Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Neural Network for Estimating Energy Expenditure in Paraplegics from Heart Rate
2014
The aim of the present study is to obtain models for estimating energy expenditure based on the heart rates of people with spinal cord injury without requiring individual calibration. A cohort of 20 persons with spinal cord injury performed a routine of 10 activities while their breath-by-breath oxygen consumption and heart rates were monitored. The minute-by-minute oxygen consumption collected from minute 4 to minute 7 was used as the dependent variable. A total of 7 features extracted from the heart rate signals were used as independent variables. 2 mathematical models were used to estimate the oxygen consumption using the heart rate: a multiple linear model and artificial neural networks…
THE CAUSAL ROOTS OF INTEGRATION AND THE UNITY OF CONSCIOUSNESS
2016
A fundamental feature of consciousness is unity. The problem is whether unity is compatible both with the physical underpinnings of conscious experience and with the fabric of the physical world in general.
Sign Languages Recognition Based on Neural Network Architecture
2017
In the last years, many steps forward have been made in speech and natural languages recognition and were developed many virtual assistants such as Apple’s Siri, Google Now and Microsoft Cortana. Unfortunately, not everyone can use voice to communicate to other people and digital devices. Our system is a first step for extending the possibility of using virtual assistants to speech impaired people by providing an artificial sign languages recognition based on neural network architecture.
Polar bosons in one-dimensional disordered optical lattices
2013
We analyze the effects of disorder and quasi-disorder on the ground-state properties of ultra-cold polar bosons in optical lattices. We show that the interplay between disorder and inter-site interactions leads to rich phase diagrams. A uniform disorder leads to a Haldane-insulator phase with finite parity order, whereas the density-wave phase becomes a Bose-glass at very weak disorder. For quasi-disorder, the Haldane insulator connects with a gapped generalized incommesurate density wave without an intermediate critical region.
Machine Learning-Based Classification of Vector Vortex Beams.
2020
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. O…
Solvent-free microwave-assisted extraction of polyphenols from olive tree leaves: Antioxidant and antimicrobial properties
2017
International audience; Response surface methodology (RSM) and artificial neural networks (ANN) were evaluated and compared in order to decide which method was the most appropriate to predict and optimize total phenolic content (TPC) and oleuropein yields in olive tree leaf (Olea europaea) extracts, obtained after solvent-free microwave- assisted extraction (SFMAE). The SFMAE processing conditions were: microwave irradiation power 250-350 W, extraction time 2-3 min, and the amount of sample 5-10 g. Furthermore, the antioxidant and antimicrobial activities of the olive leaf extracts, obtained under optimal extraction conditions, were assessed by several in vitro assays. ANN had better predic…
Comparative study to predict toxic modes of action of phenols from molecular structures.
2013
Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. M…
Development of handcrafted and deep based methods for face and facial expression recognition
2021
The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effectiv…
Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems
2020
International audience; Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN.
Forecasting Aquaponic Systems Behaviour With Recurrent Neural Networks Models
2022
Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the m…