Recent Patents on Computer Science - Volume 12, Issue 2, 2019

Background: A spiral phase rear mounted masked scheme is proposed based on Gyrator Transform (GT) to enhance the security contribution of second lens of the existing Double Random Phase Encoding (DRPE) system by modulating the phase of the output obtained in output plane. An additional third layer of Spiral Phase Mask (SPM) is included in the output plane in the same 4f system. Objective: To develop a symmetric cryptosystems to enhance the security potential of the second lens and to prevent the comfortable realization of the cipher-image in the transform domain. Methods: The original image is first scrambled using Arnold transform with frequency and then is convoluted with a secret random phase mask, RPM in GT and then obtained result is convoluted with another secret RPM in inverse GT. The obtained result is then finally convoluted with SPM. Results: It verifies the sensitivity and achieves better performance in terms of recovering a high quality image. Results show the security, performance and quality analysis on the basis of correlation coefficient, occlusion attack, key sensitivity and noise attack, entropy and histogram. Conclusion: It enhances the security potential of second lens in DRPE and introduces diffusion in the system. The system is simulated for binary and greyscale image and achieves better performance as compared to existing DRPE variants. Key sensitivity is more secure and cannot recover original image without knowing all the parameters. Correlation coefficient are also weakly correlated and does not reveals relevant information. Simulation result demonstrates the feasibility and robustness of cryptosystem.

Background: The Planar Inverted-F Antenna (PIFA) is most widely used for wireless communication applications due to its unique properties as low Specific Absorption Rate, low profile geometry and easy fabrication. In literature a number of multiband PIFA designs are available that support various wireless applications in mobile communication, satellite communication and radio frequency field. Methods: In this paper, a miniature sized planar inverted-F antenna has been proposed for dual-band operation. The antenna consists of an asymmetrical pentagonal shaped patch over an FR4 substrate. The overall antenna dimension is 10 × 10 × 3 mm3 and resonates at 5.7 GHz frequency. A modification is done in the patch structure by introducing an asymmetrical pentagon slot. Results: The proposed pentagonal antenna resonates at 5.7 GHz frequency. Further, modified antenna resonates at two bands. The lower band resonates at 5 GHz and having a bandwidth of 1.5 GHz. This band corresponds to C-band, which is suitable for satellite communication. The upper band is at 7.9 GHz with a bandwidth of 500 MHz. Performance parameters such as return loss, VSWR, input impedance and radiation pattern are obtained and analysed using ANSYS High- Frequency Structure Simulator. The radiation patterns obtained are directional, which are suitable for mobile communication. Conclusion: The antenna is compact in size and suitable for radar, satellite and vehicular communication.

Background: The last three decades were marked by a spectacular evolution of CPUs. Both cores number on chip and shared Low Level Cache (LLC) size are increasing what makes LLC the bottleneck's system. One major weakness of future cache memory hierarchies will be to carry out memory blocks availability for vertical requests, with no consideration to horizontal proximity to cores. Simulations show that some LLC accesses cost more latency cycles than off-chip accesses. Objective: This paper presents a new adaptive and blocks behavior aware process, called NUCA-2A. It manages blocks in LLC in a purpose of reducing it's latency, and it's inner bandwidth, by studying each block's behavior, and by placing it in the most suitable location among LLC banks. Methods: LLC accesses are classified basing on each one's specific behavior. Authors establish also a two levels horizontal hierarchy in LLC. This work consists to place blocks in the zones that matches the best their behaviors. Results: In contrast to the classic S-NUCA scheme, NUCA-2A makes a reduction of up to 60,39% of global LLC latency as well as 40,74% of average inner traffic. It makes also an average speedup of 17,89 % in term of number of instructions executed by cycle. Conclusion: Behaviors study gives encouraging results. Several methods are in use in different fields to forecast a behavior basing on previous observations. We are working on a prefetching model that permits blocks migration to and from privileged banks.

Background: The World Wide Web houses an abundance of information that is used every day by billions of users across the world to find relevant data. Website owners employ webmasters to ensure their pages are ranked top in search engine result pages. However, understanding how the search engine ranks a website, which comprises numerous web pages, as the top ten or twenty websites is a major challenge. Although systems have been developed to understand the ranking process, a specialized tool based approach has not been tried. Objective: This paper develops a new framework and system that process website contents to determine search engine optimization factors. Methods: To analyze the web page dynamically by assessing the web site content based on specific keywords, elimination method was used in an attempt to reveal various search engine optimization techniques. Conclusion: Our results lead to conclude that the developed system is able to perform a deeper analysis and find factors which play a role in bringing the site on the top of the list.

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam's optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

Background: DNA and Protein sequences of an organism contain a variety of repeated structures of various types. These repeated structures play an important role in Molecular biology as they are related to genetic backgrounds of inherited diseases. They also serve as a marker for DNA mapping and DNA fingerprinting. Efficient searching of maximal and super maximal repeats in DNA/Protein sequences can lead to many other applications in the area of genomics. Moreover, these repeats can also be used for identification of critical diseases by finding the similarity between frequency distributions of repeats in viruses and genomes (without using alignment algorithms). Objective: The study aims to develop an efficient tool for searching maximal and super maximal repeats in large DNA/Protein sequences. Methods: The proposed tool uses a newly introduced data structure Induced Enhanced Suffix Array (IESA). IESA is an extension of enhanced suffix array. It uses induced suffix array instead of classical suffix array. IESA consists of Induced Suffix Array (ISA) and an additional array-Longest Common Prefix (LCP) array. ISA is an array of all sorted suffixes of the input sequence while LCP array stores the lengths of the longest common prefixes between all pairs of consecutive suffixes in an induced suffix array. IESA is known to be efficient w.r.t. both time and space. It facilitates the use of secondary memory for constructing the large suffix-array. Results: An open source standalone tool named MSR-IESA for searching maximal and super maximal repeats in DNA/Protein sequences is provided at https://github.com/sanjeevalg/MSRIESA. Experimental results show that the proposed algorithm outperforms other state of the art works w.r.t. to both time and space. Conclusion: The proposed tool MSR-IESA is remarkably efficient for the analysis of DNA/Protein sequences, having maximal and super maximal repeats of any length. It can be used for identification of well-known diseases.

Background: In just last two-years most of the world's video data has been created by the digital devices like smartphone, surveillance cameras, and wireless sensor networks. These days the IEEE 802.11n enabled devices for Wireless Local Area Networks (WLANs) are being used to achieve higher throughput at the Medium Access Control (MAC) layer. Methods: In this research, the Dynamic Aggregation Mechanism (DAM) algorithm is proposed for H.264/SVC video transmission over IEEE 802.11n WLANs. The modified Network Simulator (NS2) was used to examine the quality of received H.264/SVC video over 802.11n WLAN. For quality measurement, PSNR was used for all nine H.264/SVC video traffic. Results: The proposed mechanism improved the PSNR for received video.

Background: Social media texts are often highly unstructured in accordance with the presence of hashtags, emojis and URLs occurring in abundance. Thus, a sentiment or emotion analysis on these kinds of texts becomes very difficult. The difficulty increases even more when such texts are in local languages like Arabic. Methods: This work utilizes novel deep learning architectures in the form of character-level Convolutional Neural Network (CNN) module and the word-level Recurrent Neural Network (RNN) module to produce a hybrid architecture that makes use of the character level analysis and the word level analysis to obtain state-of-the-art results on a totally new Arabic Emotions dataset. Results: The proposed method works the best among the traditional bag-of-words and Term Frequency and Inverse Document Frequency methods for emotion analysis. It also outperforms the state-of-the-art deep learning methods which are known to perform very well in an English corpus. Conclusion: The proposed deep end-to-end architecture utilizes the character level information from a text through the Character CNN Module and the word level information from a text through the Word-Level RNN Module.