13956689
The Decidability of the First-Order Theory of Knuth-Bendix Order
Two kinds of orderings are widely used in term rewriting and theorem proving, namely recursive path ordering (RPO) and Knuth-Bendix ordering (KBO). They provide powerful tools to prove the termination of rewriting systems. They are also applied in ordered resolution to prune the search space without compromising refutational completeness. Solving ordering constraints is therefore essential to the successful application of ordered rewriting and ordered resolution. Besides the needs for decision procedures for quantifier-free theories, situations arise in constrained deduction where the truth value of quantified formulas must be decided. Unfortunately, the full first-order theory of recursive path orderings is undecidable. This leaves an open question whether the first-order theory of KBO is decidable. In this paper, we give a positive answer to this question using quantifier elimination. In fact, we shall show the decidability of a theory that is more expressive than the theory of KBO.
7279338
Decision procedures for term algebras with integer constraints
Term algebras can model recursive data structures which are widely used in programming languages. To verify programs we must be able to reason about these structures. However, as programming languages often involve multiple data domains, in program verification decision procedures for a single theory are usually not applicable. An important class of mixed constraints consists of combinations of data structures with integer constraints on the size of data structures. Such constraints can express memory safety properties such as absence of memory overflow and out-of-bound array access, which are crucial for program correctness. In this paper we extend the theory of term algebras with the length function which maps a term to its size, resulting in a combined theory of term algebras and Presburger arithmetic. This arithmetic extension provides a natural but tight coupling between the two theories, and hence the general purpose combination methods like Nelson-Oppen combination are not applicable. We present decision procedures for quantifier-free theories in structures with an infinite constant domain and with a finite constant domain. We also present a quantifier elimination procedure for the extended first-order theory that can remove a block of existential quantifiers in one step.
7781593
Kernel based 2D symmetrical principal component analysis for face classification
This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image are mapped to a high dimensional feature space (Reproducing Kernel Hilbert space, RKHS) by a nonlinear function; Thirdly, compute the eigenvectors and the eigenvectors of the even image and the odd image in RKHS, respectively; At last, select the eigenvectors with greater variance as the projection axis. We compare the performance of SPCA, 2DPCA, S2DPCA with K2DSPCA on CBCL database for binary classification, and on ORL face database for multi-category classification, respectively. The experimental results show the K2DSPCA is competitive with or superior to SPCA, 2DPCA and S2DPCA.
18335624
Static Techniques to Improve Power Efficiency of Branch Predictors
In this paper, we illustrate the application of two static techniques to reduce the activities of the branch predictor in a processor leading to its significant power reduction We introduce the use of a static branch target buffer (BTB) that achieves the similar performance to the traditional branch target buffer but which eliminates most of the state updates thus reducing the power consumption of the BTB significantly We also introduce a correlation-based static prediction scheme into a dynamic branch predictor so that those branches that can be predicted statically or can be correlated to the previous ones will not go through normal prediction algorithm This reduces the activities and conflicts in the branch history table (BHT) With these optimizations, the activities and conflicts of the BTB and BHT are reduced significantly and we are able to achieve a significant reduction (43.9% on average) in power consumption of the BPU without degradation in the performance.
34371681
Static Techniques to Improve Power Efficiency of Branch Predictors
Current power-efficient designs focus on reducing the dynamic (activity-based) power consumption in a processor through different techniques. In this paper, we illustrate the application of two static techniques to reduce the activities of the branch predictor in a processor leading to its significant power reduction. We introduce the use of a static branch target buffer (BTB) that achieves the similar performance to the traditional branch target buffer but eliminates most of the state updates thus reducing the power consumption of the BTB significantly. We also introduce a correlation-based static prediction scheme into a dynamic branch predictor so that those branches that can be predicted statically or can be correlated to the previous ones will not go through normal prediction algorithm. This reduces the activities and conflicts in the branch history table (BHT). With these optimizations, the activities and conflicts of the BTB and BHT are reduced significantly and we are able to achieve a significant reduction (43.9% on average) in power consumption of the BPU without degradation in the performance.
579951
Improving performance of distribution tracking through background mismatch
This paper proposes a new density matching method based on background mismatching for tracking of nonrigid moving objects. The new tracking method extends the idea behind the original density-matching tracker, which tracks an object by finding a contour in which the photometric density sampled from the enclosed region most closely matches a model density. This method can be quite sensitive to the initial curve placements and model density. The new method eliminates these sensitivities by adding a second term to the optimization: the mismatch between the model density and the density sampled from the background. By maximizing this term, the tracking algorithm becomes significantly more robust in practice. Furthermore, we show the enhanced ability of the algorithm to deal with target objects, which possess smooth or diffuse boundaries. The tracker is in the form of a partial differential equation, and is implemented using the level-set framework. Experiments on synthesized images and real video sequences show our proposed methods are effective and robust; the results are compared with several existing methods.
18579061
An Efficient Frequency Scaling Approach for Energy-Aware Embedded Real-Time Systems
The management of energy consumption in battery-operated embedded and pervasive systems is increasingly important in order to extend battery lifetime or to increase the number of applications that can use the system's resources. Dynamic voltage and frequency scaling (DVFS) has been introduced to trade off system performance with energy consumption. For real-time applications, systems supporting DVFS have to balance the achieved energy savings with the deadline constraints of applications. Previous work has used periodic evaluation of an application's progress (e.g., with periodic checkpoints inserted into application code at compile time) to decide if and how much to adjust the frequency or voltage. Our approach builds on this prior work and addresses the overheads associated with these solutions by replacing periodic checkpoints with iterative checkpoint computations based on predicted best-, average-, and worst-case execution times of real-time applications (e.g., obtained through compile-time analysis or profiling).