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Finite length of sequences that are modulated both in phase and amplitude and have an ideal autocorrelation function (ACF) consisting of merely a pulse have many applications in control and communication systems. They are widely applied in control and communication systems, such as in pulse compression systems for radar and deep-space ranging problems [1-5]. In radar design, the important part is to choose a waveform, which is suitable to be transmitted because the waveform controls resolution in clutter performance. In addition, it can solve a general signal problem particularly related to the digital processing. Energy ratio (ER), total side lobe energy (SLE), and peak sidelobe level (PSL) are three properties of such sequences interest. This paper presents a method using the Complementation, Cyclic Shift and Bit Addition for synthesizing and optimizing a binary sequence implemented to improve the sequences of a similar quality with the Barker sequence, particularly for lengths greater than 13. All of these methods are guided by the specific parameter with good characteristics in ACF (ER, SLE, and PSL) [6,7,8]. Such sequences can then be effectively used to improve the range and Doppler resolution of radars.

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.

Plant diseases are one of source of obstruction in the quality and productivity of plants which can lead to the shortage of food supply. Therefore, plant disease classification is essential to the agriculture industry. The objective of this research is to classify the plant diseases by assessing the images of the leaves with the application of Extreme Learning Machine (ELM), a Machine Learning classification algorithm with a single layer feed-forward neural network. This work proposed image features as input where the image is pre-processed via HSV colour space and features extraction via Haralick textures. The features are then fitted in the ELM classifier to perform the model training and testing. The accuracy of ELM is then calculated after the testing has been done. The dataset used comprises of tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM shows a better accuracy that is 84.94% when compared to other models such as the Support Vector Machine and Decision Tree.

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The image such as CT scan, x -ray image, CCTV videos and hand phone's camera is kind of low resolution image producers. Digital camera captured the continuous scenes and transform into discrete presentation in term of space and intensity. In sampling process it may create aliasing and information lost at frequency below the Nyquist sampling rates. Therefore the image suffered with an ill-posed problem by aliasing and loss of frequency. The problem ill-pose problem could be solved by applying Super Resolution (SR) techniques. The SR process contains of image registration, interpolation and image reconstruction. However this paper is focus on an analysis the best performance offered by interpolation techniques. An analysis procedure requires interpolation kernel inspection into frequency domain plotting to determine the best kernel response in pass and stop band. Otherwise use Peak Signal to Noise Ratio as indicator the similarity simulated with original image. In this study found the cubic spline interpolation is provided the smoother function frequency response with less ripples in stop band and good pass response. Besides that, it shows a superiorly in lead the highest PSNR for all type image tests with several of upscale. The best response and less distortion effect generated by kernel is preferable candidate to produce an efficient image application with low maintenances.

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