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. Signal & Image Processing: An
International Journal (SIPIJ)
ISSN: 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
Current Issue: June 2021, Volume 12, Number 3
TOC - Table of Contents

2. PRACTICAL APPROACHES TO TARGET DETECTION IN LONG RANGE AND
LOW QUALITY INFRARED VIDEOS
Chiman Kwan and David Gribben
Applied Research, LLC, Rockville, Maryland, USA
ABSTRACT
It is challenging to detect vehicles in long range and low quality infrared videos using deep
learning techniques such as You Only Look Once (YOLO) mainly due to small target size. This
is because small targets do not have detailed texture information. This paper focuses on practical
approaches for target detection in infrared videos using deep learning techniques. We first
investigated a newer version of You Only Look Once (YOLO v4). We then proposed a practical
and effective approach by training the YOLO model using videos from longer ranges.
Experimental results using real infrared videos ranging from 1000 m to 3500 m demonstrated
huge performance improvements. In particular, the average detection percentage over the six
ranges of 1000 m to 3500 m improved from 54% when we used the 1500 m videos for training to
95% if we used the 3000 m videos for training.
KEYWORDS
Deep learning; YOLO v4; infrared videos; target detection; training strategy
SOURCE URL
https://aircconline.com/sipij/V12N3/12321sipij01.pdf
VOLUME LINK
http://www.airccse.org/journal/sipij/vol12.html

3. COMPUTER AIDED DETECTION OF OBSTRUCTIVE SLEEP APNEA FROM EEG
SIGNALS
Saheed Ademola Bello and Umar Alqasemi
King AbdulAziz University, Jeddah, Saudi Arabia
ABSTRACT
Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat
sleep apnea leads to fatal complications in both psychological and physiological being of human.
Electroencephalogram (EEG) performs an important task in probing for sleep apnea through
identifying and recording the brain’s activities while sleeping. In this study, computer aided
detection of sleep apnea from EEG signals is developed to optimize and increase the prompt
recognition and diagnosis of sleep apnea in patients. The time domain, wavelets, and frequency
domain of the EEG signals were computed, and features were extracted from these domains.
These features are inputted into two machine learning algorithms: Support Vector Machine and
K-Nearest Neighbors of different kernel functions and orders. Evaluation metrics such as
specificity, accuracy, and sensitivity are computed and analyzed for the classifiers. The KNN
classifier outperforms the SVM in classifying apnea from non-apnea events in patients. The
KNN order 3 shows the highest performance sensitivity of 85.92%, specificity of 80% and
accuracy of 82.69%.
KEYWORDS
Signal Processing, Computer Aided Detection, Obstructive Sleep Apnea, EEG signal, Support
Vector Machine
SOURCE URL
https://aircconline.com/sipij/V12N3/12321sipij02.pdf
VOLUME LINK
http://www.airccse.org/journal/sipij/vol12.html

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, Faculty of Biomed. Eng., Al Kadmous, Syria
2
Damascus University, Department of Biomedical Engineering, Syria
3
Al Andalus University Hospital, Al Kadmous, Syria
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
SOURCE URL
https://aircconline.com/sipij/V12N3/12321sipij03.pdf
VOLUME LINK
http://www.airccse.org/journal/sipij/vol12.html