A Survey on Flip Flop Replacement to Latch on Various Design

This paper presents survey for the replacement of flip flop to latches and the advantages of the latch based sequential design Flip flop are the major part of the design a sequential elements and this flip flop has more disadvantages as performance decreases and area increases. An alternate method to increase the performance and reduce the area size latches. Latches are used instead of flip flops in certain places to increase the performance and decrease the area

A Survey on Flip Flop Replacement to Latch on Various Design

This paper presents survey for the replacement of flip flop to latches and the advantages of the latch based sequential design Flip flop are the major part of the design a sequential elements and this flip flop has more disadvantages as performance decreases and area increases. An alternate method to increase the performance and reduce the area size latches. Latches are used instead of flip flops in certain places to increase the performance and decrease the area

Optimized Ensemble Approach for Multi-model Event Detection in Big data

Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately

A Review on Software Performance Analysis for Early Detection of Latent Faults in Design Models

Organizations and society could face major breakdown if IT strategies do not comply with performance requirements. This is more so in the era of globalization and emergence of technologies caused more issues. Software design models might have latent and potential issues that affect performance of software. Often performance is the neglected area in the industry. Identifying performance issues in the design phase can save time, money and effort. Software engineers need to know the performance requirements so as to ensure quality software to be developed. Software performance engineering a quantitative approach for building software systems that can meet performance requirements. There are many design models based on UML, Petri Nets and Product-Forms. These models can be used to derive performance models that make use of LQN, MSC, QNM and so on. The design models are to be mapped to performance models in order to predict performance of system early and render valuable feedback for improving quality of the system. Due to emerging distributed technologies such as EJB, CORBA, DCOM and SOA applications became very complex with collaboration with other software. The component based software systems, software systems that are embedded, distributed likely need more systematic performance models that can leverage the quality of such systems. Towards this end many techniques came into existence. This paper throws light into software performance analysis and its present state-of-the-art. It reviews different design models and performance models that provide valuable insights to make well informed decisions

Identity-Based Cryptosystem Based on Tate Pairing

Tate Pairings on Elliptic curve Cryptography are important because they can be used to build efficient Identity-Based Cryptosystems as well as their implementation essentially determines the efficiency of cryptosystems In this work we propose an identity-based encryption based on Tate Pairing on an elliptic curve The scheme was chosen ciphertext security in the random oracle model assuming a variant of computational problem Diffie-Hellman This paper provides precise definitions to encryption schemes based on identity it studies the construction of the underlying ground field their extension to enhance the finite field arithmetic and presents a technique to accelerate the time feeding in Tate pairing algorith

Beneficiation studies on beach placer sample for steel making industries

Beneficiation studies were carried out on the Talashil beach placer sample of South Maharastra Coast, India. The sample contains magnetite, ilmenite, rutile, hematite, goethite and chromite as opaque minerals in the sample. The total heavy minerals fraction reaches 53.8 % by weight whereas the total magnetic minerals are 56.9%. It is observed that the 2nd stage DHIMS magnetic fraction contains 65.2 % Fe2O3 with an over all yield of 37.8 % and a 86 % recovery from a containing 26.8 % Fe2O3 feed. This product can be used in the pellet feed for steel making after suitable blending with high-grade iron ore fines

Experimental evidence for fast cluster formation of chain oxygen vacancies in YBa2Cu3O7-d being at the origin of the fishtail anomaly

We report on three different and complementary measurements, namely magnetisation measurements, positron annihilation spectroscopy and NMR measurements, which give evidence that the formation of oxygen vacancy clusters is on the origin of the fishtail anomaly in YBa2Cu3O7-d. While in the case of YBa2Cu3O7.0 the anomaly is intrinsically absent, it can be suppressed in the optimally doped state where vacancies are present. We therefore conclude that the single vacancies or point defects can not be responsible for this anomaly but that clusters of oxygen vacancies are on its origin.Comment: 10 pages, 4 figures, submitted to PR