In elevator-group control, the average number of running cars should be finely adjusted by the dynamically controlling the number of running cars (DCNRC). Traffic demand in an office building varies throughout the day. In this paper, we propose a new energy-saving method for elevator-group control that adjusts the number of running cars according to the traffic demand, simulate the proposed energy-saving method under nearly real traffic demand conditions of an office building, and reduce the daily energy consumption to the target level after several days.

This paper presents a synchronization mechanism to effectively implement the lock and barrier protocols in a decentralized manner through explicit message passing. In the proposed solution, a simple and efficient synchronization control mechanism is proposed to support queued synchronization without contention. By using state-of-the-art Application-Specific Instruction-set Processor (ASIP) technology, we embed the synchronization functionality into a baseline processor, making the proposed mechanism feature ultra-low overhead. Experimental results show the proposed synchronization achieves ultra-low latency and almost ideal scalability when the number of processors increases.

In this letter, we propose a low complexity fixed sphere decoder (FSD) with statistical threshold for multiple-input and multiple-output (MIMO) systems. The proposed algorithm is developed by applying two threshold-based pruning algorithms using an initial detection and statistical noise constraint to the FSD. The proposed FSD algorithm is suitable for a fully pipelined hardware implementation and also has low complexity because the threshold of the proposed pruning algorithm is pre-calculated and independently applied to the path without sorting operation. Simulation results show that the proposed FSD has the performance of the original FSD as well as a low complexity compared to the original FSD and other low complexity FSD algorithms.

We focus on forward-looking radar network systems for automotive usages. By using multiple radars, the radar network systems will achieve reliable detection and wide observation area. The forward-looking systems by cameras are famous. In order to realize more reliable safety, the cameras had better be used with other sensing devices such as the radar network. In the radar network, processing of the data, which is derived from the multiple receivers, is important because the processing decides the estimation performance. In this paper, we will introduce our estimation algorithm which focuses on target existence probability and virtual receivers. The performance will be evaluated by simulated targets which are both single point model and 3D target model.

In this paper, we consider a decentralized failure diagnosis problem for discrete event systems. For a conjunctively codiagnosable system, there exists a conjunctive decentralized diagnoser that can detect the occurrence of any failure within a uniformly bounded number of steps. We present a method for synthesizing such a conjunctive decentralized diagnoser as an online diagnoser.

Robust yet efficient techniques for detecting and tracking targets in infrared (IR) images are a significant component of automatic target recognition (ATR) systems. In our previous works, we have proposed infrared target detection and tracking systems based on sparse representation method. The proposed infrared target detection and tracking algorithms are based on sparse representation and Bayesian probabilistic techniques, respectively. In this paper, we adopt Naïve Bayes Nearest Neighbor (NBNN) that is an extremely simple, efficient algorithm that requires no training phase. State-of-the-art image classification techniques need a comprehensive learning and training step (e.g., using Boosting, SVM, etc.) In contrast, non-parametric Nearest Neighbor based image classifiers need no training time and they also have other more advantageous properties. Results of tracking in infrared sequences demonstrated that our algorithm is robust to illumination changes, and the tracking algorithm is found to be suitable for real-time tracking of a moving target in infrared sequences and its performance was quite good.

To represent interactive objects, we propose a choice-disjunctive declaration statement of the form $S add R$ where S, R are the (procedure or field) declaration statements within a class. This statement has the following semantics: request the user to choose one between S and R when an object of this class is created. This statement is useful for representing interactive objects that require interaction with the user.

When Zero-Forcing (ZF) is adopted as a detector, decreasing the condition number of the channel matrix increases the BER performance. In this paper, we propose a new detection algorithm which reduces the condition number of channel matrix down to nearly 2 on average. Since the least singular value of the channel matrix is a major factor determining the condition number, we, first, project the received signal into a space spanned by singular vectors that are orthogonal to the one corresponding to the least singular value. Then, LR decomposition is performed to reduce further the condition number of the projected channel matrix. Computer simulations show that the performance of the proposed algorithm is comparable to that of the ML detector for both correlated and uncorrelated channels. And also the proposed algorithm provides an at least 2dB improvement compared to the conventional LR-based Ordered Successive Interference Cancellation (LR-OSIC) detector with a Bit Error Rate (BER) of 10-3 and a comparable computation load.

In the scattering problem of dielectric gratings in conical mounting, we have considered and formulated scattering fields using transverse electric (TE) and transverse magnetic (TM) waves. This paper formulates scattering fields by superpositions of right-circularly (RC) and left-circularly (LC) polarized waves through the matrix eigenvalue method.

Feature Selection (FS) is widely used to resolve the problem of selecting a subset of information-rich features; Fuzzy-Rough QuickReduct (FRQR) is one of the most successful FS methods. This paper presents two variants of the FRQR algorithm in order to improve its performance: 1) Combining Fuzzy-Rough Dependency Degree with Correlation-based FS merit to deal with a dilemma situation in feature subset selection and 2) Hybridizing the newly proposed method with the threshold based FRQR. The effectiveness of the proposed approaches are proven over sixteen UCI datasets; smaller subsets of features and higher classification accuracies are achieved.

The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.

In wireless communication, it is hard to set the optimal route between a source and a destination through relays, since for optimal relaying, the system operator should know all channel conditions from a source to a destination through relays and determine the path with all channel conditions. In this letter, a multiple relay selection strategy is proposed for the reliability of transmission. The proposed strategy establishes a relaying route to a destination and provides an efficient relay selection process regardless of all channel conditions.

So far, many approaches for detecting code clones have been proposed based on the different degrees of normalizations (e.g. removal of white spaces, tokenization, and regularization of identifiers). Different degrees of normalizations lead to different granularities of source code to be detect as code clones. To investigate how the normalizations impact the code clone detection, this study proposes six approaches for detecting code clones with preprocessing input source files using different degrees of normalizations. More precisely, each normalization is applied to the input source files and then equivalence class partitioning is performed to the files in the preprocessing. After that, code clones are detected from a set of files that are representatives of each equivalence class using a token-based code clone detection tool named CCFinder. The proposed approaches can be categorized into two types, approaches with non-normalization and normalization. The former is the detection of only identical files without any normalization. Meanwhile, the latter category is the detection of identical files with different degrees of normalizations such as removal of all lines containing macros. From the case study, we observed that our proposed approaches detect code clones faster than the approach that uses only CCFinder. We also found the approach with non-normalization is the fastest among the proposed approaches in many cases.

We demonstrated a novel technique to fabricate nanosized structures on a Nafion membrane, using thermal nanoimprinting with alinebreak $5 imes 5$,$mu $m$^{mathrm{2}}$ square pattern Si mold without any polymer damage. A 24,MPa thermal imprinting pressure was used for 10,min. We observed high aspect ratio ($sim$1:10) pillars on the surface after imprinting at 200$^{circ}$C. Finally, we used a novel quartz mold with a 200,nm resolution dot pattern.

In this paper, we discuss performance modeling of 3-D stencil computing on an FPGA accelerator with a high-level synthesis environment, aiming for efficient exploration of user-space design parameters. First, we analyze resource utilization and performance to formulate these relationships as mathematical models. Then, in order to evaluate our proposed models, we implement heat conduction simulations as a benchmark application, by using MaxCompiler, which is a high-level synthesis tool for FPGAs, and MaxGenFD, which is a domain specific framework of the MaxCompiler for finite-difference equation solvers. The experimental results with various settings of architectural design parameters show the best combination of design parameters for pipeline structure can be systematically found by using our models. The effects of changing arithmetic accuracy and using data stream compression are also discussed.

The high efficiency video coding (HEVC) standard has significantly improved compression performance for many applications, including remote desktop and desktop sharing. Screen content video coding is widely used in applications with a high demand for real-time performance. HEVC usually introduces great computational complexity, which makes fast algorithms necessary to offset the limited computing power of HEVC encoders. In this study, a statistical analysis of several screen content sequences is first performed to better account for the completely different statistics of natural images and videos. Second, a fast coding unit (CU) splitting method is proposed, which aims to reduce HEVC intra coding computational complexity, especially in screen content coding. In the proposed scheme, CU size decision is made by checking the smoothness of the luminance values in every coding tree unit. Experiments demonstrate that in HEVC range extension standard, the proposed scheme can save an average of 29% computational complexity with 0.9% Bjøntegaard Delta rate (BD-rate) increase compared with HM13.0+RExt6.0 anchor for screen content sequences. For default HEVC, the proposed scheme can reduce encoding time by an average of 38% with negligible loss of coding efficiency.

Password-protected secret sharing (PPSS, for short) schemes were proposed by Bagherzandi, Jarecki, Saxena and Lu. In this paper, we consider another attack for PPSS schemes which is based on public parameters and documents. We show that the protocol proposed by Bagherzandi et al. is broken with the attack. We then propose an enhanced protocol which is secure against the attack.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.

Our society has been getting more privacy-sensitive. Diverse information is given by users to information and communications technology (ICT) systems such as IC cards benefiting them. The information is stored as so-called big data, and there is concern over privacy violation. Visual information such as images and videos is also considered privacy-sensitive. The growing deployment of surveillance cameras and social network services has caused a privacy problem of information given from various sensors. To protect privacy of subjects presented in visual information, their face or figure is processed by means of pixelization or blurring. As image analysis technologies have made considerable progress, many attempts to automatically process flexible privacy protection have been made since 2000, and utilization of privacy information under some restrictions has been taken into account in recent years. This paper addresses the recent progress of privacy protection for visual information, showing our research projects: PriSurv, Digital Diorama (DD), and Mobile Privacy Protection (MPP). Furthermore, we discuss Harmonized Information Field (HIFI) for appropriate utilization of protected privacy information in a specific area.

In recent years, there has been growing interest in systems for sharing resources, which were originally used for personal purposes by individual users, among many unspecified users via a network. An example of such systems is a peer-to-peer (P2P) data storage system that enables users to share a portion of unused space in their own storage devices among themselves. In a recent paper on a P2P data storage system, the user behavior model was defined based on supply and demand functions that depend only on the storage space unit price in a virtual marketplace. However, it was implicitly assumed that other factors, such as unused space of storage devices possessed by users and additional storage space asked by users, did not affect the characteristics of the supply and demand functions. In addition, it was not clear how the values of parameters used in the user behavior model were determined. Therefore, in this paper, we modify the supply and demand functions and determine the values of their parameters by taking the above mentioned factors as well as the price structure of storage devices in a real marketplace into account. Moreover, we provide a numerical example to evaluate the social welfare realized by the P2P data storage system as a typical application of the modified supply and demand functions.