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Multiple-input multiple-output (MIMO) technology with high order signal modulation method has been proposed recently as a scheme to achieve high throughput requirements of next-generation communication applications. However, the use of high-order modulation signals degrades the error rate performance. Generalized frequency division multiplexing (GFDM) has attracted enormous consideration for deploying in new communication scenarios due to its advantages in terms of out-of-band (OOB) emission, spectral efficiency (SE), and latency. Index modulation (IM) methods convey digital information by utilizing transmission entities with adjustable index parameters to provide flexible system design and offer attractive advantages such as energy and SE without increasing the computational complexity. The Multiple-Mode (MM) method permits multiple subcarriers to transmit multiple modes and employs the whole permutation of these modes to convey additional information bits. The proposed MIMO MM-GFDM-IM scheme improves the bit error rate and the spectral efficiency compared to the existing schemes. The proposed method can select the modulation order and the number of modes to achieve the design goals. Furthermore, a system with high modulation order can improve the spectral efficiency when the constellation point is fixed. This paper also proposes a practical and effective MM selection strategy and two low-complexity detectors.

The CAN Bus Messages are mostly customized on various types of vehicles. A PID-CAN-Bus-Message-Pairs parser has been designed to analyze those customized CAN Bus message stream from vehicle network. It is composed of a downstream module and a upstream module. The downstream module works for vehicle dynamic behavior fusion and real-time CAN Bus message acquisition. The upstream module gets fused vehicle dynamic behavior and real-time CAN Bus messages from downlink module to autonomously parse out the parameter identify codes which are subsequently stored to serve as the basis for autonomous driving and vehicle network information security protection research.

This study based on Hartmann-Shack system to provide a non-contact system measuring Wavefront Aberration. Hartmann-Shack system set by optical component can realize that changing optical component to adjust the system. At the end, getting the aberration of normal lens between the simulation by ZEMAX software and comparing with the actual aberration that calculated by Zernike polynomials in software Matlab. To ensure the stability of system.

Our article establishes a mechanism to remind residents whether there are guests visiting through related electronic devices for range finding, identification, etc.A vibrating bracelet (or ring) and a flashing light bar are worn on the resident’s wrist (or neck), that is, to remind the resident by means of vibration and light bar flashing. Other related hardware aspects include LED dot matrix modules, voice broadcast, and infrared door locks; LED dot matrix modules are used to display the current date, time, and visitor information, and the voice broadcast will play the speakers of the visiting guests. The LED dot matrix module can provide information about visiting guests for the hearing impaired or residents with reduced mobility, and the voice broadcast can provide information about the visiting guests for the visually impaired or residents with reduced mobility. If the resident completes the identification of the visiting guest and wants to open the door lock, he can use infrared light wave to open the door lock. This is a convenient and safe way for residents with limited mobility or the elderly.

The orthogonal frequency division multiplexing (OFDM)‐based modulation scheme is widely utilized in asymmetric digital subscriber lines (ADSL), wireless local area networks (WLAN), and the...

This paper revealed an effective method to judge the quality levels of apples with optical images which could avoid the damages caused by touching measurement. When only using photographs of the apples, the quality levels cannot be effectively identified via auto optical inspection methods. We proposed a structured light projection system for detecting the apple surface topography which is corresponding to the small changes during storage periods. This system uses cosine waves with different phases to project onto the apple surface and then uses three phases to reconstruct the object. In order to get the most suitable spatial frequency for this system, we tested six spatial frequencies: f = 0.001, f = 0.003, f = 0.005, f = 0.008, f = 0.01, and f = 0.08. Test results show that the spatial frequency f = 0.008 is best for our samples and hardware equipment. A convolutional neural network modified from LeNet is used to classify both the pictures: directly captured from apples and the reconstructed pictures from the structured lighting system. Experiments were performed on 30 fresh apples which then storage for a period of 7 days, 14 days and 21 days. The results of the convolutional neural network trained on structured light system samples showed that the accuracy was 87%, but the accuracy of the results of only using photography of apples was just 46%.

Due to high cost of the high-resolution polarimetric synthetic aperture radar (PolSAR) image, there are few literature on the automatic target recognition (ATR) of small target using polarimetric features. With the limitation of high-resolution POLSAR dataset, SAR image simulation can play an important role in the investigation of polarimetric features for man-made target. This paper presents a SAR image simulator and several polarimetric features extraction methods for small complex targets. The proposed SAR simulator is composed of a SAR echo simulator and an image formation algorithm. In order to generate the SAR echo efficiently and accurately, the shooting and bouncing rays (SBR) and physical optics (PO) hybrid method is employed for high frequency electromagnetic scattering prediction of complex objects. Based on the parameters of the moving and stationary target acquisition and recognition (MSTAR) dataset, the PolSAR images of military vehicles are created. In addition, the multi-reflection analysis can be done by decomposing the simulated SAR image into the images corresponding to the first, second, and third order reflections since SBR is a ray tracing based approach. After the simulated SAR image is generated, we employ target decomposition theorems to obtain polarimetric features for target interpretation. Using polarimetric decomposition, the scattering signals of the vehicle are decomposed into a weighted combination of various simple scattering mechanisms, which can be used to form a set of images representing different scattering features. In addition, a polarimetric feature map can be obtained by coloring the scattering centers according to their scattering mechanisms. The testing results show that the polarimetric features fit the multi-reflection analysis well for the simulated SAR image.

In recent years, convolutional neural network (CNN) has been increasingly considered as a promising technology for military and homeland security applications. The fusion of CNN and Support vector machine (SVM), a popular traditional machine learning approach, has received intensive attention in the field of synthetic aperture radar (SAR) automatic target recognition (ATR). This paper, firstly, discusses the effects of some preprocessing and image enhancement methods on the performance of SAR ATR, starting with the pre-trained AlexNet in a transfer-learning based approach. Secondly, the architecture of AlexNet is modified to form a new model suitable for SAR ATR. Finally, we propose a hybrid model associated with the success of the learning feature of our CNN model and the ability of SVM to process high-dimensional dataset effectively. To evaluate the proposed method, experiments are performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database. The comparative results demonstrate that these preprocessing and enhancement methods prior to the deep-learning process are not necessary since the feature representation ability of AlexNet is already powerful. Furthermore, experimental results on the benchmark MSTAR dataset illustrate the effectiveness of the proposed new model. On classification of ten-class targets, the commonly used translation augmentation for training data has been performed. By combining the CNN and SVM, the classification accuracy percentages can be slightly improved for our proposed new model.

This paper presents the design of a remote monitoring system for open ocean aquaculture, which is based on the integration of two long-range wireless communication technologies: the IEEE 802.11ac wireless bridge and the LoRa-Android embedded system. The system is composed of sensors and underwater camera that can measure the seawater quality and observe the fish behavior in aquaculture cages in real time. The sensor and video electrical cables were connected to the watertight control unit attached to the upper frame of the cage with stainless steel brackets and wire rope. The sensor data and video recorded in control unit were sent to the remote data center located on land by LoRa wireless module and IEEE 802.11ac wireless bridge, respectively. Finally, all these data were uploaded to NTOU (National Taiwan Ocean University) Cloud center. To extend the system run time for the daily operation period under the fixed battery capacity, a remote relay controller has been used in the watertight control unit. The entire system can be shut down by remote commands except the relay itself and the wireless subsystem. In addition, the wireless subsystem can be turned off by setting a periodic wake-up time in the relay controller for power saving. The preliminary experiment was performed at Gongliao in New Taipei City to test the proposed system. After successful testing, the whole system has been setup at the fish farm at Pingtung for long-term testing. The experiment results show that the proposed wireless sensor network has good suitability and sustainability when applied in the field of open ocean aquaculture.