This research paper deals with a system-on-chip (SoC) architecture design where multiple processors are inbuilt with other blocks of memory and control logic developed by nanomaterials. The multiprocessing-based SoC architecture is commonly used in the latest electronic devices such as smartphones, tablets, and smart wristwatches with large memory sizes. The data handing in these highly memory-dense devices is a critical task, and it needs special attention for the smooth operation of the device. This research proposed a smart controller to exchange data between various processors and input-output devices to tackle this challenge. A proposed controller block controls the data flow between memory and different SoC components and processors. A memory access controller (MAC) is presented in this research study to manage and accelerate data transmission speed and reduce the processors’ activity for SoC-based devices. The proposed MAC will integrate into the SoC with multiprocessing units, including gaming processors, at minimum hardware overhead and low power consumption. It improves the memory accessing efficiency and reduces the processors’ activity of a system. As a result, the system’s performance and power consumption improve at an acceptable level compared with the other conventional methods. This research is aimed at enhancing the performance of any SoC-based device where multiprocessing engines are inbuilt and flexible enough to serve various SoCs.
Food crop classification and identification are crucial aspects of modern agriculture. With progression of drones or unmanned aerial vehicles (UAVs), crop detection from RGB images goes through a paradigm shift from traditional image processing methods to deep learning (DL) methods due to effective breakthroughs in convolutional neural networks (CNN). Drone images are reliable for identifying different crops because of its higher spatial resolution. Food crop classification utilizing deep learning on drone images includes machine learning techniques for distinguishing and identifying different types of crops in images captured by UAVs. It is beneficial for various applications, like crop monitoring and precision agriculture. This paper presents a new Satin Bowerbird Optimization with deep learning for Food Crop Classification (SBODL-FCC) technique on UAV images. The presented SBODL-FCC technique exploits DL models with hyperparameter optimizers for food crop classification on UAV images. To accomplish this, the presented SBODL-FCC technique employs adaptive bilateral filtering technique for image preprocessing. Besides, the SBODL-FCC technique uses MobileNetv2 feature extractor with Bayesian optimization (BO) algorithm for parameter optimization. Moreover, the food crop classification process is performed through convolutional long short-term memory (ConvLSTM) model. Furthermore, the hyperparameter tuning of the ConvLSTM method is accomplished through SBO algorithm. The experimental validation of the SBODL-FCC technique is validated on UAV image database and the results are inspected under different aspects. The simulation outcomes inferred that the SBODL-FCC technique reaches better performance over other models in terms of several performance measures.
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