Stability and transition of three-dimensional rotating boundary layers

The flow over a rotating cone in still fluid is susceptible to crossflow and centrifugal instability modes of spiral nature, depending on the cone half-angle. For parameters ranging from propeller nose cones to rotating disks, the instability triggers co-rotating vortices, whereas for slender spinning missiles, counter-rotating vortices are observed. Upon introduction of an oncoming flow, the problem essentially becomes a battle between the streamwise and azimuthal shear flow, due to the rotating surface. The boundary layer instability is again visualized by the formation of spiral vortices, which wrap around the cone surface in a helical nature. For both crossflow and centrifugal instabilities, we derive the mean flow boundary layer equations and investigate the high Reynolds number asymptotic linear stability of the flow to inviscid crossflow modes (type I), type II modes, which arise from a viscous-Coriolis force balance, and neutral modes for a slender cone. The influence of the cone half-angle (ψ) and axial flow strength (s or Ts) on the number and orientation of the spiral vortices is examined, with comparisons made with previous experimental and numerical results

Impact of peak/mid luteal estradiol on pregnancy outcome after intracytoplasmic sperm injection

Abstract Objective: To compare peak to mid estradiol ratio with the probability of successful conception after intracytoplasmic sperm injection. Method: The quasi-experimental study was conducted in an infertility clinic at Islamabad from June 2010 till August 2011, and comprised couples subjected to intra-cytoplasmic sperm injection. Down-regulation of ovaries was followed by calculated stimulation, ovulation induction, oocytes retrieval, intra cytoplasmic sperm injection, in vitro maturation of embryos and finally blastocysts transfer. Serum estradiol was measured by enzyme-linked immunosorbent assay on ovulation induction day and the day of embryo transfer. Failure of procedure was detected by beta human chorionic gonadotropin5-25mIU/ml (Group I; non-pregnant).Females with beta human chorionic gonadotropin\u3e25mIU/ml and no cardiac activity after 4 weeks of transfer were placed in Group II (pre-clinical abortion), and confirmation of foetal heart in the latter comprised Group III (clinical pregnancy). Data was analysed using SPSS 15. Results: Of the 323 couples initially enrolled, embryo transfer was carried out in 282(87.3%) females. Clinical pregnancy was achieved in 101(36%) of the cases, while 61(21.63%) had pre-clinical abortion, and 120(42%) remained non-pregnant. The peak/mid-luteal estradiolratio was low (2.3) in patients who had high oocyte maturity (p=0.001) and fertilisation rate (p=0.003) compared to non-pregnant patients with high peak/mid-luteal estradiolratio (2.56). Conclusion: High peak estradiol with maintenance of optimal levels in mid-luteal phase is required for implantation of fertilised ovum and accomplishment of clinical pregnancy

Simulation of Models and BER Performances of DWT-OFDM versus FFT-OFDM

Simulation approaches using MATLAB for wavelet based OFDM, particularly in DWT-OFDM as alternative substitutions for Fourier based OFDM are demonstrated. Conventional OFDM systems use IFFT and FFT algorithms at the transmitter and receiver respectively to multiplex the signals and transmit them simultaneously over a number of subcarriers. The system employs guard intervals or cyclic prefixes (CP) so that the delay spread of the channel becomes longer than the channel impulse response. The system must make sure that the cyclic prefix is a small fraction of the per carrier symbol duration. The purpose of employing the CP is to minimize inter-symbol interference (ISI). However a CP reduces the power efficiency and data throughput. The CP also has the disadvantage of reducing the spectral containment of the channels. Due to these issues, an alternative method is to use the wavelet transform to replace the IFFT and FFT blocks. The wavelet transform is referred as Discrete Wavelet Transform OFDM (DWT-OFDM). By using the transform, the spectral containment of the channels is better since they are not using CP. The wavelet based OFDM (DWT-OFDM) is assumed to have ortho-normal bases properties and satisfy the perfect reconstruction property. We use different wavelet families and compare with conventional FFT-OFDM system. BER performances of both OFDM systems are also obtained. It is found that the DWT-OFDM platform is superior as compared to others as it has less error rate, especially using bior5.5 or rbior3.3 wavelet family

Local And Semi-Global Feature-Correlative Techniques For Face Recognition

Face recognition is an interesting field of computer vision with many commercial and scientific applications. It is considered as a very hot topic and challenging problem at the moment. Many methods and techniques have been proposed and applied for this purpose, such as neural networks, PCA, Gabor filtering, etc. Each approach has its weaknesses as well as its points of strength. This paper introduces a highly efficient method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in different views (poses) of facial images. Feature extraction techniques are applied on the images (faces) based on Zernike moments and structural similarity measure (SSIM) with local and semi-global blocks. Pre-processing is carried out whenever needed, and numbers of measurements are derived. More specifically, instead of the usual approach for applying statistics or structural methods only, the proposed methodology integrates higher-order representation patterns extracted by Zernike moments with a modified version of SSIM (M-SSIM). Individual measurements and metrics resulted from mixed SSIM and Zernike-based approaches give a powerful recognition tool with great results. Experiments reveal that correlative Zernike vectors give a better discriminant compared with using 2D correlation of the image itself. The recognition rate using ORL Database of Faces reaches 98.75%, while using FEI (Brazilian) Face Database we got 96.57%. The proposed approach is robust against rotation and noise

Oriented crossover in genetic algorithms for computer networks optimization

Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the genetic algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case where one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and dampen the undesired attitudes. The proposed crossover can be achieved in two stages: The first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system show improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work

CRADG: A chaotic RADG security system

A high-performance ciphering algorithm is presented. The proposed method combines old school ciphering (Reaction Automata Direct Graph (RADG)) with chaotic systems to obtain higher level of security. Chaotic sequences are highly sensitive to any changes in their parameters, adding a higher level of security to the proposed approach, called CRADG

Hybrid mamdani fuzzy rules and convolutional neural networks for analysis and identification of animal images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052

Deep learning versus spectral techniques for frequency estimation of single tones: Reduced complexity for software-defined radio and iot sensor communications

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications

Gradient Descent Localization in Wireless Sensor Networks

Meaningful information sharing between the sensors of a wireless sensor network (WSN) necessitates node localization, especially if the information to be shared is the location itself, such as in warehousing and information logistics. Trilateration and multilateration positioning methods can be employed in two-dimensional and three-dimensional space respectively. These methods use distance measurements and analytically estimate the target location; they suffer from decreased accuracy and computational complexity especially in the three-dimensional case. Iterative optimization methods, such as gradient descent (GD), offer an attractive alternative and enable moving target tracking as well. This chapter focuses on positioning in three dimensions using time-of-arrival (TOA) distance measurements between the target and a number of anchor nodes. For centralized localization, a GD-based algorithm is presented for localization of moving sensors in a WSN. Our proposed algorithm is based on systematically replacing anchor nodes to avoid local minima positions which result from the moving target deviating from the convex hull of the anchors. We also propose a GD-based distributed algorithm to localize a fixed target by allowing gossip between anchor nodes. Promising results are obtained in the presence of noise and link failures compared to centralized localization. Convergence factor issues are discussed, and future work is outlined

Detection and recognition of moving video objects: Kalman filtering with deep learning

© 2021. All rights reserved. Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers. The first layer, which is the input image, is of 100 * 100 size. The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling. The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms