Search results for "DETECT"

Biologically Inspired Vision Architectures: a Software/Hardware Perspective

2007

Even tough the field of computer vision has seen huge improvement in the last few decades, computer vision systems still lack, in most cases, the efficiency of biological vision systems. In fact biological vision systems routinely accomplish complex visual tasks such as object recognition, obstacle avoidance, and target tracking, which continue to challenge artificial systems. The study of biological vision system remains a strong cue for the design of devices exhibiting intelligent behaviour in visually sensed environments but current artificial systems are vastly different from biological ones for various reasons. First of all, biologically inspired vision architectures, which are continu…

Improved locally adaptive least-squares detection of differences in images

2007

We introduce a method for change detection under nonuniform changes of intensity using an improved least-squares method. A locally adaptive normalizing window is correlated with the two images, and a morphological postprocessing is then applied to isolate objects that have been added or removed from the scene. We use a modification of the least-squares solution to get rid of clutter caused by intensity changes that do not satisfy the model assumed for the least-squares solution.

Does signal detection methodology allow to measure discrimination, but not pain?

1979

Exploring FPGA Based Lock-in Techniques for Brain Monitoring Applications

2017

Functional Near Infrared Spectroscopy (fNIRS) systems for e-health applications usually suffer of poor signal detection mainly due to a low end-to-end signal to noise ratio of the electronics chain. Lock-In Amplifiers (LIA) historically represent a powerful technique helping to improve performances in such circumstances. In this work it has been designed and implemented a digital LIA system, based on a Zynq® Field Programmable Gate Array (FPGA), trying to explore if this technique might improve fNIRS system performances. More broadly, FPGA based solution flexibility has been investigated, with particular emphasis applied to digital filter parameters, needed in the digital LIA, and i…

Image Recognition through Incremental Discriminative Common Vectors

2010

An incremental approach to the discriminative common vector (DCV) method for image recognition is presented. Two different but equivalent ways of computing both common vectors and corresponding subspace projections have been considered in the particular context in which new training data becomes available and learned subspaces may need continuous updating. The two algorithms are based on either scatter matrix eigendecomposition or difference subspace orthonormalization as with the original DCV method. The proposed incremental methods keep the same good properties than the original one but with a dramatic decrease in computational burden when used in this kind of dynamic scenario. Extensive …

Filecasting for streaming content delivery in IP datacast over DVB-H systems

2008

In this paper we investigate the potential gain that can be obtained in DVB-H by delivering streaming content as a succession of time-constrained flies using Application Layer - Forward Error Correction (AL-FEC) for improving the reception for mobile terminals. Compared to the conventional approach with link layer MPE-FEC, this technique allows to increase the robustness of the DVB-H transmission not only as a function of the capacity devoted for error repair (FEC overhead), but also as a function of the number of data bursts coded jointly ("file" or, source data block, size). The main drawback of this approach is an increase of the network latency proportional to the source block size, tha…

ERP qualification exploiting waveform, spectral and time-frequency infomax

2008

The present contribution briefly introduces an event related potential (ERP) detector. The specified detector includes three kinds of features of ERP. They are the ERP waveform feature, ERP spectral feature and ERP time-frequency feature respectively. According to these characteristics, two parameters are defined to reflect the timing feature of ERP. The mismatch negativity (MMN) is taken as the example to design an exact qualification detector. The experiment validates that the computer can automatically detect the raw trace to reflect the quality of the dataset, qualify the filtered trace to test whether the artifacts have been filtered out, and select the ERP-like component to reject art…

Subpixel determination of imperfect circles characteristics

2008

This article deals with the problem of the determination of characteristics of imperfect circular objects in discrete images, namely the radius and center coordinates. To limit distortion, a multi-level method based on active contours was developed. Its originality is to furnish a set of geometric envelopes in one pass, with a correspondence between grayscale and a regularity scale. The adequacy of this approach was tested with several methods, among them is the Radon-based method. More particularly, this study indicates the relevance of the use of active contours combined with a Radon transform-based method which was improved using a fitting considering the discrete implementation of the R…

Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection

2017

The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing be…

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

2019

In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …