Volume 11, Issue 2, February 2019

 

 

Data Analysis of Different Flip Flop Configurations
 

Data Analysis of Different Flip Flop Configurations

Pages: 24-29 (6) | [Full Text] PDF (664 KB)
SO Ogunlere, OS Onilede, BS Adeniyi
Department of Computer Science, School of Computing and Engineering Sciences, Babcock University, Ilishan-Remo, Ogun State, Nigeria

Abstract -
The design of a high performance memory element known as Flip-Flop Extension data analysis is carried out to ascertain its efficiency and effectiveness over conventional SR and JK Flip Flops. This is achieved through the analysis of Flip Flop Extension data in comparison with the existing conventional Flip Flops data to examine and evaluate the significant advantages of the Flip Flops Extension at 87.5% and or 100% active states against SR at 50% and JK at 75% active states utilizations. From the data analysis carried out, the Flip Flop Extension at 87.5% is found suitable to be used as memory element with speed, size and power consumption performance advantage over the conventional SR and JK Flip Flops; while the Flip Flop ‘No Rest state’ at 100% active state utilization cannot be used to build Storage devices.
 
Index Terms - Conventional Flip Flop, Flip Flop Extension, Memory Element, Active State Utilization

Citation - SO Ogunlere, OS Onilede, BS Adeniyi. "Data Analysis of Different Flip Flop Configurations ." International Journal of Computer Engineering and Information Technology 11, no. 2 (2019): 24-29.

Efficiency of LSB and PVD Algorithms Used in Steganography Applications
 

Efficiency of LSB and PVD Algorithms Used in Steganography Applications

Pages: 30-39 (10) | [Full Text] PDF (2.67 MB)
B Rexha, P Rama, B Krasniqi, G Seferi
Faculty of Electrical and Computer Engineering, University of Prishtina, Kodra e Diellit p.n.10000 - Prishtina, Kosovo

Abstract -
Steganography is the science of hiding secret information in other non-suspicious information allowing secret communication between parties. The steganographic process consists of secret information, the carrier file and steganographic algorithm. Each carrier has its own characteristics which affects the steganographic algorithm. However, what differentiates a steganographic algorithm from another is the efficiency for data hiding in the carrier. An algorithm is more efficient if it hides more secret information while maintaining the quality of the carrier. This paper compares different parameters that affect efficiency of LSB and PVD algorithms, impact of carrier type, format, and size. All these analyzes were done using SteganoFIEK application, developed in the framework of this paper, for experimental purposes. Furthermore, SteganoFIEK implementation of LSB is compared against other open-source applications.
 
Index Terms - Steganography, Steganalysis, LSB, PVD, Efficiency, Security

Citation - B Rexha, P Rama, B Krasniqi, G Seferi. "Efficiency of LSB and PVD Algorithms Used in Steganography Applications." International Journal of Computer Engineering and Information Technology 11, no. 2 (2019): 30-39.

A Novel Real-time Human Activity Based Anomaly Detection Model Using Graph Based Clustering and Classification Model
 

A Novel Real-time Human Activity Based Anomaly Detection Model Using Graph Based Clustering and Classification Model

Pages: 40-46 (7) | [Full Text] PDF (469 KB)
D Kishore, MC Mohan, AA Rao
Asst.Prof, Department of Computer Science & Engineering, Andhrapradesh, India

Abstract -
Detecting online abnormality in the video surveillance is a challenging issue due to streaming, video noise, outliers and resolution. Traditional trajectory based anomaly detection model which analyzes the video training patterns for anomaly detection. This paper aims to solve the problem of video noise and anomaly detection .In this paper, a novel filtered based ensemble clustering and classification model was implemented using the threshold based method, graph based clustering algorithm and classification model. Experimental results proved that the proposed model has high computation detection rate compared to traditional real-time anomaly detection models.
 
Index Terms - Anomaly Detection, Video Anomaly, Graph Based Clustering Model

Citation - D Kishore, MC Mohan, AA Rao. "A Novel Real-time Human Activity Based Anomaly Detection Model Using Graph Based Clustering and Classification Model." International Journal of Computer Engineering and Information Technology 11, no. 2 (2019): 40-46.