Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems

This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on their retro-reflective material. Then, classification is carried out over the point cloud projection on RGB images applying a Deep Neural Network which comprises convolutional and spatial transformer layers. This network is evaluated in three European traffic sign datasets. On the GTSRB, it outperforms previous state-of-the-art published works and achieves top-1 rank with an accuracy of 99.71%. Furthermore, a Spanish traffic sign recognition dataset is released.     

A comparison of models for fusion of the auditory and visual sensors in speech perception

Though a large amount of psychological and physiological evidence of audio-visual integration in speech has been collected in the last 20 years, there is no agreement about the nature of the fusion process. We present the main experimental data, and describe the various models proposed in the literature, together with a number of studies in the field of automatic audiovisual speech recognition. We discuss these models in relation to general proposals arising from psychology in the field of intersensory interaction, or from the field of vision and robotics in the field of sensor fusion. Then we examine the characteristics of four main models, in the light of psychological data and formal properties, and we present the results of a modelling study on audio-visual recognition of French vowels in noise. We conclude in favor of the relative superiority of a model in which the auditory and visual inputs are projected and fused in a common representation space related to motor properties of speech objects, the fused representation being further classified for lexical access.     

A chaotic time series prediction model for speech signal encoding based on genetic programming

In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal's chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay τ. And then, the speech signal's chaotic time series models were built. By standardized processing of these models and optimizing parameters, a speech signal's coding model of chaotic time series with certain generalization ability was obtained. At last, the experimental results showed that the proposed method can get the speech signal chaotic time series prediction models much more effectively, and had a better coding accuracy than linear predictive coding (LPC) algorithms and neural network model.     

Exploiting purity for atomicity

Multithreaded programs often exhibit erroneous behavior because of unintended interactions between concurrent threads. This paper focuses on the noninterference property of atomicity. A procedure is atomic if, for every execution, there is an equivalent serial execution in which the actions of the atomic procedure are not interleaved with actions of other threads. This key property makes atomic procedures amenable to sequential reasoning techniques, which significantly facilitates subsequent validation activities such as code inspection and testing. Several existing tools verify atomicity by using commutativity of actions to show that every execution reduces to a corresponding serial execution. However, experiments with these tools have highlighted a number of interesting procedures that, while intuitively atomic, are not reducible. In this paper, we exploit the notion of pure code blocks to verify the atomicity of such irreducible procedures. If a pure block terminates normally, then its evaluation does not change the program state and, hence, these evaluation steps can be removed from the program trace before reduction. We develop a static typed-based analysis for atomicity based on this insight, and we illustrate this analysis on a number of interesting examples that could not be verified using earlier tools based purely on reduction.     

Exploiting Random Walks for Learning

In this paper we consider an approach to passive learning. In contrast to the classical PAC model we do not assume that the examples are independently drawn according to an underlying distribution, but that they are generated by a time-driven process. We define deterministic and probabilistic learning models of this sort and investigate the relationships between them and with other models. The fact that successive examples are related can often be used to gain additional information similar to the information gained by membership queries. We show how this can be used to design on-line prediction algorithms. In particular, we present efficient algorithms for exactly identifying Boolean threshold functions and 2-term RSE, and for learning 2-term-DNF, when the examples are generated by a random walk on {0,1}ⁿ.     

Audio-visual speech modeling for continuous speech recognition

This paper describes a speech recognition system that uses both acoustic and visual speech information to improve recognition performance in noisy environments. The system consists of three components: a visual module; an acoustic module; and a sensor fusion module. The visual module locates and tracks the lip movements of a given speaker and extracts relevant speech features. This task is performed with an appearance-based lip model that is learned from example images. Visual speech features are represented by contour information of the lips and grey-level information of the mouth area. The acoustic module extracts noise-robust features from the audio signal. Finally the sensor fusion module is responsible for the joint temporal modeling of the acoustic and visual feature streams and is realized using multistream hidden Markov models (HMMs). The multistream method allows the definition of different temporal topologies and levels of stream integration and hence enables the modeling of temporal dependencies more accurately than traditional approaches. We present two different methods to learn the asynchrony between the two modalities and how to incorporate them in the multistream models. The superior performance for the proposed system is demonstrated on a large multispeaker database of continuously spoken digits. On a recognition task at 15 dB acoustic signal-to-noise ratio (SNR), acoustic perceptual linear prediction (PLP) features lead to 56% error rate, noise robust RASTA-PLP (relative spectra) acoustic features to 7.2% error rate and combined noise robust acoustic features and visual features to 2.5% error rate     

Exploiting location information for Web search

Most Web pages contain location information, which are usually neglected by traditional search engines. Queries combining location and textual terms are called as spatial textual Web queries. Based on the fact that traditional search engines pay little attention in the location information in Web pages, in this paper we study a framework to utilize location information for Web search. The proposed framework consists of an offline stage to extract focused locations for crawled Web pages, as well as an online ranking stage to perform location-aware ranking for search results. The focused locations of a Web page refer to the most appropriate locations associated with the Web page. In the offline stage, we extract the focused locations and keywords from Web pages and map each keyword with specific focused locations, which forms a set of <keyword, location> pairs. In the second online query processing stage, we extract keywords from the query, and computer the ranking scores based on location relevance and the location-constrained scores for each querying keyword. The experiments on various real datasets crawled from nj.gov, BBC and New York Time show that the performance of our algorithm on focused location extraction is superior to previous methods and the proposed ranking algorithm has the best performance w.r.t different spatial textual queries.     

Exploiting Regularities for Boolean Function Synthesis

The "regularity" of a Boolean function can be exploited for decreasing its minimization time. It has already been shown that the notion of autosymmetry is a valid measure of regularity, however such a notion has been studied thus far either in the theoretical framework of self-dual Boolean functions, or for the synthesis of a particular family of three-level logic networks. In this paper we show that the degree of autosymmetry of an arbitrary function can be computed implicitly in a very efficient way, and autosymmetry can then be exploited in any logic minimization context. Our algorithms make crucial use of Binary Decision Diagrams. A set of experimental results on the synthesis of standard benchmark functions substantiates the practical relevance of our theoretical results.     

Exploiting coherence for multiprocessor ray tracing

The scalability and cost effectiveness of general-purpose distributed-memory multiprocessor systems makes them particularly suitable for ray-tracing applications. However, the limited memory available to each processor in such a system requires schemes to distribute the model database among the processors. The authors identify a form of coherence in the ray-tracing algorithm that can be exploited to develop optimum schemes for data distribution in a multiprocessor system. This in turn gives rise to high processor efficiency for systems with limited distributed memory     

Exploiting Ontology for Software License Agreements

Software end-users need to sign licenses to seal an agreement with the product providers. Habitually, users agree with the license (i.e. terms and conditions) without fully understanding the agreement. To address this issue, an ontological model is developed to formulate the user requirements and license agreements formally. This paper, introduces ontological model that includes the abstract license ontology of common features found in di?erent license agreements. The abstract license ontology is then extended to a few real world license agreements. The resulting model can be used for di?erent purposes such as querying the appropriate licenses for a speciˉc requirement or checking the license terms and conditions with user requirements.     

Exploiting Network Locality for CC-NUMA Multiprocessors

The Journal of Supercomputing - Rapid advances in interconnection networks in multiprocessors are closing the gap between computation and communication. Given this trend, how can we utilize fast...     

Exploiting locality for irregular scientific codes

Irregular scientific codes experience poor cache performance due to their irregular memory access patterns. In this paper, we present two new locality improving techniques for irregular scientific codes. Our techniques exploit geometric structures hidden in data access patterns and computation structures. Our new data reordering (GPART) finds the graph structure within data accesses and applies hierarchical clustering. Quality partitions are constructed quickly by clustering multiple neighbor nodes with priority on nodes with high degree and repeating a few passes. Overhead is kept low by clustering multiple nodes in each pass and considering only edges between partitions. Our new computation reordering (Z-SORT) treats the values of index arrays as coordinates and reorders corresponding computations in Z-curve order. Applied to dense inputs, Z-SORT achieves performance close to data reordering combined with other computation reordering but without the overhead involved in data reordering. Experiments on irregular scientific codes for a variety of meshes show locality optimization techniques are effective for both sequential and parallelized codes, improving performance by 60-87 percent. GPART achieved within 1-2 percent of the performance of more sophisticated partitioning algorithms, but with one third of the overhead. Z-SORT also yields the performance improvement of 64 percent for dense inputs, which is comparable with data reordering combined with computation reordering.     

Exploiting interclass rules for focused crawling

Crawling the Web quickly and entirely is an expensive, unrealistic goal because of the required hardware and network resources. We started with a focused-crawling approach designed by Soumen Chakrabarti, Martin van den Berg, and Byron Dom, and we implemented the underlying philosophy of their approach to derive our baseline crawler. This crawler employs a canonical topic taxonomy to train a naive-Bayesian classifier, which then helps determine the relevancy of crawled pages. The crawler also relies on the assumption of topical locality to decide which URLs to visit next. Building on this crawler, we developed a rule-based crawler, which uses simple rules derived from interclass (topic) linkage patterns to decide its next move. This rule-based crawler also enhances the baseline crawler by supporting tunneling. A focused crawler gathers relevant Web pages on a particular topic. This rule-based Web-crawling approach uses linkage statistics among topics to improve a baseline focused crawler's harvest rate and coverage.     

Exploiting character networks for movie summarization

Movie summarization focuses on providing as much information as possible for shorter movie clips while still keeping the content of the original movie and presenting a faster way for the audience to understand the movie. In this paper, we propose a novel method to summarize a movie based on character network analysis and the appearance of protagonist and main characters in the movie. Experiments were carried out for 2 movies (Titanic (1997) and Frozen (2013)) to show that our method outperforms conventional approaches in terms of the movie summarization rate.     

Exploiting exceptions

A novel compiler optimization for loops is presented. The optimization uses exceptions to eliminate redundant tests that are performed when code is interpretively executed, as is the case with Java bytecode executed on the Java Virtual Machine. An analysis technique based on abstract interpretation is used to discover when the optimization is applicable. Copyright © 2001 John Wiley & Sons, Ltd.     

一种提高电阻式传感器灵敏度的双传感器电路

提出一种双传感器电路,其原理是利用1个传感器在气氛变化时电阻的变化,改变通过另1个传感器的电流,使得后一传感器在气氛变化时电流和电阻的同时变化,使灵敏度得到极大提高。以SrFeO3薄膜传感器考察了这种双传感器电路的实际效果。     

Wavelet based speech presence probability estimator for speech enhancement

A reliable speech presence probability (SPP) estimator is important to many frequency domain speech enhancement algorithms. It is known that a good estimate of SPP can be obtained by having a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. Recently, the wavelet denoising with multitaper spectrum (MTS) estimation technique was suggested for such purpose. However, traditional approaches directly make use of the wavelet shrinkage denoiser which has not been fully optimized for denoising the MTS of noisy speech signals. In this paper, we firstly propose a two-stage wavelet denoising algorithm for estimating the speech power spectrum. First, we apply the wavelet transform to the periodogram of a noisy speech signal. Using the resulting wavelet coefficients, an oracle is developed to indicate the approximate locations of the noise floor in the periodogram. Second, we make use of the oracle developed in stage 1 to selectively remove the wavelet coefficients of the noise floor in the log MTS of the noisy speech. The wavelet coefficients that remained are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. To adapt to the enhanced a-posteriori SNR function, we further propose a new method to estimate the generalized likelihood ratio (GLR), which is an essential parameter for SPP estimation. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables an improvement in both the quality and intelligibility of the enhanced speeches.     

对嵌入式POS市场的实践与思考

嵌入式POS的市场定位产品的开发和定位必须适应市场需求。目前,零售业的发展已呈现出市场分层的特点,就是中心城市的零售市场基本为国际商业巨头所垄断,POS终端也多以国际品牌为主,而内资零售业的市场逐步被压缩