Dual-output look-up tables (LUTs) are supported by modern commercial Field-Programmable Gate Array (FPGA) architectures. Existing technology mapping usually generates multi-output LUTs by merging single-output LUTs. However, these mapping solutions often fail to fully leverage the multi-output capability. This paper proposes a novel FPGA mapping scheme for multi-output logic units, aiming to improve synthesis performance significantly. By introducing new metrics functions, k-input l-output cut (kl-cut) generation method, and methods to add secondary LUT outputs, the proposed approach enables thorough exploration of the design space. Results show that the proposed dual-output LUT synthesis method achieves 30.2% and 34.7% reduction in LUT usage and depth, respectively, outperforming leading-edge dual-output mapping tools. In addition, the proposed mapping scheme is applied to an emerging field-programmable unit, MCluster, proving compatibility and efficiency on non-LUT logic blocks. For the first time, we perform a system-level design flow to demonstrate the benefit of multi-output LUT at the system level and provide critical insights and design trade-offs.

Reconfigurable devices are gaining increasing attention as a viable alternative and supplementary solution to the traditional CMOS technology. In this article, we develop a more efficient field-programmable gate array (FPGA) based on the reconfigurable field-effective transistor (RFET). We use the multi-gate characteristics of RFET to redesign the key components of FPGAs, namely SRAM-controlled multiplexer and look-up tables. The compact structure of the proposed design requires fewer transistors and leads to reduced delay of the overall FPGA system. In addition, we develop a comprehensive technology/system co-design and co-optimization framework to thoroughly investigate the design space, including various device- and system-level design parameters. A series of benchmark tests show that under the optimal design, up to 32% and 13% reduction can be achieved in delay and energy-delay product (EDP), respectively, compared to the traditional CMOS FPGAs.

In this paper, the design and simulation of a CMOS ultra-wideband (UWB) transmitter using TSMC 0.18 μm CMOS technology is presented. The operating frequency band of the transmitter is from 3 to 10 GHz for the application of UWB communications. The structure of the transmitter is based on the current-steering digital-to-analog converter (DAC). The design has the feature of high-tunability and easy adoption of modulations. The simulation results show that the output of the transmitter complies with the FCC regulations and has a power consumption of 32 mW at a 50 MHz pulse repetition frequency (PRF).

An iterative Segmentation-Estimation framework for segmentation of planar surfaces in the disparity space is implemented on a Digital Signal Processor (DSP). Disparity of a scene is modeled by approximating various surfaces in the scene to be planar. The surface labels are estimated during the segmentation phase of the framework with help of the underlying plane parameters. After segmentation, planar surfaces are separated into spatially continuous regions. The largest of these regions is used to compute the estimates for the plane parameters. The iterative process is continued till convergence. The algorithm was optimized and implemented on TMS320DM642 based embedded system that operates at 3 to 5 frames per second on images of size 320 x 240.

The goal of this paper is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional maximum likelihood (ML) methods, in which classification is based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, proposed method utilizes information from all classes to minimize classification error. The proposed approach uses a HMM for shape curvature as its 2-D shape descriptor. We introduce a weighted likelihood discriminant function and present a minimum classification error strategy based on generalized probabilistic descent method. We show comparative results obtained with our approach and classic ML classification with various HMM topologies alongside Fourier descriptor and Zernike moments-based support vector machine classification for a variety of shapes.

In this paper, we present a novel occlusion detection scheme for hidden Markov modeled shapes. First, hidden Markov model (HMM) is built using multiple examples of the shape. A reference path for the shape is built from the HMM, which is nothing but optimal path followed by the most likely example. The reference path stores temporal information about the entire shape, while the HMM only retains relationship between temporal information. For the shape of interest, its optimal path through HMM is calculated and warped to match the reference path using dynamic time warping (DTW). Occluded part of the shape is detected by identifying imbalance among various components of the matching cost. Detection results obtained for two shape data sets are presented for varying degrees of occlusion.

The goal of this communication is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional Maximum Likelihood (ML) methods in which classification is carried out based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, our proposed method utilizes information from all classes to minimize classification error. Proposed approach uses a Hidden Markov Model as a curvature feature based 2D shape descriptor. In this contribution we present a Generalized Probabilistic Descent (GPD) method to weight the curvature likelihoods to achieve a discriminant function with minimum classification error. In contrast with other approaches, a weighted likelihood discriminant function is introduced. We believe that our sound theory based implementation reduces classification error by combining hidden Markov model with generalized probabilistic descent theory. We show comparative results obtained with our approach and classic maximum-likelihood calculation for fighter planes in terms of classification accuracies.

Motion segmentation is a classic and on-going research topic which is an important pre-stage for many video processes. The reliability of the motion field calculation directly determines either success or failure of such segmentation. Due to the aperture property of optical flow calculation, motion estimation at a moving object boundary remains a challenging ill-posed problem. We propose a reliable optical-flow estimation method from the view of missing data reconstruction. Furthermore, to overcome the occlusion problem at motion boundaries, we apply a motion segmentation scheme which integrates with spatial segmentation. Experimental results of the proposed and other methods are presented.