Ultra-wideband (UWB) systems based on Channel State Information (CSI) estimate the position of mobile nodes within an environment by using Channel Impulse Responses (CIRs) of multiple stationary nodes. These contain s...
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Ultra-wideband (UWB) systems based on Channel State Information (CSI) estimate the position of mobile nodes within an environment by using Channel Impulse Responses (CIRs) of multiple stationary nodes. These contain spatial information caused by environment interactions such as reflections and scattering. To estimate positions from CSI of stationary nodes, we must transmit them to a centralized node. This introduces considerable communication *** present a large-scale study to determine whether CSI can be compressed into a small set of underlying latent variables that describe the most valuable information. We evaluate multiple neural network architectures containing encoding (compressing) and decoding (reconstructing) components and compare them to the state-of-the-art compression techniques Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). We show that fully connected autoencoders achieve the lowest error, outperforming both DCT and DWT. Further experiments prove that the reconstructed CSI can be used for positioning with only mild performance deterioration at a compression of >97% and even when trained on a different environment.
The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is ana...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.
This article presents SVC-onGoing, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-sc...
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Building retrofit is of greatest importance to reduce the environmental footprint of the building stock. It typically refers to two types of interventions: the first pertaining to interventions on the building envelop...
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ISBN:
(纸本)9788361506515
Building retrofit is of greatest importance to reduce the environmental footprint of the building stock. It typically refers to two types of interventions: the first pertaining to interventions on the building envelope, for instance by enhancing the thermal insulation of a building’s walls, and the second to energy system replacements. The latter includes not only the installation of a more efficient heating system, with the necessary capacity to meet the building's heat demands, but also the integration of renewable energy technologies and energy storage. Thus, there is a huge array of retrofit measure combinations to select from with different economic and environmental impacts. Even though there are tools and methodologies to handle the complex problem of deriving optimal retrofit solutions, they typically require increased level of expertise and highly heterogeneous building data, which are usually not available or challenging to obtain. As a result, building owners can find themselves in a quandary about how to further proceed with retrofitting their buildings. In this paper, we answer the question whether a machine-learning algorithm could be used as a surrogate model that can: (a) mimic the functionality of the retrofit process, and (b) derive near-optimal solutions with the use of building features that can be easily extracted, such as the building's ground floor area or age. To answer this question an artificial neural network-based model is trained with data collected from the case study of the city of Zurich. Results show that the proposed approach can approximate very well some dimensions of the building retrofit, such as the retrofit costs (R2 ≅ 0.971) and the energy system selection (f1 score ≅ 0.894), while for others, such as the electricity storage capacity (R2 ≅ 0.505), there is still room for improvement. Such surrogate retrofit models can be more easily shared with building owners, and can contribute towards accelerating the adoption of retrofit
Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to ...
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ISBN:
(数字)9780738131115
ISBN:
(纸本)9781665415439
Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity.
Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis elec...
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Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis electrocardiography (ECG) plays an important role. The Advancement of Medical Instrumentation (AAMI) suggests, all the heartbeat of MIT-BIH dataset into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed. Then, features computed from ECG signals are RR (separation between two successive heartbeat pulses), local binary pattern(LBP), morphological changes, wavelet, high order statistics, and several amplitude values; Pre-RR, Post-RR, Local RR and Global RR. The main reason is to computing many morphological features to comparison analysis with the model when trained and tested on single features vs combining all features together. Model gives highest performance which is 90.8% accuracy using LBP as single feature compared with other features. Combined all important features together and optimal features (total 104 ) are given as an input to Single SVM classifier as well as ensembles of SVM which maps the component feature vectors to the particular class name. Principal Component Analysis (PCA) technique is also applied to the model which reduces the total number of features from 104 to 30 features. The Single SVM system can accurately classify four beat types and has an overall classification rate of 94.05% without PCA and using PCA 92.84% while the Ensemble SVM classifies and with an accuracy of 92.96%
The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially b...
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Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usag...
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ISBN:
(数字)9781728199665
ISBN:
(纸本)9781728199672
Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a real-time handwriting recognition system to be used for writing on normal paper.
Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, ...
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Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability. For instance, DNNs are vulnerable to small or even imperceptible ...
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