Forecasting commodity futures price is a challenging task. We present an algorithm to predict the trend of commodity futures price based on a type of structuring data and back propagation neural network. The random volatility of futures can be filtered out in the structuring data. Moreover, it is not restricted by the type of futures contract. Experiments show the algorithm can achieve 80% accuracy in predicting price trends.

Herugolf and Makaro are Nikoli's pencil puzzles. We study the computational complexity of Herugolf and Makaro puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

Over the last 10 years, tablets have spread to the point where we can now read electronic books (e-books) like paper books. There is a long history of studies of eye movement during reading. Remarkable results have been reported for reading experiments in which displayed letters are changed in conjunction with eye movement during reading. However, these studies were conducted in the 1970s, and it is difficult to judge the detailed descriptions of the experimental techniques and whether the display time was correctly controlled when changing letters. Here, we propose an experimental system to control the display information exactly, as well as the display time, and inspect the results of past reading research, with the aim of being at the forefront of reading research in the e-book era.

As the demand of data reliability becomes more and more larger, most of today's storage systems adopt erasure codes to assure the data could be reconstructed when suffering from physical device failures. In order to fast recover the lost data from a single failure, recovery optimization methods have attracted a lot of attention in recent years. However, most of the existing optimization methods focus on homogeneous devices, ignoring the fact that the storage devices are usually heterogeneous. In this paper, we propose a new recovery optimization method named HSR (Heterogeneous Storage Recovery) method, which uses both loads and speed rate among physical devices as the optimization target, in order to further improve the recovery performance for heterogeneous devices. The experiment results show that, compared to existing popular recovery optimization methods, HSR method gains much higher recovery speed over heterogeneous storage devices.

In order to evaluate the vehicle detection method, it is necessary to know the correct vehicle position considered as “ground truth”. We propose indices considering subjective evaluation in vehicle detection utilizing IoU. Subjective evaluation experiments were carried out with respect to misregistration from ground truth in vehicle detection.

The human visual system exhibits a characteristic known as the Helmholtz-Kohlrausch (H-K) effect: even if the hue and the lightness retain the same values, the actual lightness (perceived lightness) changes with changes in the color saturation. Quantification of this effect is expected to be useful for the future development and evaluation of high-quality displays. We have been studying the H-K effect in natural images projected by LED projectors, which play important roles in practical uses. To verify the effectiveness of the determinations of the H-K effect for natural images, we have performed a subjective-evaluation experiment by method of adjustment for natural images and compared the experimental values with values calculated from extended form of Nayatani's equation to apply to natural images. In general, we found a high correlation between the two, although there was a low correlation for some images. Therefore, we obtained a correction function derived from the subjective evaluation experiment value of 108 color (hue: 12 × saturation: 3 × lightness: 3) patterns and have applied it to estimate the equation H-K effect.

Today, trust plays a central role in services in distributed environments. Conventionally deployed trust has been based on static framework in which a server responds to a service request under statically determined policies. However, in accordance with evolution of distributed environments empowered with IoT and federated access mechanisms, dynamic behavior must be analyzed and taken into service provision, which conventional trust cannot properly handle. In this paper, we propose an extension of PDP (Policy Decision Point) in which assertions together with service requests are evaluated. Furthermore, the evaluation may be dynamically configured in dynamically evolving trust environment. We propose an elastic trust model in view of dynamic trust environment. This enables intuitionistic modeling of typical concrete elastic distributed services.

A fast cross-validation algorithm for model selection in kernel ridge regression problems is proposed, which is aiming to further reduce the computational cost of the algorithm proposed by An et al. by eigenvalue decomposition of a Gram matrix.

Receiver implementations with very low quantization resolution will play an important role in 5G, as high precision quantization and signal processing are costly in terms of computational resources and chip area. Therefore, low resolution receivers with quasi optimum performance will be required to meet complexity and latency constraints. The Information Bottleneck method allows for a novel, information centric approach to design such receivers. The method was originally introduced by Naftali Tishby et al. and mostly used in the machine learning field so far. Interestingly, it can also be applied to build surprisingly good digital communication receivers which work fundamentally different than state-of-the-art receivers. Instead of minimizing the quantization error, receiver components with maximum preservation of relevant information for a given bit width can be designed. All signal processing in the resulting receivers is performed using only simple lookup operations. In this paper, we first provide a brief introduction to the design of receiver components with the Information Bottleneck method. We keep referring to decoding of low-density parity-check codes as a practical example. The focus of the paper lies on practical decoder implementations on a digital signal processor which illustrate the potential of the proposed technique. An Information Bottleneck decoder with 4bit message passing decoding is found to outperform 8bit implementations of the well-known min-sum decoder in terms of bit error rate and to perform extremely close to an 8bit belief propagation decoder, while offering considerably higher net decoding throughput than both conventional decoders.

Conventional approaches to statistical parametric speech synthesis use context-dependent hidden Markov models (HMMs) clustered using decision trees to generate speech parameters from linguistic features. However, decision trees are not always appropriate to model complex context dependencies of linguistic features efficiently. An alternative scheme that replaces decision trees with deep neural networks (DNNs) was presented as a possible way to overcome the difficulty. By training the network to represent high-dimensional feedforward dependencies from linguistic features to acoustic features, DNN-based speech synthesis systems convert a text into a speech. To improved the naturalness of the synthesized speech, this paper presents a novel pre-training method for DNN-based statistical parametric speech synthesis systems. In our method, a deep relational model (DRM), which represents a joint probability of two visible variables, is applied to describe the joint distribution of acoustic and linguistic features. As with DNNs, a DRM consists several hidden layers and two visible layers. Although DNNs represent feedforward dependencies from one visible variables (inputs) to other visible variables (outputs), a DRM has an ability to represent the bidirectional dependencies between two visible variables. During the maximum-likelihood (ML) -based training, the model optimizes its parameters (connection weights between two adjacent layers, and biases) of a deep architecture considering the bidirectional conversion between 1) acoustic features given linguistic features, and 2) linguistic features given acoustic features generated from itself. Owing to considering whether the generated acoustic features are recognizable, our method can obtain reasonable parameters for speech synthesis. Experimental results in a speech synthesis task show that pre-trained DNN-based systems using our proposed method outperformed randomly-initialized DNN-based systems, especially when the amount of training data is limited. Additionally, speaker-dependent speech recognition experimental results also show that our method outperformed DNN-based systems, by setting the initial parameters of our method are the same as that in the synthesis experiments.

In recent years, MIMO technology which uses multiple antennas has been introduced to the mobile terminal to increase communication capacity per unit frequency. However, if MIMO antennas are put closely, a strong mutual coupling occurred. Moreover, CA which uses multiple frequencies is also utilized to improve communication speed. Therefore, reducing mutual coupling in multiple frequencies is required. In this paper, we propose a dual-band decoupling method by using a short stub and a branch element and confirmed that the proposed model performed decoupling, increased radiation efficiency.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

Aircraft landing scheduling (ALS) is one of the most important challenges in air traffic management. The target of ALS is to decide a landing scheduling sequence and calculate a landing time for each aircraft in terminal areas. These landing times are within time windows, and safety separation distances between aircraft must be kept. ALS is a complex problem, especially with a large number of aircraft. In this study, we propose a novel heuristic called CGIC to solve ALS problems. The CGIC consists of four components: a chunking rule based on costs, a landing subsequence generation rule, a chunk improvement heuristic, and a connection rule. In this algorithm, we reduce the complexity of the ALS problem by breaking it down into two or more subproblems with less aircraft. First, a feasible landing sequence is generated and divided into several subsequences as chunks by a chunking rule based on aircraft cost. Second, each chunk is regenerated by a constructive heuristic, and a perturbative heuristic is applied to improve the chunks. Finally, all chunks constitute a feasible landing sequence through a connection rule, and the landing time of each aircraft is calculated on the basis of this sequence. Simulations demonstrate that (a) the chunking rule based on cost outperforms other chunking rules based on time or weight for ALS in static instances, which have a large number of aircraft; (b) the proposed CGIC can solve the ALS problem up to 500 aircraft optimally; (c) in dynamic instances, CGIC can obtain high-quality solutions, and the computation time of CGIC is low enough to enable real-time execution.

In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

Accurate segmentation of the region in the iris picture has a crucial influence on the reliability of the recognition system. In this letter, we present an end to end deep neural network based on U-Net. It uses dense connection blocks to replace the original convolutional layer, which can effectively improve the reuse rate of the feature layer. The proposed method takes U-net's skip connections to combine the same-scale feature maps from the upsampling phase and the downsampling phase in the upsampling process (merge layer). In the last layer of downsampling, it uses dilated convolution. The dilated convolution balances the iris region localization accuracy and the iris edge pixel prediction accuracy, further improving network performance. The experiments running on the Casia v4 Interval and IITD datasets, show that the proposed method improves segmentation performance.

In order to cope with recent growth of mobile data traffic and emerging various services, world-wide system trials for the fifth-generation (5G) mobile communication system that dramatically extends capability of the fourth-generation mobile communication system are being performed to launch its commercial service in 2020. In addition, research and development of new radio access technologies for 5G evolution and beyond 5G systems are beginning to be made all over the world. This paper introduces our recent activities on 5G transmission experiments that aim to validate Massive MIMO technologies using higher frequency bands such as SHF/EHF bands, that is, 5G experimental trials. Recent results of 5G system trials to create new services and applications in 5G era in cooperation with partners in vertical industries are also introduced.