU on the Field-Programmable Gate Array (FPGA) platform and various vision tasks are tested on it. The results show that this VPU achieves a high efficiency for both CNN processing and ISP and shows a significant reduction in energy consumption for vision tasks consisting of CNNs and ISP. For various CNN tasks, it maintains an average multiply accumulator utilization of over 94% and achieves a performance of 163.2 GOPS with a frequency of 200 MHz.

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