Circuits Systems and Signal Processing 

About: Circuits Systems and Signal Processing is an academic journal published by Birkhäuser. The journal publishes majorly in the area(s): Computer science & Nonlinear system. It has an ISSN identifier of 0278-081X. Over the lifetime, 4214 publications have been published receiving 48032 citations. The journal is also known as: C.S.S.P. & CSSP.

A survey of linear singular systems

Abstract: This paper is a brief historical review of linear singular systems, followed by a survey of results on their solution and properties. The frequency domain and time domain approaches are discussed together to sketch an overall picture of the current status of the theory.

An overview of morphological filtering

Abstract: This paper consists of a tutorial overview of morphological filtering, a theory introduced in 1988 in the context of mathematical morphology. Its first section is devoted to the presentation of the lattice framework. Emphasis is put on the lattices of numerical functions in digital and continuous spaces. The basic filters, namely the openings and the closings, are then described and their various versions are listed. In the third section morphological filters are defined as increasing idempotent operators, and their laws of composition are proved. The last sections are concerned with two special classes of filters and their derivations: first, the alternating sequential filters allow us to bring into play families of operators depending on a positive scale parameter. Finally, the center and the toggle mappings modify the function under study by comparing it, at each point, with a few reference transforms.

Eigenstructure methods for direction finding with sensor gain and phase uncertainties

Abstract: An eigenstructure-based method for direction finding in the presence of sensor gain and phase uncertainties is presented. The method provides estimates of the Directions of Arrival (DOA) of all the radiating sources as well as calibration of the gain and phase of each sensor in the observing array. The technique is not limited to a specific array configuration and can be implemented in a'ny eigenstructure-based DOA system to improve its performance.

A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

Abstract: Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.

Fast Normalized Cross-Correlation

Abstract: Normalized cross-correlation has been used extensively for many signal processing applications, but the traditional normalized correlation operation does not meet speed requirements for time-critical applications. In this paper, a new fast algorithm for the computation of the normalized cross-correlation (NCC) without using multiplications is presented. For a search window of size M and a template of size N the fast NCC requires only approximately 2N?(M?N+1) additions/subtractions without multiplications. Simulation results with 100,000 test signals show that the use of the fast NCC instead of the traditional approaches for the determination of the degree of similarity between a test signal and a reference signal (template) brings about a significant improvement in terms of false negative rate, identification rate and computational cost without a significant increase in false positive rate, especially when the signal-to-noise ratio (SNR) is higher than 3 dB.