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[Display omitted] • This study shows how the delta robot was designed to physically pick-and-place MSW. • Combined with the YOLO-based model, the robot forms the Intelligent MSW Sorter. • IMSWS could automatically sort the multiple MSWs on the conveyor belt. • The mechanical efficiency of the robot dominated IMSWS performance. • Compared with the MSW detection, IMSWS must improve the mechanical efficiency first. Sorting Municipal Solid Waste (MSW) has helped promote the awareness of sustainable development of environment. A robot equipped with an intelligent deep learning (DL) detection algorithm have been proposed to improve the sorting task. But most of the related studies aimed to better the DL algorithms on MSW detection, and few studies integrated the DL algorithms with a robot to identify the dominated factors to Intelligent MSW Sorter (IMSWS). Therefore, this study is to develop IMSWS prototype to better sort MSW, based on the pick-and-place process, and preliminarily evaluate the dominated factors. First, the delta robot prototype was manufactured, and IMSWS was performed with a camera to acquire the RGB image and the height of a MSW in the conveyor belt. The DL algorithm, YOLOv3 or YOLOv4, detected the type and plane location of the MSWs in the conveyor belt. Next, the sequence program transferred the valid MSW data to the delta robot. After the calculation of the absorbed location of the target MSW was made, the arm of this delta robot moved to absorb and then transfer the MSW to the bin. Results showed that the IMSWS prototype could sort the multi-object MSWs in the MSW stream. Both YOLOv3 and YOLOv4 reached high detection accuracy on the MSW image dataset. However, the improvement should be made in the actually moving MSW stream even though the YOLOv4 performed the acceptable detection accuracy. The gripping stability of the arm mainly dominated the performance of IMSWS, and this should be improved first. [ABSTRACT FROM AUTHOR]

Waste sorting is highly labor intensive because the wide variety of waste items prohibits automation. More recently, deep learning (DL) and computer vision technology has presented an opportunity to streamline the sorting process, but many important developmental steps remain. If computer vision technology can increase the efficiency of automated waste sorting, this would be beneficial for society and the environment. Accordingly, this study used the You Only Look Once-v3 (Yolo-v3) detection model based on DL to enhance recognition performance of household waste products. TrashNet, a commonly used waste image database, was used to train an initial Yolo-v3 model, however each image used for training only had a single waste object, and this study found that the detection model trained with a single object dataset was not only unsuitable for sorting multiple waste objects, but that this has rarely been addressed in academic literature. It was also discovered that nations and regions will need to develop their own unique databases that reflect the types of waste products found. Samples images need to account for the various appearances and colors and be combined in multiple waste object images when training the system. This paper documents the training and testing of an object detection model suitable for detecting domestic waste specific to Taiwan; however, the approach taken would be of use to other countries seeking to automate waste sorting. To achieve this, it was necessary to compile the Taiwan Recycled Waste Database (TRWD). This was then used to train the Yolo-v3, and the efficiencies of this, versus the standard TrashNet model were compared. Results showed that the TRWD-trained Yolo-v3 achieved mAP @0.5 of 92.12% and could detect waste in real-time. Relative to the TrashNet-trained Yolo-v3, the TRWD counterpart performed better due to the multiple waste objects and more relevant image repository. Further studies are recommended to investigate the effect of combining additional sensors that would enable improved detection of specific wastes. Combining the TRWD-trained Yolo-v3 with a robot system for waste sorting would potentially be another rewarding avenue of research. [Display omitted] • Automatic waste detection improves waste recycling efficiency. • Different nations require customized datasets to train Yolo-v3 detection model. • Taiwan recycled waste dataset (TRWD) was expanded to improve detection rates. • Yolo-v3 trained on the TRWD outperformed the same system using TrashNet. [ABSTRACT FROM AUTHOR]

Abstract: Multipath effects are the major source of error in GPS differential positioning. In this paper, an adaptive multipath mitigation tracking system is presented for GPS applications. It is comprised of four function blocks, those being (1) adaptive path estimator (APE), (2) multipath interference reproducer (MPIR), (3) Rake-based delay locked loop (RB-DLL), and (4) Rake-based phase locked loop (RB-PLL). Only the short delay condition with delay less than 1.5 PN chip is considered here, because GPS pseudorange error envelope decreases to zero for delay time greater than 1.5 PN chip. In order to estimate reflection profile in the correlation domain, the FFT-based circular correlation and block average method (BAM) are utilized to offer significant savings in computational complexity. The APE estimates the delayed profiles and coefficients of the reflection signals. By using the path parameters from APE, the corresponding multipath arms are activated to accomplish the multipath reproduction. These replica profiles are used for subtracting the reflection components from carrier and code discriminators before sending it into the Rake-based carrier/code tracking loops. Simulation results show that our proposed method provides a better performance in terms of multipath error envelope and carrier-phase error. [Copyright &y& Elsevier]

Abstract: Global positioning system (GPS) is a system combining code division multiple access (CDMA) and trilateration techniques to obtain precision position and timing information. The main goal of this paper is to present a high-sensitivity receiver for acquisition of weak GPS signals. A new peak-finding algorithm is developed to search the C/A code phase in the FFT-based correlation domain. This method can estimate the peak location accurately and provides a faster performance in software-based signal acquisition. Because the received frequency increases as the satellite advances the receiver and decreases as it recedes from the user in the Doppler circumstance, the code delay compensation (CDC) algorithm is proposed to enhance the capability of waveform search under indoor conditions. Using the carrier-aiding information, the CDC method can find the autocorrelation peak value effectively under both Doppler shift and weak signal environments. A step-by-step acquisition procedure is designed for indoor GPS receivers to detect the C/A code delayed phase effectively and reliable operation in a diversity of weak signal and Doppler environments. Simulation results demonstrate that our proposed scheme can achieve a significant improvement in processing gain over the traditional methods and acquire the GPS signal efficiently under the power levels of in frequency domain processing. [Copyright &y& Elsevier]

Abstract: The GPS provides accurate positioning and timing information that is useful in various applications. A new adaptive neural predictor for GPS jamming suppression applications is proposed. The effective and computationally efficient square-root extended Kalman filter (SREKF) algorithm is adopted to adjust the synaptic weights in the nonlinear recurrent architecture and thereby estimate the stationary and non-stationary narrowband/FM waveforms. Cholesky factorization is employed in Riccati recursion to improve numerical stability because of the propagation of round-off errors in conventional KF equations. The main characteristics of the proposed SREKF-based canceller are their rapid convergence and favorable tracking performance. Simulation results reveal that its SNR improvement factor exceeds the factors of conventional LMS, RLS, ENA and TLFN filters in single-tone CWI, multi-tone CWI, pulse CWI and FM jamming environments, respectively. [Copyright &y& Elsevier]

Abstract: Powder mixture rules derived from the filler particles with the shapes of sphere, cylinder or rod, and lamella or disk with random distributions are studied in this paper. Three ceramic powders of fillers with dielectric constants of 10, 20, and 36, respectively, are adopted. The experimental dielectric constants of ceramic dispersions in the polyethylene matrix at microwave frequency are compared to those obtained by using different mixing laws. The mixing rules are also adopted to estimate the dielectric constants of pure ceramics from the measured dielectric constants of composites with various concentrations. The theory for the error of estimation is studied. In contrast to the traditional concept of obtaining the best curve fitting of mixture rule with the experimental data, this study conclude a very important concept on the powder mixture rule, that is, the most adequate mixture law for estimating the dielectric constants of pure ceramics requires both good curve fitting and potential of less error. [Copyright &y& Elsevier]