The increased performance of mobile terminals has made it feasible to collect data using users' terminals. By making the best use of the network performance data widely collected in this way, network operators should deeply understand the current network conditions, identify the performance-degraded components in the network, and estimate the degree of their performance degradation. For their demands, one powerful solution with such end-to-end data measured by users' terminals is network tomography. Meanwhile, with the advance of network virtualization by software-defined networking, routing is dynamically changed due to congestion or other factors, and each end-to-end measurement flow collected from users may pass through different paths between even the same origin-destination node pair. Therefore, it is difficult and costly to identify through which path each measurement flow has passed, so it is also difficult to naively apply conventional network tomography to such networks where the measurement paths cannot be uniquely determined. We propose a novel network tomography for the networks with undeterministic routing where the measurement flows pass through multiple paths in spite of the origin-destination node pair being the same. The basic idea of our method is to introduce routing probability in accordance with the aggregated information of measurement flows. We present two algorithms and evaluate their performances by comparing them with algorithms of conventional tomography using determined routing information. Moreover, we verify that the proposed algorithms are applicable to a more practical network.

The purpose of this paper is to find an automated pricing algorithm to calculate the real cost of each product by considering the associate costs of the business. The methodology consists of two main stages. A brief semi-structured survey and a mathematical calculation the expenses and adding them to the original cost of the offered products and services. The output of this process obtains the minimum recommended selling price (MRSP) that the business should not go below, to increase the likelihood of generating profit and avoiding the unexpected loss. The contribution of this study appears in filling the gap by calculating the minimum recommended price automatically and assisting businesses to foresee future budgets. This contribution has a certain limitation, where it is unable to calculate the MRSP of the in-house created products from raw materials. It calculates the MRSP only for the products bought from the wholesaler to be sold by the retailer.

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.

We investigated the function in the Lateral Geniculate Nucleus of avoidance behavior due to the inconsistency between binocular retinal images due to blue from vergence eye movement based on avoidance behavior caused by the inconsistency of binocular retinal images when watching the rim of a blue-yellow equiluminance column.

This paper formulates minimal word-line (WL) delay time with pre-emphasis pulses to design the pulse width as a function of the overdrive voltage for large memory arrays such as 3D NAND. Circuit theory for a single RC line only with capacitance to ground and that only with coupling capacitance as well as a general case where RC lines have both grounded and coupling capacitance is discussed to provide an optimum pre-emphasis pulse width to minimize the delay time. The theory is expanded to include the cases where the resistance of the RC line driver is not negligibly small. The minimum delay time formulas of a single RC delay line and capacitive coupling RC lines was in good agreement (i.e. within 5% error) with measurement. With this research, circuit designers can estimate an optimum pre-emphasis pulse width and the delay time for an RC line in the initial design phase.

We propose a video magnification method for magnifying subtle color and motion changes under the presence of non-meaningful background motions. We use frequency variability to design a filter that passes only meaningful subtle changes and removes non-meaningful ones; our method obtains more impressive magnification results without artifacts than compared methods.

In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.

In this paper, we propose an active calibration algorithm to tackle both gain-phase errors and position perturbations. Unlike many other active calibration methods, which fix the array while changing the location of the source, our approach rotates the array but does not change the location of the source, and knowledge of the direction-of-arrival (DOA) of the far-field calibration source is not required. The superiority of the proposed method lies in the fact that measurement of the direction of a far-field calibration source is not easy to carry out, while measurement of the rotation angle via the proposed calibration strategy is convenient and accurate. To obtain the receiving data from different directions, the sensor array is rotated to three different positions with known rotation angles. Based on the eigen-decomposition of the data covariance matrices, we can use the direction of the auxiliary source to represent the gain-phase errors and position perturbations. After that, we estimate the DOA of the calibration source by a one-dimensional search. Finally, the sensor gain-phase errors and position perturbations are calculated by using the estimated direction of the calibration source. Simulations verify the effectiveness and performance of the algorithm.

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.

Content-based image retrieval has been a hot topic among computer vision researchers for a long time. There have been many advances over the years, one of the recent ones being deep metric learning, inspired by the success of deep neural networks in many machine learning tasks. The goal of metric learning is to extract good high-level features from image pixel data using neural networks. These features provide useful abstractions, which can enable algorithms to perform visual comparison between images with human-like accuracy. To learn these features, supervised information of image similarity or relative similarity is often used. One important issue in deep metric learning is how to define similarity for multi-label or multi-object scenes in images. Traditionally, pairwise similarity is defined based on the presence of a single common label between two images. However, this definition is very coarse and not suitable for multi-label or multi-object data. Another common mistake is to completely ignore the multiplicity of objects in images, hence ignoring the multi-object facet of certain types of datasets. In our work, we propose an approach for learning deep image representations based on the relative similarity of both multi-label and multi-object image data. We introduce an intuitive and effective similarity metric based on the Jaccard similarity coefficient, which is equivalent to the intersection over union of two label sets. Hence we treat similarity as a continuous, as opposed to discrete quantity. We incorporate this similarity metric into a triplet loss with an adaptive margin, and achieve good mean average precision on image retrieval tasks. We further show, using a recently proposed quantization method, that the resulting deep feature can be quantized whilst preserving similarity. We also show that our proposed similarity metric performs better for multi-object images than a previously proposed cosine similarity-based metric. Our proposed method outperforms several state-of-the-art methods on two benchmark datasets.

In medical fields, detecting traumatic bleedings has always been a difficult task due to the small size, low contrast of targets and large number of images. In this work we propose an automatic traumatic bleeding detection approach from contrast enhanced CT images via deep CNN networks, containing segmentation process and classification process. CT values of DICOM images are extracted and processed via three different window settings first. Small 3D patches are cropped from processed images and segmented by a 3D CNN network. Then segmentation results are converted to point cloud data format and classified by a classifier. The proposed pre-processing approach makes the segmentation network be able to detect small and low contrast targets and achieve a high sensitivity. The additional classification network solves the boundary problem and short-sighted problem generated during the segmentation process to further decrease false positives. The proposed approach is tested with 3 CT cases containing 37 bleeding regions. As a result, a total of 34 bleeding regions are correctly detected, the sensitivity reaches 91.89%. The average false positive number of test cases is 1678. 46.1% of false positive predictions are decreased after being classified. The proposed method is proved to be able to achieve a high sensitivity and be a reference of medical doctors.

Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.

In this paper, we present a novel portrait impression estimation method using nine pairs of semantic impression words: bitter-majestic, clear-pure, elegant-mysterious, gorgeous-mature, modern-intellectual, natural-mild, sporty-agile, sweet-sunny, and vivid-dynamic. In the first part of the study, we analyzed the relationship between the facial features in deformed portraits and the nine semantic impression word pairs over a large dataset, which we collected by a crowdsourcing process. In the second part, we leveraged the knowledge from the results of the analysis to develop a ranking network trained on the collected data and designed to estimate the semantic impression associated with a portrait. Our network demonstrated superior performance in impression estimation compared with current state-of-the-art methods.

The hybrid precoding (HP) technique has been widely considered as a promising approach for millimeter wave communication systems. In general, the existing HP structure with a complicated high-resolution phase shifter network can achieve near-optimal spectral efficiency, however, it involves high energy consumption. The HP architecture with an energy-efficient switch network can significantly reduce the energy consumption. To achieve maximum energy efficiency, this paper focuses on the HP architecture with switch network and considers a novel adaptive analog network HP structure for such mmWave MIMO systems, which can provide potential array gains. Moreover, a multiuser adaptive coordinate update algorithm is proposed for the HP design problem of this new structure. Simulation results verify that our proposed design can achieve better energy efficiency than other recently proposed HP schemes when the number of users is small.

In this paper, we propose a direct digital RF transmitter with a 1-bit band-pass delta-sigma modulator (BP-DSM) that uses high order image components of the 7th Nyquist zone in Manchester coding for microwave and milimeter wave application. Compared to the conventional non-return-to-zero (NRZ) coding, in which the high order image components of 1-bit BP-DSM attenuate severely in the form of sinc function, the proposed 1-bit direct digital RF transmitter in Manchester code can improve the output power and signal-to-noise ratio (SNR) of the image components at specific (4n-1)th and (4n-2)th Nyquist Zone, which is confirmed by calculating of the power spectral density. Measurements are made to compare three types of 1-bit digital-to-analog converter (DAC) signal in output power and SNR; NRZ, 50% duty return-to-zero (RZ) and Manchester coding. By using 1 Vpp/8Gbps DAC output, 1-bit signals in Manchester coding show the highest output power of -20.3dBm and SNR of 40.3dB at 7th Nyquist Zone (26GHz) in CW condition. As a result, compared to NRZ and RZ coding, at 7th Nyquist zone, the output power is improved by 8.1dB and 6dB, respectively. Meanwhile, the SNR is improved by 7.6dB and 4.9dB, respectively. In 5Mbps-QPSK condition, 1-bit signals in Manchester code show the lowest error vector magnitude (EVM) of 2.4% and the highest adjacent channel leakage ratio (ACLR) of 38.2dB with the highest output power of -18.5dBm at 7th Nyquist Zone (26GHz), respectively, compared to the NRZ and 50% duty RZ coding. The measurement and simulation results of the image component of 1-bit signals at 7th Nyquist Zone (26GHz) are consistent with the calculation results.

We fabricated silicon nanowires (SiNWs) using a metal-catalyzed electroless etching method, which is known to be a low-cost and simple technique. The SiNW arrays with a length of 540 nm were used as a substrate of SiNWs/PEDOT:PSS hybrid solar cell. Furthermore, gold nanoparticles (AuNPs) were used to improve the light absorption of the device due to localized surface plasmon excitation. The results show that the short-circuit current density and the power conversion efficiency increased from 22.1 mA/cm2 to 26.0 mA/cm2 and 6.91% to 8.56%, respectively. The advantage of a higher interface area between the organic and inorganic semiconductors was established by using SiNW arrays and higher absorption light incorporated with AuNPs for improving the performance of the developed solar cell.

In this paper, we explore the physical layer security of an Internet of Things (IoT) network comprised of multiple relay-user pairs in the presence of multiple malicious eavesdroppers and channel estimation error (CEE). In order to guarantee secure transmission with channel estimation error, we propose a channel estimation error oriented joint relay-user pair and friendly jammer selection (CEE-JRUPaFJS) scheme to improve the physical layer security of IoT networks. For the purpose of comparison, the channel estimation error oriented traditional round-robin (CEE-TRR) scheme and the channel estimation error oriented traditional pure relay-user pair selection (CEE-TPRUPS) scheme are considered as benchmark schemes. The exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the CEE-TRR and CEE-TPRUPS schemes as well as the CEE-JRUPaFJS scheme are derived over Rayleigh fading channels, which are employed to characterize network reliability and security, respectively. Moreover, the security-reliability tradeoff (SRT) is analyzed as a metric to evaluate the tradeoff performance of CEE-JRUPaFJS scheme. It is verified that the proposed CEE-JRUPaFJS scheme is superior to both the CEE-TRR and CEE-TPRUPS schemes in terms of SRT, which demonstrates our proposed CEE-JRUPaFJS scheme are capable of improving the security and reliability performance of IoT networks in the face of multiple eavesdroppers. Moreover, as the number of relay-user pairs increases, CEE-TPRUPS and CEE-JRUPaFJS schemes offer significant increases in SRT. Conversely, with an increasing number of eavesdroppers, the SRT of all these three schemes become worse.

It is found that the electrical resistance-length characteristic in an electroactive supercoiled polymer artificial muscle strongly depends on the temperature. This may come from the thermal expansion of coils in the artificial muscle, which increases the contact area of neighboring coils and results in a lower electrical resistance at a higher temperature. On the other hand, the electrical resistance-length characteristic collected during electrical driving seriously deviates from those collected at constant temperatures. Inhomogeneous heating during electrical driving seems to be a key for the deviation.

Automatic generation of textual stories from visual data representation, known as visual storytelling, is a recent advancement in the problem of images-to-text. Instead of using a single image as input, visual storytelling processes a sequential array of images into coherent sentences. A story contains non-visual concepts as well as descriptions of literal object(s). While previous approaches have applied external knowledge, our approach was to regard the non-visual concept as the semantic correlation between visual modality and textual modality. This paper, therefore, presents new features representation based on a canonical correlation analysis between two modalities. Attention mechanism are adopted as the underlying architecture of the image-to-text problem, rather than standard encoder-decoder models. Canonical Correlation Attention Mechanism (CAAM), the proposed end-to-end architecture, extracts time series correlation by maximizing the cross-modal correlation. Extensive experiments on VIST dataset ( http://visionandlanguage.net/VIST/dataset.html ) were conducted to demonstrate the effectiveness of the architecture in terms of automatic metrics, with additional experiments show the impact of modality fusion strategy.

Online learning is a method which updates the model gradually and can modify and strengthen the previous model, so that the updated model can adapt to the new data without having to relearn all the data. However, the accuracy of the current online multiclass learning algorithm still has room for improvement, and the ability to produce sparse models is often not strong. In this paper, we propose a new Multiclass Truncated Gradient Confidence-Weighted online learning algorithm (MTGCW), which combine the Truncated Gradient algorithm and the Confidence-weighted algorithm to achieve higher learning performance. The experimental results demonstrate that the accuracy of MTGCW algorithm is always better than the original CW algorithm and other baseline methods. Based on these results, we applied our algorithm for phishing website recognition and image classification, and unexpectedly obtained encouraging experimental results. Thus, we have reasons to believe that our classification algorithm is clever at handling unstructured data which can promote the cognitive ability of computers to a certain extent.