In this paper, we propose a precise indoor localization method using visible light communication (VLC) with dual-facing cameras on a smart device (mobile phone, smartphone, or tablet device). This approach can assist the visually impaired with navigation, or provide mobile-robot control. The proposed method is different from conventional techniques in that dual-facing cameras are used to expand the localization area. The smart device is used as the receiver, and light-emitting diodes on the ceiling are used as localization landmarks. These are identified by VLC using a rolling shutter effect of complementary metal-oxide semiconductor image sensors. The front-facing camera captures the direct incident light of the landmarks, while the rear-facing camera captures mirror images of landmarks reflected from the floor face. We formulated the relationship between the poses (position and attitude) of the two cameras and the arrangement of landmarks using tilt detection by the smart device accelerometer. The equations can be analytically solved with a constant processing time, unlike conventional numerical methods, such as least-squares. We conducted a simulation and confirmed that the localization area was 75.6% using the dual-facing cameras, which was 3.8 times larger than that using only the front-facing camera. As a result of the experiment using two landmarks and a tablet device, the localization error in the horizontal direction was less than 98 mm at 90% of the measurement points. Moreover, the error estimation index can be used for appropriate route selection for pedestrians.

Novel roll-to-roll (R2R) deposition and patterning of ITO on ultra-thin glass were developed with no photolithography and applied to flexible organic light emitting diodes (OLEDs). The developed deposition consists of low temperature sputtering and annealing. The developed patterning utilizes an etching paste printed by novel R2R screen printing.

In this paper, we propose a safe and comprehensive route finding algorithm for pedestrians based on lighting and landmark conditions. Safety and comprehensiveness can be predicted by the five possible indicators: (1) lighting conditions, (2) landmark visibility, (3) landmark effectiveness, (4) turning counts along a route, and (5) road widths. We first investigate impacts of these five indicators on pedestrians' perceptions on safety and comprehensiveness during route findings. After that, a route finding algorithm is proposed for pedestrians. In the algorithm, we design the score based on the indicators (1), (2), (3), and (5) above and also introduce a turning count reduction strategy for the indicator (4). Thus we find out a safe and comprehensive route through them. In particular, we design daytime score and nighttime score differently and find out an appropriate route depending on the time periods. Experimental simulation results demonstrate that the proposed algorithm obtains higher scores compared to several existing algorithms. We also demonstrate that the proposed algorithm is able to find out safe and comprehensive routes for pedestrians in real environments in accordance with questionnaire results.

This study investigates whether learners consider constraints while posing arithmetic word problems. Through log data from an interactive learning environment, we analyzed actions of 39 first grade elementary school students and conducted correlation analysis between the frequency of actions and validity of actions. The results show that the learners consider constraints while posing arithmetic word problems.

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.

This letter considers the robust Tomlinson-Harashima Precoding(THP) transceiver design for Multiple-Input Multiple-Output (MIMO) interference channel (IC). Assuming bounded channel state information (CSI) error, we deal with the optimization for minimizing the worst case per-user mean square error (MSE) and sum MSE. We present an approximate approach to derive the upper bound of the constraint leading to less semidefinite. Then the alternate approach is adopted to update the receiver matrix by solving second-order-cone programming (SOCP), and update the transmitter matrix and feedback matrix by solving semidefinite program (SDP), respectively. Simulation results show that the proposed method achieves similar performance of the S-procedure method, whereas the computation complexity is reduced significantly, especially for the system with large number of transmit antennas.

Mixed noise removal is a major problem in image processing. Different noises have different properties and it is required to use an appropriate removal method for each noise. Therefore, removal of mixed noise needs the combination of removal algorithms for each contained noise. We aim at the removal of the mixed noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN). Many conventional methods cannot remove the mixed noise effectively and may lose image details. In this paper, we propose a new mixed noise removal method utilizing Direction Weighted Median filter (DWM filter) and Block Matching and 3D filtering method (BM3D). Although the combination of the DWM filter for RVIN and BM3D for AWGN removes almost all the mixed noise, it still loses some image details. We find the cause in the miss-detection of the image details as RVIN and solve the problem by re-detection with the difference of an input noisy image and the output by the combination. The re-detection process removes only salient noise which BM3D cannot remove and therefore preserves image details. These processes lead to the high performance removal of the mixed noise while preserving image details. Experimental results show our method obtains denoised images with clearer edges and textures than conventional methods.

This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.

Coaxial connectors are extensively used in electrical systems and the degradation of the connector can alter the signal that is being transmitted and leads to faults, which is one of the major causes of low communication quality. In this work, the failure features caused by the degraded connector contact surface were studied. The relationship between the DC resistance and decreased real contact areas was given. Considering the inductance properties and capacitive coupling at high frequencies, the impedance characteristics of the degraded connector were discussed. Based on the transmission line theory and experimental measurement, an equivalent lump circuit of the coaxial connector was developed. For the degraded contact surface, the capacitance was analyzed, and the frequency effect was investigated. According to the high frequency characteristics of the degraded connector, a fault detection and location method for coaxial connectors in RF system was developed using a neural network method. For connectors suffering from different levels of pollution, their impedance modulus varies continuously. Considering the range of the connector's impedance parameters, the fault modes were determined. Based on the scattering parameter simulation of a RF receiver front-end circuit, the S11 and S21 parameters were obtained as feature parameters and Monte Carlo simulations were conducted to generate training and testing samples. Based on the BP neural network algorithm, the fault modes were classified and the results show the diagnosis accuracy was 97.33%.

Cloud bursting temporarily expands the capacity of a cloud-based service hosted in a private data center by renting public data center capacity when the demand for capacity spikes. To determine the optimal resources of a business-critical web system deployed over private and public data centers, this paper presents a cloud bursting approach based on long- and short-term predictions of requests to the system. In a private data center, a dedicated pool of virtual machines (VMs) is assigned to the web system on the basis of one-week predictions. Moreover, in both private and public data centers, VMs are activated on the basis of one-hour predictions. We formulate a problem that includes the total cost and response time constraints and conduct numerical simulations. The results indicate that our approach is tolerant of prediction errors and only slightly dependent on the processing power of a single VM. Even if the website receives bursty requests and one-hour predictions include a mean absolute percentage error (MAPE) of 0.2, the total cost decreases to half the existing cost of provisioning in the private date center alone. At the same time, 95% of response time is kept below 0.15s.

Recent Network on Chip (NoC) design must take the thermal issue into consideration due to its great impact on the network performance and reliability, especially for 3D NoC. In this work, we design a virtual channel based fully adaptive routing algorithm for the runtime 3D NoC thermal-aware management. To improve the network throughput and latency, we use two virtual channels for each horizontal direction and design a routing function which can not only avoid deadlock and livelock, but also ensure high adaptivity and routability in the throttled network. For path selection, we design a strategy that takes priority to the distance, but also considers path diversity and traffic state. For throttling information collection, instead of transmitting the topology information of the whole network, we use a 12 bits register to reserve the router state for one hop away, which saves the hardware cost largely and decreases the network latency. In the experiments, we test our proposed routing algorithm in different states with different sizes, and the proposed algorithm shows better network latency and throughput with low power compared with traditional algorithms.

In this paper, we consider the crosscorrelation of two interleaved sequences of period 4N constructed by Gong and Tang which has been proved to possess optimal autocorrelation. Results show that the interleaved sequences achieve the largest crosscorrelation value 4.

Microwave imaging techniques, in particular, synthetic aperture radar (SAR), are promising tools for terrain surface measurement, irrespective of weather conditions. The coherent change detection (CCD) method is being widely applied to detect surface changes by comparing multiple complex SAR images captured from the same scanning orbit. However, in the case of a general damage assessment after a natural disaster such as an earthquake or mudslide, additional about surface change, such as surface height change, is strongly required. Given this background, the current study proposes a novel height change estimation method using a CCD model based on the Pauli decomposition of fully polarimetric SAR images. The notable feature of this method is that it can offer accurate height change beyond the assumed wavelength, by introducing the frequency band-divided approach, and so is significantly better than InSAR based approaches. Experiments in an anechoic chamber on a 1/100 scaled model of the X-band SAR system, show that our proposed method outputs more accurate height change estimates than a similar method that uses single polarimetric data, even if the height change amount is over the assumed wavelength.

We study the budgeted multi-armed bandit problem with stochastic action costs. In this problem, a player not only receives a reward but also pays a cost for an action of his/her choice. The goal of the player is to maximize the cumulative reward he/she receives before the total cost exceeds the budget. In the classical multi-armed bandit problem, a policy called KL-UCB is known to perform well. We propose KL-UCB-SC, an extension of this policy for the budgeted bandit problem. We prove that KL-UCB-SC is asymptotically optimal for the case of Bernoulli costs and rewards. To the best of our knowledge, this is the first result that shows asymptotic optimality in the study of the budgeted bandit problem. In fact, our regret upper bound is at least four times better than that of BTS, the best known upper bound for the budgeted bandit problem. Moreover, an empirical simulation we conducted shows that the performance of a tuned variant of KL-UCB-SC is comparable to that of state-of-the-art policies such as PD-BwK and BTS.

This paper presents convex filter networks that are obtained from extensions of morphological filters. The proposed filter network consists of a convex and concave filter that are extensions of the dilation and erosion of mathematical morphology with the maxout activation function. Maxout can approximate arbitrary convex functions as piecewise linear functions, including the max function. The class of the convex function hence includes the morphological dilation and can be trained for specific image processing tasks. In this paper, the closing filter is extended to a convex-concave filter network with maxout. The convex-concave filter is trained by the stochastic gradient method for noise and mask removal. The examples of noise and mask removal show that the convex-concave filter can obtain a recovered image, whose quality is comparable to inpainting by using the total variation minimization with reduced computational cost without mask information of the corrupted pixels.

Head-mounted displays (HMDs) and augmented reality (AR) are actively being studied. However, ordinary AR HMDs for visual assistance have a problem in which users have difficulty simultaneously focusing their eyes on both the real target object and the displayed image because the image can only be displayed at a fixed distance from an user's eyes in contrast to where the real object three-dimensionally exists. Therefore, we considered incorporating a holographic technology, an ideal three-dimensional (3D) display technology, into an AR HMD system. A few studies on holographic HMDs have had technical problems, and they have faults in size and weight. This paper proposes a compact holographic AR HMD system with the purpose of enabling an ideal 3D AR HMD system which can correctly reconstruct the image at any depth. In this paper, a Fourier transform optical system (FTOS) was implemented using only one lens in order to achieve a compact and lightweight structure, and a compact holographic AR HMD system was constructed. The experimental results showed that the proposed system can reconstruct sharp images at the correct depth for a wide depth range. This study enabled an ideal 3D AR HMD system that enables simultaneous viewing of both the real target object and the reconstructed image without feeling visual fatigue.

Preserving hue is an important issue for color image processing. In order to preserve hue, color image processing is often carried out in HSI or HSV color space which is translated from RGB color space. Transforming from RGB color space to another color space and processing in this space usually generate gamut problem. We propose image enhancement methods which conserve hue and preserve the range (gamut) of the R, G, B channels in this paper. First we show an intensity processing method while preserving hue and saturation. In this method, arbitrary gray-scale transformation functions can be applied to the intensity component. Next, a saturation processing method while preserving hue and intensity is proposed. Arbitrary gray-scale transform methods can be also applied to the saturation component. Two processing methods are completely independent. Therefore, two methods are easily combined by applying two processing methods in succession. The combination method realizes the hue-preserving color image processing with a high arbitrariness without gamut problem. Furthermore, the concrete enhancement algorithm based on the proposed processing methods is proposed. Numerical results confirm our theoretical results and show that our processing algorithm performs much better than the conventional hue-preserving methods.

Magnetic Anomaly Detection (MAD) is a passive method for the detection of ferromagnetic objects. Currently, the performance of a MAD system is limited by the magnetic background noise that is non-stationary and shows self-similarity and long-range correlation. In this paper, we propose an empirical mode decomposition (EMD) trend filtering based energy detector for adaptively detecting the magnetic anomaly signal from the background noise. The input data is first detrended adaptively with the energy-ratio trend filtering approach. Then, the magnetic anomaly signal is detected using an energy detector. The proposed detector does not need any a priori knowledge about the target or assumptions regarding the background noise. Experiments also prove that the proposed detector shows a more stable performance than the existing undecimated discrete wavelet transform (UDWT) based energy detector.

The definitions of the Lee complete ρ weight enumerator and the exact complete ρ weight enumerator over Mn×s(F2[u,v]/) are introduced, and the MacWilliams identities with respect to the RT metric for these two weight enumerators of linear codes over Mn×s(F2[u,v]/) are obtained. Finally, we give two examples to illustrate the results we obtained.

In this letter, we investigate the price-based power allocation with rate proportional fairness constraint in downlink non-orthogonal multiple access (NOMA) systems. The Stackelberg game is utilized to model the interaction between the base station (BS) and users. The revenue maximization problem of the BS is first converted to rate allocation problem, then the optimal rate allocation for each user is obtained by variable substitution. Finally, a price-based power allocation with rate proportional fairness (PAPF) algorithm is proposed based on the relationship between rate and transmit power. Simulation results show that the proposed PAPF algorithm is superior to the previous price-based power allocation algorithm in terms of fairness index and minimum normalized user (MNU) rate.