During the detection of maritime targets, the jitter of the shipborne camera usually causes the video instability and the false or missed detection of targets. Aimed at tackling this problem, a novel algorithm for maritime target detection based on the electronic image stabilization technology is proposed in this study. The algorithm mainly includes three models, namely the points line model (PLM), the points classification model (PCM), and the image classification model (ICM). The feature points (FPs) are firstly classified by the PLM, and stable videos as well as target contours are obtained by the PCM. Then the smallest bounding rectangles of the target contours generated as the candidate bounding boxes (bboxes) are sent to the ICM for classification. In the experiments, the ICM, which is constructed based on the convolutional neural network (CNN), is trained and its effectiveness is verified. Our experimental results demonstrate that the proposed algorithm outperformed the benchmark models in all the common metrics including the mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean average precision (mAP) by at least -47.87%, 8.66%, 6.94%, and 5.75%, respectively. The proposed algorithm is superior to the state-of-the-art techniques in both the image stabilization and target ship detection, which provides reliable technical support for the visual development of unmanned ships.

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.

The reconnaissance mode with the cooperation of two unmanned aerial vehicles (UAVs) equipped with airborne visual tracking platforms is a common practice for localizing a target. Apart from the random noises from sensors, the localization performance is much dependent on their cooperative trajectories. In our previous work, we have proposed a cooperative trajectory generating method that proves better than EKF based method. In this letter, an improved online trajectory generating method is proposed to enhance the previous one. First, the least square estimation method has been replaced with a geometric-optimization based estimation method, which can obtain a better estimation performance than the least square method proposed in our previous work; second, in the trajectory optimization phase, the position error caused by estimation method is also considered, which can further improve the optimization performance of the next way points of the two UAVs. The improved method can well be applied to the two-UAV trajectory planning for corporative target localization, and the simulation results confirm that the improved method achieves an obviously better localization performance than our previous method and the EKF-based method.

With the spread of the broadband Internet and high-performance devices, various services have become available anytime, anywhere. As a result, attention is focused on the service quality and Quality of Experience (QoE) is emphasized as an evaluation index from the user's viewpoint. Since QoE is a subjective evaluation metric and deeply involved with user perception and expectation, quantitative and comparative research was difficult because the QoE study is still in its infancy. At present, after tremendous devoted efforts have contributed to this research area, a shape of the QoE management architecture has become clear. Moreover, not only for research but also for business, video streaming services are expected as a promising Internet service incorporating QoE. This paper reviews the present state of QoE studies with the above background and describes the future prospect of QoE. Firstly, the historical aspects of QoE is reviewed starting with QoS (Quality of Service). Secondly, a QoE management architecture is proposed in this paper, which consists of QoE measurement, QoE assessment, QoS-QoE mapping, QoE modeling, and QoE adaptation. Thirdly, QoE studies related with video streaming services are introduced, and finally individual QoE and physiology-based QoE measurement methodologies are explained as future prospect in the field of QoE studies.

This letter presents an efficient technique to reduce the computational complexity involved in training binary convolutional neural networks (BCNN). The BCNN training shall be conducted focusing on the optimization of the sign of each weight element rather than the exact value itself in convention; in which, the sign of an element is not likely to be flipped anymore after it has been updated to have such a large magnitude to be clipped out. The proposed technique does not update such elements that have been clipped out and eliminates the computations involved in their optimization accordingly. The complexity reduction by the proposed technique is as high as 25.52% in training the BCNN model for the CIFAR-10 classification task, while the accuracy is maintained without severe degradation.

In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.

In vacuum-deposited film composed of organic polar molecules, polarization charges appear on the film surface owing to spontaneous orientation of the molecule. Because its density (σpol) determines an amount of accumulation charge (σacc) in organic light-emitting diodes and output power in polar molecular-based vibrational energy generators (VEGs), control of molecular orientation is highly required. Recently, several groups have reported that dipole-dipole interaction between polar molecules induces anti-parallel orientation which does not contribute to σpol. In other words, perturbation inducing the attenuation of the dipole interaction is needed to enhance σpol. In this study, to investigate an effect of light irradiation on σpol, we prepared 1,3,5-tris(1-phenyl-1H-benzimidazol-2-yl)benzene (TPBi) film under illumination during its deposition, and evaluated the σacc in TPBi-based bilayer device, which equals to σpol. We found that the σacc was increased by light irradiation, indicating that average orientation of TPBi is enhanced. These results suggest that light irradiation during device fabrication is promising process for organic electronic devices including polar molecule-based VEGs.

We previously developed a new terahertz (THz) wave detection method that utilizes the effect of nonlinear optical (NLO) polymers. The new method provided us with a gapless detection, a wide detection bandwidth, and a simpler optical geometry in the THz wave detection. In this paper, polarization dependences in THz wave detection by the Stark effect were investigated. The projection model was employed to analyze the polarization dependences and the consistency with experiments was observed qualitatively, surely supporting the use of the first-order Stark effect in this method. The relations between THz wave detection by the Stark effect and Stark spectroscopy or electroabsorption spectroscopy are also discussed.

Improvement of output and lifetime is a problem for biofuel cells. A structure was adopted in which gelation mixed with agarose and fuel (fructose) was sandwiched by electrodes made of graphene-coated carbon fiber. The cathode surface not contacting the gel was exposed to air. In addition, the anode surface not contacting the gel was in contact with fuel liquid to prevent the gel from being dry. The power density of the fuel cell was improved by increasing oxygen supply from air and the lifetime was improved by maintaining wet gel, that is, the proposed structure was a hybrid type having advantages of both fuel gel and fuel liquid. The output increased almost up to that of just using fuel gel and did not decrease significantly over time. The maximum power density in the proposed system was approximately 74.0 µW/cm2, an enhancement of approximately 1.5 times that in the case of using liquid fuel. The power density after 24 h was approximately 46.1 µW/cm2, which was 62% of the initial value.

Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.

This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multi-objective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.

Scalability of distributed DNN training can be limited by slowdown of specific processes due to unexpected hardware failures. We propose a dynamic process exclusion technique so that training throughput is maximized. Our evaluation using 32 processes with ResNet-50 shows that our proposed technique reduces slowdown by 12.5% to 50% without accuracy loss through excluding the slow processes.

We paid attention the amount of change for each resolution by specifying the gaze position of images, and measured accommodation and convergence eye movement when watching high-resolution images. Change of convergence angle and accommodation were like the actual depth composition in the image when images were presented in the high-resolution.

In order to erase data including confidential information stored in storage devices, an unrelated and random sequence is usually overwritten, which prevents the data from being restored. The problem of minimizing the cost for information erasure when the amount of information leakage of the confidential information should be less than or equal to a constant asymptotically has been introduced by T. Matsuta and T. Uyematsu. Whereas the minimum cost for overwriting has been given for general sources, a single-letter characterization for stationary memoryless sources is not easily derived. In this paper, we give single-letter characterizations for stationary memoryless sources under two types of restrictions: one requires the output distribution of the encoder to be independent and identically distributed (i.i.d.) and the other requires it to be memoryless but not necessarily i.i.d. asymptotically. The characterizations indicate the relation among the amount of information leakage, the minimum cost for information erasure and the rate of the size of uniformly distributed sequences. The obtained results show that the minimum costs are different between these restrictions.

In a network topology design problem, it is important to analyze the reliability and construction cost of complex network systems. This paper addresses a topological optimization problem of minimizing the total cost of a network system with separate subsystems under a reliability constraint. To solve this problem, we develop three algorithms. The first algorithm finds an exact solution. The second one finds an exact solution, specialized for a system with identical subsystems. The third one is a heuristic algorithm, which finds an approximate solution when a network system has several identical subsystems. We also conduct numerical experiments and demonstrate the efficacy and efficiency of the developed algorithms.

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.

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.

We have proposed a waveguide to microstrip line transition, which perpendicularly connects one waveguide into two microstrip lines. It consists of only a waveguide and a dielectric substrate with copper foils. A backshort waveguide for typical transitions is not needed. Additionally, the transition does not require via holes on the substrate. These innovations simplify the structure and the manufacturing process. We assume that our transition and antennas are co-located on the substrate. We reduced the undesirable radiation from the transition so as not to contaminate the desirable radiation pattern. In this paper, we address output phase of our transition. Since the transition has two MSL output ports connecting to different radiation elements, the phase error between two dividing signals leads to beam shift in the radiation pattern. Unfortunately, misalignment of etching pattern between copper layers of the substrate is unavoidable. The structural asymmetry causes the phase error. In order to tolerate the misalignment, we propose to add a pair of open stubs to the transition. We show that the structure drastically stabilizes the output phase. Though the stubs create some extra radiation, we confirm that the impact is not significant. Moreover, we fabricate and measure a prototype antenna that uses the transition. In the case of with stubs, the radiation pattern is unchanged even if the misalignment is severe.