Search results for "signal processing"
showing 10 items of 2451 documents
Application of Model Predictive Control in Discrete Displacement Cylinders to Drive a Knuckle Boom Crane
2018
In this paper, two Discrete Displacement Cylinders (DDCs) are used to drive the boom of a knuckle boom crane. DDCs operate by connecting one of several available pressure levels to each chamber in order to produce different forces. A trade-off exists with such systems, between the accuracy of tracking and energy dissipation due to switching. A popular way to approach this problem is a Force Shifting Algorithm (FSA). However, in this paper, the trade-off is managed by use of a Model Predictive Control (MPC) algorithm. The tracking accuracy and energy efficiency of the MPC and FSA strategies for DDCs are compared to a PID strategy for standard cylinders. The comparison is obtained by use of a…
Automatic fringe pattern enhancement using truly adaptive period-guided bidimensional empirical mode decomposition.
2020
Fringe patterns encode the information about the result of a measurement performed via widely used optical full-field testing methods, e.g., interferometry, digital holographic microscopy, moiré techniques, structured illumination etc. Affected by the optical setup, changing environment and the sample itself fringe patterns are often corrupted with substantial noise, strong and uneven background illumination and exhibit low contrast. Fringe pattern enhancement, i.e., noise minimization and background term removal, at the pre-processing stage prior to the phase map calculation (for the measurement result decoding) is therefore essential to minimize the jeopardizing effect the mentioned error…
Fuzzy Logic based model for self-optimizing energy consumption in IoT environment
2021
Energy optimization is essential in IoT environments due to energy constraints for some IoT components. In fact, energy consumption has a direct impact on IoT system lifetime, which represents an important Quality of Service (QoS) parameter for IoT environments. In order to extend the IoT system lifetime, energy consumption optimization should be considered in several IoT components. In this paper, we specify an energy self-optimizing mechanism allowing to minimize data transmission energy consumption of IoT objects. This mechanism enables selecting specific objects to send the desired data while minimizing the energy consumed for the corresponding communication. Our proposal is made of a M…
Quantification and automatized adaptive detection of in vivo and in vitro neuronal bursts based on signal complexity.
2015
In this paper, we propose employing entropy values to quantify action potential bursts in electrophysiological measurements from the brain and neuronal cultures. Conventionally in the electrophysiological signal analysis, bursts are quantified by means of conventional measures such as their durations, and number of spikes in bursts. Here our main aim is to device metrics for burst quantification to provide for enhanced burst characterization. Entropy is a widely employed measure to quantify regularity/complexity of time series. Specifically, we investigate the applicability and differences of spectral entropy and sample entropy in the quantification of bursts in in vivo rat hippocampal meas…
Power allocation for two-cell two-user joint transmission
2012
In this paper, we develop a power allocation scheme for the downlink of a two-cell two-user joint transmission system. The objective is to maximize the sum rate under per-cell power constraints. We study a worst case scenario where the carrier phases between the two base stations are un-synchronized, so that joint transmission must be performed without precoding. The derived power allocation scheme is remarkably simple, i.e., each cell transmits with full power to only one user. Note that joint transmission is still possible, when two cells select the same user for data transmission. Moreover, we prove that, in this scenario, the joint transmission case happens with higher probability when …
Reducing Power Consumption of Wireless Networks Through Collaborative DMC Mobile Clusters
2017
Reducing the energy consumption of the wireless network is significantly important to the economic and ecological sustainability of the ICT industry, as high energy consumption may limit the performance of wireless networks and is one of the main network costs. To solve energy consumption problem, especially on the terminal side, a scheme known as distributed mobile cloud (DMC) is considered to be a potential solution. Multiple mobile terminals (MTs) can cooperative to take advantage of good quality links among the MTs to save energy when receiving from the Base Station (BS). In this paper, we aim to find the optimal transmit power to further reduce the energy consumption of DMC. From simul…
A naive relevance feedback model for content-based image retrieval using multiple similarity measures
2010
This paper presents a novel probabilistic framework to process multiple sample queries in content based image retrieval (CBIR). This framework is independent from the underlying distance or (dis)similarity measures which support the retrieval system, and only assumes mutual independence among their outcomes. The proposed framework gives rise to a relevance feedback mechanism in which positive and negative data are combined in order to optimally retrieve images according to the available information. A particular setting in which users interactively supply feedback and iteratively retrieve images is set both to model the system and to perform some objective performance measures. Several repo…
Performance Comparison of Duobinary Modulation Formats for 40 Gb/s Long-Haul WDM Transmissions
2006
With their compact spectrum and high tolerance to residual chromatic dispersion, duobinary formats are attractive for the deployment of 40 Gb/s technology on 10 Gb/s WDM Long-Haul transmission infrastructures. Here, we compare the robustness of various duobinary formats when facing 40 Gb/s transmission impairments.
A multi-process system for HEp-2 cells classification based on SVM
2016
An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we de…
Spectrogram analysis of multipath fading channels
2015
The analysis of the Doppler power spectral density (PSD) of measured and simulated data is an important topic in the area of mobile radio channel modelling. In this paper, we estimate the Doppler PSD of multipath fading channels by using the concept of the spectrogram. The spectrogram is a spectral representation that gives insight into how the distribution of the spectral density of a signal changes over time. The multipath fading channel is modelled by a sum-of-cisoids (SOC) process. A closed-form solution is presented for the spectrogram and the corresponding time-dependent autocorrelation function (ACF). The closed-form solutions disclose several unwanted effects that come with the limi…