The document summarizes research on using the Teaching-Learning Based Optimization (TLBO) algorithm to synthesize non-uniform circular antenna arrays. TLBO was used to determine excitation amplitudes that achieve a side lobe level of -15dB while maintaining beamwidth. Radiation patterns and convergence plots are presented for arrays with 20, 30, 40, and 50 elements, both with and without beam steering. The results show TLBO can control side lobe level and beamwidth, outperforming uniform circular arrays. Excitation amplitudes for different array sizes are reported.
Image Matting via LLE/iLLE Manifold LearningITIIIndustries
Accurately extracting foreground objects is the problem of isolating the foreground in images and video, called image matting which has wide applications in digital photography. This problem is severely ill-posed in the sense that, at each pixel, one must estimate the foreground and background pixels and the so-called alpha value from only pixel information. The most recent work in natural image matting rely on local smoothness assumptions about foreground and background colours on which a cost function has been established. In this paper, we propose an extension to the class of affinity based matting techniques by incorporating local manifold structural
information to produce both a smoother matte based on the socalled improved Locally Linear Embedding. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained.
Raw 2009 -THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO E...Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
Image Matting via LLE/iLLE Manifold LearningITIIIndustries
Accurately extracting foreground objects is the problem of isolating the foreground in images and video, called image matting which has wide applications in digital photography. This problem is severely ill-posed in the sense that, at each pixel, one must estimate the foreground and background pixels and the so-called alpha value from only pixel information. The most recent work in natural image matting rely on local smoothness assumptions about foreground and background colours on which a cost function has been established. In this paper, we propose an extension to the class of affinity based matting techniques by incorporating local manifold structural
information to produce both a smoother matte based on the socalled improved Locally Linear Embedding. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained.
Raw 2009 -THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO E...Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
Text Independent Speaker Identification Using Imfcc Integrated With IcaIOSR Journals
Abstract: Over the years, more research work has been reported in literature regarding text independent
speaker identification using MFC coefficients. MFCC is one of the best methods modeled on human auditory
system. Murali et al (2011) [1] has developed a Text independent speaker identification using MFC coefficients
which follows Generalized Gaussian mixer model. MFCC, because of its filter bank structure it captures the
characteristics of information more effectively in lower frequency region than higher region, because of this,
valuable information in high frequency region may be lost. In this paper we rectify the above problem by
retrieving the information in high frequency region by inverting the Mel bank structure. The dimensionality and
dependency of above features were reduced by integrating with ICA. Here Text Independent Speaker
Identification system is developed by using Generalized Gaussian Mixer Model .By the experimentation, it was
observed that this model outperforms the earlier existing models.
Keywords: Independent Component Analysis; Generalized Gaussian Mixer Model; Inverted Mel frequency
cepstral coefficients; Bayesian classifier; EM algorithm.
Dynamic texture based traffic vehicle monitoring systemeSAT Journals
Abstract Dynamic Textures are function of both space and time which exhibit spatio-temporal stationary property. They have spatially repetitive patterns which vary with time. In this paper, importance of phase spectrum in the signals is utilized and a novel method for vehicle monitoring is proposed with the help of Fourier analysis, synthesis and image processing. Methods like Doppler radar or GPS navigation are used commonly for tracking. The proposed image based approach has an added advantage that the clear image of the object (vehicle) can be used for future reference like proof of incidence, identification of owner and registration number. Keywords-Fourier Transform, dynamic texture, phase spectrum
Stanford ICME Lecture on Why Deep Learning WorksCharles Martin
Random Matrix Theory (RMT) is applied to analyze the weight matrices
of Deep Neural Networks (DNNs), including production quality,
pre-trained models, and smaller models trained from scratch. Empirical
and theoretical results indicate that the DNN training process itself
implements a form of self-regularization, evident in the empirical
spectral density (ESD) of DNN layer matrices. To understand this, we
provide a phenomenology to identify 5+1 Phases of Training,
corresponding to increasing amounts of implicit self-regularization.
For smaller and/or older DNNs, this implicit self-regularization is
like traditional Tikhonov regularization, with a "size scale"
separating signal from noise. For state-of-the-art DNNs, however, we
identify a novel form of heavy-tailed self-regularization, similar to
the self-organization seen in the statistical physics of disordered systems.
To that end, building on the statistical mechanics of generalization,
and applying recent results from RMT, we derive a new VC-like
complexity metric that resembles the familiar product norms, but is
suitable for studying average-case generalization behavior in real
systems. We then demonstrate its effectiveness by testing how well
this new metric correlates with trends in the reported test accuracies
across models for over 450 pretrained DNNs covering a range of data
sets and architectures.
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Optimal Synthesis of Array Pattern for Concentric Circular Antenna Array Usin...IDES Editor
In this paper one optimization heuristic search
technique, Hybrid Evolutionary Programming (HEP) is
applied to the process of synthesizing three-ring Concentric
Circular Antenna Array (CCAA) focused on maximum
sidelobe-level reduction. This paper assumes non-uniform
excitations and uniform spacing of excitation elements in each
three-ring CCAA design. Experimental results reveal that the
design of non-uniformly excited CCAAs with optimal current
excitations using the method of HEP provides a considerable
sidelobe level reduction with respect to the uniform current
excitation with d=λ/2 element-to-element spacing. Among the
various CCAA designs, the design containing central element
and 4, 6 and 8 elements in three successive concentric rings
proves to be such global optimal design with global minimum
SLL (-40.22 dB) as determined by HEP.
The fourier transform for satellite image compressioncsandit
The need to transmit or store satellite images is growing rapidly with the development of
modern communications and new imaging systems. The goal of compression is to facilitate the
storage and transmission of large images on the ground with high compression ratios and
minimum distortion. In this work, we present a new coding scheme for satellite images. At first,
the image will be downloaded followed by a fast Fourier transform FFT. The result obtained
after FFT processing undergoes a scalar quantization (SQ). The results obtained after the
quantization phase are encoded using entropy encoding. This approach has been tested on
satellite image and Lena picture. After decompression, the images were reconstructed faithfully
and memory space required for storage has been reduced by more than 80%
Extended Fuzzy C-Means with Random Sampling Techniques for Clustering Large DataAM Publications
Big data are any data that you cannot load into your computer’s primary memory. Clustering is a primary
task in pattern recognition and data mining. We need algorithms that scale well with the data size. The former
implementation, literal Fuzzy C-Means is linear or serialized. FCM algorithm attempts to partition a finite collection
of n elements into collection of c fuzzy clusters. So, given a finite set of data, this algorithm returns a list of c cluster
centers. However it doesn't scale well and slows down with increase in the size of data and is thus impractical and
sometimes undesirable. In this paper, we propose an extended version of fuzzy c-means clustering algorithm by means of various random sampling techniques to study which method scales well for large or very large data.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
Implementation of XOR Based Pad Generation Mutual Authentication Protocol for...IOSR Journals
In RF link, without security the messages exchange between the two devices are monitoring by an
eavesdropper. So the exclusive-OR (XOR) based pad generation protocol is used to safely transfer the data to
the other point with necessary security and it maintaining confidentiality. This protocol produce the cover
coding pad to mask the access password before the datas are transmitted. A specially designed pad generation
will be implemented in digital domain to solve the insecurity problem in data communication RF link. This
protocol developed under regulation of ISO 18000 – 6 type C protocol also known as EPC C1G2 RFID
protocol. The linear feed back shift register (LFSR) generate the pseudo random binary sequence (PRBS) and it
is used as data source to the pad generation function. The Xilinx 13.x software is used for synthesize and
modelsim SE6.0 is used for simulating the result. The pad generation algorithm has been implemented in FPGA
Spartan 3 PQ208-4 board to verify the result
Text Independent Speaker Identification Using Imfcc Integrated With IcaIOSR Journals
Abstract: Over the years, more research work has been reported in literature regarding text independent
speaker identification using MFC coefficients. MFCC is one of the best methods modeled on human auditory
system. Murali et al (2011) [1] has developed a Text independent speaker identification using MFC coefficients
which follows Generalized Gaussian mixer model. MFCC, because of its filter bank structure it captures the
characteristics of information more effectively in lower frequency region than higher region, because of this,
valuable information in high frequency region may be lost. In this paper we rectify the above problem by
retrieving the information in high frequency region by inverting the Mel bank structure. The dimensionality and
dependency of above features were reduced by integrating with ICA. Here Text Independent Speaker
Identification system is developed by using Generalized Gaussian Mixer Model .By the experimentation, it was
observed that this model outperforms the earlier existing models.
Keywords: Independent Component Analysis; Generalized Gaussian Mixer Model; Inverted Mel frequency
cepstral coefficients; Bayesian classifier; EM algorithm.
Dynamic texture based traffic vehicle monitoring systemeSAT Journals
Abstract Dynamic Textures are function of both space and time which exhibit spatio-temporal stationary property. They have spatially repetitive patterns which vary with time. In this paper, importance of phase spectrum in the signals is utilized and a novel method for vehicle monitoring is proposed with the help of Fourier analysis, synthesis and image processing. Methods like Doppler radar or GPS navigation are used commonly for tracking. The proposed image based approach has an added advantage that the clear image of the object (vehicle) can be used for future reference like proof of incidence, identification of owner and registration number. Keywords-Fourier Transform, dynamic texture, phase spectrum
Stanford ICME Lecture on Why Deep Learning WorksCharles Martin
Random Matrix Theory (RMT) is applied to analyze the weight matrices
of Deep Neural Networks (DNNs), including production quality,
pre-trained models, and smaller models trained from scratch. Empirical
and theoretical results indicate that the DNN training process itself
implements a form of self-regularization, evident in the empirical
spectral density (ESD) of DNN layer matrices. To understand this, we
provide a phenomenology to identify 5+1 Phases of Training,
corresponding to increasing amounts of implicit self-regularization.
For smaller and/or older DNNs, this implicit self-regularization is
like traditional Tikhonov regularization, with a "size scale"
separating signal from noise. For state-of-the-art DNNs, however, we
identify a novel form of heavy-tailed self-regularization, similar to
the self-organization seen in the statistical physics of disordered systems.
To that end, building on the statistical mechanics of generalization,
and applying recent results from RMT, we derive a new VC-like
complexity metric that resembles the familiar product norms, but is
suitable for studying average-case generalization behavior in real
systems. We then demonstrate its effectiveness by testing how well
this new metric correlates with trends in the reported test accuracies
across models for over 450 pretrained DNNs covering a range of data
sets and architectures.
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Optimal Synthesis of Array Pattern for Concentric Circular Antenna Array Usin...IDES Editor
In this paper one optimization heuristic search
technique, Hybrid Evolutionary Programming (HEP) is
applied to the process of synthesizing three-ring Concentric
Circular Antenna Array (CCAA) focused on maximum
sidelobe-level reduction. This paper assumes non-uniform
excitations and uniform spacing of excitation elements in each
three-ring CCAA design. Experimental results reveal that the
design of non-uniformly excited CCAAs with optimal current
excitations using the method of HEP provides a considerable
sidelobe level reduction with respect to the uniform current
excitation with d=λ/2 element-to-element spacing. Among the
various CCAA designs, the design containing central element
and 4, 6 and 8 elements in three successive concentric rings
proves to be such global optimal design with global minimum
SLL (-40.22 dB) as determined by HEP.
The fourier transform for satellite image compressioncsandit
The need to transmit or store satellite images is growing rapidly with the development of
modern communications and new imaging systems. The goal of compression is to facilitate the
storage and transmission of large images on the ground with high compression ratios and
minimum distortion. In this work, we present a new coding scheme for satellite images. At first,
the image will be downloaded followed by a fast Fourier transform FFT. The result obtained
after FFT processing undergoes a scalar quantization (SQ). The results obtained after the
quantization phase are encoded using entropy encoding. This approach has been tested on
satellite image and Lena picture. After decompression, the images were reconstructed faithfully
and memory space required for storage has been reduced by more than 80%
Extended Fuzzy C-Means with Random Sampling Techniques for Clustering Large DataAM Publications
Big data are any data that you cannot load into your computer’s primary memory. Clustering is a primary
task in pattern recognition and data mining. We need algorithms that scale well with the data size. The former
implementation, literal Fuzzy C-Means is linear or serialized. FCM algorithm attempts to partition a finite collection
of n elements into collection of c fuzzy clusters. So, given a finite set of data, this algorithm returns a list of c cluster
centers. However it doesn't scale well and slows down with increase in the size of data and is thus impractical and
sometimes undesirable. In this paper, we propose an extended version of fuzzy c-means clustering algorithm by means of various random sampling techniques to study which method scales well for large or very large data.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
Implementation of XOR Based Pad Generation Mutual Authentication Protocol for...IOSR Journals
In RF link, without security the messages exchange between the two devices are monitoring by an
eavesdropper. So the exclusive-OR (XOR) based pad generation protocol is used to safely transfer the data to
the other point with necessary security and it maintaining confidentiality. This protocol produce the cover
coding pad to mask the access password before the datas are transmitted. A specially designed pad generation
will be implemented in digital domain to solve the insecurity problem in data communication RF link. This
protocol developed under regulation of ISO 18000 – 6 type C protocol also known as EPC C1G2 RFID
protocol. The linear feed back shift register (LFSR) generate the pseudo random binary sequence (PRBS) and it
is used as data source to the pad generation function. The Xilinx 13.x software is used for synthesize and
modelsim SE6.0 is used for simulating the result. The pad generation algorithm has been implemented in FPGA
Spartan 3 PQ208-4 board to verify the result
Factor analysis as a tool for evaluation of spatial and temporal variations i...IOSR Journals
In this case study, factor analysis was applied for evaluation of temporal/spatial variations in the
groundwater quality of Aravakurichi block, Karur district, Tamil Nadu, India. This statistical technique was
employed for the better interpretation of large complex water quality data set obtained from twenty five
groundwater locations in four seasons during the year 2012. The water samples were characterized for the
physico-chemical parameters such as pH, total alkalinity, electrical conductivity, total hardness, calcium ions,
magnesium ions, total dissolved solids, fluorides, chlorides and sulphates. Factor analysis indicated four factors
initially and when rotation of the factor axis was executed, it yielded two factors with clear indication of high
loadings for some variable and low loadings for others, facilitating data interpretation in terms of original
variables. Overall, this case study demonstrated the effectiveness of factor analysis to identify marker variables
for assessing the chemistry of groundwater besides earmarking representative sampling stations to undertake
suitable water quality management in a shortest possible time.
Fault Detection Technique for Compact AES DesignIOSR Journals
Abstract: Cryptography is a method that has been developed to ensure the secrecy of messages and transfer data securely. Advanced Encryption Standard (AES) has been made as the first choice for many critical applications because of the high level of security and the fast hardware and software implementations, many of which are power and resource constrained and requires reliable and efficient hardware implementations. Naturally occurring and maliciously injected faults reduce the reliability of Advanced Encryption Standard (AES) and may leak confidential information. In this paper, a lightweight concurrent fault detection scheme for the AES is presented. In the proposed approach, the composite field S-box and inverse S-box are divided into blocks and the predicted parities of these blocks are obtained. For high speed applications, S-box implementation based on lookup tables is avoided. Instead, logic gate implementations based on composite fields are utilized. A compact architecture for the AES Mix-columns operation and its inverse is also presented. This parity-based fault detection scheme reaches the maximum fault coverage when compared to other methods of fault detection. The proposed fault detection technique for AES encryption and decryption has the least area and power consumption compared to their counterparts with similar fault detection capabilities. Index terms: AES, composite fields, parity prediction, fault detection, S-box.
Neural Network For The Estimation Of Ammonia Concentration In Breath Of Kidne...IOSR Journals
Neural networks are an extremely powerful tool for data mining. They are especially useful in cases
involving data classification where it is difficult to establish a pattern in the search space. In an era when
artificial intelligence is increasingly being utilised in industrial and medical applications throughout the world,
it is becoming evident that this is an emerging trend. This paper explores the idea of artificial intelligence by
employing the use of a feed-forward neural network with two process layers to determine the concentration of
ammonia in exhaled human breath. The human mouth contains many kinds of substances both in liquid and
gaseous form. The individual concentrations of each of these substances could provide useful insight to the
health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has
revealed the presence or absence of diseases in the body. Kidney failure is one diesease which is identified by
an extremely high ammonia content in human breath. This disease is as a result of the kidneys’ inability to
process the body’s liquid waste. The result of this is the release of urea throughout the body which is dissipated
in the form of ammonia through oral breath. The neural simulation is carried out using NeuroSolutions version
5 software. The neural network correctly identified the concentration of oral ammonia as an indication of
kidney failure with an accuracy of 85%.
Synthesis, Characterization and Antibacterial Activity of New Complexes of So...IOSR Journals
Complexes of some lanthanide picrates (Ln3+ = Pr3+, Nd3+ and Dy3+) with benzo-18-crown-6 and 221-cryptand were synthesized and characterized by elemental analysis, FTIR, and UV-Visible. Spectrophotometric methods, thermal analysis (TGA & DTG), melting point, magnetic susceptibility and molar conductance. Also an in-vitro study on gram positive (Staphylococcus aureus) and gram negative bacteria (Escherichia coli, Salmonella and pseudomonas aeruginosa) was performed and the results were compared to those of the broad spectrum antibiotic Chloramphinicol. The benzo-18-crown-6 complexes have the general formula of [Ln.L.(Pic)2]Pic.nH2O , where; (Ln3+ = Pr3+, Nd3+, and Dy3+) , (L = Benzo-18-crown-6) , (Pic = Picrate anion) , (n = 1-2). In these complexes two picrate anions are coordinated to the metal ion through the phenolic oxygen and oxygen of the ortho nitro group, thus, the metal ions in these complexes have a coordination number of (10). The complexes of 221-cryptand have the general formula of [Ln.L.(Pic)]Pic2.nH2O where; (Ln3+ = Pr3+, Nd3+, and Dy3+), (L = 221-cryptand), (Pic = Picrate anion), (n = 1,2 or 7). In these complexes one picrate anion is coordinated to the metal ion, also through the phenolic oxygen and the oxygen from the ortho nitro group, thus the metal ions in the cryptand complexes have a coordination number of (9).
“Trade-Off between Detection and Resolution of Two Point Objects Under Variou...IOSR Journals
It is a well-experienced fact that whenever one tries to detect a weak object point in the vicinity of an intense point object, viz., a binary star-SIRUS and its companion weak satellite star, there is always loss of resolution of the optical system. In other words, one wants to improve the defectively of the system, there is always a loss of resolution capabilities of the system. Thus, there is a trade-off between Detection and Resolution of optical systems under various imaging situations. In this first paper of discussion of this trade-off, we have derived the Fourier analytical formulation of this problem. This formulation will be used to find out a compatible trade-off between Detection and Resolution in our further publications
Blow Mould Tool Design and Manufacturing Process for 1litre Pet BottleIOSR Journals
the concepts of Blow molding is a process used to produce hollow objects from thermoplastic. The
basic blow molding process has two fundamental phases. First, a parson (or a perform) of hot plastic resin in a
somewhat tubular shape is created. Second, compressed air is used to expand the hot perform and press it
against mould cavities. The pressure is held until the plastic cools. Blow molding process is used for which has
thin wall sections.In this thesis, blow mould design is to be done for a bottle having 0.5mm thickness. This
thickness cannot be filled in pressure injection molding. So blow molding is considered for pet bottle design.
The mould is prepared by first modeling the part, extracting core & cavity and generating CNC program. Blow
mould tool design is done in Pro/Engineer according to HASCO standards. A prototype of the pet bottle using
blow mould design is also included.
“Proposed Model for Network Security Issues Using Elliptical Curve Cryptography”IOSR Journals
Abstract: Elliptic Curve Cryptography (ECC) plays an important role in today’s public key based security
systems. . ECC is a faster and more secure method of encryption as compared to other Public Key
Cryptographic algorithms. This paper focuses on the performance advantages of using ECC in the wireless
network. So in this paper its algorithm has been implemented and analyzed for various bit length inputs. The
Private key is known only to sender and receiver and hence data transmission is secure.
Bangla Optical Digits Recognition using Edge Detection MethodIOSR Journals
Abstract:This paper is based on Bangla Optical Digit Recognition (ODR) by the Edge detection technique. In this method, Bangla digit image converted into gray-scale which distributed by an M by N array form. Here input data are considered off-line printed digit’s image which collected from computer generated image, scanned documents or printed text. After addressing the gray-scale image against a variable in the form of an M by N array, where the value of array pointers are shown 255 for total white space, 0 (zero) for total dark space and value between 255 and 0 for mix of white and dark space of the image. At the next process, four edgestouch points as well as each touch point’s ratio use as parameters to determine each Bangla digit uniquely. Keywords-Edge, image,gray-scale, Matrix,ODR.
Investigation on the Pattern Synthesis of Subarray Weights for Low EMI Applic...IOSRJECE
In modern radar applications, it is frequently required to produce sum and difference patterns sequentially. The sum pattern amplitude coefficients are obtained by using Dolph-Chebyshev synthesis method where as the difference pattern excitation coefficients will be optimized in this present work. For this purpose optimal group weights will be introduced to the different array elements to obtain any type of beam depending on the application. Optimization of excitation to the array elements is the main objective so in this process a subarray configuration is adopted. However, Differential Evolution Algorithm is applied for optimization method. The proposed method is reliable and accurate. It is superior to other methods in terms of convergence speed and robustness. Numerical and simulation results are presented.
An Automatic Medical Image Segmentation using Teaching Learning Based Optimiz...idescitation
Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
For high performance communication systems, Side Lobe Level (SLL) reduction and improved directivity are the goal of antenna designers. In the recent years, many optimization techniques of antenna design are occupying demanding place over the analytical techniques. Though they have contributed attractive solutions, it is often obvious to select one that meets the particular design need at hand. In this paper, an optimization technique called Self-adaptive Differential Evolution (SaDE) that can be able to learn and behave intelligently along with hyper beam forming is integrated to determine an optimal set of excitation weights in the design of EcAA. Non-uniform excitation weights of the individual array elements of EcAA are performed to obtain reduced SLL, high directivity and flexible radiation pattern. To evaluate the improved performance of the proposed SaDE optimized hyper beam, comparison are done with uniformly excited, SaDE without hyper beam and Genetic Algorithm (GA). In general, the proposed work of pattern synthesis has resulted in much better reduction of SLL and FNBW than both the uniformly excited and thinned EcAA. The results of this study clearly reveal that the SLL highly reduced at a very directive beamwidth.
Development of deep reinforcement learning for inverted pendulumIJECEIAES
This paper presents a modification of the deep Q-network (DQN) in deep reinforcement learning to control the angle of the inverted pendulum (IP). The original DQN method often uses two actions related to two force states like constant negative and positive force values which apply to the cart of IP to maintain the angle between the pendulum and the Y-axis. Due to the changing of too much value of force, the IP may make some oscillation which makes the performance system could be declined. Thus, a modified DQN algorithm is developed based on neural network structure to make a range of force selections for IP to improve the performance of IP. To prove our algorithm, the OpenAI/Gym and Keras libraries are used to develop DQN. All results showed that our proposed controller has higher performance than the original DQN and could be applied to a nonlinear system.
Designing a pencil beam pattern with low sidelobesPiyush Kashyap
In this paper, a system has been designed for an operational frequency of 1.27 GHz consisting of an 8 element array of parasitic dipoles illuminated by a 4 element center fed array of active dipoles with Dolph-Chebyshev excitation coefficients. The array is designed to achieve a fairly pencil beam pattern suitable for direction of arrival estimation purposes. Array geometry and configuration is optimized for both active and parasitic elements using the PSO tool in FEKO. A directive radiation pattern is obtained with a gain of 14.5 dBi in the broadside direction along with a beamwidth of 30.29o. VSWR of 1.58 is achieved. Further, an iterative least square valued error estimation approach using phase control to achieve a desired array factor pattern for an n-element linear array, has been shown to be effective for larger number of iterations. The array excitation coefficients achieved were consistent with the Dolph-Chebyshev coefficients used in our antenna array design. With the ability to introduce nulls and steering the main beam in desired directions along with a pencil beam radiation pattern, beamsteering has been illustrated and the MUSIC algorithm for direction of arrival estimation has been implemented
Synthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for...Sivaranjan Goswami
A technique for synthesizing a sparse array from a 16×16 URA is presented.
An ANN model is proposed for calculation of the excitation phase of the 2D array that shows accurate results for both the original URA and the sparse array.
It is observed that the PSLL of the synthesized sparse array is almost the same as that of the URA except at the extreme ends of the scanning range (-45 degree to +45 degree in azimuth and elevation plane).
The overall scan angle of the proposed antenna array is 90 degree for both the azimuth plane and the elevation plane.
The array comprises cosine antenna elements that represent printed antennas used in 5G millimeter-wave wireless communication. Thus, the proposed sparse array has possible applications in 5G wireless communication and radar systems.
Local Binary Fitting Segmentation by Cooperative Quantum Particle OptimizationTELKOMNIKA JOURNAL
Recently, sophisticated segmentation techniques, such as level set method, which using valid
numerical calculation methods to process the evolution of the curve by solving linear or nonlinear elliptic
equations to divide the image availably, has become being more popular and effective. In Local Binary
Fitting (LBF) algorithm, a simple contour is initialized in an image and then the steepest-descent algorithm
is employed to constrain it to minimize the fitting energy functional. Hence, the initial position of the contour
is difficult or impossible to be well chosen for the final performance. To overcoming this drawback, this
work treats the energy fitting problem as a meta-heuristic optimization algorithm and imports a varietal
particle swarm optimization (PSO) method into the inner optimization process. The experimental results of
segmentations on medical images show that the proposed method is not only effective to both simple and
complex medical images with adequate stochastic effects, but also shows the accuracy and high
efficiency.
Comparative and comprehensive study of linear antenna arrays’ synthesisIJECEIAES
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F010333844
1. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 3, Ver. III (May - Jun.2015), PP 38-44
www.iosrjournals.org
DOI: 10.9790/2834-10333844 www.iosrjournals.org 38 | Page
Non-Uniform Circular Array Synthesis Using Teaching Learning
Based Optimization
VVSSS Chakravarthy1
, SVRAN Sarma2
, K Naveen Babu1
, PSR Chowdary1
,
T Sudheer Kumar3
1
(Department of ECE, Raghu Institute of Technology, India)
2
(Department of Basic Sciences, Vignan Institute of Information Technology, India)
3
(Department of ECE, Shri Vishnu Engineering College for Women, India)
Abstract : Teaching-Learning Based Optimization (TLBO) is a novel population based optimization algorithm
that has proved to be worthy in solving many multimodal problems in production engineering. In this paper, the
application of TLBO is extended to electromagnetics. Non-uniform circular array synthesis is performed using
TLBO with amplitude only technique. With the objective of -15dB side love level (SLL). Along with SLL the
beam-width is controlled and almost made equal to that of uniform circular array with 10% relaxation. The
synthesized radiation pattern for 20, 30, 40 and 50 elements circular array are presented here along with
corresponding excitation amplitude as stem plots and convergence plots for both scanned and non-scanned
conditions.
Keywords: Circular array, side lobe level, beam scanning, non-scanned beams, TLBO.
I. Introduction
In communication systems, an antenna is designed to radiate in or to receive from desired direction [1-
4]. Any radiation in the unwanted direction must be suppressed by reducing the energy in the side lobes.
Traditionally this was achieved by using a group of antennas arranged in a geometrically well-structured
manner, which are generally referred to as antenna arrays. To obtain the desired radiation characteristics such as
required side lobe level, beam-width etc., various numerical methods for antenna array synthesis such as
Schelkunoff, Dolph-Chebyscheff and Taylor's have evolved over the course of time. These are mathematically
rigorous, time consuming and could not handle multi-modal problems. Hence heuristic methods are employed to
determine the excitation coefficients required to generate the desired radiation pattern. Evolutionary methods
such as genetic algorithm [5,6], particle swarm optimization algorithm [7-10], ant colony optimization [11],
invasive weed optimization [12], bees algorithm [13], Taguchi's algorithm [14] and flower pollination algorithm
[15] were used to obtain solutions for of non-uniform linear and circular arrays synthesis problems with several
single and multiple objectives.
Side lobe level (SLL) has been a prominent issue in a communication system because it effects the
level of interference in the direction outside of main beam area. The reduction in interference gives the
possibility of increasing the capacity of the communication system. Circular arrays have become popular in
wireless communications ever since they have employed in this field due to several inherent features like beam
scanning. There are three parameters predominantly used to arrive at required pattern of an array. These are
amplitude and phase of the excitation, spacing between the antenna elements in the array.
In this paper, a non-uniform circular array is considered. The coefficients are generated for the
amplitude of excitation using a novel evolutionary method called Teaching and Learning Based Optimization
(TLBO) algorithm [16-21] to keep the side lobe level to as low as -15db while the main beam is scanned to 200
.
The other two steering parameters were kept constant. Non-scanned beams with the lower SLL are reported
earlier by the same authors for circular arrays [20] and linear arrays [21]. Initially the array factor was
formulated for a non-uniform circular array followed by the elaboration of the teaching learning based
optimization algorithm and its fitness function. Finally the results are presented comparing it with the pattern of
a uniform circular array.
II. Array Factor Formulation
The geometry of non-uniform circular array is shown in fig.1. The figure shows isotropic radiators
placed in the form of a circle having a radius ‗r‘. The array factor of this geometry is,
N
n
nnn krjIAF
1
cosexp
(1)
2. Non-Uniform Circular Array Synthesis Using Teaching Learning Based Optimization
DOI: 10.9790/2834-10333844 www.iosrjournals.org 39 | Page
Fig.1.Geometry of N Element Non-Uniform Circular Array
where,
n is element number
In is current excitation of the nth
element
N is number of elements in the array
n is the phase of excitation of the nth
element
N
i
id
r
kr
1
2
(2)
n
i
in d
kr 1
2
(3)
III. Fitness Evaluation
The fitness is evaluated using the following expression.
90 90min max dB tof AF (4)
Where, )9090( todBAF refers to the array factor values excluding the main beam. The
expression inside gives the maximum of dBAF values. The term min refers to minimization problem. In brief,
optimization problem involves in achieving lower SLL by minimizing the maximum SLL in the region of the
radiation pattern not covered by the principal beam.
IV. TLBO Algorithm
TLBO algorithm is a novel meta-heuristic optimization algorithm based on the exchange of knowledge
which happens possibly in two different ways in a class room environment viz., between the teacher and the
student and learner and fellow learner [16-19]. This process in divided into Teaching phase and Student phase.
Each student is treated as an array and the corresponding subjects in the class are array elements. The best
student is in the class is treated as the array with best fitness function. The algorithm is applied to synthesize the
non-uniform circular antenna array to obtain the desired SLL.
The algorithm is classified into initialization, teaching phase, learning phase and termination criterion.
In the first phase, the population is generated with specified lower and higher boundary. During the teaching
3. Non-Uniform Circular Array Synthesis Using Teaching Learning Based Optimization
DOI: 10.9790/2834-10333844 www.iosrjournals.org 40 | Page
phase the mean of each of the subjects for all generations is calculated and presented as Mg
. in the minimization
problem the learner with the least mean is considered as teacher for that iteration. All the learners now takes a
shift towards the teacher with the following expression [16,17]:
gg
teacher
gg
new MtfXrandXX ** (5)
where, X is the learner, g is generation, tf is teaching factor ranging from 1 to 2.
In the learning phase the learner‘s knowledge is updated using the flowing equation:
OtherwiseXXrandX
XfitnessXfitnessXXrandX
X
g
i
g
r
g
i
g
r
g
i
g
r
g
i
g
i
g
new
*
* (6)
where, ‗i‘ refers to considered learner and ‗r‘ to another randomly selected learner. The program will
be terminated if the desired criterion is obtained. The criterion can be number of generations or the desired value
of the fitness.
V. Results And Discussions
The simulation was carried out for N=20, 30, 40 and 50 elements. The resultant plots without steering the
main beam and with main beam steered to 200
are shown below:
(a) Radiation Pattern (b) Convergence Plot
(i) Plots without beam-steering
(c) Radiation Pattern (d) Convergence Plot
(ii) Plots with main beam steered to 200
Fig.2 Plots for non-uniform circular array of 20 Elements
The above figures (a) and (c) shows the radiation pattern without and with beam-steering for a 20
element non-uniform circular array respectively. In both the cases the side lobe level is maintained at -15 dB.
The beam-width is also maintained consistently with uniform circular array with 10% relaxation. As it is evident
from figures (b) and (d) the plots converge after 1900 and 10000 generations respectively
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degress
ArrayFactorindB
Radiation Pattern for N = 20
TLBO
Uniform
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
2
4
6
8
10
12
14
Generation
Fitness
Convergence Plot for N = 20
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB
Radiation Pattern for N = 20
TLBO
Uniform
0 2000 4000 6000 8000 10000
0
1
2
3
4
5
6
7
8
9
Generation
Fitness
Convergence Plot for N = 20
4. Non-Uniform Circular Array Synthesis Using Teaching Learning Based Optimization
DOI: 10.9790/2834-10333844 www.iosrjournals.org 41 | Page
(a) Radiation Pattern (b) Convergence Plot
(i) Plots without beam-steering
(c) Radiation Pattern (d) Convergence Plot
(ii) Plots with main beam steered to 200
Fig.3 Plots for non-uniform circular array of 30 Elements
The above figures (a) and (c) shows the radiation pattern without and with beam-steering for a 30
element non-uniform circular array respectively. In both the cases the side lobe level is maintained at -15 dB.
The beam-width is also maintained consistently with uniform circular array with 10% relaxation. As it is evident
from figures (b) and (d) the plots converge after 5500 and 15000 generations respectively.
(a) Radiation Pattern (b) Convergence Plot
(i) Plots without beam-steering
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB Radiation Pattern for N = 30
TLBO
Uniform
0 1000 2000 3000 4000 5000 6000
0
2
4
6
8
10
12
14
16
Generation
Fitness
Convergence Plot for N = 20
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB
Radiation Pattern for N = 30
TLBO
Uniform
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB
Radiation Pattern for N = 40
TLBO
Uniform
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0
1
2
3
4
5
6
7
8
Generation
Fitness
Convergence Plot for N = 40
0 5000 10000 15000
5
10
15
20
25
30
Generation
Fitness
Convergence Plot for N = 30
5. Non-Uniform Circular Array Synthesis Using Teaching Learning Based Optimization
DOI: 10.9790/2834-10333844 www.iosrjournals.org 42 | Page
(c) Radiation Pattern (d) Convergence Plot
(ii) Plots with main beam steered to 200
Fig.4 Plots for non-uniform circular array of 40 Elements
As is evident from figures (a) and (c) the side lobe level is maintained below -15 dB even while the main beam
is steered for a 40 element array as well.
(a) Radiation Pattern (b) Convergence Plot
(i) Plots without beam-steering
(c) Radiation Pattern (d) Convergence Plot
(ii) Plots with main beam steered to 200
Fig.5 Plots of non-uniform circular array having 50 Elements
In figures (a) and figures (c), of all the plots shown above, the radiation pattern of non-uniform circular
array are plotted and compared with that of uniform circular array. It is obvious from the plots that the side lobe
level is reduced by 7 dB by using non-uniform amplitude distribution. The excitation coefficients for the sizes of
the non-uniform circular array considered above are tabulated in Table 1.
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB Radiation Pattern for N = 40
TLBO
Uniform
0 2000 4000 6000 8000 10000 12000
0
2
4
6
8
10
12
14
Generation
Fitness
Convergence plot for N = 40
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB
Radiation Pattern for N = 50
TLBO
Uniform
-80 -60 -40 -20 0 20 40 60 80
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
in degrees
ArrayFactorindB
Radiation Pattern for N = 50
TLBO
Uniform
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
3
4
5
6
7
8
9
10
11
12
Generation
Fitness Convergence Plot for N = 50
0 2000 4000 6000 8000 10000 12000 14000
0
2
4
6
8
10
12
14
16
Generation
Fitness
Convergence Plot for N = 50
6. Non-Uniform Circular Array Synthesis Using Teaching Learning Based Optimization
DOI: 10.9790/2834-10333844 www.iosrjournals.org 43 | Page
Table 1: Amplitude distribution for non-scanned and scanned beams
S.No
Number
of
Elements
Amplitude coefficients without Beam scanning Amplitude coefficients with Beam scanning (200
)
1 20
0.99887 0.39963 0.25807 0.51404 0.31376
0.15784 0.01000 0.12706 0.42894 0.16340
0.55307 0.53109 0.23445 0.03295 0.13247
0.17522 0.05017 0.27300 0.44578 0.24161
0.48302 0.58274 0.65326 0.06680 0.33814
0.09420 0.12040 0.22002 0.25096 0.27596
0.53185 0.90125 0.33578 0.55301 0.01028
0.60786 0.07332 0.11553 0.22953 0.33421
2 30
0.59518 0.41890 0.42279 0.24161 0.24185
0.40210 0.03291 0.01399 0.16903 0.29305
0.15150 0.28982 0.18305 0.44031 0.74780
0.30610 0.54546 0.29218 0.11367 0.06665
0.08031 0.43144 0.34637 0.10481 0.19880
0.19504 0.52639 0.49467 0.11814 0.95539
0.15595 0.20247 0.12306 0.02868 0.19924
0.03218 0.08625 0.07028 0.01201 0.06227
0.03291 0.10504 0.07812 0.06492 0.10860
0.17364 0.07991 0.19824 0.29498 0.07164
0.03555 0.07257 0.03983 0.05520 0.07366
0.06462 0.03993 0.03872 0.02722 0.05548
3 40
0.64067 0.76952 0.69126 0.13673 0.52632
0.48691 0.09877 0.27575 0.03409 0.03819
0.55009 0.01000 0.34868 0.07318 0.13483
0.26571 0.39519 0.65351 0.39117 0.63488
0.27555 0.45659 0.72464 0.43303 0.20735
0.01175 0.15885 0.19189 0.06702 0.11686
0.24899 0.02694 0.57324 0.19424 0.37058
0.38721 0.40950 0.65085 1.00000 0.19946
0.59218 0.44869 0.57375 0.60308 0.25032
0.23286 0.17171 0.04818 0.05364 0.37777
0.05188 0.16567 0.09223 0.42654 0.11095
0.03841 0.54389 0.25614 0.62593 0.76522
0.51171 0.59347 0.80309 0.44418 0.39216
0.71987 0.41901 0.08146 0.35873 0.11769
0.24993 0.01562 0.08853 0.12222 0.07807
0.18186 0.25073 0.42319 0.17460 0.20565
4 50
0.90792 0.62133 0.22757 0.49018 0.36449
0.28283 0.14071 0.34417 0.29222 0.38332
0.21530 0.52455 0.47622 0.12033 0.09200
0.13557 0.72302 0.53800 0.01000 0.40289
0.73890 0.20181 0.87449 0.36330 0.93152
0.63849 0.66351 0.46902 0.86279 0.24310
0.77243 0.21307 0.14805 0.03081 0.10077
0.29064 0.18872 0.36722 0.34743 0.15887
0.33854 0.43397 0.02634 0.31201 0.06549
0.20791 0.87053 0.68613 0.79435 0.15358
0.18263 0.22499 0.36861 0.44848 0.27281
0.29385 0.15210 0.30748 0.31313 0.18843
0.01050 0.16650 0.15030 0.04802 0.16277
0.09473 0.14521 0.10977 0.05435 0.02987
0.06483 0.14682 0.11161 0.20323 0.27789
0.13567 0.31271 0.21375 0.13602 0.22442
0.36722 0.32789 0.22441 0.15175 0.17386
0.06055 0.07194 0.03687 0.23440 0.02319
0.08191 0.27199 0.03827 0.05135 0.13643
0.10240 0.20273 0.09185 0.22548 0.28261
VI. Conclusion
The circular array synthesis is formulated as an optimization problem with control over the two
conflicting parameters known as SLL and BW. The BW is maintained at magnitude of that of the uniform
circular array with SLL much less than the uniform case. TLBO algorithm is successfully applied to determine
the coefficients required to obtain desired side lobe level of -15 dB and simultaneously scanning the main beam
to desired direction in radiation pattern of non-uniform circular arrays. The table gives the amplitude
distribution for both scanned and non-scanned cases.
References
[1]. C.A Balanis, Antenna Theory: Analysis and Design, 2nd
ed. Singapore, John Wiley and Sons (Asia), 2003.
[2]. P. S. R. Chowdary, A. Mallikarjuna Prasad, P. Mallikarjuna Rao, and Jaume Anguera, " Simulation of
Radiation Characteristics of Sierpinski Fractal Geometry for Multiband Applications," International
Journal of Information and Electronics Engineering vol. 3, no. 6, pp. 618-621, 2013.
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