Search results for "Intelligence"
showing 10 items of 6959 documents
POLARIZATION-BASED CAR DETECTION
2018
International audience; Road scene understanding is a vital task for driving assistance systems. Robust vehicle detection is a precondition for diverse applications particularly for obstacle avoidance and secure navigation. Color images provide limited information about the physical properties of the object. This results in unstable vehicle detection caused mainly from road scene complexity (strong reflexions, noises and radiometric distortions). Instead, polarimetric images, characteristic of the light wave, can robustly describe important physical properties of the object (e.g., the surface geometric structure, material and roughness etc). This modality gives rich physical informations wh…
Estimation of brain connectivity through Artificial Neural Networks
2019
Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…
A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking
2019
P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to insufficient therapeutic targeting of tumor populations, representing a major drawback of cancer chemotherapy. Therefore, P-gp is a target for pharmacological inhibitors to overcome MDR. In the present study, we utilized machine learning strategies to establish a model for P-gp modulators to predict whether a given compound would behave as substrate or inhibitor of P-gp. Random forest feature selection algorithm-based leave-one-out random sampl…
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…
Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals.
2019
In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features. In this work, a deep exploration of pain-based emotion classification was conducted to better understand differences in the results of the related literature. In total, 155 different time domain and frequency domain features were e…
Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering
2016
Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It i…
The Impact of Human Walking on the Time-Frequency Distribution of In-Home Radio Channels
2018
Passive activity recognition of home occupants has become a very hot topic in the area of radio communications, as it enables the development of cutting-edge healthcare monitoring solutions. Thanks to ubiquitous radio waves, such as WiFi signals, at today's homes, one can process radio waves reflected off a person's body for identifying certain mobility patterns. This new approach ignores the need for any wearable sensors. This paper reports a challenging indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of walking persons on the temporal and spectral properties of the channel. In particular, the time-frequency distribution of the channel has…
Computational Intelligence and Citizen Communication in the Smart City
2016
Information and communication are at the core of the intelligent city of tomorrow, and the key components of a smart city cannot prescind from data exchanges and interconnectedness. Citizen communication is an integral part of the smart city’s development plans: freedom of information and involvement in collective decisions, e-democracy and decision-making feedback can be greatly enhanced in an intelligent city, and, among other smart city components, foster a new era of participation and wise decisions. In this contribution we describe the methodologies that can be implemented in order to correctly develop automatic recognition systems for citizen communication, paying special attention to…
A Linguistic Model in Component Oriented Programming
2016
Abstract It is a fact that the component-oriented programming, well organized, can bring a large increase in efficiency in the development of large software systems. This paper proposes a model for building software systems by assembling components that can operate independently of each other. The model is based on a computing environment that runs parallel and distributed applications. This paper introduces concepts as: abstract aggregation scheme and aggregation application. Basically, an aggregation application is an application that is obtained by combining corresponding components. In our model an aggregation application is a word in a language.
Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction
2021
Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this w…