Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.

Pose estimation is a research hot spot in computer vision tasks and the key to computer perception of human activities. The core concept of human pose estimation involves describing the motion of the human body through major joint points. Large receptive fields and rich spatial information facilitate the keypoint localization task, and how to capture features on a larger scale and reintegrate them into the feature space is a challenge for pose estimation. To address this problem, we propose a multi-scale convergence network (MSCNet) with a large receptive field and rich spatial information. The structure of the MSCNet is based on an hourglass network that captures information at different scales to present a consistent understanding of the whole body. The multi-scale receptive field (MSRF) units provide a large receptive field to obtain rich contextual information, which is then selectively enhanced or suppressed by the Squeeze-Excitation (SE) attention mechanism to flexibly perform the pose estimation task. Experimental results show that MSCNet scores 73.1% AP on the COCO dataset, an 8.8% improvement compared to the mainstream CMUPose method. Compared to the advanced CPN, the MSCNet has 68.2% of the computational complexity and only 55.4% of the number of parameters.

Accurate fish detection is of great significance in aquaculture. However, the non-uniform strong reflection in aquaculture ponds will affect the precision of fish detection. This paper combines YOLOv4 and CVAE to accurately detect fishes in the image with non-uniform strong reflection, in which the reflection in the image is removed at first and then the reflection-removed image is provided for fish detecting. Firstly, the improved YOLOv4 is applied to detect and mask the strong reflective region, to locate and label the reflective region for the subsequent reflection removal. Then, CVAE is combined with the improved YOLOv4 for inferring the priori distribution of the Reflection region and restoring the Reflection region by the distribution so that the reflection can be removed. For further improving the quality of the reflection-removed images, the adversarial learning is appended to CVAE. Finally, YOLOV4 is used to detect fishes in the high quality image. In addition, a new image dataset of pond cultured takifugu rubripes is constructed,, which includes 1000 images with fishes annotated manually, also a synthetic dataset including 2000 images with strong reflection is created and merged with the generated dataset for training and verifying the robustness of the proposed method. Comprehensive experiments are performed to compare the proposed method with the state-of-the-art fish detecting methods without reflection removal on the generated dataset. The results show that the fish detecting precision and recall of the proposed method are improved by 2.7% and 2.4% respectively.

Pseudorandom sequences with large linear complexity can resist the linear attack. The trace representation plays an important role in analysis and design of pseudorandom sequences. In this letter, we present the construction of a family of new binary sequences derived from Euler quotients modulo pq, where pq is a product of two primes and p divides q-1. Firstly, the linear complexity of the sequences are investigated. It is proved that the sequences have larger linear complexity and can resist the attack of Berlekamp-Massey algorithm. Then, we give the trace representation of the proposed sequences by determining the corresponding defining pair. Moreover, we generalize the result to the Euler quotients modulo pmqn with m≤n. Results indicate that the generalized sequences still have high linear complexity. We also give the trace representation of the generalized sequences by determining the corresponding defining pair. The result will be helpful for the implementation and the pseudorandom properties analysis of the sequences.

A new zero-forcing block diagonalization (ZF-BD) scheme that enables both a more simplified ZF-BD and further increase in sum rate of MU-MIMO channels is proposed in this paper. The proposed scheme provides the improvement in BER performance for equivalent SU-MIMO channels. The proposed scheme consists of two components. First, a permuted channel matrix (PCM), which is given by moving the submatrix related to a target user to the bottom of a downlink MIMO channel matrix, is newly defined to obtain a precoding matrix for ZF-BD. Executing QR decomposition alone for a given PCM provides null space for the target user. Second, a partial MSQRD (PMSQRD) algorithm, which adopts MSQRD only for a target user to provide improvement in bit rate and BER performance for the user, is proposed. Some numerical simulations are performed, and the results show improvement in sum rate performance of the total system. In addition, appropriate bit allocation improves the bit error rate (BER) performance in each equivalent SU-MIMO channel. A successive interference cancellation is applied to achieve further improvement in BER performance of user terminals.

In mobile communication systems, the channel state information (CSI) is severely affected by the noise effect of the receiver. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. Although it can reduce the additive noise effect by increasing samples to derive the array weight for co-channel interference suppression, it needs to know the signal-to-noise ratio (SNR) in advance to set the threshold for subcarrier grouping. This paper newly proposes adaptive zero padding (AZP) in the time domain to improve the weight accuracy of the SMI matrix. This method does not need to estimate the SNR in advance, and even if the threshold is always constant, it can adaptively identify the position of zero-padding to eliminate the noise interference of the received signal. Simulation results reveal that the proposed method can achieve superior bit error rate (BER) performance under various Rician K factors.

This research develops a new automatic path following control method for a car model based on just-in-time modeling. The purpose is that a lot of basic driving data for various situations are accumulated into a database, and we realize automatic path following for unknown roads by using only data in the database. Especially, just-in-time modeling is repeatedly utilized in order to follow the desired points on the given road. From the results of a numerical simulation, it turns out that the proposed new method can make the car follow the desired points on the given road with small error, and it shows high computational efficiency.

In this letter, we consider the problem of joint selection of transmitters and receivers in a distributed multi-input multi-output radar network for localization. Different from previous works, we consider a more mathematically challenging but generalized situation that the transmitting signals are not perfectly orthogonal. Taking Cramér Rao lower bound as performance metric, we propose a scheme of joint selection of transmitters and receivers (JSTR) aiming at optimizing the localization performance under limited number of nodes. We propose a bi-convex relaxation to replace the resultant NP hard non-convex problem. Using the bi-convexity, the surrogate problem can be efficiently resolved by nonlinear alternating direction method of multipliers. Simulation results reveal that the proposed algorithm has very close performance compared with the computationally intensive but global optimal exhaustive search method.

As the active safety of vehicles has become essential, vehicular communication has been gaining attention. The IETF IPWAVE working group has proposed the shared prefix model-based vehicular link model. In the shared prefix model, a prefix is shared among RSUs to prevent changes in IPv6 addresses of a vehicle within a shared prefix domain. However, vehicle movement must be tracked to deliver packets to the serving RSU of the vehicle within a shared prefix domain. The Identifier/Locator Separation Protocol (ILSP) is one of the techniques used to handle vehicle movement. It has several drawbacks such as the inability to communicate with a standard IPv6 module without special components and the requirement to pass signaling messages between end hosts. Such drawbacks severely limit the service availability for a vehicle in the Internet. We propose an ILSP for a shared prefix model over IEEE WAVE IPv6 networks. The proposed protocol supports IPv6 communication between a standard IPv6 node in the Internet and a vehicle supporting the proposed protocol. In addition, the protocol hides vehicle movement within a shared prefix domain to peer hosts, eliminating the signaling between end hosts. The proposed protocol introduces a special NDP module based on IETF IPWAVE vehicular NDP to support vehicular mobility management within a shared prefix domain and minimize link-level multicast in WAVE networks.

A drone swarm is a robotic architecture having multiple drones cooperate to accomplish a mission. Nowadays, heterogeneous drone swarms, in which a small number of gateway drones (GWs) act as protocol translators to enable the mixing of multiple swarms that use independent wireless protocols, have attracted much attention from many researchers. Our previous work proposed Path Optimizer — a method to minimize the number of end-to-end path-hops in a remote video monitoring system using heterogeneous drone swarms by autonomously relocating GWs to create a shortcut in the network for each communication request. However, Path Optimizer has limitations in improving communication quality when more video sessions than the number of GWs are requested simultaneously. Path Coordinator, which we propose in this paper, achieves a uniform reduction in end-to-end hops and maximizes the allowable hop satisfaction rate regardless of the number of sessions by introducing the cooperative and synchronous relocation of all GWs. Path Coordinator consists of two phases: first, physical optimization is performed by geographically relocating all GWs (relocation phase), and then logical optimization is achieved by modifying the relaying GWs of each video flow (rerouting phase). Computer simulations reveal that Path Coordinator adapts to various environments and performs as well as we expected. Furthermore, its performance is comparable to the upper limits possible with brute-force search.

Clipping is an efficient and simple method that can reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However, clipping causes in-band distortion referred to as clipping noise. To resolve this problem, a novel iterative estimation and cancellation (IEC) scheme for clipping noise is one of the most popular schemes because it can significantly improve the performance of clipped OFDM systems. However, IEC exploits detected symbols at the receiver to estimate the clipping noise in principle and the detected symbols are not the sufficient statistic in terms of estimation theory. In this paper, we propose the post-processing technique of IEC, which fully exploits given sufficient statistic at the receiver and thus further enhances the performance of a clipped OFDM system as verified by simulations.

Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.

In recent years, physical layer security (PLS), which is based on information theory and whose strength does not depend on the eavesdropper's computing capability, has attracted much attention. We have proposed a chaos modulation method as one PLS method that offers channel coding gain. One alternative is based on polar codes. They are robust error-correcting codes, have a nested structure in the encoder, and the application of this mechanism to PLS encryption (PLS-polar) has been actively studied. However, most conventional studies assume the application of conventional linear modulation such as BPSK, do not use encryption modulation, and the channel coding gain in the modulation is not achieved. In this paper, we propose a PLS-polar method that can realize high-quality transmission and encryption of a modulated signal by applying chaos modulation to a polar-coding system. Numerical results show that the proposed method improves the performance compared to the conventional PLS-polar method by 0.7dB at a block error rate of 10-5. In addition, we show that the proposed method is superior to conventional chaos modulation concatenated with low-density parity-check codes, indicating that the polar code is more suitable for chaos modulation. Finally, it is demonstrated that the proposed method is secure in terms of information theoretical and computational security.

In this paper, we propose an interpretation method on amplitude intensities for response waveforms of backward transient scattered field components for both E- and H-polarizations by a 2-D coated metal cylinder. A time-domain (TD) asymptotic solution, which is referred to as a TD Fourier transform method (TD-FTM), is derived by applying the FTM to a backward transient scattered field expressed by an integral form. The TD-FTM is represented by a combination of a direct geometric optical ray (DGO) and a reflected GO (RGO) series. We use the TD-FTM to derive amplitude intensity ratios (AIRs) between adjacent backward transient scattered field components. By comparing the numerical values of the AIRs with those of the influence factors that compose the AIRs, major factor(s) can be identified, thereby allowing detailed interpretation method on the amplitude intensities for the response waveforms of backward transient scattered field components. The accuracy and practicality of the TD-FTM are evaluated by comparing it with three reference solutions. The effectiveness of an interpretation method on the amplitude intensities for response waveforms of backward transient scattered field components is revealed by identifying major factor(s) affecting the amplitude intensities.

In this study, we discuss a structure that realizes a wideband polarization splitter comprising fiber 1 with a single core and fiber 2 with circular pits, which touch the top and bottom of a single core. The refractive index profile of the W type was adopted in the core of fiber 1 to realize the wideband. We compared the maximum bandwidth of BW-15 (bandwidth at an extinction ratio of -15dB) for the W type obtained in this study with those (our previous results) of BW-15 for the step and graded types with cores and pits at the same location; this comparison clarified that the maximum bandwidth of BW-15 for the W type is 5.22 and 4.96 times wider than those of step and graded types, respectively. Furthermore, the device length at the maximum bandwidth improved, becoming slightly shorter. The main results of the FPS in this study are all obtained by numerical analysis based on our proposed MM-DM (a method that combines the multipole method and the difference method for the inhomogeneous region). Our MM-DM is a quite reliable method for high accuracy analysis of the FPS composed of inhomogeneous circular regions.

An approach to dedicated computers is discussed in this study as a possibility for portable, low-cost, and low-power consumption high-performance computing technologies. Particularly, dataflow architecture dedicated computer of the finite integration technique (FIT) for 2D magnetostatic field simulation is considered for use in industrial applications. The dataflow architecture circuit of the BiCG-Stab matrix solver of the FIT matrix calculation is designed by the very high-speed integrated circuit hardware description language (VHDL). The operation of the dedicated computer's designed circuit is considered by VHDL logic circuit simulation.

This article presents a 28GHz high-accuracy phase and amplitude detection circuit for dual-polarized phased-array calibration. With dual-polarized calibration scheme, external LO signal is not required for calibration. The proposed detection circuit detects phase and amplitude independently, using PDC and ADC. By utilizing a 28GHz-to-140kHz downconversion scheme, the phase and amplitude are detected more accurately. In addition, reference signal for PDC and ADC is generated from 28GHz LO signal with divide-by-6 dual-step-mixing injection locked frequency divider (ILFD). This ILFD achieves 24.5-32.5GHz (28%) locking range with only 3.0mW power consumption and 0.01mm2 area. In the measurement, the detection circuit achieves phase and amplitude detections with RMS errors of 0.17degree and 0.12dB, respectively. The total power consumption of the proposed circuit is 59mW with 1-V supply voltage.

Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.

As the Internet has become prevalent, the popularity of net media has been growing, to a point that it has taken over conventional mass media. However, TWtrends, the Twitter trends visualization system operated by our research team since 2019, indicates that many topics on TV programs frequently appear on Twitter trendlines. This study investigates the relationship between Twitter and TV programs by collecting information on Twitter trends and TV programs simultaneously. Although this study provides a rough estimation of the volume of tweets that mention TV programs, the results show that several tweets mention TV programs at a constant rate, which tends to increase on the weekend. This tendency of TV-related tweets stems from the audience rating survey results. Considering the study outcome, and the fact that many TV programs introduce topics popular in social media, implies codependency between Internet media (social media) and mass media.

Animating 3D characters using motion capture data requires basic expertise and manual labor. To support the creativity of animation design and make it easier for common users, we present a sketch-based interface DualMotion, with rough sketches as input for designing daily-life animations of characters, such as walking and jumping. Our approach enables to combine global motions of lower limbs and the local motion of the upper limbs in a database by utilizing a two-stage design strategy. Users are allowed to design a motion by starting with drawing a rough trajectory of a body/lower limb movement in the global design stage. The upper limb motions are then designed by drawing several more relative motion trajectories in the local design stage. We conduct a user study and verify the effectiveness and convenience of the proposed system in creative activities.