INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES

Journal Information
EISSN : 2457-0370
Published by: XLE Science (10.29284)
Total articles ≅ 60
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DOAJ
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Mukil Alagirisamy
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 7, pp 1-10; https://doi.org/10.29284/ijasis.7.1.2021.1-10

Abstract:
A fully automatic Computer Aided diagnosis (CAD) of glaucoma is developed that aims to reduce the false positive detection rate and increasing the sensitivity of classification. It consists of three main steps: Region of Interest (ROI) extraction (Optic Disc (OD) region), feature extraction (micro textures) and classification using Linear Vector Quantizer-Artificial Neural Network (LVQ-ANN). The search area for glaucoma is the OD region wherein the cupping occurs, so in the first step ROI is extracted from the whole image. Feature extraction and classification are the most challenging tasks as the performance of the system depend both of them. Laws defined five spatial filters to extract micro-statistical estimators such as Level, Edge, Spot, Wave, and Ripple. Fundus images in three databases; DRISHTI-GS1, ORIGA, and RIM-ONE are classified using LVQ-ANN classifier. Results indicate the strength of the LVQ-ANN classifier for glaucoma diagnosis with sensitivity of 95.71% (DRISHTI-GS1), 83.33% (ORIGA) and 94.87% (RIM-ONE).
Ahmed A. Mustafa, Ahmed Ak. Tahir
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 7, pp 38-56; https://doi.org/10.29284/ijasis.7.1.2021.38-56

Abstract:
A new system for finger-vein recognition is proposed based on the Complete Local Binary pattern (CLBP) as afeature extractor and the Phase Only Correlation (POC) for post-processing alignment and for speeding up the system. The CLBP produces three components of image descriptors and thus holds more details compared to the previous methods such as the Local Binary Pattern (LBP), the Local Directional Pattern (LDP), the Local Line Binary Pattern (LLBP), the Repeated Line Tracking (RLT), the Maximum Curvature (MC) and the Wide Line Detector (WLD). In the proposed system, POC is used for two purposes. First, to increase the performance of the system the alignment between the CLBP components of the test image and the enrolled CLBP components are performed. Second, to speed up the matching stage, a portion of the enrolled images is excluded that are highly misaligned with the test image from the Hamming Distance (HD) measure competition in the matching stage. To make the system more secure against attacks targeting personal information, only CLBP components are enrolled in the system and the alignment process POC is implemented on these components without the need to original images. For image pre-processing a novel scheme of pre-processing methods is adopted including finger-vein localization, alignment, and the Region-Of-Interest (ROI) extraction and enhancement. Two databases, UTFVP and SDUMLA-HMT, are used to evaluate the performance of the system. The results have shown that the values for the Identification Recognition Rate (IRR) and the Equal Error Rate (EER) are respectively (99.66%) and (0.139) for the UTFVP database and (98.95%, and 0.53%) for SDUMLA-HMT database. These results are competitive compared to those achieved by the state-of-art systems.
Arnold Sachith A Hans, Smitha Rao
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 7, pp 11-20; https://doi.org/10.29284/ijasis.7.1.2021.11-20

Abstract:
Human beings while communicating use emotions as a medium to understand the other person. Face being the primary source of contact while communicating and being the most communicative component of the body for exhibiting emotions, facial emotion detection in videos has been a challenging and an interesting problem to be addressed. The Facial expressions fall under the category of non-verbal type of communication and understanding Emotional state of a person through Facial Expressions has many use cases such as in the field of marketing research – understanding the customers responses for various products, Virtual classroom – understanding the comprehension level of the students, Job Interview – in understanding the changes in emotional state of the Interviewee, etc. This research paper proposes a CNN- LSTM based Neural Network which has been trained on CREMA-D dataset and tested on RAVDEES dataset for six basic emotions i.e. Angry, Happy, Sad, Fear, Disgust, and Neutral. The Faces in the videos were masked using Open Face software which gets the attention on the Face ignoring the background, which were further fed to the Convolutional Neural Network. The research focuses on using LSTM networks which have the capability of using the series of data which will aid in the final prediction of emotions in a video. We achieved an accuracy of 78.52% on CREMA-D dataset and further also tested the model on RAVDEES dataset and achieved an accuracy of 63.35%. This research work will help in making machines understand emotions, can help systems make better decisions and respond accordingly to the user.
Ramitha M A, MohanaSundaram N
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 7, pp 30-37; https://doi.org/10.29284/ijasis.7.1.2021.30-37

Abstract:
In Deep Learning, a Convolutional Neural Network (CNN) extracts the features from the visual imagery. These features can be used for various complex tasks such as image classification and segmentation and detection of different objects. The convolutional layers are stacked over each other to form the state-of-the-art models. A modified SENet architecture is introduced in this study to classify pneumonia from chest x-ray images. Six ResNet blocks are connected back to back. The output from the sixth ResNet and the side outputs from the last three ResNets are fused together. This output is fed as input to the SENet block. The validation accuracy of this fusion architecture is 91.84% on chest x-ray images.
Stalin Jacob, Jenifer Darling Rosita
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 7, pp 21-29; https://doi.org/10.29284/ijasis.7.1.2021.21-29

Abstract:
The early detection of skin cancer can lead to high prognosis rate. Thus it is very important to identify abnormalities in skin as early as possible. However, the detection of abnormalities at their early stages is a challenging task since the shape and colour of the abnormalities vary with different persons. In this study, fractal model for skin cancer diagnosis is developed. Differential Box Counting (DBC) method is implemented to get the fractal dimension from the dermoscopic images from two databases; International Skin Imaging Collaboration (ISIC) and PH2 database. The fractal features are classified using a parametric and non-parametric classification approach. The system provides promising results for skin cancer diagnosis with 96.5% accuracy on PH2 images and 91.5% accuracy on ISIC database images using the non-parametric classifier whereas parametric classifier gives 95% (PH2) and 90% (ISIC) images.
Mustafa Mashali, Miftah Addeif, Mohamed Embarak
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 6, pp 8-19; https://doi.org/10.29284/ijasis.6.2.2020.8-19

Abstract:
This paper aims for optimizing links length that consumed the minimum energy, for a customized Selective Compliant Assembly Robot Arm (SCARA) robot. Nine link length combinations are tested and simulated. This research is a part of a project of designing a robotic arm for a packing task. Kinematic and dynamic studies are performed for a 2R robotic arm. The results of kinematic study which are angular displacement, angular velocity and angular acceleration for each joint are determined and exported to the dynamic study to obtain the torque and power consumed. The dynamic study is performed with the aid of MATLAB code, MATLAB/SimMechanics and Solidworks are used to simulate and analyze the dynamic of the robotic arm. The energy consumed for each link length combination using the three methods is calculated.
Ibrahim A. Farhat, Abdullah O. Hawal
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 6, pp 1-7; https://doi.org/10.29284/ijasis.6.2.2020.1-7

Abstract:
The problem of Maximum Loadability of power systems is addressed in this paper using a proposed dynamic JAYA algorithm. The maximum loadability problem is a typical optimization problem in which the maximum loadability point is to be determined optimally. Voltage stability of power systems is maintained by determining the estimated margin between the system operating point and the maximum loadability limit. The basic JAYA algorithm has been introduced to solve foremost optimization problems with small-scaled nature. However, when applied to large-scale, nonlinear and non-convex constrained problems, it showed a poor convergence characteristic. In order to deal with these weaknesses, the original algorithm has been improved by adding some dynamic features to its convergence behavior. The modified algorithm has been presented and validated when applied to well-known typical power systems. The obtained results were compared to the results achieved by other equivalent optimization techniques.
Salem A. S. Ahbil, Hamid H. Sherwali
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 6, pp 29-39; https://doi.org/10.29284/ijasis.6.2.2020.29-39

Abstract:
In this paper, a methodology for estimating end-use load shapes using the hourly whole-house metered load data, the household demographic survey data and the weather data (temperature) is presented. End use load shapes presents a method of generating realistic electricity load profile data for some of city of Tripoli domestic buildings. This method could help in predicting the daily load profile from individual flats to community. The results obtained show that the overall methodology provides an effective means for end-use load shape modeling and estimation.
Mohamed Elsharif, Tareq Elgargani
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 6, pp 20-28; https://doi.org/10.29284/ijasis.6.2.2020.20-28

Abstract:
High performance motor drives require high accuracy, fast response, wide range of control, robustness and immunity from the effect of parameter variations. Three phase motors have a complex and highly nonlinear mathematical model associated with interactive parameters. This makes designing a conventional controller for such a system is a hard task. Researchers are paying more attention to fuzzy logic controllers (FLCs) since they can be employed to control complex or nonlinear systems even without knowing their mathematical model. The main task of this paper is to design and implement an FLC for indirect field orientated control of a three phase induction motor drive. The proposed controller is a proportional-derivative (PD) FLC. It uses the speed and its derivative as input and the electromagnetic torque as output. The input and output are coupled with simple linguistic if-then rules. The spread of each input and output is adjusted using a gain block to achieve the best performance in a trial-and-error process. Also, an incremental counter is attached to the output of the controller to yield the desired electromagnetic torque. The design was implemented and tested using MATLAB/SIMULINK. Finally, the simulation results and figures were presented.
Abdusalam Yahya, Ashraf Gasim Elsid Abdalla
INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, Volume 6, pp 40-49; https://doi.org/10.29284/ijasis.6.2.2020.40-49

Abstract:
Voiceover Internet Protocol (VoIP) application is a vital technology that is quickly growing in the Mobile Ad hoc NETwork (MANET). Packet loss is a factor that can significantly affect the Quality of Service (QoS) for VoIP performance. Due to the dynamic nature of MANET, it is a challenging task to maintain the desired packet loss rate. This paper aims to enhance the performance of VoIP in the MANET using a fuzzy logic model. The input for the model is VoIP packet loss and the outputs are the optimal parameters of MANET (node number, pause time, maximum speed, and maximum connection).Network Simulator (NS2) was used to perform all simulations. MATLAB was used to implement the proposed fuzzy model. Moreover, the performance of the model was evaluated using NS2, and the results show that our proposed fuzzy model offers a significant enhancement in terms of the VoIP packet loss rate (P.LR).
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