Low Complexity Multiplier-less Modified FRM Filter Bank using MPGBP Algorithm

The design of a low complexity multiplier-less narrow transition band filter bank for the channelizer of multi-standard software-defined radio (SDR) is investigated in this paper. To accomplish this, the modal filter and complementary filter in the upper and lower branches of the conventional Frequency Response Masking (FRM) architecture are replaced with two power-complementary and linear phase filter banks. Secondly, a new masking strategy is proposed to fully exploit the potential of the numerous spectra replicas produced by the interpolation of the modal filter, which was previously ignored in the existing FRM design. In this scheme, the two masking filters are appropriately modulated and alternately masked over the spectra replicas from 0 to 2$\pi$, to generate even and odd channels. This Alternate Masking Scheme (AMS) increases the potency of the Modified FRM (ModFRM) architecture for the design of a computationally efficient narrow transition band uniform filter bank (termed as ModFRM-FB). Finally, by combining the adjoining ModFRM-FB channels, Non-Uniform ModFRM-FB (NUModFRM-FB) for extracting different communication standards in the SDR channelizer is created. To reduce the total power consumption of the architecture, the coefficients of the proposed system are made multiplier-less using the Matching Pursuits Generalized Bit-Planes (MPGBP) algorithm. In this method, filter coefficients are successively approximated using a dictionary of vectors to give a sum-of-power-of-two (SOPOT) representation. In comparison to all other general optimization techniques, such as genetic algorithms, the suggested design method stands out for its ease of implementation, requiring no sophisticated optimization or exhaustive search schemes. Another notable feature of the suggested approach is that, in comparison to existing methods, the design time for approximation has been greatly reduced. To further bring down the complexity, adders are reused in recurrent SOPOT terms using the Common Sub-expression Elimination (CSE) technique without compromising the filter performance

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes