Hanadi Ahmed Hakami

Bio: Hanadi Ahmed Hakami is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Image compression & Human visual system model. The author has an hindex of 3, co-authored 6 publications receiving 29 citations. Previous affiliations of Hanadi Ahmed Hakami include IT University.

Review of Human Motion Detection based on Background Subtraction Techniques

Abstract: For the majority of computer vision applications, the ability to identify and detect objects in motion has become a crucial necessity. Background subtraction, also referred to as foreground detection is an innovation used with image processing and computer vision fields when trying to detect an object in motion within videos from static cameras. This is done by deducting the present image from the image in the background or background module. There has been comprehensive research done in this field as an effort to precisely obtain the region for the use of further processing (e.g. object recognition). This paper provides a review of the human motion detection methods focusing on background subtraction technique.

Review of Big Data Storage Based on DNA Computing

Abstract: There is a need of noteworthy scaling down in the information approached may be saved in the most recent decade. Delicate and advanced version hard paper duplicate which helps in two ways that they increased the effectiveness from claiming data management but also improved the distribution of entrance of information. On engineered DNA, it may be a chance to view the late improvement on the possibility about data capacity. Similarly in this way we have figured out how leap forward engineering could dramatically change the lifestyle out of our information capacity. This topic ' Big Data Storage based DNA' is described from the first research to newer one, their advantages and disadvantages, their techniques and how it will become a practice in the future. We also propose an approach is proposed as simple method to store data into DNA. The experiment work is done to validate the proposed approach result clearly show advantages merits of proposed method.

Review of HVS-based Image Compression Methods

Abstract: Digital images can be compressed by using data compression processes with the aim of lessening image data redundancy and save or send out an efficient form of data. This is not an easy task because large volumes of data comprise an original image, requiring huge memory allocation and inconvenient transmission. In this paper, the Human Visual System-based Image Compression is reviewed based on set criteria. Specifically, the characteristics, compression techniques and wavelet-based compression are discussed.

Improve data compression performance using wavelet transform based on HVS

Abstract: In recent years, multimedia computing has emerged as a major area of research. This led to the development of various other types of applications like video, images and graphics. Data compression is specifically valuable during communication as it enables the electronic devices to store and transmit the data in a smaller number of bits. The following research considers data compression as a possible solution to retrieve, store and transmit data. In relation to this, this research also aims to have a balance between the processing time, the quality and compression rate based on Human Visual System (HVS) perception. The best compression method for multimedia conversion would be to convert at the highest possible rate with the minimum amount of distortion. For this reason, the proposed method using wavelet based on HVS method with integrated JPEG2000 to achieve a high compression ratio, faster execution and better quality all in real time. This experiment work has improved the performance of the previous method and achieved the best result of data compression using wavelet based on HVS.

Door wave home automation system

Abstract: The technological developments are focused on automation of control systems. Technology is used in homes to create a digital environment such as controlling room temperature, sundry devices, security and lighting. The design of home automation systems is geared towards the automation of processes like remote control of the home environment appliances. The use of Wireless Sensor and Actuators Networks (WSANs) in home automation is a growing trend. WSANs are based on network architecture and protocols in order to enable a network of integrated devices which monitor and control household apparatus.

New Trends of Digital Data Storage in DNA

Abstract: With the exponential growth in the capacity of information generated and the emerging need for data to be stored for prolonged period of time, there emerges a need for a storage medium with high capacity, high storage density, and possibility to withstand extreme environmental conditions. DNA emerges as the prospective medium for data storage with its striking features. Diverse encoding models for reading and writing data onto DNA, codes for encrypting data which addresses issues of error generation, and approaches for developing codons and storage styles have been developed over the recent past. DNA has been identified as a potential medium for secret writing, which achieves the way towards DNA cryptography and stenography. DNA utilized as an organic memory device along with big data storage and analytics in DNA has paved the way towards DNA computing for solving computational problems. This paper critically analyzes the various methods used for encoding and encrypting data onto DNA while identifying the advantages and capability of every scheme to overcome the drawbacks identified priorly. Cryptography and stenography techniques have been analyzed in a critical approach while identifying the limitations of each method. This paper also identifies the advantages and limitations of DNA as a memory device and memory applications.

Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

Abstract: Forest fires represent a real threat to human lives, ecological systems, and infrastructure. Many commercial fire detection sensor systems exist, but all of them are difficult to apply at large open spaces like forests because of their response delay, necessary maintenance needed, high cost, and other problems. In this paper a forest fire detection algorithm is proposed, and it consists of the following stages. Firstly, background subtraction is applied to movement containing region detection. Secondly, converting the segmented moving regions from RGB to YCbCr color space and applying five fire detection rules for separating candidate fire pixels were undertaken. Finally, temporal variation is then employed to differentiate between fire and fire-color objects. The proposed method is tested using data set consisting of 6 videos collected from Internet. The final results show that the proposed method achieves up to 96.63% of true detection rates. These results indicate that the proposed method is accurate and can be used in automatic forest fire-alarm systems.

A Hybrid Deep Model Using Deep Learning and Dense Optical Flow Approaches for Human Activity Recognition

Abstract: Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose a hybrid deep model to understand and interpret videos focusing on human activity recognition. The proposed architecture is constructed combining dense optical flow approach and auxiliary movement information in video datasets using deep learning methodologies. To the best of our knowledge, this is the first study based on a novel combination of 3D-convolutional neural networks (3D-CNNs) fed by optical flow and long short-term memory networks (LSTM) fed by auxiliary information over video frames for the purpose of human activity recognition. The contributions of this paper are sixfold. First, a 3D-CNN, also called multiple frames is employed to determine the motion vectors. With the same purpose, the 3D-CNN is secondly used for dense optical flow, which is the distribution of apparent velocities of movement in captured imagery data in video frames. Third, the LSTM is employed as auxiliary information in video to recognize hand-tracking and objects. Fourth, the support vector machine algorithm is utilized for the task of classification of videos. Fifth, a wide range of comparative experiments are conducted on two newly generated chess datasets, namely the magnetic wall chess board video dataset (MCDS), and standard chess board video dataset (CDS) to demonstrate the contributions of the proposed study. Finally, the experimental results reveal that the proposed hybrid deep model exhibits remarkable performance compared to the state-of-the-art studies.

Wavelet based image compression

Abstract: The aim of this paper is to discuss the important features of wavelet transform in compression of still images Wavelet transform is the technique that provides both spatial and frequency domain information. These properties of wavelet transform help in identification and selection of significant and non-significant coefficients. Discrete wavelet Transform represents image as a sum of wavelet function on different resolution levels. There exists a large selection of wavelet families depending on the choice of wavelet function. The choice of wavelet function for image compression depends on the content of image. A review of the fundamentals of image compression based on wavelets is given here. This also discussed the important features of wavelet transform in compression of images.

Trends to store digital data in DNA: an overview

Abstract: There has been an ascending growth in the capacity of information being generated. The increased production of data in turn has put forward other challenges as well thus, and there is the need to store this information and not only to store it but also to retain it for a prolonged time period. The reliance on DNA as a dense storage medium with high storage capacity and its ability to withstand extreme environmental conditions has increased over the past few years. There have been developments in reading and writing different forms of data on DNA, codes for encrypting data and using DNA as a way of secret writing leading towards new styles like stenography and cryptography. The article outlines different methods adopted for storing digital data on DNA with pros and cons of each method that has been applied plus the advantages and limitations of using DNA as a storage medium.