Because solar energy is intermittent and a ship's power-system load fluctuates and changes abruptly, in this work, the solar radiation parameters were adjusted according to the latitude and longitude of the ship and the change of the sea environment. An objective function was constructed that accounted for the cost and service life simultaneously to optimize the configuration of the marine diesel engine hybrid energy system. Finally, the improved artificial bee colony algorithm was used to optimize and obtain the optimal system configuration. The feasibility of the method was verified by ship navigation tests. This method exhibited better configuration performance optimization than the traditional methods.

A modified whale optimization algorithm (MWOA) with dynamic leader selection mechanism and novel population updating procedure is introduced for pattern synthesis of linear antenna array. The current best solution is dynamic changed for each whale agent to overcome premature with local optima in iteration. A hybrid crossover operator is embedded in original algorithm to improve the convergence accuracy of solution. Moreover, the flow of population updating is optimized to balance the exploitation and exploration ability. The modified algorithm is tested on a 28 elements uniform linear antenna array to reduce its side lobe lever and null depth lever. The simulation results show that MWOA algorithm can improve the performance of WOA obviously compared with other algorithms.

Coherent transceivers are now regarded as promising candidates for upgrading the current 400Gigabit Ethernet (400GbE) transceivers to 800G. However, due to the complicated structure of a dual-polarization IQ modulator (DP-IQM) with its bulky polarization-beam splitter/comber (PBS/PBC), the increase in the transmitter size and cost is inevitable. In this paper, we propose a compact PBS/PBC-free transmitter structure with a straight-line configuration. By using the concept of polarization differential modulation, the proposed transmitter is capable of generating a DP phase-shift-keyed (DP-PSK) signal, which makes it directly applicable to the current coherent systems. A detailed analysis of the system performance reveals that the imperfect equalization and the bandwidth limitation at the receiver are the dominant penalty factors. Although such a penalty is usually unacceptable in long-haul applications, the proposed transmitter can be attractive due to its significant simplicity and compactness for short-reach applications, where the cost and the footprint are the primary concerns.

This paper proposes a parallel peak cancellation (PC) process for the computational complexity-efficient algorithm called PC with a channel-null constraint (PCCNC) in the adaptive peak-to-average power ratio (PAPR) reduction method using the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. By simultaneously adding multiple PC signals to the time-domain transmission signal vector, the required number of iterations of the iterative algorithm is effectively reduced along with the PAPR. We implement a constraint in which the PC signal is transmitted only to the null space in the MIMO channel by beamforming (BF). By doing so the data streams do not experience interference from the PC signal on the receiver side. Since the fast Fourier transform (FFT) and inverse FFT (IFFT) operations at each iteration are not required unlike the previous algorithm and thanks to the newly introduced parallel processing approach, the enhanced PCCNC algorithm reduces the required total computational complexity and number of iterations compared to the previous algorithms while achieving the same throughput-vs.-PAPR performance.

In this letter we analyze the economic worth of GPU on analytical processing of GPU-accelerated database management systems (DBMSes). To this end, we conducted rigorous experiments with TPC-H across three popular GPU DBMSes. Consequently, we show that co-processing with CPU and GPU in the GPU DBMSes was cost-effective despite exposed concerns.

With the widespread diffusion of Internet of Things (IoT), the number of applications using wireless sensor devices are increasing, and Quality of Service (QoS) required for these applications is diversifying. Thus, it becomes difficult to satisfy a variety of QoS with a single wireless system, and many kinds of wireless systems are working in the same domains; time, frequency, and place. This paper considers coexistence environments of ZigBee and Wi-Fi networks, which use the same frequency band channels, in the same place. In such coexistence environments,ZigBee devices suffer radio interference from Wi-Fi networks, which results in severe ZigBee packet losses because the transmission power of Wi-Fi is much higher than that of ZigBee. Many existing methods to avoid interference from Wi-Fi networks focus on only one of time, frequency, or space domain. However, such avoidance in one domain is insufficient particularly in near future IoT environments where more ZigBee devices and Wi-Fi stations transfer more amount of data. Therefore, in this paper, we propose joint channel allocation and routing in both frequency and space domains. Finally, we show the effectiveness of the proposed method by computer simulation.

Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.

In this paper, we used the data set of domain names Global Top 1M provided by Alexa to analyze the effectiveness of Fallback in ECN. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable. By extracting the header fields of the TCP/IP packets, we confirmed that in most regions, connectivity will be slightly improved after Fallback is enabled and Fallback has a positive effect on the total time of the whole access process. Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers.

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.

Computer networks are facing serious threats from the emergence of sophisticated new DGA bots. These DGA bots have their own dictionary, from which they concatenate words to dynamically generate domain names that are difficult to distinguish from human-generated domain names. In this letter, we propose an approach for identifying the callback communications of DGA bots based on relations among the words that constitute the character string of each domain name. Our evaluation indicates high performance, with a recall of 0.9977 and a precision of 0.9869.

Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary flux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to compare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1-FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The developed software, minL1-FMDL, implemented in MATLAB, is available on https://sunflower.kuicr.kyoto-u.ac.jp/~tamura/software, and can be used for genome-scale metabolic network design for metabolite production.

The progress of immersive technology enables researchers and developers to construct work spaces that are freed from real-world constraints. This has motivated us to investigate the role of the human body. In this research, we examine human cognitive behaviors in obtaining an understanding of the width of their virtual body through simple yet meaningful experiments using virtual reality (VR). In the experiments, participants were modeled as an invisible board, and a spherical object was thrown at the participants to provide information for exploring the width of their invisible body. Audio and visual feedback were provided when the object came into contact with the board (body). We first explored how precisely the participants perceived the virtual body width. Next, we examined how the body perception was generated and changed as the trial proceeded when the participants tried to move right or left actively for the avoidance of collision with approaching objects. The results of the experiments indicated that the participants could become successful in avoiding collision within a limited number of trials (14 at most) under the experimental conditions. It was also found that they postponed deciding how much they should move at the beginning and then started taking evasive action earlier as they become aware of the virtual body.

An encoder-decoder (Enc-Dec) model is one of the fundamental architectures in many computer vision applications. One desired property of a trained Enc-Dec model is to feasibly encode (and decode) diverse input patterns. Aiming to obtain such a model, in this paper, we propose a simple method called curiosity-guided fine-tuning (CurioFT), which puts more weight on uncommon input patterns without explicitly knowing their frequency. In an experiment, we evaluated CurioFT in a task of future frame generation with the CUHK Avenue dataset and found that it reduced the mean square error by 7.4% for anomalous scenes, 4.8% for common scenes, and 6.6% in total. Some other experiments with the UCSD dataset further supported the reasonability of the proposed method.

In recent years, checking sleep quality has become essential from a healthcare perspective. In this paper, we propose a respiratory rate (RR) monitoring system that can be used in the bedroom without wearing any sensor devices directly. To develop the system, passive radio-frequency identification (RFID) tags are introduced and attached to a blanket, instead of attaching them to the human body. The received signal strength indicator (RSSI) and phase values of the passive RFID tags are continuously obtained using an RFID reader through antennas located at the bedside. The RSSI and phase values change depending on the respiration of the person wearing the blanket. Thus, we can estimate the RR using these values. After providing an overview of the proposed system, the RR estimation flow is explained in detail. The processing flow includes noise elimination and irregular breathing period estimation methods. The evaluation demonstrates that the proposed system can estimate the RR and respiratory status without considering the user's body posture, body type, gender, or change in the RR.

Thanks to the excellent learning capability of deep convolutional neural networks (CNNs), CNN-based methods have achieved great success in computer vision and image recognition tasks. However, it has turned out that these methods often have inherent vulnerabilities, which makes us cautious of the potential risks of using them for real-world applications such as autonomous driving. To reveal such vulnerabilities, we propose a method of simultaneously attacking monocular depth estimation and optical flow estimation, both of which are common artificial-intelligence-based tasks that are intensively investigated for autonomous driving scenarios. Our method can generate an adversarial patch that can fool CNN-based monocular depth estimation and optical flow estimation methods simultaneously by simply placing the patch in the input images. To the best of our knowledge, this is the first work to achieve simultaneous patch attacks on two or more CNNs developed for different tasks.

Recently, researchers paid more attention on designing optical routers, since they are essential building blocks of all photonic interconnection architectures. Thus, improving them could lead to a spontaneous improvement in the overall performance of the network. Optical routers suffer from the dilemma of increased insertion loss and crosstalk, which upraises the power consumed as the network scales. In this paper, we propose a new 7×7 non-blocking optical router based on the Dimension Order Routing (DOR) algorithm. Moreover, we develop a method that can ensure the least number of MicroRing Resonators (MRRs) in an optical router. Therefore, by reducing these optical devices, the optical router proposed can decrease the crosstalk and insertion loss of the network. This optical router is evaluated and compared to Ye's router and the optimized crossbar for 3D Mesh network that uses XYZ routing algorithm. Unlike many other proposed routers, this paper evaluates optical routers not only from router level prospective yet also consider the overall network level condition. The appraisals show that our optical router can reduce the worst-case network insertion loss by almost 8.7%, 46.39%, 39.3%, and 41.4% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. Moreover, it decreases the Optical Signal-to-Noise Ratio (OSNR) worst-case by almost 27.92%, 88%, 77%, and 69.6% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. It also reduces the power consumption by 3.22%, 23.99%, 19.12%, and 20.18% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively.

In order to improve tracking, interference and multipath mitigation performance from that possible with existing signals, a new Global Navigation Satellite System (GNSS) signal is needed that can offer additional degrees of freedom for shaping its pulse waveform and spectrum. In this paper, a new modulation scheme called Quinary Offset Carrier modulation (QOC) is proposed as a new GNSS signal design. The pulse waveforms of QOC modulation are divided into two types: convex and concave waveforms. QOC modulations can be easily constructed by selecting different modulation parameters. The spectra and autocorrelation characteristics of QOC modulations are investigated and discussed. Simulations and analyses show that QOC modulation can achieve similar performance to traditional BOC modulation in terms of code tracking, anti-multipath, and compatibility. QOC modulation can provide a new option for satellite navigation signal design.

During the recent several years, deep learning has achieved excellent results in image recognition, voice processing, and other research areas, which has set off a new upsurge of research and application. Internal defects and external malicious attacks may threaten the safe and reliable operation of a deep learning system and even cause unbearable consequences. The technology of testing deep learning systems is still in its infancy. Traditional software testing technology is not applicable to test deep learning systems. In addition, the characteristics of deep learning such as complex application scenarios, the high dimensionality of input data, and poor interpretability of operation logic bring new challenges to the testing work. This paper focuses on the problem of test case generation and points out that adversarial examples can be used as test cases. Then the paper proposes MTGAN which is a framework to generate test cases for deep learning image classifiers based on Generative Adversarial Network. Finally, this paper evaluates the effectiveness of MTGAN.

Scalable networking for scientific research data transfer is a vital factor in the progress of data-intensive research, such as collaborative research on observation of black hole. In this paper, investigations of the nature of practical research traffic allow us to introduce optical flow switching (OFS) and contents delivery network (CDN) technologies into a wide area network (WAN) to realize highly scalable networking. To measure the scalability of networks, energy consumption in the WAN is evaluated by considering the practical networking equipment as well as reasonable assumptions on scientific research data transfer networks. In this study, we explore the energy consumption performance of diverse Japan and US topologies and reveal that the energy consumption of a routing and wavelength assignment algorithm in an OFS scheduler becomes the major hurdle when the number of nodes is high, for example, as high as that of the United States of America layer 1 topology. To provide computational scalability of a network dimensioning algorithm for the CDN based WAN, a simple heuristic algorithm for a surrogate location problem is proposed and compared with an optimal algorithm. This paper provides intuitions and design rules for highly scalable research data transfer networks, and thus, it can accelerate technology advancements against the encountering big-science problems.

This letter proposes a downlink multiple-input multiple-output (MIMO) non-orthogonal multiple access technique that mitigates multi-cell interference (MCI) at cell-edge users, regardless of the number of interfering cells, thereby improving the spectral efficiency. This technique employs specific receive beamforming vectors at the cell-edge users in clusters to minimize the MCI. Based on the receive beamforming vectors adopted by the cell-edge users, the transmit beamforming vectors for a base station (BS) and the receive beamforming vectors for cell-center users are designed to eliminate the inter-cluster interference and maximize the spectral efficiency. As each user can directly obtain its own receive beamforming vector, this technique does not require channel feedback from the users to a BS to design the receive beamforming vectors, thereby reducing the system overhead. We also derive the upper bound of the average sum rate achievable using the proposed technique. Finally, we demonstrate through simulations that the proposed technique achieves a better sum rate performance than the existing schemes and that the derived upper bound is valid.