scholarly journals Design of amplitude and phase modulated pulse trains with good auttocorrelation properties for radar communications

2019 ◽  
Vol 13 (3) ◽  
pp. 990
Author(s):  
S.J Rosli ◽  
H.A. Rahim ◽  
K.N. Abdul Rani
Keyword(s):  
Low Complexity ◽  
Side Lobe ◽  
Inverse Filtering ◽  
Compression Technique ◽  
Pulse Trains ◽  
Filtering Technique ◽  
Radar Applications ◽  
Generating Sequence ◽  
The Stability ◽  
Correlation Properties

Development of technique for synthesizing multilevel sequences with good correlation properties is very useful for several radar applications where as a set of phase and amplitude coded sequences will be synthesized directly for compression technique. In reality, the signal processing also been significant to transmit or store signals, to enhance desired signal component and to extract useful information carried by signals. Consequently, this paper describes affectively methods to generating the finite length multilevel sequence of any length that have low side lobe energy (SLE) and improved energy ratio (ER) in their autocorrelation function (ACF). Testing for the stability and the analyzing of systems zero pattern using z-transform for the generating sequence indicates a possible position of roots in the radius of circle lies. This is illustrated by application of 13-element Huffman code as a starting sequence, this technique more low complexity and compatible compared inverse filtering technique.

Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

2012 ◽  
Vol 488-489 ◽  
pp. 1587-1591
Author(s):  
Amol G. Baviskar ◽  
S. S. Pawale
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Low Complexity ◽  
Contractive Mapping ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Image Encoding ◽  
Fractal Image ◽  
Coding Method ◽  
The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

scholarly journals Low complexity state metric compression technique in turbo decoder

2013 ◽  
Vol 10 (15) ◽  
pp. 20130485-20130485 ◽  
Author(s):  
Qingqing Yang ◽  
Xiaofang Zhou ◽  
Gerald E. Sobelman ◽  
Xinxin Li
Keyword(s):  
Low Complexity ◽  
Compression Technique ◽  
Turbo Decoder

Gain and side‐lobe improvement of W‐band microstrip array antenna with CSRR for radar applications

2017 ◽  
Vol 53 (11) ◽  
pp. 702-704 ◽  
Author(s):  
Aulia Dewantari ◽  
Jaeheung Kim ◽  
Se‐Yeon Jeon ◽  
Seok Kim ◽  
Min‐Ho Ka
Keyword(s):  
Side Lobe ◽  
Array Antenna ◽  
W Band ◽  
Radar Applications ◽  
Microstrip Array ◽  
Microstrip Array Antenna

On the stability of surface acoustic pulse trains in coated elastic media

Wave Motion ◽  
2001 ◽  
Vol 34 (1) ◽  
pp. 35-49 ◽  
Author(s):  
C. Eckl ◽  
J. Schöllmann ◽  
A.P. Mayer ◽  
A.S. Kovalev ◽  
G.A. Maugin
Keyword(s):  
Acoustic Pulse ◽  
Elastic Media ◽  
Pulse Trains ◽  
The Stability

scholarly journals Application of Derivative Transform Spectroscopy in Gas Detection

2021 ◽  
Vol 299 ◽  
pp. 02005
Author(s):  
Tingting Chen ◽  
Chun Wang ◽  
Jingqiu Liang ◽  
Jingsong Li
Keyword(s):  
Signal Processing ◽  
Noise Suppression ◽  
Time Signal ◽  
Spectroscopy Analysis ◽  
Signal Processing Algorithm ◽  
Methane Absorption ◽  
Laser Absorption ◽  
Derivative Spectroscopy ◽  
Filtering Technique ◽  
The Stability

Digital filtering technique is of great significance in real-time signal processing and analysis, but the stability, efficiency and flexibility of filter algorithm are important indicators to reflect its application value. In this paper, an adaptive derivative transformation based on Savitzky-Golay filter algorithm was proposed for laser absorption spectroscopy analysis. To demonstrate this analysis algorithm, first-order and second-order derivative spectroscopy are evaluated for the analysis of infrared methane absorption spectra, and compared with the original direct absorption spectral signals. The results indicated that the proposed signal processing algorithm has good performance on noise suppression and spectral resolution improvement, and the 2nd derivative spectroscopy shows better de-noising efficiency.

scholarly journals Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data

2021 ◽  
Vol 3 (4) ◽  
pp. 357-366
Author(s):  
Haoxiang Wang
Keyword(s):  
Remote Sensing ◽  
Signal To Noise Ratio ◽  
Structural Data ◽  
Low Complexity ◽  
Industrial Internet Of Things ◽  
Remote Sensing Images ◽  
Compression Technique ◽  
Industrial Internet ◽  
Space Saving ◽  
Intelligent Devices

Industrial internet of things has grown quite popular in recent years and involves a large number of intelligent devices linked together to build a system that can investigate, communicate, gather and observe information. Due to this requirement, there is more demand for compression techniques which compresses data, leading to less usage of resources and low complexity. This is where Convolutional Neural Networks (CNN) play a large role in the field of computer vision, especially in places where high applications such as interpretation coupled with detection is required. Similarly, low-level applications such as image compression cannot be resolved using this methodology. In this paper, a compression technique for remote sensing images using CNN is proposed. This methodology incorporates CNN in a compact learning environment wherein the actual image that consists of structural data is coded using Lempel Ziv Markov chain algorithm. This process is followed by image reconstruction in order to obtain the actual image in high quality. Other methodologies such as optimized trunctiona, JPEG2000, JPEC and binary tree were compared using a large number of experiments in terms of space saving, reconstructed image quality and efficiency. The output obtained indicates that the proposed methodology shows effective improvement, attaining a 50 dB signal to noise ratio and space saving of 90%.

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