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Akshay Dalal

Bio: Akshay Dalal is an academic researcher from Rajiv Gandhi College of Engineering. The author has contributed to research in topics: Injection locking & Phase detector. The author has an hindex of 1, co-authored 2 publications receiving 16 citations.

Papers
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Proceedings ArticleDOI
26 Feb 2015
TL;DR: The proposed micro strip-feed antenna consists of glass shaped with defected ground structure for cognitive radio application and uses FR-4 substrate having thickness of 1.6mm and dielectric constant of 4.4.
Abstract: The proposed micro strip-feed antenna consists of glass shaped with defected ground structure for cognitive radio application. The proposed antenna uses FR-4 substrate having thickness of 1.6mm and dielectric constant of 4.4. By using two switches a combination of wideband and narrowband antennas into the same substrate is designed. The antenna can be used as a wideband antenna when both switches are in on position. Micro strip-fed planer glass-shaped monopole operates from 2.6 GHz to 12.6 GHz which is used as a sensing antenna. The narrowband antenna designed to operate from 9.4- 12.08GHz (s1 on, s2 off), 6.68-9.4GHz, 10.76-12.37GHz (s1 off, s2 on), 9.62-12.62GHz, 4.09-4.2GHz (s1 off, s2 off). The proposed antenna was designed by using software HFSS 13.0 (Higher frequency simulation software).

16 citations

Journal ArticleDOI
18 May 2022
TL;DR: Krishi Mandi website is providing assistance to farmers sell their agriculture products directly to wholesaler and any food processing company, retail stores and institutional buyers as discussed by the authors , so that the farmers and retailer can get the better price of agriculture products.
Abstract: Krishi Mandi website is providing assistance to farmers sell their agriculture products directly to wholesaler and any food processing company, retail stores and institutional buyers. So, that the farmers and retailer can get the better price of agriculture products. The farmer will be able to get the best value for his crops. Farmer can know about government subsidies. Farmer will get information about crop yields and tools. And agri manufacturer/distributor can sell agri machines, agri seed directly to farmers. Farmer as well as manufacturing company will be able to advertise their product through giving product detail, picture, video.
Proceedings ArticleDOI
26 Feb 2015
TL;DR: A comparison for the lock time and the capture ranges is made between two technologies i.e. 0.18 μm and 50nm and it is found that there is great reduction in the locking time when 50nm technology is used.
Abstract: Phase locked loop (PLL) is a control system that generates a signal that has a fixed relation to the phase of a reference signal. The performance of PLL is primarily dependent on the lock time, it is the time the PLL takes to adapt and settle after a sudden change of the input signal frequency. It is desired to design a novel fast locking digital PLL. The high speed high throughput applications needed for information technology demand that the lock time should be as small as possible. The fast locking digital PLL proposed is consisting of two main stages, namely fine stage and coarse stage. The fine stage is having a phase detector, a multiple charge ump and voltage control oscillator while the coarse stage mainly consists the fast locking algorithm. It is having a frequency comparator array, a 3:8 decoder and an encoder. The PLL designed is having a centre frequency 500MHz with 20% deviation. The efforts are made to reduce the lock time and to achieve a high degree of accuracy in the work. A comparison for the locking time and the capture ranges is made between two technologies i.e. 0.18 µm and 50nm. It is found that there is great reduction in the locking time when 50nm technology is used. The locking time is found around 60 to 70nsec. The comparison is also made between the locking times of respective conventional and fast locking digital phase locked loop. The simulations are done using Advance Design System (ADS) software.

Cited by
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Journal Article
01 Jan 2010-Scopus
TL;DR: In this article, two designs of an ultra-wideband antenna with reconfigurable band notches are presented, one based on several nested complementary split-ring resonators and the other based on two identical split ring resonators.
Abstract: In this paper, two designs of an ultra-wideband antenna with reconfigurable band notches are presented. The first design is based on several nested complementary split-ring resonators, whereas the second has two identical split-ring resonators. Electronic switches mounted across or along these resonators serve to activate or deactivate their corresponding band notches, thus leading to band-notch reconfigurability. Prototypes of the two designs are fabricated and tested, and their results are compared.

34 citations

Journal ArticleDOI
TL;DR: The proposed hybrid fingerprint extracting using simhash (SH) and Huffman coding (HC) algorithms and GWO based clustering method for de-duplication is compared with the existing methods in terms of accuracy, True Positive Rate (TPR), True negative rate (TNR) and performance time.
Abstract: This paper intends to perform de-duplication for enhancing the storage optimization. Hence, this paper contributes by proposing a hybrid fingerprint extracting using simhash (SH) and Huffman coding (HC) algorithms. Secondly, the data is clustered using the latest technique called as grey wolf optimization (GWO) to extract the metadata. The extracted metadata is stored in metadata server which provides better storage optimization and de-duplication. Euclidean distance based GWO is adopted as it provides minimum Euclidean distance in the GWO based clustering for de-duplication. The proposed GWO based clustering method is compared with the existing methods such as k-means, k-mode, Euclidean distance based Particle Swarm Optimization and Euclidean distance based genetic algorithm in terms of accuracy, True Positive Rate (TPR), True Negative Rate (TNR) and performance time and the significance of the GWO based clustering method is described.

14 citations

Journal ArticleDOI
TL;DR: Self-Adaptive Grey Wolf Optimization (GWO) is proposed for optimizing 2-dimensional Logistic Chaotic Mapping (2DCM) and the experimental result stated that the proposed method is key sensitive and opposed to the general attacks during the encryption and decryption of images.
Abstract: The development of encryption and decryption plays a vital role in the field of security. Recently, Chaos-based security has suggested the reliable and efficient way of securing the images. In this paper, Self-Adaptive Grey Wolf Optimization (GWO) is proposed for optimizing 2-dimensional Logistic Chaotic Mapping (2DCM). Further, the security analysis of the proposed method is performed using different comparison such as key sensitivity, histogram analysis, adjacent pixel autocorrelation, information entropy, attacks, quality of encryption, Chi square test etc. Moreover, analytical outcomes are compared with the conventional algorithms like standard encryption and decryption, Genetic Algorithm (GA) and GWO. The experimental result stated that the proposed method is key sensitive and opposed to the general attacks during the encryption and decryption of images.

13 citations

Journal ArticleDOI
TL;DR: A hybrid algorithm termed as genetically modified glowworm swarm is used for both data sanitization and data restoration process, and the dominance of the developed model is proved.
Abstract: Cloud computing is a computing paradigm that provides vibrant accessible infrastructure for data, application and file storage as well. This technology advancement benefits in a significant lessening of consumption cost, application hosting, content storage as well as delivery, and hence the concept appear gradually more in all entities that exploited in the healthcare sector. Under such circumstances, efficient analysis and data extraction from a cloud environment is more challenging. Moreover, the extracted data has to be preserved for privacy. To handle these challenges, this paper has come out with a privacy-preserving algorithm in both data sanitization and data restoration process. Further, several researchers have contributed advancement in the restoration process, yet the accuracy of restoration seems to be very low. As a solution to this problem, this paper uses a hybrid algorithm termed as genetically modified glowworm swarm for both data sanitization and data restoration process. Further, the developed hybridization model compares its performance with other conventional models like conventional glowworm swarm optimization, firefly, particle swarm optimization, artificial bee colony, crow search, group search optimization and genetic algorithm in terms of statistical analysis, sanitization and restoration effectiveness, convergence analysis and key sensitivity analysis, and the dominance of the developed model is proved.

13 citations

Journal ArticleDOI
TL;DR: This model tells the severity of retinopathy from the given fundus image and compares its performance over other conventional classifiers like support vector machine (SVM), k nearest neighbor (k-NN) and Navies Bayes (NB).
Abstract: Till now, the detection of diabetic retinopathy seems to be one of the sensitive research topics since it is related to health care of any individual A number of contributions in terms of detection already exists in the dice; still, there present some problems regarding the detection accuracy This issue motivates to develop a new detection model of diabetic retinopathy, and moreover, this model tells the severity of retinopathy from the given fundus image The proposed model includes preprocessing, segmentation, feature extraction and classification stages Here, Triplet Half band Filterbank (THFB) Segmentation is performed, local vector pattern (LVP) is used for extracting the features, principle component analysis (PCA) procedure is used to reduce the dimensions of the feature vector, and neural network (NN) is used for classification purpose The proposed model compares its performance over other conventional classifiers like support vector machine (SVM), k nearest neighbor (k-NN) and Navies Bayes (NB) in terms of positive and negative measures The positive measures are accuracy, specificity, sensitivity, precision, negative predictive value (NPV), F1-Score and Matthews Correlation Coefficient (MCC) Similarly, the negative measures are the false positive rate (FPR), false negative rate (FNR) and false discovery rate (FDR), and the efficiency of the proposed model is proven

12 citations