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Non-orthogonal multiple access (NOMA) is a multiple access technology that can provide efficient spectrum utilization and increased channel capacity on Internet of Things (IoT) networks. One factor affecting NOMA system performance in the IoT network is the allocation of power for each NOMA user. To improve the channel capacity of the system, this paper presents a method for optimizing power allocation for NOMA in an IoT network with a Lagrange multiplier using the Karush-Kuhn-Tucker (KKT) condition. The optimization will result in an optimal power allocation solution to maximize the system channel capacity with the maximum transmit power constraint function and the minimum data rate for NOMA users. The channel capacity achieved by the system was observed in the Nakagami- $m$ fading channel with imperfect successive interference cancellation (imp-SIC). The proposed power allocation method was compared with orthogonal multiple access (OMA) and the conventional NOMA (C-NOMA) power allocation techniques. The results show that the proposed power allocation coefficient optimization solution with the Lagrange multiplier using the KKT conditions significantly increases the channel capacity of the system compared to the OMA and C-NOMA methods.

The global demand for coconut and coconut-based products has increased rapidly over the past decades. Coconut price continues to fluctuate; thus, it is not easy to make predictions. Good price modelling is important to accurately predict the future coconut price. Several studies have been conducted to predict the price of coconut using various models. One of the most important and widely used models in time series forecasting is the autoregressive integrated moving average (ARIMA). However, price fluctuations is considered a problem with uncertain behaviour. The existing ARIMA time series model is unsuitable for solving this problem, because of the nonlinear series. Artificial neural networks (ANN) have been an effective method in solving nonlinear data pattern problems in the last two decades. The non-linear autoregressive neural network (NARNET) gives good forecast, most especially when series are non-linear. Therefore ARIMA- NARNET is considered a universal approach to forecasting the coconut price. The aim of the study is to establish a linear and nonlinear model in time series to forecast coconut prices. The ability of a hybrid approach that combines ARIMA and NARNET(ANN) models is investigated. Based on the experimental study, the experimental results show that the proposed method ARIMA- NARNET, is better at forecasting the price of coconut, an agriculture commodity, than both the ARIMA model and NARNET models. The expected benefit of the proposed forecasting model is it can help farmers, exporters, and the government to maximize profits in the future.

In this article, airborne broadband (maximum 400 MHz bandwidth) C-band circularly polarized synthetic aperture radar (SAR) is proposed and developed for further study on airborne compact polarized synthetic aperture radar (CP SAR) system using circular polarization. This article explains the scattering characteristic of circular polarization as the concept of circularly polarized SAR, system configuration, RF system and antenna, ground test, and flight test of circularly polarized SAR in Hinotori-C2 (Firebird-C2) mission onboard CN235MPA aircraft on 14 and 15 March 2018 at South Celebes, Indonesia. The result of the flight test depicts multipath full polarimetric circularly polarized images that show good performance of circularly polarized SAR and matched well to the result of the ground test of a multipolarized single pulse. The circularly polarized scattering clarification using trihedral, cylindrical, and omni corner reflectors (TCR, CCR, and OCR), assessment of circularly polarized image analysis, and image classification using the conventional axial ratio (AR), ellipticity (ε), and polarization ratio (ρ) are discussed. The proposed circularly polarized SAR will enrich the existing CP SAR systems and could be employed in further study of CP SAR calibration technique, also applications development for the environment and disaster monitoring using CP SAR.

The main problem in designing a multiband bandpass filter (BPF) is making each passband response highly independent, where each bandwidth of multiband BPF can be controlled and adjusted separately. To overcome this problem, this paper proposes a highly independent multiband BPF based on a multicoupled line stub-SIR with a folding structure. The proposed multiband BPF is constructed as a multicoupled line to generate a highly independent inter-passband. Moreover, the multiband performance is produced separately and independently by using three sets of resonators: resonator A1/A2 (RA1 and RA2), resonator B1/B2 (RB1 and RB2), and resonator C1/C2 (RC1 and RC2). The three passband frequencies can be independently arranged and designed. To miniaturize the multiband BPF, a folding structure is also proposed. As a result, the multiband BPF has a compact size that is reduced by over 61.29 % compared to previous structures. The even-odd excitation model and the equivalent circuit model are used to analyze the multiband BPF structure. This BPF is designed for GPS applications at 1.57 GHz, WCDMA (3G) at 1.8 GHz, WLAN (WiFi) at 2.4 GHz, LTE (4G) at 2.6 GHz, and 5G communication at 3.5 GHz. To evaluate and validate the proposed structure of the multiband BPF, the circuits are fabricated and tested. The simulated and measured results of the multiband BPF show good agreement. In conclusion, the proposed multiband BPF structure has a highly independent inter passband response and a compact size.

The design of cryptographic engines for the Internet of Things (IoT) edge devices and other ultralightweight devices is a crucial challenge. The emergence of such resource-constrained devices raises significant challenges to current cryptographic algorithms. PHOTON is an ultra-lightweight cryptographic hash function targeting low-resource devices. The currently implemented hardware architectures of PHOTON hash function utilize a large amount of resources and have low operating frequencies with a low rate of throughputs. Maximum operating frequency and throughput of PHOTON architecture can be improved but at the cost of larger area utilization. Therefore, to improve the area-performance trade-offs of PHOTON hash function, an iterative architecture is implemented in this work. The concern is with the most lightweight version of PHOTON hash function with the hash size of 80 bits. It is implemented and verified on several Xilinx and Altera Field Programmable Gate Array (FPGA) devices using their synthesis and simulation tools. Low-cost and high-processing FPGA devices were both considered. The design is optimized for performance, whereas the area utilization is also taken into consideration. The overall performance and logic utilization are benchmarked with the existing implementations. The results show an improvement rate of 10.26% to 51.04% in the speed performance and a reduction rate of 7.55% to 60.64% in area utilization compared to existing implementations of PHOTON hash functions.

In conventional hybrid reversible data hiding in encrypted images (RDHEI), the error-free extracted-bit rate condition in recovered images cannot be fully achieved (reversible) as the block size decreases because of the fluctuation function used, which cannot reduce the bit error, as indicated by the high extracted-bit error rate (EER) and low peak signal-to-noise ratio (PSNR). Therefore, this work proposes improving the accuracy of hybrid RDHEI performance for remote sensing satellite images by modifying the fluctuation function in the data extraction process with and without the Reed-Solomon (RS) codes in the data embedding process. The proposed fluctuation function takes the absolute difference in the actual value of two adjacent pixels in horizontal and vertical pixels. The modified fluctuation function algorithm in the extraction process both with and without RS codes in the embedding data process is derived, and performance results are obtained through simulations of SPOT-6, SPOT-7, and Pleiades-1A satellite images. The simulation results show that the proposed hybrid RDHEI algorithm with modification of the fluctuation function without an RS encoder can achieve error-free extracted-bit and maximum PSNR (infinity) values at a block size of 18 × 18 for SPOT-6 and SPOT-7 test images, as well as a block size of 20 × 20 for the Pleiades-1A test image. It is proven that the proposed hybrid RDHEI succeeds in reducing the minimum block size from reference systems. In addition, it can also be seen that the proposed hybrid RDHEI with modification of the fluctuation function and RS coding in data embedding can reduce the minimum block size to achieve error-free extracted bits to 9 × 9 for SPOT-6 and SPOT-7 test images and 10 × 10 for the Pleiades-1A test image.

In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest neighbor (KNN) is proposed for the WiFi-based indoor positioning system. It is referred to as A Fast-Accurate-Reliable Localization System (AFARLS). AFARLS exploits the speed advantage of COSELM to reduce the computational cost, and the accuracy advantage of WSRC to enhance the classification performance, by utilizing KNN as the adaptive sub-dictionary selection strategy. The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. AFARLS unifies their complementary strengths to resolve each other’s limitation. In large-scale multi-building and multi-floor environments, AFARLS estimates a location that considers the building, floor, and position (longitude and latitude) in a hierarchical and sequential approach according to a discriminative criterion to the COSELM output. If the classifier result is unreliable, AFARLS uses KNN to achieve the best relevant sub-dictionary. The sub-dictionary is fed to WSRC to re-estimate the building and the floor, while the position is predicted by the ELM regressor. AFARLS has been verified on two publicly available datasets, the EU Zenodo and the UJIIndoorLoc. The experimental results demonstrate that AFARLS outperforms the state-of-the-art algorithms on the former dataset, and it provides near state-of-the-art performance on the latter dataset. When the size of the dataset increases remarkably, AFARLS shows that it can maintain its real-time high-accuracy performance.

A major concern in implementing white-space technology in a television broadcasting system is the ability of the system to provide information about the vacant channels while guaranteeing protection to its primary users. Moreover, in the developing countries, the allocation of television broadcasting channels is not as good as that in the developed countries. Therefore, there is a high possibility of overlaps between the digital television terrestrial coverage areas and white-space areas. Herein, we propose a method involving the use of four zones—prohibited, strict, moderate, and loose (PSML)—to model the television white-space system. We use a television coverage area that has less than 70% location probability to accommodate the possibility of overlaps between the white space and the protection areas. We also propose a protection ratio value to complement the standard given by the Federal Communications Committee (FCC) for countries implementing the digital video broadcasting system (DVB-T2). We conduct a test to analyze the implementation of the proposed PSML method in Bali Island, Indonesia. The results show that our model can provide more white-space channels than the models proposed by Villardi and the FCC when applied to transition areas between urban and rural regions.