As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

This letter derives the packet error rate (PER) in terms of the retry limit and the channel error probability in wireless local area networks (WLANs), when an additional number of retries is allocated to a block of packets to be transmitted. We prove that the lower bound of the PER is the dropping probability which is defined as the probability of any given packet being dropped after its retry limit has been reached.

In this paper we apply a parallel adaptive solution algorithm to simulate nanoscale double-gate metal-oxide-semiconductor field effect transistors (MOSFETs) on a personal computer (PC)-based Linux cluster with the message passing interface (MPI) libraries. Based on a posteriori error estimation, the triangular mesh generation, the adaptive finite volume method, the monotone iterative method, and the parallel domain decomposition algorithm, a set of two-dimensional quantum correction hydrodynamic (HD) equations is solved numerically on our constructed cluster system. This parallel adaptive simulation methodology with 1-irregular mesh was successfully developed and applied to deep-submicron semiconductor device simulation in our recent work. A 10 nm n-type double-gate MOSFET is simulated with the developed parallel adaptive simulator. In terms of physical quantities and refined adaptive mesh, simulation results demonstrate very good accuracy and computational efficiency. Benchmark results, such as load-balancing, speedup, and parallel efficiency are achieved and exhibit excellent parallel performance. On a 16 nodes PC-based Linux cluster, the maximum difference among CPUs is less than 6%. A 12.8 times speedup and 80% parallel efficiency are simultaneously attained with respect to different simulation cases.

In this letter, a quantization error-aware Tomlinson-Harashinma Precoding (THP) is proposed based on the equivalent zero-forcing (ZF) criterion in Multiuser Multiple-Input Single-Output (MU-MISO) systems with limited feedback, where the transmitter has only quantized channel direction information (CDI). This precoding scheme is robust to the channel uncertainties arising from the quantization error and the lack of channel magnitude information (CMI). Our simulation results show that the new THP scheme outperforms the conventional precoding scheme in limited feedback systems with respect to Bit Error Ratio (BER).

Handwriting difficulties (HWDs) in children have adverse effects on their confidence and academic progress. Detecting HWDs is the first crucial step toward clinical or teaching intervention for children with HWDs. To date, how to automatically detect HWDs is still a challenge, although digitizing tablets have provided an opportunity to automatically collect handwriting process information. Especially, to our best knowledge, there is no exploration into the potential of combining machine learning algorithms and the handwriting process information to automatically detect Chinese HWDs in children. To bridge the gap, we first conducted an experiment to collect sample data and then compared the performance of five commonly used classification algorithms (Decision tree, Support Vector Machine (SVM), Artificial Neural Network, Naïve Bayesian and k-Nearest Neighbor) in detecting HWDs. The results showed that: (1) only a small proportion (13%) of children had Chinese HWDs and each classification model on the imbalanced dataset (39 children at risk of HWDs versus 261 typical children) produced the results that were better than random guesses, indicating the possibility of using classification algorithms to detect Chinese HWDs; (2) the SVM model had the best performance in detecting Chinese HWDs among the five classification models; and (3) the performance of the SVM model, especially its sensitivity, could be significantly improved by employing the Synthetic Minority Oversampling Technique to handle the class-imbalanced data. This study gains new insights into which handwriting features are predictive of Chinese HWDs in children and proposes a method that can help the clinical and educational professionals to automatically detect children at risk of Chinese HWDs.

This CMOS transceiver IC exploits the superheterodyne architecture to implement a low-cost RF front-end with only 6.25 mm2 die area for IEEE 802.11b standard. The transceiver is implemented in 0.25 µm CMOS process with 2.7 V supply voltage, and achieves a -86 dBm 11 Mb/s receive sensitivity and a 2 dBm transmit output power.

Using a self-developed ASTM test system of contact material electrical properties under low voltage (LV), small-capacity, the current-frequency variable and a photoelectric analytical balance, the electric performance comparison experiments and material weighing of silver-based electrical contact materials, such as silver/tungsten and silver/cadmium oxide contact materials, are completed under LV, pure resistive load and small current at 400 Hz/50 Hz. The surface profiles and constituents of silver/tungsten contact material were observed and analyzed by SEM and EDAX. Researches indicate that the form of the contact material arc burnout at 400 Hz is stasis, not an eddy flow style at 50 Hz; meanwhile, the area of the contact burnout at 400 Hz is less than that of 50 Hz, and the local ablation on the surface layer at 400 Hz is more serious. Comparing the capacities of the silver-based contact materials with different second element such as CAgW50, CAgNi10, CAgC4 and CAgCdO15 at 400 Hz, no matter what the performances of arc erosion resistance or welding resistance, it can be found that the capacities of the silver/tungsten material is the best.

The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.

The issues of comparing the similarity or dissimilarity (distance) between structures have been studied. Especially, several distances between trees and their efficient algorithms have been proposed. However, all of the tree distances are defined based on mapping between vertices only, and they are helpless to compare the tree structures whose vertices and edges hold information. In this paper, we will propose a mapping between rooted and unordered trees based on vertex translation and edge translation, and then define a distance based on proposed mapping, and develop an efficient algorithm for computing proposed distance. Proposed distance can be used to compare the similarity or distance between two natural language sentences.

In this letter, we present an automatic approach of objective singing performance evaluation for untrained singers by relating acoustic measurements to perceptual ratings of singing voice quality. Several acoustic parameters and their combination features are investigated to find objective correspondences of the perceptual evaluation criteria. Experimental results show relative strong correlation between perceptual ratings and the combined features and the reliability of the proposed evaluation system is tested to be comparable to human judges.

The large computational complexity makes stereo matching a big challenge in real-time application scenario. The problem of stereo matching in a video sequence is slightly different with that in a still image because there exists temporal correlation among video frames. However, no existing method considered temporal consistency of disparity for algorithm acceleration. In this work, we proposed a scheme called the dynamic disparity range (DDR) to optimize matching cost calculation and cost aggregation steps by narrowing disparity searching range, and a scheme called temporal cost aggregation path to optimize the cost aggregation step. Based on the schemes, we proposed the DDR-SGM and the DDR-MCCNN algorithms for the stereo matching in video sequences. Evaluation results showed that the proposed algorithms significantly reduced the computational complexity with only very slight loss of accuracy. We proved that the proposed optimizations for the stereo matching are effective and the temporal consistency in stereo video is highly useful for either improving accuracy or reducing computational complexity.

Context-aware applications are a key aspect of pervasive computing. The core issue of context-aware application development is how to make the application behave suitably according to the changing context without coupling such context dependencies in the program. Several programming paradigms and languages have been proposed to facilitate the development, but they are either lack of sufficient flexibility or somewhat complex for programming and deploying. A reference programming model is proposed in this paper to make up inadequacy of those approaches. In the model, virtual tables constructed by system and maintained by space manager connect knowledge of both developer and space manager while separating dependency between context and application logic from base program. Hierarchy and architecture of the model are presented, and implementation suggestions are also discussed. Validation and evaluation show that the programming model is lightweight and easy to be implemented and deployed. Moreover, the model brings better flexibility for developing context-aware applications.

Spectral-amplitude coding (SAC) techniques in fiber-Bragg-grating (FBG)-based optical code-division multiple-access (OCDMA) systems were investigated in our previous work. This paper adopts the same network architecture to investigate the simultaneous reductions of multiple-access interference (MAI) and optical beat interference (OBI). The MAI is caused by overlapping wavelengths from undesired network coder/decoders (codecs) while the OBI is induced by interaction of simultaneous chips at adjacent gratings. It is proposed that MAI and OBI reductions may be obtained by use of: 1) a source spectrum that is divided into equal chip spacing; 2) coded FBGs characterized by approximately the same number of "0" and "1" code elements; and 3) spectrally balanced photo-detectors. With quasi-orthogonal Walsh-Hadamard coded FBGs, complementary spectral chips is employed as signal pairs to be recombined and detected in balanced photo-detectors, thus achieving simultaneous suppression of both MAIs and OBIs. Simulation results showed that the proposed OCDMA spectral-amplitude coding scheme achieves significant MAI and OBI reductions.

In this paper, we propose an on-line e-coin system with four parties: Consumer, Merchant, Bank and Issuer. The proposed system not only circulates anonymous e-coins but also protects the profits of the Merchant and the Consumer during a transaction. An e-coin, consisting of a secret value c and a public value c'=h(c) where h() is a secure one-way hash function with collision resistant property, is generated by its owner. The public value of a legal e-coin is published on the bulletin board of Issuer. Only the owner who releases the secret values of the published e-coins can spend money. Instead of Bank, Issuer has to be on-line to verify and replace the public values of the Consumer's e-coins with the Merchant's while the Consumer pays money to the Merchant in a transaction. Such a replacement represents that the coins are passed from one person to another.

In this paper, we investigate the electron-hole energy states and energy gap in three-dimensional (3D) InAs/GaAs quantum rings and dots with different shapes under external magnetic fields. Our realistic model formulation includes: (i) the effective mass Hamiltonian in non-parabolic approximation for electrons, (ii) the effective mass Hamiltonian in parabolic approximation for holes, (iii) the position- and energy-dependent quasi-particle effective mass approximation for electrons, (iv) the finite hard wall confinement potential, and (v) the Ben Daniel-Duke boundary conditions. To solve the 3D nonlinear problem without any fitting parameters, we have applied the nonlinear iterative method to obtain self-consistent solutions. Due to the penetration of applied magnetic fields into torus ring region, for ellipsoidal- and rectangular-shaped quantum rings we find nonperiodical oscillations of the energy gap between the lowest electron and hole states as a function of external magnetic fields. The nonperiodical oscillation is different from 1D periodical argument and strongly dependent on structure shape and size. The result is useful to study magneto-optical properties of the nanoscale quantum rings and dots.

This paper discusses the largest common similar substructure (in short, LCSS) problem for trees. The problem is, for all pairs of "substructure of A and that of B," to find one of them, denoted by A and B', such that A is most similar to B' and the sum of the number of vertices of A and that of B' is largest. An algorithm for the LCSS problem for unrooted and unordered trees (in short, trees) and that for trees embedded in a plane (in short, Co-trees) are proposed. The time complexity of the algorithm for trees is O (max (ma, mb)2 NaNb) and that for CO-trees is O (mambNaNb), where, ma (mb) and Na (Nb) are the largest degree of a vertex of tree Ta (Tb) and the number of vertices of Ta (Tb), respectively. It is easy to modify the algorithms for enumerating all of the LCSSs for trees and CO-trees. The algorithms can be applied to structure-activity studies in chemistry and various structure comparison problems.

Several distances between trees have been proposed. However, most of the reports on distances have dealt with rooted and ordered trees. This paper proposes two distances between unrooted and cyclically ordered trees (CO-trees) and their computing methods. A CO-tree is a tree embedded in a plane. These distances are defined based on Tai's mapping (TM) and a strongly structure preserving mapping (SSPM) between CO-trees. The time complexities to compute the distances between two CO-trees Ta and Tb are OT (N 2aN 2b) for the distance based on a TM and OT(mambNaNb) for that on an SSPM, respectively, where ma(mb) and Na(Nb) are the largest degree of a vertex and the number of vertices of Ta(Tb), respectively. The space complexities of both methods are Os(NaNb). Those distances can be applied to the clustering of CO-trees.

To achieve more accurate measurements of the mobile station (MS) location, it is possible to integrate many kinds of measurements. In this paper we proposed several simpler methods that utilized time of arrival (TOA) at three base stations (BSs) and the angle of arrival (AOA) information at the serving BS to give location estimation of the MS in non-line-of-sight (NLOS) environments. From the viewpoint of geometric approach, for each a TOA value measured at any BS, one can generate a circle. Rather than applying the nonlinear circular lines of position (LOP), the proposed methods are much easier by using linear LOP to determine the MS. Numerical results demonstrate that the calculation time of using linear LOP is much less than employing circular LOP. Although the location precision of using linear LOP is only reduced slightly. However, the proposed efficient methods by using linear LOP can still provide precise solution of MS location and reduce the computational effort greatly. In addition, the proposed methods with less effort can mitigate the NLOS effect, simply by applying the weighted sum of the intersections between different linear LOP and the AOA line, without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods can always yield superior performance in comparison with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.

Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.