Vik Tor Goh

Multiple error detection and correction based on redundant residue number systems

This paper presents some results on multiple error detection and correction based on the Redundant Residue Number System (RRNS). RRNS is often used in parallel processing environments because of its ability to increase the robustness of information passing between the processors. The proposed multiple error correction scheme utilizes the Chinese Remainder Theorem(CRT) together with a novel algorithm that significantly simplifies the error correcting process for integers. An extension of the scheme further reduces the computational complexity without compromising its error correcting capability. Proofs and examples are provided for the coding technique.

Network packet payload parity based steganography

Network steganography is an emerging research field, which exploits packet protocol headers, protocol mechanism, packet payload, etc. to establish secret communication between two parties over a computer network. The detection of such hidden communication has not been part of intrusion detection systems that are primarily used to detect malicious activities such as viruses and malwares. In this paper, we propose two packet-length based steganographic techniques to implement a covert channel. We examine and analyze the packet lengths of normal traffic to show that our techniques can cope with traffic anomaly detection methods and does not introduce noticeable traffic overhead.

A novel error correction scheme based on the Chinese remainder theorem

The Chinese remainder theorem (CRT) plays an important role in error detection and correction in redundant residue number systems. This paper presents novel algorithms that significantly simplify the process of correcting both single and multiple errors based on the CRT. Proofs are provided for the new decoding techniques

document signature generation and insertion

Internet plays the role of a huge information repository in the modern era. It is the most rapidly growing and ever expanding media for information interchange. The easiest way to distribute articles, theses, papers and legal law documents is through the Internet or file sharing systems, but it poses copyrights violation issues. To protect these intellectual property rights, only a few techniques have been proposed. We present a content-based technique in conjunction with horizontal word shift coding to provide a two level security and a mechanism for inserting and generating unique signatures and copyrights information

Autonomous Road Potholes Detection on Video

This research work explores the possibility of using deep learning to produce an autonomous system for detecting potholes on video to assist in road monitoring and maintenance. Video data of roads was collected using a GoPro camera mounted on a car. Region-based Fully Convolutional Networks (RFCN) was employed to produce the model to detect potholes from images, and validated on the collected videos. The R-FCN model is able to achieve a Mean Average Precision (MAP) of 89% and a True Positive Rate (TPR) of 89% with no false positive.

Residential Neighbourhood Security using WiFi

This paper focuses on the design of a WiFi-based tracking and monitoring system that can detect people’s movements in a residential neighbourhood. The proposed system uses WiFi access points as scanners that detect signals transmitted by the WiFi-enabled smartphones that are carried by most people. Our proposed system is able to track these people as they move through the neighbourhood. We implement our WiFi-based tracking system in a prototype and demonstrate that it is able to detect all WiFi devices in the vicinity of the scanners. We describe the implementation details of our system as well as discuss some of the results that we obtained.

Daily Activities Classification on Human Motion Primitives Detection Dataset

The study is to classify human motion data captured by a wrist worn accelerometer. The classification is based on the various daily activities of a normal person. The dataset is obtained from Human Motion Primitives Detection [1]. There is a total of 839 trials from 14 activities performed by 16 volunteers (11 males and 5 females) ages between 19 to 91 years. A wrist worn tri-axial accelerometer was used to accrue the acceleration data of X, Y and Z axis during each trial. For feature extraction, nine statistical parameters together with the energy spectral density and the correlation between the accelerometer readings are employed to extract 63 features from the raw acceleration data. Particle Swarm Organization, Tabu Search and Ranker are applied to rank and select the positive roles for the later classification process. Classification is implemented using Support Vector Machine, k-Nearest Neighbors and Random Forest. From the experimental results, the proposed model achieved the highest correct classification rate of 91.5% from Support Vector Machine with radial basis function kernel.

Identification of Road Surface Conditions using IoT Sensors and Machine Learning

The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using k-Nearest Neighbour (k-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.

Using IP identification for fragmentation resilient data embedding

In this work, we propose a fragmentation resilient IP identification based data embedding method. First, we analyze the IP identification generation in various operating systems to validate the feasibility of our proposal. Then, we put forward our proposal to embed data into IP identification field while considering data from the payload field. Results suggest that the proposed method resembles the ordinary IP identification generation pattern and can mitigate the problem of packet fragmentation.