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Über dieses Buch

This, the 29th issue of the Transactions on Computational Science journal, is comprised of seven full papers focusing on the area of secure communication. Topics covered include weak radio signals, efficient circuits, multiple antenna sensing techniques, modes of inter-computer communication and fault types, geometric meshes, and big data processing in distributed environments.



Analysis of Relationship Between Modes of Intercomputer Communications and Fault Types in Redundant Computer Systems

This paper analyzes the reasons of appearance of non - Byzantine and Byzantine fault types in redundant computer systems. The proposed approach is based on analysis of the relationship between the modes of intercomputer communications and fault types. This analysis allows the users to design the redundant computer systems in such a way that Byzantine faults cannot appear. Consequently, designing the redundant computer systems, in which Byzantine faults cannot appear, allows the designers to increase the degree of reliability by preventing the masking of any forms of appearance of faults and by decreasing the time period of checkpoints. In addition, this approach decreases the cost of software and hardware involved in the execution of fault-tolerant procedures.
Refik Samet, Nermin Samet

Efficient Circuit Design of Reversible Square

In the midst of emerging technology, reversible computing is promising due to its application in the field of quantum computing. The computing hardware plays a significant role in digital signal processing (DSP) and multimedia application; one such major computing hardware is multiplier. It is a practice to choose multiplier to compute square of an operand. Multiplication hardware requires more elementary computations which leads to performance degradation in terms of reversible performance metrics like quantum cost, garbage outputs, and ancilla inputs. Ancilla inputs and garbage outputs are overhead bits in a reversible circuit. Reversible quantum computers of many qubits are extremely difficult to realize, thus we propose garbageless circuit design for reversible square computation. The proposed design methodology is based on recursion. Recursion technique is adapted from Karatsuba’s recursive method to compute square of an operand; we designed inverse computation units to retrieve the inputs and obtain garbageless circuit. On comparing proposed circuit design with existing reversible square designs and Karatsuba multiplier design, we observed that our work improves number of input lines which includes data lines and ancilla lines.
H. V. Jayashree, Himanshu Thapliyal, Vinod Kumar Agrawal

Methods of Registration of Weak Radio Signals

In this paper we will consider a problem of registration of radio signals from distant sources, natural (pulsars) or artificial (SETI signals). These signals possess a number of common properties, i.e. they are weak, almost indistinguishable from the background noise, are strongly localized on celestial sphere, have spectral characteristics smeared by dispersion on interstellar medium and Doppler drift, suffer from near-Earth electromagnetic interference. In this paper we will overview existing methods for registration of such signals and discuss some alternatives. We implement selected methods as data filters connected to data processing workflow, with 3D Virtual Environment as a frontend, integrate the methods into a system for radio astronomical monitoring StarWatch and apply them for detection of pulsar signals from BSA telescope at Pushchino Radio Astronomy Observatory and narrow band signals in SETI database (
Stanislav Klimenko, Andrey Klimenko, Kira Konich, Igor Nikitin, Lialia Nikitina, Valery Malofeev, Sergey Tyul’bashev

A Novel Multiple Antennas Based Centralized Spectrum Sensing Technique

In wireless communication, sensing failure, reliability, and fading affects the radio signals. Adaptive threshold and multiple antennas are one of the solutions of such problems. In this paper, authors introduced a novel multiple antennas based centralized spectrum sensing (SS) technique for cognitive radio networks (CRNs). This paper is divided into two parts: part A uses multiple antennas based improved sensing detector (MA_ISD), and part B uses multiple antennas based centralized spectrum sensing (MA_CSS) technique. Now, in the part A: the presented scheme uses two detectors (TD) concept, first one is an energy detector with a single adaptive threshold (ED-SAT) and the second one is an energy detector with two adaptive thresholds (ED-TAT). Both detectors imply multiple antennas, following selection combination to select best signals. The proposed model enhances the detection performance and takes less sensing or detection time. The thresholds are adaptive as they are dependent on noise variance (\( \sigma_{\omega }^{2} \)), and the value of this noise variance changes according to the noise signal. Both the detectors work simultaneously and their output is then fed to a decision device which takes the decision using an OR rule. Results confirm that the presented multiple antennas based improved sensing detector (MA_ISD) technique improves the detection performance by 24.6%, 53.4%, 37.9%, and 49.6%, as compared to existing schemes (i.e. EDT-ASS-2015 scheme, ED and cyclo-2010, adaptive SS-2012, and conventional-ED) scheme at −12 dB signal-to-noise ratio (SNR), respectively, while the number of antennas (N r ) = 2. Meanwhile, proposed technique also decreases sensing time in the order of 47.0 ms, 49.0 ms, and 53.2 ms as compared to existing schemes (EDT-ASS-2015, Adaptive SS-2012, and ED and Cyclo-2010) scheme at −20 dB SNR respectively. Further, in the part B: cooperative SS (CSS) is introduced in which the local decisions from each cognitive radio are transferred to a fusion center (FC) that decides the final decision and shares the decision to every cognitive radio. It is also found that the proposed detection technique with CSS when a number of cognitive radio (CR) users (k) = 10, and N r  = 2, achieves detection performance as per IEEE 802.22 at very low SNR i.e. −20 dB.
Jyotshana Kanti, Geetam Singh Tomar, Ashish Bagwari

ImatiSTL - Fast and Reliable Mesh Processing with a Hybrid Kernel

A novel approach is presented to deal with geometric computations while joining the efficiency of floating point representations with the robustness of exact arithmetic. Our approach is based on a hybrid geometric kernel where a floating point number is made fully interoperable with an exact rational number, so that the latter can be used only within critical parts of the program or within restricted portions of the input. The whole program can dynamically change the level of precision used to produce new values and to evaluate expressions. Around such a kernel, a mesh processing library has been implemented whose API functions can be classified depending on their precision as always exact, always approximated, or exact if the current level of precision is sufficient. Such a classification allows implementing algorithms with a full control of the robustness at an unprecedented level of granularity. Experiments show that this interoperability comes at a nearly negligible cost: on average, a test algorithm implemented on our hybrid kernel is just 8% slower than the same algorithm implemented on a standard floating point version of the same kernel while providing the possibility to be fully robust if necessary.
Marco Attene

Processing Large Geometric Datasets in Distributed Environments

We describe an innovative Web-based platform to remotely perform complex geometry processing on large triangle meshes. A graphical user interface allows combining available algorithms to build complex pipelines that may also include conditional tasks and loops. The execution is managed by a central engine that delegates the computation to a distributed network of servers and handles the data transmission. The overall amount of data that is flowed through the net is kept within reasonable bounds thanks to an innovative mesh transfer protocol. A novel distributed divide-and-conquer approach enables parallel processing by partitioning the dataset into subparts to be delivered and handled by dedicated servers. Our approach can be used to process an arbitrarily large mesh represented either as a single large file or as a collection of files possibly stored on geographically scattered servers. To prove its effectiveness, we exploited our platform to implement a distributed simplification algorithm which exhibits a significant flexibility, scalability and speed.
Daniela Cabiddu, Marco Attene

Decision Fusion for Classification of Content Based Image Data

Information recognition by means of content based image identification has emerged as a prospective alternative to recognize semantically analogous images from huge image repositories. Critical success factor for content based recognition process has been reliant on efficient feature vector extraction from images. The paper has introduced two novel techniques of feature extraction based on image binarization and Vector Quantization respectively. The techniques were implemented to extract feature vectors from three public datasets namely Wang dataset, Oliva and Torralba (OT-Scene) dataset and Corel dataset comprising of 14,488 images on the whole. The classification decisions with multi domain features were standardized with Z score normalization for fusion based identification approach. Average increase of 30.71% and 28.78% in precision were observed for classification and retrieval respectively when the proposed methodology was compared to state-of-the art techniques.
Rik Das, Sudeep Thepade, Saurav Ghosh


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