Search results for " processing"

Constraints on Minute-Scale Transient Astrophysical Neutrino Sources

2019

High-energy neutrino emission has been predicted for several short-lived astrophysical transients including gamma-ray bursts (GRBs), core-collapse supernovae with choked jets, and neutron star mergers. IceCube's optical and x-ray follow-up program searches for such transient sources by looking for two or more muon neutrino candidates in directional coincidence and arriving within 100 s. The measured rate of neutrino alerts is consistent with the expected rate of chance coincidences of atmospheric background events and no likely electromagnetic counterparts have been identified in Swift follow-up observations. Here, we calculate generic bounds on the neutrino flux of short-lived transient so…

The time course of processing handwritten words: An ERP investigation

2021

Available online 25 June 2021. Behavioral studies have shown that the legibility of handwritten script hinders visual word recognition. Furthermore, when compared with printed words, lexical effects (e.g., word-frequency effect) are magnified for less intelligible (difficult) handwriting (Barnhart and Goldinger, 2010; Perea et al., 2016). This boost has been interpreted in terms of greater influence of top-down mechanisms during visual word recognition. In the present experiment, we registered the participants’ ERPs to uncover top-down processing effects on early perceptual encoding. Participants’ behavioral and EEG responses were recorded to high- and low-frequency words that varied in scr…

Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA

2022

Object detection is one of the most challenging yet essential computer vision research areas. It means labeling and localizing all known objects of interest on an input image using tightly fit rectangular bounding boxes around the objects. Object detection, having passed through several evolutions and progressions, nowadays relies on the successes of image classification networks based on deep convolutional neural networks. However, as the depth and complication of convolutional neural networks increased, detection speed reduced, and accuracy increased. Unfortunately, most computer vision applications, such as real-time object tracking on an embedded system, requires lightweight, fast and a…

Editorial

2002

Pathological voice analysis via digital signal processing

2015

The interest in pathological voice analysis for specific neurological diseases is growing up aiming to offer more Health-care tele monitoring services since new high performing electronic devices are available for the end-user. In this article we show some parameters that can be digitally extracted and analyzed from pathological voices, in order to find a distinctive sign of the Parkinson disease. As a result, we will show a parameter that gives some information about the Parkinson disease characterization, particularly for male patients. We will also discuss about the needed computational cost related to parameters extraction and elaboration, aiming to target a possible tough yet portable …

Optimization of Application-Specific L1 Cache Translation Functions of the LEON3 Processor

2020

Reconfigurable caches offer an intriguing opportunity to tailor cache behavior to applications for better run-times and energy consumptions. While one may adapt structural cache parameters such as cache and block sizes, we adapt the memory-address-to-cache-index mapping function to the needs of an application. Using a LEON3 embedded multi-core processor with reconfigurable cache mappings, a metaheuristic search procedure, and Mibench applications, we show in this work how to accurately compare non-deterministic performances of applications and how to use this information to implement an optimization procedure that evolves application-specific cache mappings.

Parallel macro pipelining on the intel SCC many-core computer

2013

In this paper we present how Intel's Single-Chip-Cloud processor behaves for parallel macro pipeline applications. Subsets of the SCC's available cores can be arranged as a pipeline where each core processes one stage of the overall workload. Each of the independent cores processes a small part of a larger task and feeds the following core with new data after it finishes its work. Our case-study is a parallel rendering system which renders successive images and applies different filters on them. On normal graphics adapters this is usually done in multiple cycles, we do this in a single pipeline pass. We show that we can achieve a significant speedup by using multiple parallel pipelines on t…

Fast Image Restoration Algorithms Based on PDE Models Using Modified Hopfield Neural Network

2010

Two image restoration algorithms based on modified Hop field neural network and variational partial differential equations (PDE) were proposed in our previous work [1, 2]. But the convergence rate of the proposed algorithms was slow. In this paper, we develop a fast update rule based on modified Hop field neural network (MHNN) of continuous state change and two fast image restoration algorithms. Experimental results show that, when compared with the previous algorithms, our proposed algorithms have better performance both in convergence rate and in image restoration quality.

A recap on Linear Mixed Models and their hat-matrices

2017

This working paper has a twofold goal. On one hand, it provides a recap of Linear Mixed Models (LMMs): far from trying to be exhaustive, this first part of the working paper focusses on the derivation of theoretical results on estimation of LMMs that are scattered in the literature or whose mathematical derivation is sometimes missing or too quickly sketched. On the other hand, it discusses various definitions that are available in the literature for the hat-matrix of Linear Mixed Models, showing their limitations and proving their equivalence.

3d mesh denoising using normal based myriad filter

2011

We propose a new filtering scheme for denoising of 3D objects which are represented by a triangular mesh. This scheme consists on applying myriad filter to face normals and then updating the vertices positions in order to preserve the original shape of the object. The choice of the Myriad is justified by the assumption of Cauchy distributed angles between surface normals. This filter improves the performance of a normal-based method which is adapted to the underlying mesh structure. To evaluate these methods of filtering, we use three error metrics. The first is based on the vertices, the second is based on the normals and the third is based on Hausdorff distance. Experimental results demon…