In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

Optimally designed arrays are used to obtain high gain and directivity. Based on the desired application, different radiation patterns can be generated. These patterns have generally a main beam and some sidelobes. These last ones are undesirable because they can cause electromagnetic interference and compatibility issues. Therefore, sidelobe levels (SLLs) reduction is one of the most important factors for the design of antenna arrays and arrays must be designed in a way to reduce SLLs. In this paper, the optimal design of circular antenna arrays (CAA) for maximum SLLs reduction is investigated using a newly developed metaheuristic that is the most valuable player algorithm. This algorithm is inspired from sport and it has been proven to be an excellent optimization algorithm for mathematical benchmarks. Three cases with different number of arrays are investigated. For comparison purposes, ten other well-known optimization algorithms are used. The design results obtained show the superiority of the proposed algorithm for the optimal design of CAA and for sidelobe reduction over many other algorithms.

Notice of Retraction: Most Valuable Player Algorithm for Circular Antenna Arrays Optimization to Maximum Sidelobe Levels Reduction

Most Valuable Player Algorithm for Circular Antenna Arrays Optimization to Maximum Sidelobe Levels Reduction

Linear sparse antenna arrays have been widely studied in array processing literature. They belong to the general class of non-uniform linear arrays (NULAs). Sparse arrays need fewer sensor elements than uniform linear arrays (ULAs) to realize a given aperture. Alternately, for a given number of sensors, sparse arrays provide larger apertures and higher degrees of freedom than full arrays (ability to detect more source signals through direction-of-arrival (DOA) estimation). Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Different types of linear sparse arrays have been studied in the past. While minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) existed for more than five decades, other sparse arrays such as nested arrays, co-prime arrays and super-nested arrays have been introduced in the past decade. Subsequent to the introduction of co-prime and nested arrays in the past decade, many modifications, improvements and alternate sensor array configurations have been presented in the literature in the past five years (2015–2020). The use of sparse arrays in future communication systems is promising as they operate with little or no degradation in performance compared to ULAs. In this chapter, various linear sparse arrays have been compared with respect to parameters such as the aperture provided for a given number of sensors, ability to provide large hole-free co-arrays, higher degrees of freedom (DOFs), sharp angular resolutions and susceptibility to mutual coupling. The chapter concludes with a few recommendations and possible future research directions.

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Sparse Linear Antenna Arrays: A Review

Metaheuristic algorithms, in the field of engineering, have attracted researchers for problem-solving of complex and non-linear optimization. Many algorithms have been designed to address wide-ranging applications such as GSA—gravitational search algorithms (GSA), PSO—particle swarm optimization, GWO—grey wolf optimization, and various hybrid plus evolutionary algorithms. Hybrid algorithms also made for such wide-range application, but the drawback of such algorithms are convergence time is very high and challenging to implement for multiple wide range applications. Cuckoo Search (CS) is an optimization technique, developed in 2009, is a highly efficient algorithm. It is an algorithm that is based on population and is also a nature-inspired metaheuristic algorithm, which is easy to implement for such applications. The success of the algorithm has been fueled because of its characteristics, i.e. its simplicity, few parameter, ease of implementation. Cuckoos are delightful birds, which have attracted people not only because of their melodious sound but also because of their aggressive reproduction capability. The algorithm addresses two important behavioral aspects of some cuckoos i.e. brood parasitism and levy flights. The two Ani and Guira cuckoo species will all lay eggs in a communal nest. The cuckoo can then remove the eggs laid by others to improve the probability of hatching the laid eggs. Cuckoo immigration along with environmental factors make the cuckoos to find an appropriate and also a place for reproduction and breeding. Cuckoos presents the random walk Levy flight behavior, which enables the algorithm to completely explore the search space. In CS algorithm fixed number of better fitness cuckoos survive in the environment. This chapter introduces with the mathematical concept of CS algorithm and summarizes different research articles where the algorithm has been explored in the field of engineering. Furthermore, the resent version of CS algorithm are addressed which mainly focuses on modified and hybrid versions. The novelty of this chapter is that it presents current trending research aspects of CS algorithm in the field of engineering, machine and deep learning. The chapter concludes with the future direction which can be investigated using CS algorithm in the field of science and technology.

Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications

Antenna arrays have potential applications in Radar, mobile, broadcasting, astronomy and other defense and commercial platforms. The design of antenna arrays is essential as they are capable of controlling the radiation pattern in terms of sidelobe level (SLL), beamwidth (BW), nulls and beam steering. Among several geometries of antenna arrays, the circular antenna arrays have the advantage in terms of inherent capability to control the phase implicitly. In this paper, circular antenna array synthesis is presented using novel metaheuristic techniques like the Social Group Optimization Algorithm (SGOA) and Modified SGOA (MSGOA). The main objective of the synthesis process is to synthesize radiation patterns with suppressed SLL while the BW being equal to the uniform distribution. A comparative study is performed to analyze the performance of various synthesis techniques like amplitude only, inter-element spacing only, and amplitude spacing. The MGSOA and SGOA have proved to be performing well in accomplishing the objective. Further, the CAA synthesis problem is translated into single-variable and multivariable optimization problems following which the obvious advantage associated with the inclusion of additional variable as an additional degree of freedom is demonstrated. The simulation is carried out in Matlab®, and results are analyzed using the plotted radiation patterns.

Circular antenna array optimization using modified social group optimization algorithm

Study on image processing using deep learning techniques

In linear arrays the element failure is common issues which degrades the radiation characteristics. It is required to compensate to the failure and recover the pattern in terms of several radiation characteristics like sidelobe level (SLL) and beam-width (BW). In this paper, pattern recovery in linear arrays using amplitude only technique using flower pollination algorithm is presented. A 10 element linear array is used for demonstration of the recovery process and the analysis is carried out using the radiation pattern plots.

Pattern Recovery in Linear Array Antenna using Flower Pollination Algorithm

Technologies are advancing day by day after the successful implementation of 4G networks Now, mobile technology is footed into 5G communication To provide antenna solutions for 5G communications, a T-shaped multiband antenna has been proposed The T-shaped microstrip patch antenna is intended to operate on 28/38 GHz frequency T-shaped antenna has a compact size and planar geometry with high gain To increase the bandwidth of the antenna, defected ground structures are used These structures are formed by etching rectangular slots in ground

DGS-Based T-Shaped Patch Antenna for 5G Communication Applications

Array pattern synthesis is one of the most important problems in communication and radar applications. The synthesis is usually carried out by controlling amplitude and phase levels (or) element spacing functions of the array. Although, uniform linear arrays cost effective in terms of sources it is not preferred due to high presence of high first side lobe levels. For point to the point communications, and high angular resolutions radars, it is essential to generate low side lobe narrow beams. In view of these facts, a linear array is considered in the present work and it is optimally designed using CSA & APSO algorithms. The desired amplitude levels are evaluated using the algorithm with element spacing d=0.50. The main objective of this work is to minimize the sidelobe level with a constraint on beam width.

M. Vamshi Krishna

Papers

Circular antenna array optimization using modified social group optimization algorithm

Study on image processing using deep learning techniques

Pattern Recovery in Linear Array Antenna using Flower Pollination Algorithm

DGS-Based T-Shaped Patch Antenna for 5G Communication Applications

Sidelobe level reduction in linear antenna array synthesis using Cuckoo Search & Accelerated Particle Swarm algorithms