We describe a novel idea to improve machine translation by combining multiple candidate translations and extra translations. Without manual work, extra translations can be generated by identifying and hybridizing the syntactic equivalents in candidate translations. Candidate and extra translations are then combined on sentence level for better general translation performance.

The traditional reinforcement learning (RL) methods can solve Markov Decision Processes (MDPs) online, but these learning methods cannot effectively use a priori knowledge to guide the learning process. The exploration of the optimal policy is time-consuming and does not employ the information about specific issues. To tackle the problem, this paper proposes heuristic function negotiation (HFN) as an online learning framework. The HFN framework extends MDPs and introduces heuristic functions. HFN changes the state-action dual layer structure of traditional RL to the triple layer structure, in which multiple heuristic functions can be set to meet the needs required to solve the problem. The HFN framework can use different algorithms to let the functions negotiate to determine the appropriate action, and adjust the impact of each function according to the rewards. The HFN framework introduces domain knowledge by setting heuristic functions and thus speeds up the problem solving of MDPs. Furthermore, user preferences can be reflected in the learning process, which improves the flexibility of RL. The experiments show that, by setting reasonable heuristic functions, the learning results of the HFN framework are more efficient than traditional RL. We also apply HFN to the air combat simulation of unmanned aerial vehicles (UAVs), which shows that different function settings lead to different combat behaviors.

In modern hardware security applications, silicon physical unclonable functions (PUFs) are of interest for their potential use as a unique identity or secret key that is generated from inherent characteristics caused by process variations. However, arbiter-based PUFs utilizing the relative delay-time difference between equivalent paths have a security issue in which the generated challenge-response pairs (CRPs) can be predicted by a machine learning attack. We previously proposed the RG-DTM PUF, in which a response is decided from divided time domains allocated to response 0 or 1, to improve the uniqueness of the conventional arbiter-PUF in a small circuit. However, its resistance against machine learning attacks has not yet been studied. In this paper, we evaluate the resistance against machine learning attacks by using a support vector machine (SVM) and logistic regression (LR) in both simulations and measurements and compare the RG-DTM PUF with the conventional arbiter-PUF and with the XOR arbiter-PUF, which strengthens the resistance by using XORing output from multiple arbiter-PUFs. In numerical simulations, prediction rates using both SVM and LR were above 90% within 1,000 training CRPs on the arbiter-PUF. The machine learning attack using the SVM could never predict responses on the XOR arbiter-PUF with over six arbiter-PUFs, whereas the prediction rate eventually reached 95% using the LR and many training CRPs. On the RG-DTM PUF, when the division number of the time domains was over eight, the prediction rates using the SVM were equal to the probability by guess. The machine learning attack using LR has the potential to predict responses, although an adversary would need to steal a significant amount of CRPs. However, the resistance can exponentially be strengthened with an increase in the division number, just like with the XOR arbiter-PUF. Over one million CRPs are required to attack the 16-divided RG-DTM PUF. Differences between the RG-DTM PUF and the XOR arbiter-PUF relate to the area penalty and the power penalty. Specifically, the XOR arbiter-PUF has to make up for resistance against machine learning attacks by increasing the circuit area, while the RG-DTM PUF is resistant against machine learning attacks with less area penalty and power penalty since only capacitors are added to the conventional arbiter-PUF. We also attacked RG-DTM PUF chips, which were fabricated with 0.18-µm CMOS technology, to evaluate the effect of physical variations and unstable responses. The resistance against machine learning attacks was related to the delay-time difference distribution, but unstable responses had little influence on the attack results.

Combining the strong anti-interference advantages of OFDM technology and the time-frequency analysis features of fractional Fourier transform (FFT), we apply OFDM as the coding modulation technology for digital watermarking. Based on the Arnold scrambling and OFDM coding, an innovative DFRFT digital watermarking algorithm is proposed. First, the watermark information is subjected to the Arnold scrambling encryption and OFDM coding transform. Then it is embedded into the FFT domain amplitude. The three parameters of scrambling iterations number, t, FFT order, p, and the watermark information embedded position, L, are used as keys, so that the algorithm has high safety. A simulation shows that the algorithm is highly robust against noise, filtering, compression, and other general attacks. The algorithm not only has strong security, but also makes a good balance between invisibility and robustness. But the possibility of using OFDM technique in robust image watermarking has drawn a very little attention.

Space-time block code (STBC) with complex orthogonal designs achieves full diversity with a simple maximum-likelihood (ML) decoding, however, do not achieve a full transmission rate for more than two antennas. To attain a higher transmission rate, STBC with quasi-orthogonal designs were proposed, whereas there are interference terms caused by relaxing the orthogonality. It has an impact on decoding complexity because a receiver needs to decode two symbols at a time. Moreover, QO-STBC does not achieve full diversity. In this paper, we propose a scheme which makes possible to decode symbols one by one, and two schemes which gain full transmission diversity by upsetting the balance of the transmit power and rotating constellation.

This letter proposes a fast intra prediction method to reduce encoding time for the high-efficiency video coding (HEVC) standard, which involves an increase in the number of intra-modes. The proposed intra-mode coding method uses correlation between intra-modes and adjacent pixels. The proposed method skips half of the intra-modes in certain blocks, specifically those that satisfy predetermined conditions. Using the half of intra-modes reduces number of bits for intra-mode coding and offsets a decline of coding performance caused by mode skipping. The experimental results show that the proposed method achieved 5.87% reduction in encoding time compared to the HEVC test model 7.1 encoder with almost no loss in coding performance.

Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.

This paper deals with carrier frequency offset (CFO) estimation based on the minimum variance distortionless response (MVDR) criterion without using specific training sequences for interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. In the presence of large CFOs, the estimator is proposed to find a new CFO vector based on the first-order Taylor series expansion of the one initially given. The problem of finding the new CFO vector is formulated as the closed form of a generalized eigenvalue problem, which allows one to readily solve it. Since raising the accuracy of residual CFO estimation can provide more accurate residual CFO compensation, this paper also present a decision-directed MVDR approach to improve the CFO estimation performance. However, the proposed estimator can estimate CFOs with less computation load. Several computer simulation results are provided for illustrating the effectiveness of the proposed blind estimate approach.

Recently, small cell systems such as femto cell are being considered as a good alternative that can support the increasing demand for mobile data traffic because they can significantly enhance network capacity by increasing spatial reuse. In this paper, we analyze the coverage and capacity of a femto cell when it is deployed in a hotspot to reduce the traffic loads of neighboring macro base stations (BSs). Our analysis results show that the coverage and capacity of femto cell are seriously affected by surrounding signal environment and they can be greatly enhanced by adapting power allocation for channels to the surrounding environment. Thus, we propose an adaptive power partitioning scheme where power allocation for channels can be dynamically adjusted to suit the environment surrounding the femto cell. In addition, we numerically derive the optimal power allocation ratio for channels to optimize the performance of the femto cell in the proposed scheme. It is shown that the proposed scheme with the optimal channel power allocation significantly outperforms the conventional scheme with fixed power allocation for channels.

The main target of compressed sensing is recovery of one-dimensional signals, because signals more than two-dimension can also be treated as one-dimensional ones by raster scan, which makes the sensing matrix huge. This is unavoidable for general sensing processes. In separable cases like discrete Fourier transform (DFT) or standard wavelet transforms, however, the corresponding sensing process can be formulated using two matrices which are multiplied from both sides of the target two-dimensional signals. We propose an approximate message passing (AMP) algorithm for the separable sensing process. Typically, we suppose DFT for the sensing process, in which the measurements are complex numbers. Therefore, the formulation includes cases in which both target signal and measurements are complex. We show the effectiveness of the proposed algorithm by computer simulations.

In wireless networks, how to provide reliable data transfer is an important and challenging issue due to channel fading and interference. Several approaches, e.g., Automatic Repeat reQuest (ARQ), Hybrid ARQ (HARQ) and Network Coding (NC), are used to enhance reliability of transmission in wireless networks. However, we note that these schemes implement the data recovery process for mixed unicast and multicast (MUM) communications by simply separating the process into two phases, unicast and multicast phase. This is inefficient and expensive. In this paper, we propose an efficient retransmission scheme with network coding for MUM transmission, aiming at improving bandwidth utilization. UMNC searches for coding opportunities from both unicast and multicast flows, which offer the potential benefit of improved recovery in the event of packet loss. We theoretically prove that UMNC can effectively reduce the total number of retransmissions and thus improve bandwidth efficiency, compared with existing schemes.

To achieve high recording density in a bit-patterned media recording system, the spacing between data bit islands in both the along-track and the across-track directions must be decreased, thus leading to the increase of two-dimensional (2D) interference. One way to reduce the 2D interference is to apply a 2D coding scheme on a data sequence before recording; however, this method usually requires many redundant bits, thus lowering a code rate. Therefore, we propose a novel 2D coding scheme referred to as a recorded-bit patterning (RBP) scheme to mitigate the 2D interference, which requires no redundant bits at the expense of using more buffer memory. Specifically, an input data sequence is first split into three tracks in which will then be rotated to find the best 3-track data pattern based on a look-up table before recording, such that the shifted data tracks yield the least effect of 2D interference in the readback signal. Numerical results indicate that the proposed RBP scheme provides a significant performance improvement if compared to a conventional system (without 2D coding), especially when the recording density is high and/or the position jitter noise is large.

In spite of continuous improvement of computational power of multi/many-core processors, the memory access performance of the processors has not been improved sufficiently, and thus the overall performance of recent processors is often restricted by the delay of off-chip memory accesses. Low-delay data compression for last level cache (LLC) would be effective to improve the processor performance because the compression increases the effective size of LLC, and thus reduces the number of off-chip memory accesses. This paper proposes a novel data compression method suitable for high-speed parallel decoding in the LLC. Since cache line data often have periodicity of certain lengths, such as 32- or 64-bit instructions, 32-bit integers, and 64-bit floating point numbers, an information word is encoded as a base pattern and a differential pattern between the original word and the base pattern. Evaluation using a GPU simulator shows that the compression ratio of the proposed coding is comparable to LZSS coding and X-Match Pro and superior to other conventional compression algorithms for cache memories. Also this paper presents an experimental decoder designed for ASIC, and the synthesized result shows that the decoder can decompress cache line data of length 32bytes in four clock cycles. Evaluation of the IPC on the GPU simulator shows that, for several benchmark programs, the IPC achieved by the proposed coding is higher than that by the conventional BΔI coding, where the maximum improvement of the IPC is 20%.

The equivalent circuit of aperture-coupled cavities filled with a lossy dielectric is considered by means of an eigenmode expansion technique founded on the segmentation concept. It is different from a series LCR resonant circuit, and the resistor which symbolizes the dielectric loss is connected to the capacitor in parallel. If the cavities are formed by a short-circuited oversize waveguide, then the input admittance can be represented by the product of a coupling factor to the connected waveguide port and the equivalent admittance of the short-circuited waveguide. The transmission line model is effective even if lossy wall effect and dielectric partially-loading effect are considered. As a result, three-dimensional eigenmode parameters, such as the resonant frequency and the Q-factor, become dispensable and the computational complexity for the cavity simulation in the field of microwave heating is dramatically reduced.

This paper presents a real-time and memory-efficient arrhythmia detection system with binary classification that uses antidictionary coding for the analysis and classification of electrocardiograms (ECGs). The measured ECG signals are encoded using a lossless antidictionary encoder, and the system subsequently uses the compression rate to distinguish between normal beats and arrhythmia. An automated training data procedure is used to construct the automatons, which are probabilistic models used to compress the ECG signals, and to determine the threshold value for detecting the arrhythmia. Real-time computer simulations with samples from the MIT-BIH arrhythmia database show that the averages of sensitivity and specificity of the proposed system are 97.8% and 96.4% for premature ventricular contraction detection, respectively. The automatons are constructed using training data and comprise only 11 kilobytes on average. The low complexity and low memory requirements make the system particularly suitable for implementation in portable ECG monitors.

With the rapid development of multi-view video coding (MVC) and light field rendering (LFR), Free-View Television (FTV) has emerged as new entrainment equipment, which can bring more immersive and realistic feelings for TV viewers. In FTV broadcasting system, the TV-viewer can freely watch a realistic arbitrary view of a scene generated from a number of original views. In such a scenario, the ownership of the multi-view video should be verified not only on the original views, but also on any virtual view. However, capacities of existing watermarking schemes as copyright protection methods for LFR-based FTV are only one bit, i.e., presence or absence of the watermark, which seriously impacts its usage in practical scenarios. In this paper, we propose a robust multi-bit watermarking scheme for LFR-based free-view video. The direct-sequence code division multiple access (DS-CDMA) watermark is constructed according to the multi-bit message and embedded into DCT domain of each view frame. The message can be extracted bit-by-bit from a virtual frame generated at an arbitrary view-point with a correlation detector. Furthermore, we mathematically prove that the watermark can be detected from any virtual view. Experimental results also show that the watermark in FTV can be successfully detected from a virtual view. Moreover, the proposed watermark method is robust against common signal processing attacks, such as Gaussian filtering, salt & peppers noising, JPEG compression, and center cropping.

Recently, various research efforts have been conducted to develop strategies for accelerating multi-dimensional query processing using the graphics processing units (GPUs). However, well-known multi-dimensional access methods such as the R-tree, B-tree, and their variants are hardly applicable to GPUs in practice, mainly due to the characteristics of a hierarchical index structure. More specifically, the hierarchical structure not only causes frequent transfers of small volumes of data but also provides limited opportunity to exploit the advanced data parallelism of GPUs. To address these problems, we propose an approach that uses GPUs as a buffer. The main idea is that object entries in recently visited leaf nodes are buffered in the global memory of GPUs and processed by massive parallel threads of the GPUs. Through extensive performance studies, we observed that the proposed approach achieved query performance up to five times higher than that of the original R-tree.

Optical Code Division Multiplexing (OCDM) is a multiplexing technology for constructing future all-optical networks. Compared with other multiplexing technologies, it can be easily controlled and can establish lightpaths of smaller granularity. However, previous research has revealed that OCDM networks are vulnerable to cycle attacks. Cycle attacks are caused by multi-access interference (MAI), which is crosstalk noise on the same wavelength in OCDM networks. If cycle attacks occur, they disrupt all network services immediately. Previous research has proposed a logical topology design that is free of cycle attacks. However, this design assumes that path assignment is centrally controlled. It also does not consider the delay between each node and the centralized controller. In this paper, we propose novel logical topology designs that are free of cycle attacks and methods of establishing paths. The basic concepts underlying our methods are to autonomously construct a cycle-attack-free logical topology and to establish lightpaths by using a distributed controller. Our methods can construct a logical network and establish lightpaths more easily than the previous method can. In addition, they have network scalability because of their distributed control. Simulation results show that our methods have lower loss probabilities than the previous method and better mean hop counts than the centralized control approach.

In this paper, joint transmit/receive frequency-domain equalization (FDE) is proposed for analog network coded (ANC) single-carrier (SC) bi-directional multi-antenna relay. In the proposed scheme, diversity transmission using transmit FDE is performed at relay station (RS) equipped with multiple antennas while receive FDE is carried out at base station (BS) and mobile terminal (MT) both equipped with single antenna. The transmit and receive FDE weights are jointly optimized so as to minimize the end-to-end mean square error (MSE). We evaluate, by computer simulation, the throughput performance and show that the joint transmit/receive FDE obtains the spatial and frequency diversity gains and accordingly achieve better throughput performance compared to either the transmit FDE only or the receive FDE only. It is also shown that ANC SC bi-directional multi-antenna relay can extend the communication coverage area for the given required throughput compared to conventional direct transmission.

In this study, we investigate the use of scheduling algorithms to support coordinated multipoint (CoMP) operation for Long Term Evolution (LTE)-Advanced systems studied in the 3rd Generation Partnership Project (3GPP). CoMP, which improves cooperative transmission among network nodes (transmission points: TPs) and reduces or eliminates interTP interference, enabling performance improvements in cell edge throughputs. Although scheduling algorithms in LTE systems have been extensively investigated from the single cell operation perspective, those extension to CoMP where each user equipment (UE) has multiple channel state information (CSI) feedbacks require further consideration on proportional fairness (PF) metric calculation while maintaining PF criteria. To this end, we propose to apply a scaling factor in accordance with the number of CSI feedbacks demanded for the UE. To evaluate the benefits of this scaling factor, multicell system-level simulations that take account of channel estimation errors are performed, and the results confirmed that our improved algorithm enables fairness to be maintained.