Correction to: Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

Water Resources Management - A correction to this paper has been published: https://doi.org/10.1007/s11269-021-02861-z     

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.     

Head circumference measurement with deep learning approach based on multi-scale ultrasound images

Multimedia Tools and Applications - Checking up on the health of the fetus during pregnancy is an important issue that should be taken into account. Ultrasound imaging, as one useful medical...     

Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method

Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6.

     

Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

Water Resources Management - Rainfall-runoff (r-r) modeling at different time scales is considered as a significant issue in hydro-environmental planning. As a first hydrological implementation,...     

Correlation‐based visual odometry for ground vehicles

Reliable motion estimation is a key component for autonomous vehicles. We present a visual odometry method for ground vehicles using template matching. The method uses a downward‐facing camera perpendicular to the ground and estimates the motion of the vehicle by analyzing the image shift from frame to frame. Specifically, an image region (template) is selected, and using correlation we find the corresponding image region in the next frame. We introduce the use of multitemplate correlation matching and suggest template quality measures for estimating the suitability of a template for the purpose of correlation. Several aspects of the template choice are also presented. Through an extensive analysis, we derive the expected theoretical error rate of our system and show its dependence on the template window size and image noise. We also show how a linear forward prediction filter can be used to limit the search area to significantly increase the computation performance. Using a single camera and assuming an Ackerman‐steering model, the method has been implemented successfully on a large industrial forklift and a 4×4 vehicle. Over 6 km of field trials from our industrial test site, an off‐road area and an urban environment are presented illustrating the applicability of the method as an independent sensor for large vehicle motion estimation at practical velocities. © 2011 Wiley Periodicals, Inc.     

Clock Delayed Domino Logic With Efficient Variable Threshold Voltage Keeper

In this paper, efficient clock delayed domino logic with variable strength voltage keeper is proposed. The variable strength of the keeper is achieved through applying two different body biases to the keeper. The circuits used to generate the body biases are called capacitive body bias generator and cross-coupled capacitive body bias generator. Compared to a previous work, the body bias generator circuits presented in this paper are simpler and do not require double or triple power supply while consuming less area and power. To show the efficiency of the proposed technique, the implementation of a carry generator circuit by the proposed techniques and the previous work are compared. The simulation results for standard CMOS technologies of 0.18 mum and 70 nm show considerable improvements in terms of power and power delay product. In addition, the proposed technique shows much less temperature dependence when compared to that of previous work     

False path exclusion in delay analysis of RTL structures

In this paper, we present an accurate delay-estimation algorithm at the register-transfer level. We study three important sources of false paths in register-transfer-level (RTL) structures, i.e., 1) resource binding; 2) interdependent conditions; and 3) datapath-controller path mismatching. The existence and creation of such paths and their effects in delay analysis are discussed. We show that in a RTL datapath structure the accuracy of the delay estimators is affected by the existence of false paths. Specifically, the accuracy drops significantly for structures synthesized from condition-dominated behaviors. We propose a mechanism to efficiently avoid false paths in delay analysis. This is achieved by introducing the propagation delay graph (PDG), whose traversal for delay analysis is equivalent to the traversal of sensitizable paths in the datapath. Comparison with the timing verifier in commercial computer-aided design (CAD) tools, show that estimated delays are within 14% accuracy of those reported by CAD tools     

Preparation and evaluation of novel nano-bioglass/gelatin conduit for peripheral nerve regeneration

Peripheral nerves are exposed to physical injuries usually caused by trauma that may lead to a significant loss of sensory or motor functions and is considered as a serious health problem for societies today. This study was designed to develop a novel nano bioglass/gelatin conduit (BGGC) for the peripheral nerve regeneration. The bioglass nanoparticles were prepared by sol–gel technique and characterized using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction analysis. The interfacial bonding interaction between the nano-bioglass and gelatin in the developed conduits was assessed by FTIR. The surface morphology and pore size of the nanocomposite were investigated through scanning electron microscopy with the pore size of the conduits being 10–40 μm. Biocompatibility was assessed by MTT assay which indicated the BGGC to have good cytocompatibility. The guidance channel was examined and used to regenerate a 10 mm gap in the right sciatic nerve of a male Wistar rat. Twenty rats were randomly divided into two experimental groups, one with the BGGC and the other being normal rats. The gastrocnemius muscle contractility was also examined at one, two and three months post-surgery in all groups using electromyography (EMAP). Histological and functional evaluation and the results obtained from electromyography indicated that at three months, nerve regeneration of the BGGC group was statistically equivalent to the normal group (p > 0.05). Our result suggests that the BGGC can be a suitable candidate for peripheral nerve repair.     

Detecting signal-overshoots for reliability analysis in high-speed system-on-chips

The rising level of complexity and speed of SoC makes it increasingly vital to test adequately the system for signal integrity. Voltage overshoot is one of the integrity factors that has not been sufficiently addressed for the purpose of testing and reliability. Overshoots are known to inject hot-carriers into the gate oxide and cause permanent degradation of MOSFET transistors' performance. This performance degradation creates a serious reliability concern. Unfortunately, accurate parasitic extraction and simulation to detect the interconnect problems is very time consuming and very sensitive to the circuit characteristics and thus is not practical for large SoC. This paper presents a built-in chip methodology to detect and measure the signal overshoots occurring on the interconnects of high-speed system-on-chips. This built-in test strategy does not require external probing or signal waveform monitoring. Instead, the overshoot detector cells monitor signals received by a core (e.g. from the system bus) and record the occurrence of overshoots over a period of operation. The overshoot information accumulated by these cells can be compressed and scanned out efficiently And inexpensively for final quality grading, reliability analysis and diagnosis.