In digital systems design, the high-speed adders play an essential role in modules like adders, multipliers, division, etc. to obtain the response in quickly. In digital signal processor and computer datapath circuits, carry look-ahead adder (CLA), carry save adder (CSA), carry select adder (CSeA) and carry skip adder are the mostly used fast adders to improve the performance of the system. This work mainly focuses on fault-tolerant carry save adder using hardware redundancy configurations for the betterment of reliability at the cost of the area. The popular methods like dual (DMR) and triple modular redundancy (TMR) are implemented to detect an error and tolerate single event upset (SEU) error respectively. To tolerate multiple errors, for example, two, three and four errors, the 5-modular (quintuple), 7-modular (septuple), and 9-modular (nonuple) redundancy configurations are used respectively. For experimental results, the implementation of a simple addition of four 4-bit numbers is considered on Altera FPGA EP4CE115F29C7 device using Quartus II synthesis software tool. Simulation results reveal that for example in nonuple MR, CSA using CSeA final stage obtain higher performance (45.7 MHz) with moderate power dissipation as 173.36 mW and at the cost of more LEs (216).

Abstract: Background: Nowadays, image forgery is a major issue for digital images when used in surveillance purposes, as evidence in court and crime investigation. In spite of this image forgery, yet no one system exists to accomplish the image tampering detection with high. Aim / Objective: to improve the trustworthiness to assess the digital images in the field of blind image forensics, this work has been focused Copy-Move based image Forgery Detection (CMFD) and classification using Improved Relevance Vector Machine (IRVM). Methodology Initially, the input images are divided into overlapping blocks. Then, Biorthogonal Wavelet Transform with Singular Value Decomposition (BWT-SVD) is applied to extract the feature vector from the blocks. After that, the feature vectors are sorted in lexicographically and the duplicate vectors are identified by similarity between two successive vectors. To decide the similarity of vectors, Minkowski distance and Threshold value is used. Finally, the forgergy detected and classified as authentic image and forged image by IRVM and it will increase the accuracy of process. Results: the performance of proposed scheme evaluated by using CoMoFoD database which is collected from internet and the simulation results shows that the proposed IRVM scheme attained high performance with high accuracy rate of 92.22%, sensitivity rate of 88.4%, specificity rate of 97.6%, F-measure rate of 92.24%, G-mean rate of 92.31%, precision rate of 96.87% and recall rate of 88.4% compared than existing CMFD based Support Vector Machine (SVM) classifier and Hybridization of Hidden Markov Model (HMM) and SVM Classifier in MATLAB environment.

Collembola family abundance from three localities with different land-use types were studied: forest, agroforestry, grassland and corn crop, all located in the Santa Marta Range, Los Tuxtlas Biosphere Reserve, Veracruz. Litter and soil samples were taken in dry season (February and March 2005) in each land use and processed by Berlese-Tullgren funnels. Soil physical and chemical parameters were measured. A nested MANOVA was applied to evaluate land-use and site effect on edaphic parameters, and a nested ANOVA was use to evaluate their effect on the Collembola abundance. Also a Cluster Analysis and Canonical Correspondence Analysis (CCA) were applied, as well Shannon diversity index was calculated. A total of 1,088 collembolans from seven families were gathered, with the most abundant being Isotomidae, Entomobryidae and Hypogastruridae. Nested MANOVA and ANOVA revealed significant effect of the site and land-use on the soil parameters and Collembola abundance, respectively. CA formed two main groups based mainly on sites and biotopes. The CCA, show that abundance of Onychiuridae and Odontellidae are related with altitude, Na and CEC (Cation Exchange Capacity). Diversity was higher in forest than corn crop and grassland. The corn crop showed a higher incidence of Isotomidae and Entomobryidae than the other sites.