Perception and characterization of materials using signal processing techniques

2001 ◽  
Vol 50 (5) ◽  
pp. 1203-1211 ◽  
Author(s):  
S. Femmam ◽  
N.K. M'Sirdi ◽  
A. Ouahabi
Keyword(s):  
Signal Processing ◽  
Characterization Of Materials ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals Characterization of Brain Stroke Using Image and Signal Processing Techniques

2021 ◽  
Author(s):  
Abdullah Alamoudi ◽  
Yousif Abdallah
Keyword(s):  
Signal Processing ◽  
Brain Injuries ◽  
Cross Sectional ◽  
Manual Contour ◽  
Cross Sectional Imaging ◽  
Brain Stroke ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques

Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods.

Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management

2008 ◽  
Vol 367 (1887) ◽  
pp. 235-253 ◽  
Author(s):  
Leif Sörnmo ◽  
Martin Stridh ◽  
Daniela Husser ◽  
Andreas Bollmann ◽  
S. Bertil Olsson
Keyword(s):  
Atrial Fibrillation ◽  
Signal Processing ◽  
Clinical Management ◽  
Advanced Characterization ◽  
Non Invasive ◽  
Therapy Efficacy ◽  
Electrocardiogram Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

The analysis of atrial fibrillation in non-invasive ECG recordings has received considerable attention in recent years, spurring the development of signal processing techniques for more advanced characterization of the atrial waveforms than previously available. The present paper gives an overview of different approaches to the extraction of atrial activity in the ECG and to the characterization of the resulting atrial signal with respect to its spectral properties. So far, the repetition rate of the atrial waves is the most studied parameter and its significance in clinical management is briefly considered, including the identification of pathomechanisms and prediction of therapy efficacy.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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