The design of multiple feedback topology Chebyshev low-pass active filter with average differential evolution algorithm

Neural Computing and Applications - This study presents the design of a tenth-order multiple feedback Chebyshev low-pass filter (MF-C-LPF). Component selection and gain calculation of filters are...     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

ANT-colony based disjoint set assortment in wireless sensor networks

Wireless Networks - Wireless sensor network (WSN) consists of small sized devices containing different sensors to monitor physical, environmental and medical conditions during surveillance of...     

Role of Nd-Ni on structural,spectral and dielectric properties of strontium-barium based nano-sized X-type ferrites

The influence of neodymium and nickel substitution on structural and dielectric parameters was investigated in strontium-barium X-type hexagonal ferrites having composition SrBaCu_2?xNi_xNd_yFe_28?yO₄₆ (x = 0, 0.2, 0.4, 0.6, 0.8, 1 and y = 0, 0.02, 0.04, 0.06, 0.08, 0.1). Sol-gel method was employed for synthesizing these hexagonal ferrites. The XRD plots of all studied materials which were annealed at 1250 °C show single phase characteristics. Lattice parameter ‘c’ increased as a consequence of larger radius of rare earth ion (Nd³⁺) as compared to (Fe³⁺), while lattice parameter ‘a’ showed very small variation. The cell volume was obtained in the range 2508.32–2523.75 (ų). The inclusion of Nd-Ni also affected X-ray density, bulk density and porosity. The FTIR spectroscopy indicated the particular absorption peaks of hexagonal ferrites and it was performed in the range of 500–700 cm^?1. On account of Nd-Ni doping, the dielectric constant, dielectric loss and AC-conductivity showed decreasing trend. The occupancy of Nd³⁺ ions at octahedral site impedes the valence alternation of Fe³⁺; therefore there was decrease in dielectric permittivity. Ac conductivity has been decreased from 9.14 to 6.49 (Ω cm)^?1 at frequency of 2.7 GHz. The Cole-Cole plots of synthesized materials noticeably revealed grain boundary contribution. The appearance of single semi-circle in impedance Cole-Cole graphs confirms the exceptional role of grain boundaries in the conduction process. The considerably lower dielectric parameters of investigated nano X-type ferrites propose their feasibility for high-frequency applications (phase shifters, dielectric resonators, stealth technology etc).     

A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG

Frontiers of Information Technology & Electronic Engineering - Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need...     

A Code Assignment Algorithm for Nonblocking OVSF Codes in WCDMA

OVSF codes are used as channelization codes in WCDMA. Due to code blocking property of OVSF codes, the bandwidth available in the system is severely limited. Code reassignments mitigate the impact of the blocking property at the expense of causing delays and decreasing the throughput of the system. Nonblocking OVSF (NOVSF) codes have been proposed to alleviate the adverse effect of code reassignments. This paper presents a code assignment algorithm for NOVSF codes, which does not require any code reassignments. Simulation results show that NOVSF codes achieve better throughput than OVSF codes, even though code reassignments are allowed in the assignments of OVSF codes.     

Autocorrelation model-based identification method for ARMA systems in noise

A novel method for parameter estimation of minimum-phase autoregressive moving average (ARMA) systems in noise is presented. The ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise-free ARMA signal. The AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters with guaranteed stability. The MA parameters are estimated using a correlation matching technique. Simulation results show that the proposed method can estimate the ARMA parameters with better accuracy as compared to other reported methods, in particular for low SNRs.