Search results for "Instrumentation"
showing 10 items of 4914 documents
Multiscale Granger causality analysis by à trous wavelet transform
2017
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Semi-automated evaluation tool for retinal vasculopathy.
2009
The ocular fundus is the only area of human body where vascular system is visible using relatively simple instrumentation. Furthermore, there is medical suggestive evidence of a direct relationship between certain measures of vascular characteristics in the ocular fundus (arteriolar and venular calibers and focal arteriolar narrowing) and cardiovascular diseases. In order to establish such relationship on sound statistical basis a method must be provided to measure the needed values in an easy, yet precise and repeatable way. This paper presents a system to assist physicians in signaling and storing the data associated to signs of vascular deterioration and vascular calibers in non-mydriati…
Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks
2021
[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…
Radio Frequency Spectrum Sensing by Automatic Modulation Classification in Cognitive Radio System Using Multiscale Deep CNN
2022
Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal comb…
Open Set Audio Classification Using Autoencoders Trained on Few Data.
2020
Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solution…
CultReal—A Rapid Development Platform for AR Cultural Spaces, with Fused Localization
2021
Virtual and augmented reality technologies have known an impressive market evolution due to their potential to provide immersive experiences. However, they still have significant difficulties to enable fully fledged, consumer-ready applications that can handle complex tasks such as multi-user collaboration or time-persistent experiences. In this context, CultReal is a rapid creation and deployment platform for augmented reality (AR), aiming to revitalize cultural spaces. The platform’s content management system stores a representation of the environment, together with a database of multimedia objects that can be associated with a location. The localization component fuses data from beacons …
Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments
2019
This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m ×
New Method of Microimages Generation for 3D Display
2018
In this paper, we propose a new method for the generation of microimages, which processes real 3D scenes captured with any method that permits the extraction of its depth information. The depth map of the scene, together with its color information, is used to create a point cloud. A set of elemental images of this point cloud is captured synthetically and from it the microimages are computed. The main feature of this method is that the reference plane of displayed images can be set at will, while the empty pixels are avoided. Another advantage of the method is that the center point of displayed images and also their scale and field of view can be set. To show the final results, a 3D InI dis…
Aalto-1, multi-payload CubeSat: Design, integration and launch
2021
The design, integration, testing, and launch of the first Finnish satellite Aalto-1 is briefly presented in this paper. Aalto-1, a three-unit CubeSat, launched into Sun-synchronous polar orbit at an altitude of approximately 500 km, is operational since June 2017. It carries three experimental payloads: Aalto Spectral Imager (AaSI), Radiation Monitor (RADMON), and Electrostatic Plasma Brake (EPB). AaSI is a hyperspectral imager in visible and near-infrared (NIR) wavelength bands, RADMON is an energetic particle detector and EPB is a de-orbiting technology demonstration payload. The platform was designed to accommodate multiple payloads while ensuring sufficient data, power, radio, mechanica…