Bluetooth Low Energy (BLE) is a positioning technology that is commonly used in indoor positioning systems (IPS) such as shopping malls or underground parking lots, because of its low power consumption and the low cost of Bluetooth devices. It also maintains high positioning accuracy. Since the cost of BLE itself is low, it has now been used in larger environments such as parking lots or shopping malls for a long time. However, it is necessary to configure a large number of devices in the environment to obtain accurate positioning results. The most accurate method of using signal strength for positioning is the signal pattern-matching method. The positioning result is compared through a database with the overheads of time and labor costs, since the amount of data will be proportional to the size of the environment for BLE-IPS. A planar model that conforms to the signal strength in the environment was generated, wherein the database comparison method is replaced by an equation solution, to improve various costs but diminish the positioning accuracy. In this paper, we propose to further replace the planar model with a cost-effective fitting model to both save costs and improve positioning accuracy. The experimental results demonstrate that this model can effectively reduce the average positioning error in distance by 31%.

Location‐based services (LBSs) in such indoor places as museums, airport terminals, and train stations are becoming more and more popular among smartphone users. Hence, the research on indoor positioning technologies is increasing. Indoor positioning technologies using Wi‐Fi or Bluetooth wireless signals have some problems that cannot be overcome, for example, inconsistent data caused by human body shadowing effects. Positioning accuracy and stability still need to be improved to provide higher quality LBS applications. Smartphones have built‐in geomagnetic and acceleration sensors, and the Earth's magnetic field is not affected by human body shadowing effects, so it increases the appeal of using geomagnetism to enable indoor positioning with smartphones. This article proposed approaches in light of calibration‐free fingerprinting positioning techniques with terrestrial magnetism databases for indoor environments. For the differences in measurements yielded by various mobile phone (MP) models and brands, adjustments can be made using the ratio of the total intensity of the magnetic field and its horizontal component to correct the discrepancies. Finally, with the arrangement of four landmarks, the average error distance of the research results in the experimental environment is 3.59 m. The average accuracy of the k ‐nearest neighbour ( k ‐NN) mobile phones is 71.3%.

This article puts forward a low-complexity filtering algorithm to achieve a low-complexity filtering chip design for real-time location tracking. In order to meet the need for low-complexity and real-time, the positioning tracking algorithm based on Kalman Filter (KF) is proposed. The KF itself has the functions of tracking, predicting, etc., which can correct the positioning into more accurate results. However, in the calculation of KF algorithms, each iteration often requires tedious and complex calculations of Kalman Gain (KG). Both software and hardware are very resource-intensive. Therefore, use the feature of KG in alpha-beta (α-β) filtering algorithm which can gradually balance in each iteration. Proposed a filtering algorithm that is based on low-complexity, low cost, and high efficiency. This algorithm uses DKF (Difference Kalman Filter) and PKF (Percentage Kalman Filter) depending on different environments. In other words, DKF and PKF are the algorithms which are generated based on different judging conditions. This algorithm can not only significantly reduce the time and the complexity of computing, but also greatly shorten the circuit area of the original algorithm. This algorithm has a large number of matrix operation. In the hardware calculation process, it solves matrix problems about hardware and then developed chip design. Coefficients are used by a multiple of 2 for operation. Use shifters instead of multipliers and dividers, significantly reducing complexity and circuit area. At the same time, deal with the problem of a floating-point number, achieve circuit function verification on the FPGA, and finally tape-out. The design uses the TSMC 0.18μm CMOS cell library provided by the TSRI, uses EDA to implement VLSI with the Design Vision of SYNOPSYS, the operating frequency of the circuit is 83.33 MHz, the value of gate counts is 22.84 K, the power consumption is 3.86 mW, and chip area is 582.63 μm × 580.23 μm.

In the process of economic development of a country, agriculture plays a strategic role. For precision agriculture science, data stability and accuracy of the rice seedlings are important issues in estimating rice productions for paddies. With the rapid development of wireless communication systems and aerial photography techniques, drones can provide essential full-color images for the applications of rice paddy fields. A proper cultivation density of rice seedlings is important for agricultural data. Thus, this article presents the approaches for the detection and interpretation of transplanted positions in terms of drone’s eye-view images for rice paddies. In the light of deep learning algorithms, the proposed approaches built a model for estimating the transplanted positions of paddies from the type of training set. The validation was employed to obtain an 80/20 dividing ratio of training/test data, and then the set of well-known coordinates of the seedlings of rice paddies is used to validate the efficiency. The results demonstrate that the accuracy of the detection and interpretation of transplanted positions for rice seedlings is higher than 94.88% in terms of the F 1 -measure value.

Pansharpening is a remote sensing image fusion technique that combines a high-resolution (HR) panchromatic (PAN) image with a low-resolution (LR) multispectral (MS) image to produce an HR MS image. The primary challenge in pansharpening lies in preserving the spatial details of the PAN image while maintaining the spectral integrity of the MS image. To address this, this article presents a generative adversarial network (GAN)-based approach to pansharpening. The GAN discriminator facilitated matching the generated image’s intensity to the HR PAN image and preserving the spectral characteristics of the LR MS image. The performance in generating images was evaluated using the peak signal-to-noise ratio (PSNR). For the experiment, original LR MS and HR PAN satellite images were partitioned into smaller patches, and the GAN model was validated using an 80:20 training-to-testing data ratio. The results illustrated that the super-resolution images generated by the SRGAN model achieved a PSNR of 31 dB. These results demonstrated the developed model’s ability to reconstruct the geometric, textural, and spectral information from the images.

For existing methods on YUV420 subsampling from YUV444 image format, the Y is constrained to change the UV values, or the UVs are constrained to change the Y value. We proposed a method of chroma subsampling with changing YUV values at the same time for YUV420 images. We proposed an assumption (observation) to reduce the algorithm complexity. The experimental results show that the proposed work is better than existing state-of-the-art methods by 1.64 dB and 0.61 dB on average in CPSNR (Composite Peak Signal-to-noise Ratio).