Search results for "train"

Efficient linear fusion of partial estimators

2018

Abstract Many signal processing applications require performing statistical inference on large datasets, where computational and/or memory restrictions become an issue. In this big data setting, computing an exact global centralized estimator is often either unfeasible or impractical. Hence, several authors have considered distributed inference approaches, where the data are divided among multiple workers (cores, machines or a combination of both). The computations are then performed in parallel and the resulting partial estimators are finally combined to approximate the intractable global estimator. In this paper, we focus on the scenario where no communication exists among the workers, de…

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

2020

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lo…

Structural and functional features of human muscle-tendon unit.

2006

This paper reviews the architectural details and the in vivo behavior of the human muscle-tendon unit with the focus on the triceps surae and quadriceps femoris muscles. Recent advances in experimental techniques allow in vivo measurements of muscle-tendon architecture and function. In particular, the use of ultrasonography for measurement of tendon and muscle has expanded our knowledge in the last decade. Furthermore, the nuclear magnetic resonance imaging is opening up new insights not only for three-dimensional anatomical information but also for examining musculo-skeletal motion in vivo. While these two completely non-invasive methods provide kinematic data, in vivo force measurements s…

Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data

2021

Constrained joint analysis of data from multiple sources has received widespread attention for that it allows us to explore potential connections and extract meaningful hidden components. In this paper, we formulate a flexible joint source separation model termed as group nonnegative matrix factorization with sparse regularization (GNMF-SR), which aims to jointly analyze the partially coupled multi-set data. In the GNMF-SR model, common and individual patterns of particular underlying factors can be extracted simultaneously with imposing nonnegative constraint and sparse penalty. Alternating optimization and alternating direction method of multipliers (ADMM) are combined to solve the GNMF-S…

WSN Design and Verification Using On-Board Executable Specifications

2019

The gap between informal functional specifications and the resulting implementation in the chosen programming language is notably a source of errors in embedded systems design. In this paper, we discuss a methodology and a software platform aimed at coping with this issue in programming resource-constrained wireless sensor network nodes (WSNs). Whereas the typical development model for the WSNs is based on cross compilation, the proposed approach supports high-level symbolic coding of abstract models and distributed applications, as well as their test and their execution, directly on the target hardware. As a working example, we discuss the application of our methodology to specify the func…

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

2014

Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, gras…

Automated Scenario Generation for Training of Humanitarian Responders in High-Risk Settings

2019

Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

2012

In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing s…

Deep Learning-Based Real-Time Object Detection in Inland Navigation

2019

International audience; Semi-autonomous and fully-autonomous systems must have knowledge about the objects in their environment to ensure a safe navigation. Modern approaches implement deep learning techniques to train a neural network for object detection. This project will study the effectiveness of using several promising algorithms such as Faster R-CNN, SSD, and different versions of YOLO, to detect, classify, and track objects in near real-time fluvial domain. Since no dataset is available for this purpose in literature, we first started by annotating a dataset of 2488 images with almost 35 400 annotations for training the convolutional neural network architectures. We made this data s…

A Techno-Economic Perspective of Constrained Application Protocol

2017

Among billions of Internet enabled devices that are expected to surround us in the near future, many will be resource constrained, i.e., will have limited power supply, processing power and memory. To cope with these limitations, the Constrained Application Protocol (CoAP) has been recently introduced as a lightweight alternative to HTTP for connecting the resource limited devices to the Web. Although the new protocol offers solid technical advantages, it remains uncertain whether a successful uptake will follow, as it depends also on its economic feasibility for the involved stakeholders. Therefore, this paper studies the techno-economic feasibility of CoAP using a systematic methodologica…