In this paper, to improve communication system's energy-efficiency (EE), multi-case optimization of two new transmission strategies is investigated. Firstly, with amplify-and-forward relaying and full-duplex technique, two new transmission strategies are designed. The designed transmission strategies consider direct links and non-ideal transmission conditions. At the same time, detailed capacity and energy consumption analyses of the designed transmission strategies are given. In addition, EE optimization and analysis of the designed transmission strategies are studied. It is the first case of EE optimization and it is achieved by joint optimization of transmit time (TT) and transmit power (TP). Furthermore, the second and third cases of EE optimization with respectively optimizing TT and TP are given. Simulations reveal that the designed transmission strategies can effectively improve the communication system's EE.

The existing research on Amdahl's law is limited to multi/many-core processors, and cannot be applied to the important parallel processing architecture of coarse-grained reconfigurable arrays. This paper studies the relation between the multi-level parallelism of block cipher algorithms and the architectural characteristics of coarse-grain reconfigurable arrays. We introduce the key variables that affect the performance of reconfigurable arrays, such as communication overhead and configuration overhead, into Amdahl's law. On this basis, we propose a performance model for coarse-grain reconfigurable block cipher array (CGRBA) based on the extended Amdahl's law. In addition, this paper establishes the optimal integer nonlinear programming model, which can provide a parameter reference for the architecture design of CGRBA. The experimental results show that: (1) reducing the communication workload ratio and increasing the number of configuration pages reasonably can significantly improve the algorithm performance on CGRBA; (2) the communication workload ratio has a linear effect on the execution time.

This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a twodimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.

With the spread of the 5th generation mobile phone, the increase of the output power of PA (power amplifier) has become important, and in recent years, differential amplifiers that can increase the output voltage amplitude for the power supply voltage have been examined from the viewpoint of power synthesis. In the case of a differential PA, in addition to the advantage of voltage amplitude, the load impedance can be set 4 times as much as that of a single-ended PA, which makes it possible to reduce the impact of parasitic resistance. With the study of the differential PA, many transformer matching circuits have been studied in addition to the LC matching circuits that have been widely used in the past. The transformer matching circuit can easily realize the differential-single conversion, and the transformer matching circuit is an indispensable technology in the differential PA. As with the LC matching circuit, widening the bandwidth of the transformer matching circuit is at issue. In this paper, characteristics of basic transformer matching circuits are analyzed by adding input/output shunt capacitance to transformers and the conditions of bandwidth improvement are clarified. In addition, by comparing the FBW (fractional bandwidth) with the LC 2-stage matching circuit, it is shown that the FBW can be competitive.

Millimeter wave synthetic aperture interferometric radiometers (SAIR) are very powerful instruments, which can effectively realize high-precision imaging detection. However due to the existence of interference factor and complex near-field error, the imaging effect of near-field SAIR is usually not ideal. To achieve better imaging results, a new fully connected imaging network (FCIN) is proposed for near-field SAIR. In FCIN, the fully connected network is first used to reconstruct the image domain directly from the visibility function, and then the residual dense network is used for image denoising and enhancement. The simulation results show that the proposed FCIN method has high imaging accuracy and shorten imaging time.

In the fifth-generation mobile communications system (5G), it is critical to ensure wireless security as well as large-capacity and high-speed communication. To achieve this, a chaos modulation method as an encrypted and channel-coded modulation method in the physical layer is proposed. However, in the conventional chaos modulation method, the decoding complexity increases exponentially with respect to the modulation order. To solve this problem, in this study, a hybrid modulation method that applies quadrature amplitude modulation (QAM) and chaos to reduce the amount of decoding complexity, in which some transmission bits are allocated to QAM while maintaining the encryption for all bits is proposed. In the proposed method, a low-complexity decoding method is constructed by ordering chaos and QAM symbols based on the theory of index modulation. Numerical results show that the proposed method maintains good error-rate performance with reduced decoding complexity and ensures wireless security.

This paper proposes a fast and scalable traffic monitoring system called Fast xFlow Proxy. For efficiently provisioning and operating networks, xFlow such as IPFIX and NetFlow is a promising technology for visualizing the detailed traffic matrix in a network. However, internet protocol (IP) packets in a large carrier network are encapsulated with various outer headers, e.g., layer 2 tunneling protocol (L2TP) or multi-protocol label switching (MPLS) labels. As native xFlow technologies are applied to the outer header, the desired inner information cannot be visualized. From this motivation, we propose Fast xFlow Proxy, which explores the complicated carrier's packet, extracts inner information properly, and relays the inner information to a general flow collector. Fast xFlow Proxy should be able to handle various packet processing operations possible (e.g., header analysis, header elimination, and statistics) at a wire rate. To realize the processing speed needed, we implement Fast xFlow Proxy using the data plane development kit (DPDK) and field-programmable gate array (FPGA). By optimizing deployment of processes between DPDK and FPGA, Fast xFlow Proxy achieves wire rate processing. From evaluations, we can achieve over 20 Gbps performance by using a single server and 100 Gbps performance by using scale-out architecture. We also show that this performance is sufficiently practical for monitoring a nationwide carrier network.

This paper reports on X-band Gallium Nitride (GaN) chipsets for cost-effective 20W transmit-receive (T/R) modules. The chipset components include a GaN-on-Si monolithic microwave integrated circuit (MMIC) driver amplifier (DA), a GaN-on-SiC high power amplifier (HPA) with GaAs matching circuits, a high-gain GaN-on-Si HPA with a GaAs output matching circuit, and a GaN-on-Si MMIC switch (SW). By utilizing either combination of the DA or single high-gain HPA, the configurations of two T/R module types can be realized. The GaN-on-Si MMIC DA demonstrates an output power of 6.4-7.4W, an associate gain of 22.3-24.6dB and a power added efficiency (PAE) of 32-36% over 9.0-11.0GHz. A GaN-on-SiC HPA with GaAs matching circuits exhibited an output power of 20-28W, associate gain of 7.8-10.7dB, and a PAE of 40-56% over 9.0-11.0GHz. The high-gain GaN-on-Si HPA with a GaAs output matching circuit exhibits an output power of 15-30W, associate gain of 27-30dB, and PAE of 26-33% over 9.0-11.0GHz. The GaN-on-Si MMIC switch demonstrates insertion losses of 1.1-1.3dB and isolation of 10.1-14.7dB over 8.0-11.5GHz. By employing cost-effective circuit configurations, the costs of these chipsets are estimated to be about half that of conventional chipsets.

In this paper, event-triggered control over a sensor network is studied as one of the control methods of cyber-physical systems. Event-triggered control is a method that communications occur only when the measured value is widely changed. In the proposed method, by solving an LMI (Linear Matrix Inequality) feasibility problem, an event-triggered output feedback controller such that the closed-loop system is asymptotically stable is derived. First, the problem formulation is given. Next, the control problem is reduced to an LMI feasibility problem. Finally, the proposed method is demonstrated by a numerical example.

Because processes run concurrently in multitask systems, the size of the state space grows exponentially. Therefore, it is not straightforward to formally verify that such systems enjoy desired properties. Real-time constrains make the formal verification more challenging. In this paper, we propose the following to address the challenge: (1) a way to model multitask real-time systems as observational transition systems (OTSs), a kind of state transition systems, (2) a way to describe their specifications in CafeOBJ, an algebraic specification language, and (3) a way to verify that such systems enjoy desired properties based on such formal specifications by writing proof scores, proof plans, in CafeOBJ. As a case study, we model Fischer's protocol, a well-known real-time mutual exclusion protocol, as an OTS, describe its specification in CafeOBJ, and verify that the protocol enjoys the mutual exclusion property when an arbitrary number of processes participates in the protocol*.

Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.

Autonomous driving technology is currently attracting a lot of attention as a technology that will play a role in the next generation of mobility. For autonomous driving in urban areas, it is necessary to recognize various information. Especially, the recognition of traffic lights is important in crossing intersections. In this paper, traffic light recognition technology developed by the authors was evaluated using onboard sensor data during autonomous driving in the Tokyo waterfront area as an example of traffic light recognition technology. Based on the results, it was found that traffic lights could be recognized with an accuracy of approximately 99% to carry out the decision making for intersection approaching. However, from the evaluation results, it was also confirmed that traffic light recognition became difficult under situations involving occlusion by other object, background assimilation, nighttime conditions, and backlight by sunlight. It was also confirmed that these effects are mostly temporary, and do not significantly affect decision-making to enter intersections as a result of utilizing information from multiple traffic lights installed at an intersection. On the other hand, it is expected that recognition with current onboard cameras will become technically difficult during situations in which not all traffic lights are visually recognizable due to the effects of back or front light by sunlight when stopped at the stop line of an intersection. This paper summarizes these results and presents the necessity of appropriate traffic light installation on the assumption of recognition by onboard cameras.

The unit commitment problem (UCP) is the problem of deciding up/down and generation-level patterns of energy production units. Due to the expansion of distributed energy resources and the liberalization of energy trading in recent years, solving the distributed UCP (DUCP) is attracting the attention of researchers. Once an up/down pattern is determined, the generation-level pattern can be decided distributively using the alternating direction method of multipliers (ADMM). However, ADMM does not guarantee convergence when deciding both up/down and generation-level patterns. In this paper, we propose a method to solve the DUCP using ADMM and constraint optimization programming. Numerical experiments show the efficacy of the proposed method.

We consider a reliable decentralized supervisory control problem for discrete event systems in the inference-based framework. This problem requires us to synthesize local supervisors such that the controlled system achieves the specification and is nonblocking, even if local control decisions of some local supervisors are not available for making the global control decision. In the case of single-level inference, we introduce a notion of reliable 1-inference-observability and show that reliable 1-inference-observability together with controllability and Lm(G)-closedness is a necessary and sufficient condition for the existence of a solution to the reliable decentralized supervisory control problem.

Real-time and scalable multicast services are of paramount importance to Industrial Internet of Things (IIoT) applications. To realize these services, the multicast algorithm should, on the one hand, ensure the maximum delay of a multicast session not exceeding its upper delay bound. On the other hand, the algorithm should minimize session costs. As an emerging networking paradigm, Software-defined Networking (SDN) can provide a global view of the network to multicast algorithms, thereby bringing new opportunities for realizing the desired multicast services in IIoT environments. Unfortunately, existing SDN-based multicast (SDM) algorithms cannot meet the real-time and scalable requirements simultaneously. Therefore, in this paper, we focus on SDM algorithm design for IIoT environments. To be specific, the paper first converts the multicast tree construction problem for SDM in IIoT environments into a delay-bounded least-cost shared tree problem and proves that it is an NP-complete problem. Then, the paper puts forward a shared tree (ST) algorithm called SDM4IIoT to compute suboptimal solutions to the problem. The algorithm consists of five steps: 1) construct a delay-optimal shared tree; 2) divide the tree into a set of subpaths and a subtree; 3) optimize the cost of each subpath by relaxing the delay constraint; 4) optimize the subtree cost in the same manner; 5) recombine them into a shared tree. Simulation results show that the algorithm can provide real-time support that other ST algorithms cannot. In addition, it can achieve good scalability. Its cost is only 20.56% higher than the cost-optimal ST algorithm. Furthermore, its computation time is also acceptable. The algorithm can help to realize real-time and scalable multicast services for IIoT applications.

This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.

This paper proposes a novel maximum Doppler frequency detection technique for user moving velocity estimation. The maximum Doppler frequency is estimated in the proposed detection technique by making use of the fact that user moving velocity is not distributed continuously. The fluctuation of the channel state information during a packet is applied for the proposed detection, in which likelihood estimation is performed by comparing the fluctuation with the thresholds. The thresholds are theoretically derived on the assumption that the fluctuation is distributed with an exponential function. An approximated detection technique is proposed to simplify the theoretical threshold derivation. The performance of the proposed detection is evaluated by computer simulation. The proposed detection accomplishes better detection performance as the fluctuation values are summed over more packets. The proposed detection achieves about 90% correct detection performance in a fading channel with the Eb/N0 = 35dB, when the fluctuation values are summed over only three packets. Furthermore, the approximated detection also achieves the same detection performance.

Although deep neural networks (DNNs) have achieved high performance across a variety of applications, they can often be deceived by adversarial examples that are generated by adding small perturbations to the original images. Adversaries may generate adversarial examples using the property of transferability, in which adversarial examples that deceive one model can also deceive other models because adversaries do not obtain any information on the DNNs deployed in real scenarios. Recent studies show that adversarial examples with feature space perturbations are more transferable than others. Adversarial training is an effective method to defend against adversarial attacks. However, it results in a decrease in the classification accuracy for natural images, and it is not sufficiently robust against transferable adversarial examples because it does not consider adversarial examples with feature space perturbations. We propose a novel adversarial training method to train DNNs to be robust against transferable adversarial examples and maximize their classification accuracy for natural images. The proposed method trains DNNs to correctly classify natural images and adversarial examples and also minimize the feature differences between them. The robustness of the proposed method was similar to those of the previous adversarial training methods for MNIST dataset and was up to average 6.13% and 9.24% more robust against transfer adversarial examples for CIFAR-10 and CIFAR-100 datasets, respectively. In addition, the proposed method yielded an average classification accuracy that was approximately 0.53%, 6.82%, and 10.60% greater than some state-of-the-art adversarial training methods for all datasets, respectively. The proposed method is robust against a variety of transferable adversarial examples, which enables its implementation in security applications that may benefit from high-performance classification but are at high risk of attack.

In this letter, we discuss the ambiguity function of interleaved sequences. Furthermore, using the Guassian sum and choosing binary m-sequences as column sequences, we investigate the property of a binary sequence set given by Zhou, Tang, Gong (IEEE Trans. Inf. Theory, 54(9), 2008), which has low ambiguity property in a large region. Those sequences could be used in radar systems.

Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.