This paper describes a parametric representation of ultra-wideband radar signatures and its physical interpretation. Under the scattering theory of electromagnetic waves, a transfer function of radar scattering is factorized into three elementary parts and a radar signature with three parameters is derived. To use these parameters for radar target classification and identification, the relation between them and the response waveform is analytically revealed and numerically checked. The result indicates that distortion of the response waveform is sensitive to these parameters, and thus they can be expected to be used as features for radar target classification and identification.

We study an authentication method using secret figures of Pattern Lock, called pass patterns. In recent years, it is important to prevent the leakage of personal and company information on mobile devices. Android devices adopt a login authentication called Pattern Lock, which achieves both high resistance to Brute Force Attack and usability by virtue of pass pattern. However, Pattern Lock has a problem that pass patterns directly input to the terminal can be easily remembered by shoulder-surfing attack. In this paper, we propose a shoulder-surfing resistant authentication using pass pattern of Pattern Lock, which adopts a challenge & response authentication and also uses users' short-term memory. We implement the proposed method as an Android application and measure success rate, authentication time and the resistance against shoulder surfing. We also evaluate security and usability in comparison with related work.

In this work we utilize feature transformations that are common in supervised learning without having prior supervision, with the goal to improve Dirichlet process Gaussian mixture model (DPGMM) based acoustic unit discovery. The motivation of using such transformations is to create feature vectors that are more suitable for clustering. The need of labels for these methods makes it difficult to use them in a zero resource setting. To overcome this issue we utilize a first iteration of DPGMM clustering to generate frame based class labels for the target data. The labels serve as basis for learning linear discriminant analysis (LDA), maximum likelihood linear transform (MLLT) and feature-space maximum likelihood linear regression (fMLLR) based feature transformations. The novelty of our approach is the way how we use a traditional acoustic model training pipeline for supervised learning to estimate feature transformations in a zero resource scenario. We show that the learned transformations greatly support the DPGMM sampler in finding better clusters, according to the performance of the DPGMM posteriorgrams on the ABX sound class discriminability task. We also introduce a method for combining posteriorgram outputs of multiple clusterings and demonstrate that such combinations can further improve sound class discriminability.

This paper presents a new GPU-based rasterization algorithm for Boolean operations that handles arbitary closed polygons. We construct an efficient data structure for interoperation of CPU and GPU and propose a fast GPU-based contour extraction method to ensure the performance of our algorithm. We then design a novel traversing strategy to achieve an error-free calculation of intersection point for correct Boolean operations. We finally give a detail evaluation and the results show that our algorithm has a higher performance than exsiting algorithms on processing polygons with large amount of vertices.

We propose a wideband antenna that has both vertical and horizontal polarization to create access points with enhanced connectivity. The antenna is composed of a rectangular plate and a ground plate, and the rectangular plate is fed sideways from the ground plate. Its -10dB fractional bandwidth is approximately 162%. It is shown that the offset feed of the rectangular plate is important to attain wideband impedance matching and vertical polarized wave. The results of a parametric study to characterize the first- and second-lowest resonant frequencies are presented. Moreover, the behavior of the impedance matching and polarization is interpreted by dividing the current distribution around the feed port on the rectangular plate into the same direction current mode and the opposite direction current mode. The measured results for the return loss and the radiation pattern of a prototype antenna agree well with the simulation results, therefore the wideband property was experimentally confirmed.

In this study, toward the development of smartphone-based monitoring system for life logging, we collect over 1,400 hours of data by recording including both the outdoor and indoor daily activities of 19 subjects, under practical conditions with a smartphone and a small camera. We then construct a huge human activity database which consists of an environmental sound signal, triaxial acceleration signals and manually annotated activity tags. Using our constructed database, we evaluate the activity recognition performance of deep neural networks (DNNs), which have achieved great performance in various fields, and apply DNN-based adaptation techniques to improve the performance with only a small amount of subject-specific training data. We experimentally demonstrate that; 1) the use of multi-modal signal, including environmental sound and triaxial acceleration signals with a DNN is effective for the improvement of activity recognition performance, 2) the DNN can discriminate specified activities from a mixture of ambiguous activities, and 3) DNN-based adaptation methods are effective even if only a small amount of subject-specific training data is available.

This paper reports the development of a landmine visualization system based on complex-valued self-organizing map (CSOM) by employing one-dimensional (1-D) array of taper-walled tapered slot antennas (TSAs). Previously we constructed a high-density two-dimensional array system to observe and classify complex-amplitude texture of scattered wave. The system has superiority in its adaptive distinction ability between landmines and other clutters. However, it used so many (144) antenna elements with many mechanical radio-frequency (RF) switches and cables that it has difficulty in its maintenance and also requires long measurement time. The 1-D array system proposed here uses only 12 antennas and adopts electronic RF switches, resulting in easy maintenance and 1/4 measurement time. Though we observe stripe noise specific to this 1-D system, we succeed in visualization with effective solutions.

We present the concept of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, which is defined as a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed. After formalising the syntax and security notions for KR-SU-HE schemes, we build a concrete scheme based on the Learning With Errors assumption. We then perform several careful implementations and optimizations to show that our proposed scheme is efficiently practical.

Partial blur segmentation is one of the most interesting topics in computer vision, and it has practical value. The generation of blur maps is a crucial part of partial blur segmentation because partial blur segmentation involves producing a blur map and applying a segmentation algorithm to the blur map. In this study, we address two important issues in order to improve the discrimination of blur maps: (1) estimating a robust local blur feature to consider variations in the intensity amplitude and (2) a scheme for generating blur maps. We propose the ANGHS (Amplitude-Normalized Gradient Histogram Span) as a local blur feature. ANGHS represents the heavy-tailedness of a gradient distribution, where it is calculated from an image gradient normalized using the intensity amplitude. ANGHS is robust to variations in the intensity amplitude, and it can handle local regions in a more appropriate manner than previously proposed local blur features. Blur maps are affected by local blur features but also by the contents and sizes of local regions, and the assignment of blur feature values to pixels. Thus, multiple-sized grids and the EAI (Edge-Aware Interpolation) are employed in each task to improve the discrimination of blur maps. The discrimination of the generated blur maps is evaluated visually and statistically using numerous partial blur images. Comparisons with the results obtained by state-of-the-art methods demonstrate the high discrimination of the blur maps generated using the proposed method.

In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.

In this letter, three constructions of perfect Gaussian integer sequences are constructed based on cyclic difference sets. Sufficient conditions for constructing perfect Gaussian integer sequences are given. Compared with the constructions given by Chen et al. [12], the proposed constructions relax the restrictions on the parameters of the cyclic difference sets, and new perfect Gaussian integer sequences will be obtained.

A quantized index for evaluating the pattern similarity of two different datasets is designed by calculating the number of correlated dictionary atoms. Guided by this theory, task-specific biometric recognition model transferred from state-of-the-art DNN models is realized for both face and vein recognition.

The Werewolf game is a kind of role-playing game in which players have to guess other players' roles from their speech acts (what they say). In this game, players have to estimate other players' beliefs and intentions, and try to modify others' intentions. The BDI model is a suitable one for this game, because it explicitly has notions of mental states, i.e. beliefs, desires and intentions. On the other hand, in this game, players' beliefs are not completely known. Consequently, in many cases it is difficult for players to choose a unique strategy; in other words, players frequently have to maintain probabilistic intentions. However, the conventional BDI model does not have the notion of probabilistic mental states. In this paper, we propose an extension of BDI logic that can handle probabilistic mental states and use it to model some situations in the Werewolf game. We also show examples of deductions concerning those situations. We expect that this study will serve as a basis for developing a Werewolf game agent based on BDI logic in the future.

In this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

In recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy — such as the diversity and novelty of the recommended items — are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyond-accuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness — factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

This paper proposes a novel wavelet de-noising scheme regarding the existing burst noises that consist of background and impulsive noises in power-line communications. The proposed de-noising scheme employs multi-level threshold functions to efficiently and adaptively reduce the given burst noises. The experiment results show that the proposed de-noising scheme significantly outperformed the conventional schemes.

As financial products have grown in complexity and level of risk compounding in recent years, investors have come to find it difficult to assess investment risk. Furthermore, companies managing mutual funds are increasingly expected to perform risk control and thus prevent assumption of unforeseen risk by investors. A related revision to the investment fund legal system in Japan led to establishing what is known as “the rule for investment diversification” in December 2014, without a clear discussion of its expected effects on market price formation having taken place. In this paper, we therefore used an artificial market to investigate its effects on price formation in financial markets where investors follow the rule at the time of a market crash that is caused by the collapse of an asset fundamental price. As results, we found the possibility that when the fundamental price of one asset collapses and its market price also collapses, some asset market prices also fall, whereas other asset market prices rise for a market in which investors follow the rule for investment diversification.

Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

Cooperative caching is a key technique to reduce rapid growing video-on-demand's traffic by aggregating multiple cache storages. Existing strategies periodically calculate a sub-optimal allocation of the content caches in the network. Although such technique could reduce the generated traffic between servers, it comes with the cost of a large computational overhead. This overhead will be the cause of preventing these caches from following the rapid change in the access pattern. In this paper, we propose a light-weight scheme for cooperative caching by grouping contents and servers with color tags. In our proposal, we associate servers and caches through a color tag, with the aim to increase the effective cache capacity by storing different contents among servers. In addition to the color tags, we propose a novel hybrid caching scheme that divides its storage area into colored LFU (Least Frequently Used) and no-color LRU (Least Recently Used) areas. The colored LFU area stores color-matching contents to increase cache hit rate and no-color LRU area follows rapid changes in access patterns by storing popular contents regardless of their tags. On the top of the proposed architecture, we also present a new routing algorithm that takes benefit of the color tags information to reduce the traffic by fetching cached contents from the nearest server. Evaluation results, using a backbone network topology, showed that our color-tag based caching scheme could achieve a performance close to the sub-optimal one obtained with a genetic algorithm calculation, with only a few seconds of computational overhead. Furthermore, the proposed hybrid caching could limit the degradation of hit rate from 13.9% in conventional non-colored LFU, to only 2.3%, which proves the capability of our scheme to follow rapid insertions of new popular contents. Finally, the color-based routing scheme could reduce the traffic by up to 31.9% when compared with the shortest-path routing.

Small delay caused by a resistive open is difficult to test since circuit delay varies depending on various factors such as process variations and crosstalk even in fault-free circuits. We consider the problem of discriminating a resistive open by anomaly detection using delay distributions obtained by the effect of various input signals provided to adjacent lines. We examined the circuit delay in a fault-free circuit and a faulty circuit by applying electromagnetic simulator and circuit simulator for a line structure with adjacent lines under consideration of process variations. The effectiveness of the method that discriminates a resistive open is shown for the results obtained by the simulation.