Real design practice, real design computation

The business model of architecture has been accused of being technologically deficient. Through case studies, this paper investigates alternatives to the status quo of computation in architecture, with a focus on design-oriented service providers. It first examines the discourse on computation within the field, then describes the experiments of a relevant design firm before discussing implications for the wider industry. In the process, the paper articulates the gap between theoretical computing applications and the realities of the architecture business. It addresses difficult technological adaptations within the industry with concrete use cases of integrated computing expertise, highlighting opportunities, benefits, and, more importantly, the need for a computational workforce.

Optimum range of angle tracking radars: a theoretical computing

In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other.

Permuted Pattern Matching Algorithms on Multi-Track Strings

A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth–Morris–Pratt (KMP) algorithm, has a fast theoretical computing time with O ( m k ) as the preprocessing time and O ( n k log σ ) as the matching time, where n, m, k, σ , and denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer–Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice.

Impact of Big Data over Telecom Industry

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

Membrane Computing

The theoretical computing models that are used throughout this book are described in this chapter. These models are based on the initial P system model and include: Numerical P systems, Enzymatic Numerical P systems, P colonies and P swarms. Detailed examples and execution diagrams help the reader allow the reader to understand the functioning principle of each model and also its potential in various applications. The similarity between P systems (and their variants) and robot control models is also addressed. This analysis is presented to the reader in a side-by-side manner using a table where each row represents an analysis topic. Among others we mention: (1) Architectural structure, (2) Modularity and hierarchy, (3) Input-output relationships, (4) Parallelism.

Optimization of Area and Delay for Implementation of the Composite Field Advanced Encryption Standard S-Box

Among different implementations of the Advanced Encryption Standard (AES) S-box, the implementation based on composite field arithmetic (CFA) has the smallest size. In this study, to eliminate the redundant gates in the implementation of CFA-based S-box, a new optimization algorithm named common subexpression elimination (CSE)–shortest critical path constructing (SCPC) algorithm is proposed. The CSE–SCPC algorithm combines an efficient CSE technology with the SCPC method, therefore not only area cost but also the delays are taken into account in the optimization process. For facilitating the search of common subexpressions (CSs), the main operation of CFA-based S-box — the multiplicative inverses (MI) over galois field (GF)((2[Formula: see text] — is divided into six parts and each part is expressed by logical expressions directly. Furthermore, the parts with same input are classified into the same group, as there are CSs among them. Each part of the S-box is optimized by the CSE–SCPC algorithm. For the MI over GF((2[Formula: see text], both the CSs in each part and the CSs among the parts are eliminated by the CSE–SCPC algorithm. Compared to the previous works, our design has not only the minimal area cost but also the shorter critical path in both theoretical computing evaluation and experimental evaluation.