Improving data prefetching efficacy in multimedia applications

The workload of multimedia applications has a strong impact on cache memory performance, since the locality of memory references embedded in multimedia programs differs from that of traditional programs. In many cases, standard cache memory organization achieves poorer performance when used for multimedia. A widely-explored approach to improve cache performance is hardware prefetching, which allows the pre-loading of data in the cache before they are referenced. However, existing hardware prefetching approaches are unable to exploit the potential improvement in performance, since they are not tailored to multimedia locality. In this paper we propose novel effective approaches to hardware prefetching to be used in image processing programs for multimedia. Experimental results are reported for a suite of multimedia image processing programs including MPEG-2 decoding and encoding, convolution, thresholding, and edge chain coding

Neighbor cache prefetching for multimedia image and video processing

Cache performance is strongly influenced by the type of locality embodied in programs. In particular, multimedia programs handling images and videos are characterized by a bidimensional spatial locality, which is not adequately exploited by standard caches. In this paper we propose novel cache prefetching techniques for image data, called neighbor prefetching, able to improve exploitation of bidimensional spatial locality. A performance comparison is provided against other assessed prefetching techniques on a multimedia workload (with MPEG-2 and MPEG-4 decoding, image processing, and visual object segmentation), including a detailed evaluation of both the miss rate and the memory access time. Results prove that neighbor prefetching achieves a significant reduction in the time due to delayed memory cycles (more than 97% on MPEG-4 with respect to 75% of the second performing technique). This reduction leads to a substantial speedup on the overall memory access time (up to 140% for MPEG-4). Performance has been measured with the PRIMA trace-driven simulator, specifically devised to support cache prefetching

Topographical working memory in children and adolescents with motor disabilities

Aim: The aim of the present study was to investigate topographical working memory in individuals with motor disabilities. Methods: Topographical working memory was investigated using the Walking Corsi Test in 89 participants with motor disability, mean age 11.5 years, of which 40 with cerebral palsy, 31 with spina bifida, and 18 with orthopaedic or peripheral symptoms. The participants were grouped according to everyday mobility, i.e. walking outdoors, walking indoors, and using wheelchair. A control group constituted 120 typically developing participants, mean age 9.9 years. Results: Individuals with spina bifida, orthopaedic or peripheral symptoms as well as typically developing participants performed significantly larger walking spans than the cerebral palsy group. With respect to mobility, those walking outdoors had significantly larger span than those walking indoors and those using wheelchair for mobility. Conclusions: Participants with outdoor walking in the community, apart from type of motor disability, seem to have improved topographic memory compared to individuals who don’t walk outside and individuals who are mobile through wheelchair. The results highlight the question of development of spatial cognition to enhance participation in social environments. Future research should focus on prematurity in the cerebral palsy group, and on hydrocephalus in the spina bifida group

Automatic classification of abandoned objects for surveillance of public premises

One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyzed and classified images of trolley(s), bag(s), single person(s), and group(s) of people by using various shape features with a number of uncluttered and cluttered images and applied multiframe integration to overcome partial occlusions and obtain better recognition results. We also tested the proposed techniques on data extracted from a wellrecognized and recent data set, PETS 2007 benchmark data set[16]. Our experimental results show that the features extracted are invariant to data set and classification scheme chosen. For our four-class object recognition problem, we achieved an average recognition accuracy of 70%. © 2008 IEEE

An infinite adaptive online learning model for segmentation and classification of streaming data

© 2014 IEEE. In recent years, the desire and need to understand streaming data has been increasing. Along with the constant flow of data, it is critical to classify and segment the observations on-the-fly without being limited to a rigid number of classes. In other words, the system needs to be adaptive to the streaming data and capable of updating its parameters to comply with natural changes. This interesting problem, however, is poorly addressed in the literature, as many of the common studies focus on offline classification over a pre-defined class set. In this paper, we propose a novel adaptive online system based on Markov switching models with hierarchical Dirichlet process priors. This infinite adaptive online approach is capable of segmenting and classifying the streaming data over infinite classes, while meeting the memory and delay constraints of streaming contexts. The model is further enhanced by a 'predictive batching' mechanism, that is able to divide the flowing data into batches of variable size, imitating the ground-truth segments. Experiments on two video datasets show significant performance of the proposed approach in frame-level accuracy, segmentation recall and precision, while determining the accurate number of classes in acceptable computational time

Performance analysis of MPEG-4 decoder and encoder

© 2002 Croatian Soc. Electronics in Marine-ELMAR. A performance analysis of MPEG-4 encoder and decoder programs on a standard personal computer is presented. The paper first describes the MPEG-4 computational load and discusses related works, then outlines the performance analysis. Experimental results show that while the decoder program can be easily executed in real time, the encoder requires execution times in the order of seconds per frame which call for substantial optimisation to satisfy real-time constraints

Peculiar body representation alterations in hemineglect:a case report

We report the case of FP affected by personal and extrapersonal neglect and a body representation deficit characterized by delusional ideas. When FP performed the human figure, he placed body parts to the left, despite his extrapersonal neglect. Differently, when he performed the car figure, he placed all parts to the right, in line with his deficit. Comparing FP with a small patient group with the same clinical features without delusional ideas about body emerged that he was the only one to suffer from a specific body representation deficit characterized by a lack of body ownership sense

Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction

Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach

A simulated annealing-based maximum-margin clustering algorithm

© 2018 Wiley Periodicals, Inc. Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms