Data Mining Practical Machine Learning Tools and Techniques

Environmental hazards: Assessing risk and reducing disaster

Probability theory and mathematical statistics

We present a method for segmenting and tracking vehicles on highways using a camera that is relatively low to the ground. At such low angles, 3-D perspective effects cause significant changes in appearance over time, as well as severe occlusions by vehicles in neighboring lanes. Traditional approaches to occlusion reasoning assume that the vehicles initially appear well separated in the image; however, in our sequences, it is not uncommon for vehicles to enter the scene partially occluded and remain so throughout. By utilizing a 3-D perspective mapping from the scene to the image, along with a plumb line projection, we are able to distinguish a subset of features whose 3-D coordinates can be accurately estimated. These features are then grouped to yield the number and locations of the vehicles, and standard feature tracking is used to maintain the locations of the vehicles over time. Additional features are then assigned to these groups and used to classify vehicles as cars or trucks. Our technique uses a single grayscale camera beside the road, incrementally processes image frames, works in real time, and produces vehicle counts with over 90% accuracy on challenging sequences.

Real-Time Incremental Segmentation and Tracking of Vehicle at Low Camera Angles Using Stable Features

The Internet of Things (IoT) has been identified in various applications across different domains, such as in the healthcare sector. IoT has also been recognised for its revolution in reshaping modern healthcare with aspiring wide range prospects, including economical, technological and social. This study aims to establish IoT-based smart home security solutions for real-time health monitoring technologies in telemedicine architecture. A multilayer taxonomy is driven and conducted in this study. In the first layer, a comprehensive analysis on telemedicine, which focuses on the client and server sides, shows that other studies associated with IoT-based smart home applications have several limitations that remain unaddressed. Particularly, remote patient monitoring in healthcare applications presents various facilities and benefits by adopting IoT-based smart home technologies without compromising the security requirements and potentially large number of risks. An extensive search is conducted to identify articles that handle these issues, related applications are comprehensively reviewed and a coherent taxonomy for these articles is established. A total number of (n = 3064) are gathered between 2007 and 2017 for most reliable databases, such as ScienceDirect, Web of Science and Institute of Electrical and Electronic Engineer Xplore databases. Then, the articles based on IoT studies that are associated with telemedicine applications are filtered. Nine articles are selected and classified into two categories. The first category, which accounts for 22.22% (n = 2/9), includes surveys on telemedicine articles and their applications. The second category, which accounts for 77.78% (n = 7/9), includes articles on the client and server sides of telemedicine architecture. The collected studies reveal the essential requirement in constructing another taxonomy layer and review IoT-based smart home security studies. Therefore, IoT-based smart home security features are introduced and analysed in the second layer. The security of smart home design based on IoT applications is an aspect that represents a crucial matter for general occupants of smart homes, in which studies are required to provide a better solution with patient security, privacy protection and security of users’ entities from being stolen or compromised. Innovative technologies have dispersed limitations related to this matter. The existing gaps and trends in this area should be investigated to provide valuable visions for technical environments and researchers. Thus, 67 articles are obtained in the second layer of our taxonomy and are classified into six categories. In the first category, 25.37% (n = 17/67) of the articles focus on architecture design. In the second category, 17.91% (n = 12/67) includes security analysis articles that investigate the research status in the security area of IoT-based smart home applications. In the third category, 10.44% (n = 7/67) includes articles about security schemes. In the fourth category, 17.91% (n = 12/67) comprises security examination. In the fifth category, 13.43% (n = 9/67) analyses security protocols. In the final category, 14.92% (n = 10/67) analyses the security framework. Then, the identified basic characteristics of this emerging field are presented and provided in the following aspects. Open challenges experienced on the development of IoT-based smart home security are addressed to be adopted fully in telemedicine applications. Then, the requirements are provided to increase researcher’s interest in this study area. On this basis, a number of recommendations for different parties are described to provide insights on the next steps that should be considered to enhance the security of smart homes based on IoT. A map matching for both taxonomies is developed in this study to determine the novel risks and benefits of IoT-based smart home security for real-time remote health monitoring within client and server sides in telemedicine applications.

Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review

A weighted dominant color descriptor for content-based image retrieval

Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems.

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Vehicle Detection and Tracking Techniques: A Concise Review

Information hiding researchers have been exploring techniques to improve the security of transmitting sensitive data through an unsecured channel. This paper proposes an audio steganography model for secure audio transmission during communication based on fractal coding and a chaotic least significant bit or also known as HASFC. This model contributes to enhancing the hiding capacity and preserving the statistical transparency and security. The HASFC model manages to embed secret audio into a cover audio with the same size. In order to achieve this result, fractal coding is adopted which produces high compression ratio with the acceptable reconstructed signal. The chaotic map is used to randomly select the cover samples for embedding and its initial parameters are utilized as a secret key to enhancing the security of the proposed model. Unlike the existing audio steganography schemes, The HASFC model outperforms related studies by improving the hiding capacity up to 30% and maintaining the transparency of stego audio with average values of SNR at 70.4, PRD at 0.0002 and SDG at 4.7. Moreover, the model also shows resistance against brute-force attack and statistical analysis.

High capacity, transparent and secure audio steganography model based on fractal coding and chaotic map in temporal domain

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In this research, new fractal compression technique is introduced. It is based on using moment features to index the zero-mean range on domain blocks. The moment features have been used to speed up the iterated function system (IFS) matching stage. These features are used to determine the block descriptor "moment's ratio index", which in turn is utilized to classify the image blocks in both domain and range pools. During the encoding stage the block moment ratio descriptor of each range blocks is used to filter the domain blocks and keep only those blocks whose moment descriptor is suitable to be IFS matched with the tested range block. The data of the color components (R,G,B) are transformed to (Y,U,V) component, to take the advantage of the existing spectral correlation and gain more compression. The results of the tests conducted on Lena (256 times 256) pixel, resolution 24 bits/pixel) image showed a lower encoding time (4.82 sec) with appropriate PSNR (30dB). The time speeding is about (96%) in comparison with that for traditional method.

Loay E. George

Papers

A weighted dominant color descriptor for content-based image retrieval

Vehicle Detection and Tracking Techniques: A Concise Review

High capacity, transparent and secure audio steganography model based on fractal coding and chaotic map in temporal domain

Vehicle Detection and Tracking Techniques: A Concise Review

Speeding-Up Fractal Colored Image Compression Using Moments Features