Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

1. Top Cited Articles in
Signal & ImageProcessing
2021-2022
Signal & Image Processing: An
International Journal (SIPIJ)
***WJCI Indexed***
ISSN: 0976 – 710X [Online]; 2229 – 3922 [Print]
https://www.airccse.org/journal/sipij/index.html
Citations, h-index, i10-index
Citations 5001 h-index 32 i10-index 117

2. Target Detection and Classification Improvements using Contrast Enhanced
16-bit Infrared Videos
Chiman Kwan and David Gribben, Applied Research, LLC, USA
https://aircconline.com/sipij/V12N1/12121sipij03.pdf
February 2021 | Cited by 4
Abstract
In our earlier target detection and classification papers, we used 8-bit infrared videos in the
Defense Systems Information Analysis Center(DSIAC) video dataset. In this paper, we focus on
how we can improve the target detection and classification results using 16-bit videos. One
problem with the 16-bit videos is that some image frames have very low contrast. Two methods
were explored to improve upon previous detection and classification results. The first method
used to improve contrast was effectively the same as the baseline 8-bit video data but using the
16-bit raw data rather than the 8-bit data taken from the avi files. The second method used was a
second order histogram matching algorithm that preserves the 16-bit nature of the videos while
providing normalization and contrast enhancement. Results showed the second order histogram
matching algorithm improved the target detection using You Only Look Once (YOLO) and
classificationusing Residual Network (ResNet) performance. The average precision (AP) metric
in YOLO was improved by 8%. This is quite significant. The overall accuracy (OA) of ResNet
has been improved by 12%. This is also very significant.
Keywords
Deep learning, mid-wave infrared (MWIR) videos, target detection and classification, contrast
enhancement, YOLO, ResNet.

3. Mixed Spectra for Stable Signals from Discrete Observations
Rachid Sabre, University of Burgundy, France
https://aircconline.com/sipij/V12N5/12521sipij02.pdf
October 2021 | Cited by 2
Abstract
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable
in the modeling of certain signals with indefinitely increasing variance. Particularly the case
where the spectral measurement is mixed: sum of a continuous measurement and a discrete
measurement. Our goal is to estimate the spectral density of the continuous part by observing the
signal in a discrete way. For that, we propose a method which consists in sampling the signal at
periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then
smooth by two spectral windows taking into account the width of the interval where the spectral
density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case
of estimation from discrete observations of a continuous time process.
Keywords
Spectral density, stable processes, periodogram, smoothing estimate, aliasing.

4. Using Distance Measure based Classification in Automatic Extraction of
Lungs Cancer Nodules for Computer Aided Diagnosis
Maan Ammar1
, Muhammad Shamdeen2
, MazenKasedeh2
, Kinan Mansour3
and Waad Ammar3
,
1
AL Andalus University for Medical Sciences, Syria, 2
Damascus University, Syria, 3
Al Andalus
University Hospital, Syria
https://aircconline.com/sipij/V12N3/12321sipij03.pdf
June 2021 | Cited by 2
Abstract
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT
chest images and shed the light on the details of using the Weighted Euclidean Distance (WED)
for classifying lungs connected components into nodule and not-nodule. We explain also using
Connected Component Labeling (CCL) in an effective and flexible method for extraction of
lungs area from chest CT images with a wide variety of shapes and sizes. This lungs extraction
method makes use of, as well as CCL, some morphological operations. Our tests have shown that
the performance of the introduce method is high. Finally, in order to check whether the method
works correctly or not for healthy and patient CT images, we tested the method by some images
of healthy persons and demonstrated that the overall performance of the method is satisfactory.
Keywords
Nodules classification, lungs cancer, morphological operators, weighted Euclidean distance,
nodules extraction.

5. A Comparative Study of Machine Learning Algorithms for EEG Signal
Classification
Anam Hashmi, Bilal Alam Khan and Omar Farooq, Aligarh Muslim University, India
https://aircconline.com/sipij/V12N6/12621sipij03.pdf
December 2021 | Cited by 1
Abstract
In this paper, different machine learning algorithms such as Linear Discriminant Analysis,
Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour,
and Autoencoder with SVM have been compared. This comparison was conducted to seek a
robust method that would produce good classification accuracy. To this end, a robust method of
classifying raw Electroencephalography (EEG) signals associated with imagined movement of
the right hand and relaxation state, namely Autoencoder with SVM has been proposed. The EEG
dataset used in this research was created by the University of Tubingen, Germany. The best
classification accuracy achieved was 70.4% with SVM through feature engineering. However,
our prosed method of autoencoder in combination with SVM produced a similar accuracy of
65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to
mentally control a robotic device or an exoskeleton.
Keywords
EEG. Machine learning. BCI. Motor Imagery signals. Random Forest.

6. Sensing Method for Two-Target Detection in Time-Constrained Vector
Poisson Channel
Muhammad Fahad and Daniel R. Fuhrmann, Michigan Technological University, USA
https://aircconline.com/sipij/V12N6/12621sipij01.pdf
December 2021 | Cited by 1
Abstract
It is an experimental design problem in which there are two Poisson sources with two possible
and known rates, and one counter. Through a switch, the counter can observe the sources
individually or the counts can be combined so that the counter observes the sum of the two. The
sensor scheduling problem is to determine an optimal proportion of the available time to be
allocated toward individual and joint sensing, under a total time constraint. Two different metrics
are used for optimization: mutual information between the sources and the observed counts, and
probability of detection for the associated source detection problem. Our results, which are
primarily computational, indicate similar but not identical results under the two cost functions.
Keywords
sensor scheduling, vector Poisson channels.

7. General Purpose Image Tampering Detection using Convolutional Neural
Network and Local Optimal Oriented Pattern (LOOP)
Ali Ahmad Aminu1, 2
and Nwojo Nnanna Agwu1
, 1
Nile University of Nigeria, Nigeria, 2
Gombe
State University, Nigeria
https://aircconline.com/sipij/V12N2/12221sipij02.pdf
April 2021 | Cited by 1
Abstract
Digital image tampering detection has been an active area of research in recent times due to the
ease with which digital image can be modified to convey false or misleading information. To
address this problem, several studies have proposed forensics algorithms for digital image
tampering detection. While these approaches have shown remarkable improvement, most of
them only focused on detecting a specific type of image tampering. The limitation of these
approaches is that new forensic method must be designed for each new manipulation approach
that is developed. Consequently, there is a need to develop methods capable of detecting multiple
tampering operations. In this paper, we proposed a novel general purpose image tampering
scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of
detecting five types of image tampering in both binary and multiclass scenarios. Unlike the
existing deep learning techniques which used constrained pre-processing layers to suppress the
effect of image content in order to capture image tampering traces, our method uses LOOP
features, which can effectively subdue the effect image content, thus, allowing the proposed
CNNs to capture the needed features to distinguish among different types of image tampering.
Through a number of detailed experiments, our results demonstrate that the proposed general
purpose image tampering method can achieve high detection accuracies in individual and
multiclass image tampering detections respectively and a comparative analysis of our results
with the existing state of the arts reveals that the proposed model is more robust than most of the
exiting methods.
Keywords
Image Tampering, General purpose Tampering Detection, Convolutional Neural Network, Local
Optimal Oriented Pattern.