The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.

One of the patterns that the design of parallel file systems has to solve stems from the difficulty of handling the metadata-intensive I/O generated by parallel applications accessing a single large directory. We demonstrate a middleware design called SFS to support existing parallel file systems for distributed and scalable directory service. SFS distributes directory entries over data servers instead of metadata servers to offer increased scalability and performance. Firstly, SFS exploits an adaptive directory partitioning based on extendible hashing to support concurrent and unsynchronized partition splitting. Secondly, SFS describes an optimization based on recursive split-ordering that emphasizes speeding up the splitting process. Thirdly, SFS applies a write-optimized index structure to convert slow, small, random metadata updates into fast, large, sequential writes. Finally, SFS gracefully tolerates stale mapping at the clients while maintaining the correctness and consistency of the system. Our performance results on a cluster of 32-servers show our implementation can deliver more than 250,000 file creations per second on average.

Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.

Flexible resource utilization in terms of adaptive use of optical bandwidth with agile reconfigurability is key for future metro networks. To address this issue, we focus on optical subwavelength switched network architectures that leverage high-speed optical switching technologies and can accommodate dynamic traffic cost-effectively. Although optical subwavelength switched networks have been attracting attention, most conventional studies apply static (pre-planned) protection scenarios in the networks of limited sizes. In this paper, we discuss optical switch requirements, the use of transceivers, and protection schemes to cost-effectively create large-scale reliable metro networks. We also propose a cost-effective adaptive protection scheme appropriate for optical subwavelength switched networks using our fast time-slot allocation algorithm. The proposed scheme periodically re-optimizes the bandwidth of both working and protection paths to prevent bandwidth resources from being wasted. The numerical examples verify the feasibility of our proposed scheme and the impact on network resources.

This paper proposes the use of two transmit and two receive antennas spaced at roughly the width of a human body to improve communication quality in the presence of shadowing by a human body in the 60GHz band. In the proposed method, the transmit power is divided between the two transmit antennas, and the receive antenna that provides the maximum receive level is then chosen. Although the receive level is reduced by 3dB, the maximum attenuation caused by human body shadowing is totally suppressed. The relationship between the antenna element spacing and the theoretical spacing based on the 1st. Fresnel zone theory is clarified. Experiments confirm that antenna spacing several centimeters wider than that given by the 1st. Fresnel zone theory is enough to attain a significant performance improvement.

Accidental falling among elderly people has become a public health concern. Thus, there is a need for systems that detect a fall when it happens. This paper presents a portable real-time remote health monitoring system that can remotely monitor patients' movements. The system is designed and implemented using ZigBee wireless technologies, and the data is analysed using Matlab. The purpose of this research is to determine the acceleration thresholds for fall detection, using tri-axial accelerometer readings at the head, waist, and knee. Seven voluntary subjects performed purposeful falls and Activities of Daily Living (ADL). The results indicated that measurements from the waist and head can accurately detect falls; the sensitivity and reliability measurements of fall detection ranged between 80% and 90%. In contrast, the measurements showed that the knee is not a useful position for the fall detection.

This paper describes recent progress of photonically-enabled systems for millimeter-wave and terahertz measurement applications. After briefly explaining signal generation schemes as a foundation of photonics-based approach, system configurations for specific applications are discussed. Then, practical demonstrations are presented, which include frequency-domain spectroscopy, phase-sensitive measurement, electric-field measurement, and 2D/3D imaging.

In a public-key encryption scheme, if a sender is not concerned about the security of a message and is unwilling to generate costly randomness, the security of the encrypted message can be compromised. In this work, we characterize such lazy parties, who are regarded as honest parties, but are unwilling to perform a costly task when they are not concerned about the security. Specifically, we consider a rather simple setting in which the costly task is to generate randomness used in algorithms, and parties can choose either perfect randomness or a fixed string. We model lazy parties as rational players who behave rationally to maximize their utilities, and define a security game between the parties and an adversary. Since a standard secure encryption scheme does not work in this setting, we provide constructions of secure encryption schemes in various settings.

Virtualization is no longer an emerging research area since the virtual processor and memory operate as efficiently as the physical ones. However, I/O performance is still restricted by the virtualization overhead caused by the costly and complex I/O virtualization mechanism, in particular by massive exits occurring on the guest-host switch and redundant processing of the I/O stacks at both guest and host. A para-virtual device driver may reduce the number of exits to the hypervisor, whereas the network stacks in the guest OS are still duplicated. Previous work proposed a socket-outsourcing technique that bypasses the redundant guest network stack by delivering the network request directly to the host. However, even by bypassing the redundant network paths in the guest OS, the obtained performance was still below 60% of the native device, since notifications of completion still depended on the hypervisor. In this paper, we propose vCanal, a novel network virtualization framework, to improve the performance of network access in the virtual machine toward that of the native machine. Implementation of vCanal reached 96% of the native TCP throughput, increasing the UDP latency by only 4% compared to the native latency.

Unlimited requirements for system-on-chip (SoC) facilitate three-dimensional (3D) technology as a promising alternative for extending Moore's Law. In spite of many advantages 3D technology provides, 3D technology faces testing issues because of the complexity of 3D design. Therefore, resolving the problem of test optimization and reducing test cost are crucial challenges. In this paper, we propose a novel optimization mechanism of 3D SoCs to minimize test time for mid-bond testing. To make our proposed mechanism more practical, we discuss test cost in mid-bond testing with consideration of manufacturing influence factors. Experimental results on ITC'02 SoC benchmark circuits show that our proposed mechanism reduces mid-bond test time by around 73% on average compared with one baseline solution, furthermore, the mechanism also proves its capacity in test cost reduction.

In this paper, an iterative optimal method is proposed to design the prototype filters for a fast filter bank (FFB) with low complexity, aiming to control the optimum ripple magnitude tolerance of each filter according to the overall specifications. This problem is formulated as an optimization problem for which the total number of multiplications is to be minimized subject to the constrained ripple in the passband and stopband. In the following, an iterative solution is proposed to solve this optimization problem for the purpose of obtaining the impulse response coefficients with low complexity at each stage. Simulations are conducted to verify the performance of the proposed scheme and show that compared with the original method, the proposed scheme can reduce about 24.24% of multiplications. In addition, the proposed scheme and the original method provide similar mean square error (MSE) and the mean absolute error (MAE) of the frequency response.

Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.

Time synchronization is of paramount importance in wireless sensor networks (WSNs) due to the inherent distributed characteristics of WSNs. Border surveillance WSNs, especially, require a highly secure and accurate time synchronization scheme to detect and track intruders. In this paper, we propose a Secure and Efficient Time synchronization scheme for Border surveillance WSNs (SETB) which meets the requirements of border surveillance WSNs while minimizing the resource usage. To accomplish this goal, we first define the performance and security requirements for time synchronization in border surveillance WSNs in detail. Then, we build our time synchronization scheme optimized for these requirements. By utilizing both heterogeneous WSNs and one-way key chains, SETB satisfies the requirements with much less overhead than existing schemes. Additionally, we introduce on-demand time synchronization, which implies that time synchronization is conducted only when an intruder enters the WSN, in order to reduce energy consumption. Finally, we propose a method of deploying time-source nodes to keep the synchronization error within the requirement. Our analysis shows that SETB not only satisfies the performance and security requirements, but also is highly efficient in terms of communication and computation overhead, thus minimizing energy consumption.

This paper proposes Fast Fourier Transform (FFT) based orthogonal beam selection method at the user terminals (UTs) to reduce the number of nulls for the other users except an intended user by the Block Diagonalization (BD) algorithm in multiuser MIMO (MU-MIMO) sytems. The BD algorithm has been proposed in order to realize MU-MIMO broadcast transmission with a realistic signal processing burden. The BD algorithm cancels inter-user interference by creating the weights so that the channel matrixes for the other users are set to be zero matrixes. However, when the number of transmit antennas is equals to the total number of received antennas, the transmission rate by the BD algorithm is decreased. The proposed method realizes the performance improvement compared to the conventional BD algorithm without the burden on the UTs. It is verified via bit error rate (BER) evaluation that the proposed method is effective compared to the conventional BD algorithm and antenna selection method. Moreover, the effectiveness of proposed method is verified by the performance evaluation considering medium access control (MAC) layer in a comparison with the conventional BD algorithm which needs the channel state information (CSI) feedback. Because the proposed method can be easily applied to beamforming without the CSI feedback (implicit beamforming), it is shown that the propose method is effective from a point of view on the transmission efficiency in MU-MIMO system.

Recent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Publishing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as “How many people are in Kyoto Station now?” However, analyzing these raw data will entail risks of compromising individual privacy. ε-Differential Privacy has emerged as a well-known standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. However, since user trajectories will be generated infinitely, it is difficult to protect every trajectory under ε-differential privacy. On the other hand, in real life, not all users require the same level of privacy. To this end, we propose a flexible privacy model of l-trajectory privacy to ensure every desired length of trajectory under protection of ε-differential privacy. We also design an algorithmic framework to publish l-trajectory private data in real time. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.

With extensive use of automotive radars, mutual interference between radars has become a crucial issue, since it increases the noise floor in the frequency domain triggering frequent false alarms and unsafe decision. This paper introduces a mathematical model for a frequency-modulated continuous-wave (FMCW) radar in interfering environments. In addition, this paper proposes a time-domain interference suppression method to provide anti-interference capability regardless of the signal-to-interference ratio. Numerical results are presented to verify the performance of a 77GHz FMCW radar systme with the proposed method in interference-rich environments.

In the development of inter-vehicle communication systems for the prevention of car crashes, it is important to know radio propagation characteristics at blind intersections. In field experiments and numerical simulations to investigate radio propagation characteristics, a half wavelength dipole antenna is assumed to be the wave source in many cases. However, a directivity of car antenna is changed by the effect of both car body and antenna position on car. In this paper, path loss characteristics considering antenna positions on car body at a blind intersection in urban area for inter-vehicle communications using 700MHz band are investigated. Additionally, simplified car models are proposed for the efficient analysis of radio propagation. Here, the hybrid method using both FDTD and ray-tracing methods is used for the radio propagation analysis.

Unsupervised spoken unit discovery or zero-source speech recognition is an emerging research topic which is important for spoken document analysis of languages or dialects with little human annotation. In this paper, we extend our earlier joint training framework for unsupervised learning of discrete density HMM to continuous density HMM (CDHMM) and apply it to spoken unit discovery. In the proposed recipe, we first cluster a group of Gaussians which then act as initializations to the joint training framework of nonnegative matrix factorization and semi-continuous density HMM (SCDHMM). In SCDHMM, all the hidden states share the same group of Gaussians but with different mixture weights. A CDHMM is subsequently constructed by tying the top-N activated Gaussians to each hidden state. Baum-Welch training is finally conducted to update the parameters of the Gaussians, mixture weights and HMM transition probabilities. Experiments were conducted on word discovery from TIDIGITS and phone discovery from TIMIT. For TIDIGITS, units were modeled by 10 states which turn out to be strongly related to words; while for TIMIT, units were modeled by 3 states which are likely to be phonemes.

This paper proposes a method of watermarking for digital audio signals based on adaptive phase modulation. Audio signals are usually non-stationary, i.e., their own characteristics are time-variant. The features for watermarking are usually not selected by combining the principle of variability, which affects the performance of the whole watermarking system. The proposed method embeds a watermark into an audio signal by adaptively modulating its phase with the watermark using IIR all-pass filters. The frequency location of the pole-zero of an IIR all-pass filter that characterizes the transfer function of the filter is adapted on the basis of signal power distribution on sub-bands in a magnitude spectrum domain. The pole-zero locations are adapted so that the phase modulation produces slight distortion in watermarked signals to achieve the best sound quality. The experimental results show that the proposed method could embed inaudible watermarks into various kinds of audio signals and correctly detect watermarks without the aid of original signals. A reasonable trade-off between inaudibility and robustness could be obtained by balancing the phase modulation scheme. The proposed method can embed a watermark into audio signals up to 100 bits per second with 99% accuracy and 6 bits per second with 94.3% accuracy in the cases of no attack and attacks, respectively.

Incoming GPS signals through windows can be often observed indoors. However, conventional indoor positioning systems do not use Global Positioning System (GPS) generally because the signals may come in NLOS (Non Line of Sight). In this paper, we propose a positioning method by fingerprinting based on the incoming GPS signals.