This paper proposes relay selection techniques for XOR physical layer network coding with MMSE based non-linear precoding in MIMO bi-directional wireless relaying networks. The proposed selection techniques are derived on the different assumption about characteristics of the MMSE based non-linear precoding in the wireless network. We show that the signal to noise power ratio (SNR) is dependent on the product of all the eigenvalues in the channels from the terminals to relays. This paper shows that the best selection techniques in all the proposed techniques is to select a group of the relays that maximizes the product. Therefore, the selection technique is called “product of all eigenvalues (PAE)” in this paper. The performance of the proposed relay selection techniques is evaluated in a MIMO bi-directional wireless relaying network where two terminals with 2 antennas exchange their information via relays. When the PAE is applied to select a group of the 2 relays out of the 10 relays where an antenna is placed, the PAE attains a gain of more than 13dB at the BER of 10-3.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

A practical Doherty amplifier design method has been developed based on an asymmetric configuration scheme. By embedding a load modulation function into matching circuits of a carrier amplifier (CA) and a peaking amplifier (PA) in the Doherty amplifier, an issue of the Doherty amplifier design is boiled down to the CA and PA matching circuit design. The method can be applied to transistors with unknown parasitic elements if optimum termination impedance conditions for the transistor are obtained from a source-/load-pull technique in simulation or measurement. The design method was applied to GaN HEMT Doherty amplifier MMICs. The fabricated 4.5-GHz-band GaN HEMT Doherty amplifier MMIC exhibited a maximum drain efficiency of 66% and a maximum power-added efficiency (PAE) of 62% at 4.1GHz, with a saturation output power of 36dBm. In addition, PAE of 50% was achieved at 4.1GHz on a 7.2-dB output back-off (OBO) condition. The fabricated 8.5-GHz-band GaN HEMT Doherty amplifier MMIC exhibited a maximum drain efficiency of 53% and a maximum PAE of 44% at 8.6GHz, with a saturation output power of 36dBm. In addition, PAE of 35% was achieved at 8.6GHz on a 6.7-dB (OBO). And, the fabricated 12-GHz-band GaN HEMT Doherty amplifier MMIC exhibited a maximum drain efficiency of 57% and a maximum PAE of 52% at 12.4GHz, with a saturation output power of 34dBm. In addition, PAE of 32% was achieved at 12.4GHz on a 9.5-dB (OBO) condition.

In this paper, we demonstrate that a formal approach is effective for improving reliability of cooperative robot designs, where the control logics are expressed in concurrent FSMs (Finite State Machines), especially in accordance with the standard FSM4RTC (FSM for Robotic Technology Components), by a case study of cooperative transport robots. In the case study, FSMs are modeled in the formal specification language CSP (Communicating Sequential Processes) and checked by the model-checking tool FDR, where we show techniques for modeling and verification of cooperative robots implemented with the help of the RTM (Robotic Technology Middleware).

With the advent of 5G and external pressure to reduce greenhouse gas emissions, wireless transceivers with low power consumption are strongly desired for future cellular systems. At the same time, increased modulation order due to the evolution of cellular systems will force power amplifiers to operate at much larger output power back-off to prevent EVM degradation. This paper begins with an analysis of load modulation and asymmetrical Doherty amplifiers. Measurement results will show an apparent 60% efficiency plateau for modulated signals with a large peak-to-average power ratio (PAPR). To exceed this efficiency limitation, the second part of this paper focuses on a new amplification topology based on the amalgamation between Doherty and outphasing. Measurement results of the proposed Doherty-outphasing power amplifier (DOPA) will confirm the feasibility of the approach with a modulated efficiency greater than 70% measured at 10 dB output power back-off.

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

High power amplifier technologies for base transceiver stations (BTSs) for the 5th generation (5G) mobile communication systems and so-called beyond 5G (B5G) systems are reviewed. For sub-6, which is categorized into frequency range 1 (FR1) in 5G, wideband Doherty amplifiers are introduced, and a multi-band load modulation amplifier, an envelope tracking amplifier, and a digital power amplifier for B5G are explained. For millimeter wave 5G, which is categorized into frequency range 2 (FR2), GaAs and GaN MMICs operating at around 28GHz are introduced. Finally, future prospect for THz GaN devices is described.

In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.

Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.

Long-range radars (LRRs) for higher level autonomous driving (AD) will require more antennas than simple driving assistance. The point at issue here is 50-60% of the LRR module area is used for antennas. To miniaturize LRR modules, we use horn and lens antenna with highly efficient gain. In this paper, we propose two high-density implementation techniques for radio-frequency (RF) front-end using horn and lens antennas. In the first technique, the gap between antennas was eliminated by taking advantage of the high isolation performance of horn and lens antennas. In the second technique, the RF front-end including micro-strip-lines, monolithic microwave integrated circuits, and peripheral parts is placed in the valley area of each horn. We fabricated a prototype LRR operating at 77 GHz with only one printed circuit board (PCB). To detect vehicles horizontally and vertically, this LRR has a minimum antenna configuration of one Tx antenna and four Rx antennas placed in 2×2 array, and 30 mm thickness. Evaluation results revealed that vehicles could be detected up to 320 m away and that the horizontal and vertical angle error was less than +/- 0.2 degrees, which is equivalent to the vehicle width over 280 m. Thus, horn and lens antennas implemented using the proposed techniques are very suitable for higher level AD LRRs.

Effective emergency resource allocation is essential to guarantee a successful emergency disposal, and it has become a research focus in the area of emergency management. Emergency event logs are accumulated in modern emergency management systems and can be analyzed to support effective resource allocation. This paper proposes a novel approach for efficient emergency resource allocation by mining emergency event logs. More specifically, an emergency event log with various attributes, e.g., emergency task name, emergency resource type (reusable and consumable ones), required resource amount, and timestamps, is first formalized. Then, a novel algorithm is presented to discover emergency response process models, represented as an extension of Petri net with resource and time elements, from emergency event logs. Next, based on the discovered emergency response process models, the minimum resource requirements for both reusable and consumable resources are obtained, and two resource allocation strategies, i.e., the Shortest Execution Time (SET) strategy and the Least Resource Consumption (LRC) strategy, are proposed to support efficient emergency resource allocation decision-making. Finally, a chlorine tank explosion emergency case study is used to demonstrate the applicability and effectiveness of the proposed resource allocation approach.

Crowd flow prediction in high density urban scenes is involved in a wide range of intelligent transportation and smart city applications, and it has become a significant topic in urban computing. In this letter, a CNN-based framework called Pyramidal Spatio-Temporal Network (PSTNet) for crowd flow prediction is proposed. Spatial encoding is employed for spatial representation of external factors, while prior pyramid enhances feature dependence of spatial scale distances and temporal spans, after that, post pyramid is proposed to fuse the heterogeneous spatio-temporal features of multiple scales. Experimental results based on TaxiBJ and MobileBJ demonstrate that proposed PSTNet outperforms the state-of-the-art methods.

Binary sequence pairs with optimal periodic correlation have important applications in many fields of communication systems. In this letter, four new families of binary sequence pairs are presented based on the generalized cyclotomy over Z5q, where q ≠ 5 is an odd prime. All these binary sequence pairs have optimal three-level correlation values {-1, 3}.

Sketch face recognition refers to matching photos with sketches, which has effectively been used in various applications ranging from law enforcement agencies to digital entertainment. However, due to the large modality gap between photos and sketches, sketch face recognition remains a challenging task at present. To reduce the domain gap between the sketches and photos, this paper proposes a cascaded transformation generation network for cross-modality image generation and sketch face recognition simultaneously. The proposed cascaded transformation generation network is composed of a generation module, a cascaded feature transformation module, and a classifier module. The generation module aims to generate a high quality cross-modality image, the cascaded feature transformation module extracts high-level semantic features for generation and recognition simultaneously, the classifier module is used to complete sketch face recognition. The proposed transformation generation network is trained in an end-to-end manner, it strengthens the recognition accuracy by the generated images. The recognition performance is verified on the UoM-SGFSv2, e-PRIP, and CUFSF datasets; experimental results show that the proposed method is better than other state-of-the-art methods.

Face sketch synthesis refers to transform facial photos into sketches. Recent research on face sketch synthesis has achieved great success due to the development of Generative Adversarial Networks (GAN). However, these generative methods prone to neglect detailed information and thus lose some individual specific features, such as glasses and headdresses. In this paper, we propose a novel method called Feature Learning Generative Adversarial Network (FL-GAN) to synthesize detail-preserving high-quality sketches. Precisely, the proposed FL-GAN consists of one Feature Learning (FL) module and one Adversarial Learning (AL) module. The FL module aims to learn the detailed information of the image in a latent space, and guide the AL module to synthesize detail-preserving sketch. The AL Module aims to learn the structure and texture of sketch and improve the quality of synthetic sketch by adversarial learning strategy. Quantitative and qualitative comparisons with seven state-of-the-art methods such as the LLE, the MRF, the MWF, the RSLCR, the RL, the FCN and the GAN on four facial sketch datasets demonstrate the superiority of this method.

This paper proposes a measurement-based spectrum database (MSD) with clustered fading distributions toward greater storage efficiencies. The conventional MSD can accurately model the actual characteristics of multipath fading by plotting the histogram of instantaneous measurement data for each space-separated mesh and utilizing it in communication designs. However, if the database contains all of a distribution for each location, the amount of data stored will be extremely large. Because the main purpose of the MSD is to improve spectral efficiency, it is necessary to reduce the amount of data stored while maintaining quality. The proposed method reduces the amount of stored data by estimating the distribution of the instantaneous received signal power at each point and integrating similar distributions through clustering. Numerical results show that clustering techniques can reduce the amount of data while maintaining the accuracy of the MSD. We then apply the proposed method to the outage probability prediction for the instantaneous received signal power. It is revealed that the prediction accuracy is maintained even when the amount of data is reduced.

Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.

When a failure occurs in a network element, such as switch, router, and server, network operators need to recognize the service impact, such as time to recovery from the failure or severity of the failure, since service impact is essential information for handling failures. In this paper, we propose Deep learning based Service Impact Prediction system (DeepSIP), which predicts the service impact of network failure in a network element using a temporal multimodal convolutional neural network (CNN). More precisely, DeepSIP predicts the time to recovery from the failure and the loss of traffic volume due to the failure in a network on the basis of information from syslog messages and traffic volume. Since the time to recovery is useful information for a service level agreement (SLA) and the loss of traffic volume is directly related to the severity of the failure, we regard the time to recovery and the loss of traffic volume as the service impact. The service impact is challenging to predict, since it depends on types of network failures and traffic volume when the failure occurs. Moreover, network elements do not explicitly contain any information about the service impact. To extract the type of network failures and predict the service impact, we use syslog messages and past traffic volume. However, syslog messages and traffic volume are also challenging to analyze because these data are multimodal, are strongly correlated, and have temporal dependencies. To extract useful features for prediction, we develop a temporal multimodal CNN. We experimentally evaluated DeepSIP in terms of accuracy by comparing it with other NN-based methods by using synthetic and real datasets. For both datasets, the results show that DeepSIP outperformed the baselines.

Binary neural networks (BNNs), where both activations and weights are radically quantized to be {-1, +1}, can massively accelerate the run-time performance of convolution neural networks (CNNs) for edge devices, by computation complexity reduction and memory footprint saving. However, the non-differentiable binarizing function used in BNNs, makes the binarized models hard to be optimized, and introduces significant performance degradation than the full-precision models. Many previous works managed to correct the backward gradient of binarizing function with various improved versions of straight-through estimation (STE), or in a gradual approximate approach, but the gradient suppression problem was not analyzed and handled. Thus, we propose a novel gradient corrected approximation (GCA) method to match the discrepancy between binarizing function and backward gradient in a gradual and stable way. Our work has two primary contributions: The first is to approximate the backward gradient of binarizing function using a simple leaky-steep function with variable window size. The second is to correct the gradient approximation by standardizing the backward gradient propagated through binarizing function. Experiment results show that the proposed method outperforms the baseline by 1.5% Top-1 accuracy on ImageNet dataset without introducing extra computation cost.

In this paper, we present an efficient and robust method for estimating Homography matrix for soccer field registration between a captured camera image and a soccer field model. The presented method first detects reliable field lines from the camera image through clustering. Constructing a novel directional feature of the intersection points of the lines in both the camera image and the model, the presented method then finds matching pairs of these points between the image and the model. Finally, Homography matrix estimations and validations are performed using the obtained matching pairs, which can reduce the required number of Homography matrix calculations. Our presented method uses possible intersection points outside image for the point matching. This effectively improves robustness and accuracy of Homography estimation as demonstrated in experimental results.