Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.

Implementation of ID-based Audit Protocols to Enhance Security and Productivity

Cloud storage has gained increasing popularity,as it helps cloud users arbitrarily store and access the related outsourced data.Numerous public audit buildings have been presented to ensure data transparency.However,modern developments have mostly been constructed on the public key infrastructure.To achieve data integrity,the auditor must first authenticate the legality of the public key certificate,which adds to an immense workload for the auditor,in order to ensure that data integrity is accomplished.The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server.One of the main problems for individuals,though,is how to detect data integrity on a term where people have a backup of local files.Meanwhile,a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved.In this work,a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols.The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity.The idea has already been proved safe;its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.

Investigation on Structural, Optical and Thermal Properties of Diphenyl Urea—An Organic Non-Linear Optical Crystal

Organic non-linear optical crystal diphenyl urea with molecular formula C13H12N2O was synthesized and grown successfully by slow evaporation solution growth technique. The single crystal X-ray diffraction (XRD) confirms that it crystallizes in orthorhombic crystal system with non-centrosymmetric space group Pna21. The various functional groups were identified qualitatively by Fourier transform-infra red (FT-IR) and FT-Raman techniques. The electron absorption spectrum was studied by UV-Vis spectrophotometer. Thermal behavior of the crystal was evidenced by thermogravimetric (TG) and differential scanning calorimetric (DSC) analyses. From DSC the melt ing point of the crystal is found to be 145°C. The existence of second harmonic generation (SHG) signal was evidenced using Kurtz Perry powder test and the efficiency of the crystal was found to be 0.64 times that of the standard KDP crystal.

Growth and Characterization of 8-Hydroxy Quinoline Nitrobenzoate

In this work we report the newly formed crystal structure of 8-Hydroxy quinoline nitro benzoate. The systematic investigation has been carried out on the growth and characterizations with a view of using this organic material in semiconductor devices apart from its various biological applications. Single crystals of 8-Hydroxy Quinoline Nitro Benzoate (8-HQNB) were grown successfully by slow evaporation solution growth technique. The new formation of the crystal with molecular formula C16H14N2O6 is confirmed by single crystal X-ray diffraction analysis. The crystallographic data has been deposited in Cambridge Crystallographic Data Centre [CCDC NO. 1005192]. Fourier Transform Infra Red (FTIR) spectroscopic analysis confirms the functional groups of the newly confirmed crystal. The UV-Vis-NIR studies reveal that there is no remarkable absorption in the visible region which proves its suitability for optical applications.

Optimized Features Extraction of IRIS Recognition by Using MADLA to Ensure Secure Authentication

Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.

A Modified PFD Based PLL with Frequency Dividers in 0.18-µm CMOS Technology

This paper introduces a modified design of CMOS dynamic Phase Frequency Detector (PFD). The proposed PFD circuit (PPFD) is designed, simulated and the results obtained are analyzed. In order to reduce dead zone, internal signal routing is used in the PPFD circuit. To extend, Phase Locked Loop (PLL) is designed and it is verified with two different Frequency Divider (FD) circuits. There is a decrease in area of the PPFD circuit with 16 transistors and dissipates power of 40.8 pW for 1.2 V power supply. The pre-layout simulation result shows that the PPFD circuit has an elimination of a dead zone. Further, it works with the high speed and reduced power operated in the reference frequency of 50 MHz and the feedback frequency up to 4 GHz.

A Universal BIST Approach for Virtex-Ultrascale Architecture

Interconnected cells,Configurable Logic Blocks(CLBs),and input/output(I/O)pads are all present in every Field Programmable Gate Array(FPGA)structure.The interconnects are formed by the physical paths for connecting the blocks.The combinational and sequential circuits are used in the logic blocks to execute logical functions.The FPGA includes two different tests called interconnect testing and logical testing.Instead of using an additional circuitry,the Built-in-Self-Test(BIST)logic is coded into an FPGA,which is then reconfigured to perform its specific operation after the testing is completed.As a result,additional test circuits for the FPGA board are no longer required.The FPGA BIST has no area overhead or performance reduction issues like conventional BIST.A resource-efficient testing scheme is essential to assure the appropriate operation of FPGA look-up tables for effectively testing the functional operation.In this work,the Configurable Logic Blocks(CLBs)of virtex-ultrascale FPGAs are tested using a BIST with a simple architecture.To evaluate the CLBs’capabilities including distributed modes of operation of Random Access Memory(RAM),several types of configurations are created.These setups have the ability to identify 100%stuck-at failures in every CLB.This method is suitable for all phases of FPGA testing and has no overhead or performance cost.