David Mulvaney

Thread-parallel MPEG-2, MPEG-4 and H.264 video encoders for SoC multi-processor architectures

This study utilizes thread-level parallel techniques to significantly reduce the dynamic instruction count performance metric of the MPEG-2, MPEG-4 and H.264 video encoders. Such solutions are particularly applicable in portable devices as workload distribution among a number of parallel-executing processors decreases the individual processing requirements and allows for the real time video encoding. Due to the use of multiple processing engines in a consumer SoC the required clock frequency for real-time encoding, and hence power consumption, is likely to be considerably less than that of a single high-speed processor solution. The results presented demonstrate that reductions in dynamic instruction count in the range of 84% to 96% can be achieved for each of the encoders investigated.

Mobile Robot Path Planning in Dynamic Environments

This paper introduces a genetic algorithm (GA) planner that is able to rapidly determine optimal or near-optimal solutions for mobile robot path planning problems in environments containing moving obstacles. The method restricts the search space to the vertices of the obstacles, obviating the need to search the entire environment as in earlier GA-based approaches. The new approach is able to produce an off-line plan through an environment containing dynamic obstacles, and can also re-calculate the plan on-line to deal with any motion changes encountered. A particularly novel aspect of the work is the incorporation of the selection of robot speed into the GA genes. The results from a number of realistic environments demonstrate that planning changes in robot speed significantly improves the efficiency of movement through the static and moving obstacles.

A multi-standard video coding accelerator based on a vector architecture

A multi-standard video encoding coprocessor is presented that efficiently accelerates MPEG-2, MPEG-4 (XViD) and a proprietary H.264 encoder. The proposed architecture attaches to a configurable, extensible RISC CPU to form a highly efficient solution to the computational complexity of current and emerging video coding standards. A subset of the ISA has been implemented as a VLSI macrocell for a high performance 0.13 /spl mu/m silicon process.

Monitoring heart disease and diabetes with mobile internet communications

A telemedicine system is described for monitoring vital signs and general health indicators of patients with cardiac and diabetic conditions. Telemetry from wireless sensors and readings from other instruments are combined into a comprehensive set of measured patient parameters. Using a combination of mobile device applications and web browser, the data can be stored, accessed, and displayed using mobile internet communications to the central server. As an extra layer of security in the data transmission, information embedded in the data is used in its verification. The paper highlights features that could be enhanced from previous systems by using alternative components or methods.

Genetic-based Mobile Robot Path Planning using Vertex Heuristics

This paper presents a new genetic-based path planning method for a mobile robot in which the vertices of the obstacles, described as polygonal clusters, are encoded as genes. In comparison with other approaches that either map the robots environment or need to consider the whole environment as the search space, the narrowing of the search to include only vertices is able to gear evolutionary progress, bringing potential benefits in terms of both calculation time and memory usage. The proposed vertex planning method is verified through experiments in a series of realistic simulated environments. Also, the efficiency and effectiveness of the proposed method are compared with the well-known evolutionary navigator/planner

Data acquisition in a wireless diabetic and cardiac monitoring system

A telemedicine system is described for monitoring the vital signs and general health indicators of patients with cardiac and diabetic conditions. Telemetry from wireless sensors and readings from other instruments are combined into a comprehensive patient health dataset. The data can be stored, accessed and displayed using mobile Internet communications with a server. The paper concentrates on the data acquisition process, using an alternative sensor network protocol to Bluetooth and manual data entry into a smartphone application and HTML5 web browser.

Hardware implementation of a novel genetic algorithm

This paper presents a novel genetic algorithm, termed the Optimum Individual Monogenetic Algorithm (OIMGA) and describes its hardware implementation. As the monogenetic strategy retains only the optimum individual, the memory requirement is dramatically reduced and no crossover circuitry is needed, thereby ensuring the requisite silicon area is kept to a minimum. Consequently, depending on application requirements, OIMGA allows the investigation of solutions that warrant either larger GA populations or individuals of greater length. The results given in this paper demonstrate that both the performance of OIMGA and its convergence time are superior to those of existing hardware GA implementations. Local convergence is achieved in OIMGA by retaining elite individuals, while population diversity is ensured by continually searching for the best individuals in fresh regions of the search space. Keywords—Genetic algorithms, hardware-based machine learning.

Robot Navigation by Waypoints

In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.

Design and implementation of a lossless parallel high-speed data compression system

Logic density increases have made feasible the implementation of multiprocessor systems able to meet the intensive data processing demands of highly concurrent systems. We describe the research and hardware implementation of a high-performance parallel multicompressor chip. A detailed investigation into the performances of alternative input and output routing strategies for realistic data sets demonstrate that the design of parallel compression devices involves important trade offs that affect compression performance, latency, and throughput. The most promising approach is implemented into FPGA hardware and is shown to provide a scalable compression solution at throughputs able to cope with the demands of modern high-bandwidth applications.

Wrist pulse signal classification for health diagnosis

Ancient Indian and Chinese medicine both use non-invasive wrist pulse signals for health diagnosis of patients. In this paper, data obtained from a number of patients have been used to categorize the types of pulse signals that are found in both normal and abnormal health conditions. Features were extracted from the pulse signals using both frequency and wavelet transformations and these were then ranked according to their classification power for multiclass classifier design. Linear and quadratic pulse classifiers are proposed with raw features as well as subset of ranked features. Linear classifier has found to be giving highest classification accuracy of 73.82% using 4 ranked features.