Mark Yeary

Multi-Rate Layered Decoder Architecture for Block LDPC Codes of the IEEE 802.11n Wireless Standard

We present a new multi-rate architecture for decoding block LDPC codes in IEEE 802.11n standard. The proposed architecture utilizes the value-reuse property of offset min-sum, block-serial scheduling of computations and turbo decoding message passing algorithm. Techniques of data-forwarding and out-of-order processing are used to deal with the irregularity of the codes. The decoder has the following advantages when compared to recent state-of-the-art architectures: 55% savings in memory, reduction of routers by 50% and increase of throughput by 2times.

VLSI Architectures for Layered Decoding for Irregular LDPC Codes of WiMax

We present a new multi-rate architecture for decoding irregular LDPC codes in IEEE 802.16e WiMax standard. The proposed architecture utilizes the value-reuse property of offset min-sum, block-serial scheduling of computations and turbo decoding message passing algorithm. The decoder has the following advantages: 55% savings in memory, reduction of routers by 50%, and increase of throughput by 2times when compared to the recent state-of-the-art decoder architectures.

Development of a low cost fuel cell inverter system with DSP control

In this paper, the development of a low cost fuel cell inverter system is detailed. The approach consists of a three-terminal push-pull dc-dc converter to boost the fuel cell voltage (48V) to /spl plusmn/200 VDC. A four switch [insulated gate bipolar transistor (IGBT)] inverter is employed to produce 120-V/240-V, 60-Hz ac outputs. High performance, easy manufacturability, lower component count, safety and cost are addressed. Protection and diagnostic features form an important part of the design. Another highlight of the proposed design is the control strategy, which allows the inverter to adapt to the requirements of the load as well as the power source (fuel cell). A unique aspect of the design is the use of the TMS320LF2407 DSP to control the inverter. Two sets of lead-acid batteries are provided on the high voltage dc bus to supply sudden load demands. Efficient and smooth control of the power drawn from the fuel cell and the high voltage battery is achieved by controlling the front end dc-dc converter in current mode. The paper details extensive experimental results of the proposed design on Department of Energy (DoE) National Energy Technology Laboratory (NETL) fuel cell.

A Parallel VLSI Architecture for Layered Decoding for Array LDPC Codes

The VLSI implementation complexity of a low density parity check (LDPC) decoder is largely influenced by interconnect and the storage requirements. Here, the proposed layout-aware layered decoder architecture utilizes the data-reuse properties of min-sum, layered decoding and structured properties of array LDPC codes. This results in a significant reduction of logic and interconnects requirements of the decoder when compared to the state-of-the-art LDPC decoders. The ASIC implementation of the proposed fully parallel architecture achieves throughput of 4.6 Gbps (for a maximum of 15 iterations). The chip size is 2.3 mm times 2.3 mm in 0.13 micron technology

DSP-based hierarchical neural network modulation signal classification

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time.

An Object-Tracking Algorithm Based on Multiple-Model Particle Filtering With State Partitioning

As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model. However, the single-model assumption can cause the tracker to become unstable, particularly when the target has complex motions and when the camera has abrupt ego-motions. In the new tracking algorithm, a target was assumed to operate according to one dynamic model from a finite set of models. The switching process from one model to another was governed by a jump Markov process. Based on the improved MM particle filter framework, we offer a new design strategy that adopts the state-partitioning technique and a bank of parallel extended Kalman filters to construct a better proposal distribution to achieve further estimation accuracy. We have conducted extensive testing for the proposed tracking algorithm, and key outcomes were given in the results section. It has been demonstrated by the experiments that this approach gave significantly improved estimations, enabling the new particle filter to effectively track human subjects.

Reversing the Trend of Engineering Enrollment Declines With Innovative Outreach, Recruiting, and Retention Programs

This paper discusses an all-encompassing approach to increase the number of students in engineering through innovative outreach, recruiting, and retention programs. Prior to adopting these programs, the School of Electrical and Computer Engineering (ECE) at the University of Oklahoma (OU), Norman, experienced a reduction in engineering enrollment similar to the trend that has occurred across the U.S. over the last few years. As a result, the school investigated the key factors that influence selection of engineering as a career path and initiated a corrective program to reverse this trend. The program involves focusing on the present through retention, on the immediate future through recruiting, and on the distant future through outreach. The focus of all of these programs is to mobilize the OU-ECE faculty and student body to present advanced engineering technologies, innovative demonstrations, and hands-on activities at a level that the individual student can understand and appreciate. Student surveys and interviews are used to assess the program qualitatively, and OU-ECE enrollment numbers are used as a quantitative assessment.

The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar

AbstractMobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial...

A Three-Dimensional Swept Volume Display Based on LED Arrays

Stereoscopic, or multi-view, display systems are considered as better alternatives to conventional two-dimensional (2D) displays, since such systems can provide important visual cues for the human brain to process three-dimensional (3D) objects. An auto-stereoscopic display is a device that can render 3D images for viewers without the aid of special headgear or glasses. In this paper, we present a new design of an auto-stereoscopic swept-volume display (SVD) system based on light-emitting diode (LED) arrays. This system is constituted of a display device and a graphics control sub-system. The display device is a 2D rotating panel of LEDs, relying on “persistence of vision” to generate 3D images. The graphics control sub-system is composed of a combination of PC software, field-programmable gate arrays (FPGAs), and supporting circuitry. The primary task of the graphics control sub-system is to process 3D data and control each LED. In addition, a new 3D image generation and rendering method was developed to reduce the bandwidth requirement and to facilitate 3D image display. Demonstrated in the experiments, a prototype of this system is capable of displaying 3D images and videos with full 360 ° view angles.

A New Centralized Sensor Fusion-Tracking Methodology Based on Particle Filtering for Power-Aware Systems

In this paper, we address the problem of target tracking in a collaborative acoustic sensor network. To cope with the inherent characteristics and constraints of wireless sensor networks, we present a novel target-tracking algorithm with power-aware concerns. The underlying tracking methodology is described as a multiple-sensor tracking/fusion technique based on particle filtering. As discussed in the most recent literature, particle filtering is defined as an emerging Monte Carlo state estimation technique with proven superior performance in many target-tracking applications. More specifically, in our proposed method, each activated sensor transmits the received acoustic intensity and the direction of arrival (DOA) of the target to the sensor fusion center (a dedicated computing and storage platform, such as a microserver). The fusion center uses each received DOA to generate a set of estimations based on the state partition technique, as described later in this paper. In addition, a set of sensor weights is calculated based on the acoustic intensity received by each activated sensor. Next, the weighted sum of the estimates is used to generate the proposal distribution in the particle filter for sensor fusion. This technique renders a more accurate proposal distribution and, hence, yields more precise and robust estimations of the target using fewer samples than those of the traditional bootstrap filter. In addition, since the majority of the signal processing efficiently resides on the fusion center, the computation load at the sensor nodes is limited, which is desirable for power-aware systems. Last, the performance of the new tracking algorithm in various tracking scenarios is thoroughly studied and compared with standard tracking methods. As shown in the theory and demonstrated by our experimental results, the state-partition-based centralized particle filter reliably outperforms the traditional method in all experiments.