Conventional image retargeting methods fail to avoid distortion in the case where visually important regions are distributed all over the image. To reduce distortions, this paper proposes a novel image retargeting method that incorporates letterboxing into an image warping framework. Letterboxing has the advantage of producing results without distortion or content loss although being unable to use the entire display area. Therefore, it is preferable to combine a retargeting method with a letterboxing operator when displaying images in full screen. Experimental results show that the proposed method is superior to conventional methods in terms of visual quality measured by an objective metric.

Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

A modular serial pipelined sorting architecture for continuous input sequences is presented. It supports continuous sequences, whose lengths can be dynamically changed, and does so using a very simple control strategy. It consists of identical serial cascaded sorting cells, and lends itself to high frequency implementation with any number of sorting cells, because both data and control signals are pipelined. With L cascaded sorting cells, it produces a fully sorted result for sequences whose length N is equal to or less than L+1; for longer sequences, the largest L elements are sorted out. Being modularly designed, several independent smaller sorters can be dynamically configured to form a larger sorter.

Adaptive and flexible network control technology is considered essential for efficient network resource utilization. Moreover, such technology is becoming a key to cost-effectively meet diverse service requirements and accommodate heavier traffic with limited network resources; demands that conventional static operation cannot satisfy. To address this issue, we previously studied dynamic network control technology for large-capacity network services including on-demand broad bandwidth provisioning services and layer-one VPN. Our previous study introduced a simple weighting function for achieving fairness in terms of path length and proposed two dynamic Make Before Break Routing algorithms for reducing blocking probability. These algorithms enhance network utilization by rerouting existing paths to alternative routes while completely avoiding disruption for highly reliable services. However, the impact of this avoidance of service disruption on blocking probability has not been clarified. In this paper, we propose modified versions of the algorithms that enhance network utilization while slightly increasing disruption by rerouting, which enable us to elucidate the effectiveness of hitless rerouting. We also provide extensive evaluations including a comparison of original and modified algorithms. Numerical examples demonstrate that they achieve not only a high degree of fairness but also low service blocking probability. Hitless rerouting is achieved with a small increase in blocking probability.

In conventional spread spectrum (SS) watermarking schemes, random sequences are used for the modulation of watermark information. However, because of the mutual interference among those sequences, it requires complicated removal operation to improve the performance. In this paper, we propose an efficient spread spectrum watermarking scheme by introducing the orthogonal frequency divisiion multiplexing (OFDM) technique at the modulation of watermark information. The SS sequences in the proposed method are the DCT basic vectors modulated by a pseudo-random number (PN) sequence. We investigate the SS-based method considering the host interference at the blind detection scenario and analyze the noise caused by attacks. Because every operation is invertible, the quantization index modulation (QIM)-based method is applicable for the OFDM modulated signals. We also consider the property of watermark extracting operation in SS-based and QIM-based method and formalize their models of noisy channel in order to employ an error correcting code. The performance of their methods with error correcting code is numerically evaluated under the constraints of same distortion level in watermarked content. The experimental results indicated a criteria for the selection of SS-based and QIM-based methods for given content, which is determined by the amount of host interference. In case that the host interference is 0.8 times smaller than a watermark signal, the SS-based method is suitable. When it is 1.0 times larger, the QIM-based method should be selected.

In this paper, we introduce a self-constructive Normalized Gaussian Network (NGnet) for online learning tasks. In online tasks, data samples are received sequentially, and domain knowledge is often limited. Then, we need to employ learning methods to the NGnet that possess robust performance and dynamically select an accurate model size. We revise a previously proposed localized forgetting approach for the NGnet and adapt some unit manipulation mechanisms to it for dynamic model selection. The mechanisms are improved for more robustness in negative interference prone environments, and a new merge manipulation is considered to deal with model redundancies. The effectiveness of the proposed method is compared with the previous localized forgetting approach and an established learning method for the NGnet. Several experiments are conducted for a function approximation and chaotic time series forecasting task. The proposed approach possesses robust and favorable performance in different learning situations over all testbeds.

This paper proposes secure regenerating codes that are composed of non-secure regenerating codes and a new all-or-nothing transform. Unlike the previous analysis of secure regenerating codes, the security of the proposed codes is analyzed in the sense of the indistinguishability. The advantage of the proposed codes is that the overhead caused by the security against eavesdropping is much less than that of previous secure regenerating codes. The security of the proposed codes against eavesdropping mainly depends on the new all-or-nothing transform.

A ladder lottery, known as “Amidakuji” in Japan, is one of the most popular lotteries. In this paper, we consider the problems of enumeration, counting, and random generation of the ladder lotteries. For given two positive integers n and b, we give algorithms of enumeration, counting, and random generation of ladder lotteries with n lines and b bars. The running time of the enumeration algorithm is O(n + b) time for each. The running time of the counting algorithm is O(nb3) time. The random generation algorithm takes O(nb3) time for preprocess, and then it generates a ladder lottery in O(n + b) for each uniformly at random.

Given an undirected graph G, an edge dominating set is a subset F of edges such that each edge not in F is adjacent to some edge in F, and computing the minimum size of an edge dominating set is known to be NP-hard. Since the size of any edge dominating set is at least half of the maximum size µ(G) of a matching in G, we study the problem of testing whether a given graph G has an edge dominating set of size ⌈µ(G)/2⌉ or not. In this paper, we prove that the problem is NP-complete, whereas we design an O*(2.0801µ(G)/2)-time and polynomial-space algorithm to the problem.

A comprehensive model is presented for estimating the bit error rate (BER) of write disturbance in a resistive memory composed of a cross-point array. While writing a datum into the selected address, the non-selected addresses are biased by word-line (WL) and bit-line (BL). The stored datum in the non-selected addresses will be disturbed if the bias is large enough. It is necessary for the current flowing through the non-selected address to be calculated in order to estimate the BER of the write disturbance. Since it takes a long time to calculate the current flowing in a large-scale cross-point array, several simplified circuits have been utilized to decrease the calculating time. However, these simplified circuits are available to the selected address, not to the non-selected one. In this paper, new simplified circuits are proposed for calculating the current flowing through the non-selected address. The proposed and the conventional simplified circuits are used, and on that basis the trade-off between the write disturbance and the write error is discussed. Furthermore, the error correcting code (ECC) is introduced to improve the trade-off and to provide the low-cost memory chip matching current production lines.

Antenna-coupled kinetic inductance detectors (KIDs) have recently shown great promise as microwave detection systems with a large number of channels. However, this technique, still has difficulties in eliminating the radiation loss of the resonator signals. To solve this problem, we propose a design in which the absorption area connected to an antenna is located on the ground-side of a coplanar waveguide. Thereby, radiation loss due to leakage from the resonator to the antenna can be considerably reduced. This simple design also enables the use of a contact aligner for fabrication. We have developed KIDs with this design, named as the ground-side absorption (GSA)-KIDs and demonstrated that they have higher quality factors than those of the existing KIDs, while maintaining a good total sensitivity.

A floating-point multiplier with concurrent error detection capability by partial duplication is proposed. It uses a truncated multiplier for checking of the significand (mantissa) multiplication instead of full duplication. The proposed multiplier can detect any erroneous output with error larger than one unit in the last place (1 ulp) of the significand, which may be overlooked by residue checking. Its circuit area is smaller than that of a fully duplicated one. Area overhead of a single-precision multiplier is about 78% and that of a double-precision one is about 65%.

We propose the node name routing (NNR) strategy for information-centric ad-hoc networks based on the named-node networking (3N). This strategy is especially valuable for use in disaster areas because, when the Internet is out of service during a disaster, our strategy can be used to set up a self-organizing network via cell phones or other terminal devices that have a sharing ability, and it does not rely on a base station (BS) or similar providers. Our proposed strategy can solve the multiple-name problem that has arisen in prior 3N proposals, as well as the dead loop problems in both 3N ad-hoc networks and TCP/IP ad-hoc networks. To evaluate the NNR strategy, it is compared with the optimized link state routing protocol (OLSR) and the dynamic source routing (DSR) strategy. Computer-based comprehensive simulations showed that our NNR proposal exhibits a better performance in this environment when all of the users are moving randomly. We further observed that with a growing number of users, our NNR protocol performs better in terms of packet delivery, routing cost, etc.

In text-independent online writer identification, the Gaussian Mixture Model(GMM) writer model trained with the GMM-Universal Background Model(GMM-UBM) framework has acquired excellent performance. However, the system assumes the items in the observation sequence are independent, which neglects the dynamic information between observations. This work shows that although in the text-independent application, the dynamic information between observations is still important for writer identification. In order to extend the GMM-UBM system to use the dynamic information, the hidden Markov model(HMM) with Gaussian observation model is used to model each writer's handwriting in this paper and a new training schematic is proposed. In particular, the observation model parameters of the writer specific HMM are set with the Gaussian component parameters of the GMM writer model trained with the GMM-UBM framework and the state transition matrix parameters are learned from the writer specific data. Experiments show that incorporating the dynamic information is capable of improving the performance of the GMM-based system and the proposed training method is effective for learning the HMM writer model.

In this letter, we propose a novel speech separation method based on perceptual weighted deep recurrent neural network (DRNN) which incorporate the masking properties of the human auditory system. In supervised training stage, we firstly utilize the clean label speech of two different speakers to calculate two perceptual weighting matrices. Then, the obtained different perceptual weighting matrices are utilized to adjust the mean squared error between the network outputs and the reference features of both the two clean speech so that the two different speech can mask each other. Experimental results on TSP speech corpus demonstrate that the proposed speech separation approach can achieve significant improvements over the state-of-the-art methods when tested with different mixing cases.

With the rapid development of information and Web technologies, people are facing ‘information overload’ in their daily lives. The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. In this paper, a PRS model based on the Support Vector Machine (SVM) is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.

An adaptive time-step control method is proposed for the damped pseudo-transient analysis (DPTA) method. The new method is based on the idea of switched evolution/relaxation (SER), which can automatically adapt the step size for different circuit states. Considering the number of iterations needed for the convergence of Newton-Raphson (NR) method and the states in previous steps, the proposed method can automatically optimize the time-step size. Using numerical examples, the new method is proven to improve robustness, simulation efficiency, and the convergence of DPTA for solving nonlinear DC circuit equations.

We prepared a bR thin film by the wire-bar coating technique, and investigated the transient photo-current characteristics of the bR photocell. The transient photo-current signal of bR photocells prepared by the wire-bar coating technique and the dip coating technique was compared. An almost identical transient photo-current signal intensity was obtained both for the wire-bar coating technique and dip coating technique, while the thickness of bR thin film prepared by the wire-bar coating technique is slightly thinner than that prepared by the dip-coating technique. Transparent conductive oxide dependence of the transient photo-current signal is almost the same dependence for the bR photocells with a bR thin film prepared by both techniques. Application of the wire-bar coating technique is significant from the viewpoints of the bR's sample consumption as well as simplicity of sample preparation.

Rapid process scaling and the introduction of the multilevel cell (MLC) concept have lowered costs of NAND Flash memories, but also degraded reliability. For this reason, the memories are depending on strong error correcting codes (ECCs), and this has enabled the memories to be used in wide range of storage applications, including solid-state drives (SSDs). Meanwhile, too strong error correcting capability requires excessive decoding complexity and check bits. In NAND Flash memories, cell errors to neighborhood voltage levels are more probable than those to distant levels. Several ECCs reflecting this characteristics, including limited-magnitude ECCs which correct only errors with a certain limited magnitude and low-density parity check (LDPC) codes, have been proposed. However, as most of these ECCs need the multiple bits in a cell for encoding, they cannot be used with multipage programing, a high speed programming method currently employed in the memories. Also, binary ECCs with Gray codes are no longer optimal when multilevel voltage shifts (MVSs) occur. In this paper, an error correction method reflecting the error characteristic is presented. This method detects errors by a binary ECC as a conventional manner, but a nonbinary value or whole the bits in a cell, are subjected to error correction, so as to be corrected into the most probable neighborhood value. The amount of bit error rate (BER) improvement is depending on the probability of the each error magnitude. In case of 2bit/cell, if only errors of magnitude 1 and 2 can occur and the latter occupies 5% of cell errors, acceptable BER is improved by 4%. This is corresponding to extending 2.4% of endurance. This method needs about 15% longer average latency, 19% longer maximum latency, and 15% lower throughput. However, with using the conventional method until the memories' lifetime number of program/erase cycling, and the proposed method after that, BER improvement can be utilized for extending endurance without latency and throughput degradation until the switch of the methods.

In this letter, a novel and highly efficient haze removal algorithm is proposed for haze removal from only a single input image. The proposed algorithm is built on the atmospheric scattering model. Firstly, global atmospheric light is estimated and coarse atmospheric veil is inferred based on statistics of dark channel prior. Secondly, the coarser atmospheric veil is refined by using a fast Tri-Gaussian filter based on human retina property. To avoid halo artefacts, we then redefine the scene albedo. Finally, the haze-free image is derived by inverting the atmospheric scattering model. Results on some challenging foggy images demonstrate that the proposed method can not only improve the contrast and visibility of the restored image but also expedite the process.