Using a matrix approach based on a Markov process, we investigate the reliability of a circular connected-(1,2)-or-(2,1)-out-of-(m,n):F lattice system for the i.i.d. case. We develop a modified linear lattice system that is equivalent to this circular system, and propose a methodology that allows the systematic calculation of the reliability. It is based on ideas presented by Fu and Hu [6]. A partial transition probability matrix is used to reduce the computational complexity of the calculations, and closed formulas are derived for special cases.

In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

Network coding (NC) is considered a new paradigm for distributed networks. However, NC has an all-or-nothing property. In this paper, we propose a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a finite field. The effectiveness of the proposed approach is evaluated based on a sensor network.

Driving safety related innovations received increasing interest from automotive industry. We performed an experiment to observe what situations are related to the secured feelings drivers feel when they drive, and found out that drivers need to have four to seven seconds to react possible collision when they operate onboard Human Machine Interface (HMI) devices and check display devices. We explored the distance of semantic space to see what factors of HMI interaction lead to the secured feeling in that time period, and extracted 32 types of factors that lead to the secured feelings. Furthermore, in the process of investigating the semantic space distance, the indicators relating to the secured feelings obtained in the prior studies were further determined to be ‘The layout of the operation device is the same as the driver's image' and ‘The driver can use the word he uses every day to give instructions’ in this time period.’, which were more concrete factors of the secured feelings.

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.

Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

Dry etching is one of the elemental technologies for the fabrication of optical devices. In order to obtain the desired shape using the dry etching process, it is necessary to understand the reactivity of the materials being used to plasma. In particular, III-V compound semiconductors have a multi-layered structure comprising a plurality of elements and thus it is important to first have a full understanding of the basic trends of plasma dry etching, the plasma type and the characteristics of etching plasma sources. In this paper, III-V compound semiconductor etching for use in light sources such as LDs and LEDs, will be described. Glass, LN and LT used in the formation of waveguides and MLA will be introduced as well. And finally, the future prospects of dry etching will be described briefly.

The integer least-squares (ILS) problem frequently arises in wireless communication systems. Sphere decoding (SD) is a systematic search scheme for solving ILS problem. The enumeration of candidates is a key part of SD for selecting a lattice point, which will be searched by the algorithm. Herein, the authors present a computationally efficient Schnorr-Euchner enumeration (SEE) algorithm to solve the constrained ILS problems, where the solution is limited into the finite integer lattice. To trace only valid lattice points within the underlying finite lattice, the authors devise an adaptive computation of the enumeration step and counting the valid points enumerated. In contrast to previous SEE methods based on a zig-zag manner, the proposed method completely avoids enumerating invalid points outside the finite lattice, and it further reduces real arithmetic and logical operations.

Group device-to-device (GD2D) communication is a good solution for data dissemination to devices in proximity without imposing a heavy load on cellular networks. We propose an operating strategy for GD2D communication regarding the mode selection and the power allocation in order to maximize the sum rate of the overall system satisfying QoS requirements of both cellular and D2D links. We derive the maximum sum rate for each class of distance profile of participating devices in the interference-dominant scenario. Using the result, the operating strategy of GD2D communication can be determined in a table-look-up manner.

This paper discusses a pulse generator implemented by CMOS flipped on a glass substrate aiming at low power applications with low duty cycle. The pulse generator is theoretically possible to generate a pulse at a frequency near and beyond Fmax. It also features a quick starting time and zero stand-by power. By using a simplified circuit model, analytical expressions for Q factor, energy conversion efficiency, output energy, and oscillation frequency of the pulse generator are derived. Pulse generator prototypes are designed on a 0.18 μm CMOS chip flipped over a transmission line resonator on a glass substrate. Measurement results of two different prototypes confirm the feasibility of the proposed circuit and the analytical model.

Service chaining (SC) is a method for realizing a service by transferring flows among several service functions (SFs) that process packets. A route among SFs is called a service path (SP). Service chaining is being developed to reduce costs, increase flexibility, and shorten time-to-market. SC technologies are expected to be applied to carrier networks so that large communication carriers benefit from them. We assume that SPs process the traffic of services that treat all users in the same way such as an Internet access service for home users. An SP processes flows from several users. We do not assume that each SP is assigned to a user. Because a carrier network accommodates many users, each service will be heavily utilized. Therefore, it is assumed that the amount of traffic of a service is larger than the resource of an SF apparatus. Several SPs are required to process the traffic. SPs are supposed to meet two requirements. One is guaranteeing minimum bandwidth. The other is reducing the number of SF apparatuses, i.e., high resource utilization. Resource utilization depends on the combination of the resource quantities of SF apparatuses. Network operators have to determine the bandwidth of each SP within the range from the minimum bandwidth to the resource quantities of SF apparatuses to maximize resource utilization. Methods for determining the bandwidth of each SP have not been proposed for meeting the two requirements. Therefore, we propose a resource allocation method for this purpose. The proposed method determines the bandwidth of each SP on the basis of the combination of the resource quantities of SF apparatuses for guaranteeing the minimum bandwidth and maximizing resource utilization and allocates necessary resources to each SP. We also evaluate the proposed method and confirm that it can guarantee the minimum bandwidth of SPs and achieve high resource utilization regardless of the combination of the resource quantities of SF apparatuses. Although SF apparatuses are generally produced without considering the combinations of resource quantities of SF apparatuses in SPs, the proposed method can provide more options for selecting SF apparatuses.

We propose a synthesis and automatic layout method for mixed-signal circuits with high regularity. As the first step of this research, a resistive digital-to-analog converter (RDAC) is presented. With a size calculation routine, the area of this RDAC is minimized while satisfying the required matching precision without any optimization loops. We propose to partition the design into slices comprising of both analog and digital cells. These cells are programmed to be synthesized as similar as custom P-Cells based on the calculation above, and automatically laid out to form one slice cell. To synthesize digital circuits, without using digital standard cell library, we propose a versatile unit digital block consisting of 8 transistors. With one or several blocks, the transistors' interconnections are programmed in the units to realize various logic gates. By using this block, the slice shapes are aligned so that the layout space in between the slices are minimized. The proposed mixed-signal slice-based partition facilitates the place-and-route of the whole RDAC. The post-layout simulation shows that the generated 9-bit RDAC achieves 1GHz sampling frequency, -0.11/0.09 and -0.30/0.75 DNL and INL, respectively, 3.57mW power consumption, and 0.0038mm2 active area.

We have derived the physics-based equivalent circuit model of a semiconductor-integrated bow-tie antenna (BTA) for expressing its impedance and radiation characteristics as a terahertz transmitter. The equivalent circuit branches and components, consisting of 16 RLC parameters are determined based on electromagnetic simulations. All the values of the circuit elements are identified using the particle swarm optimization (PSO) that is one of the modern multi-purpose optimization methods. Moreover, each element value can also be explained by the structure of the semiconductor-integrated BTA, the device size, and the material parameters.

Information-Centric Networking (ICN) technology has recently been attracting substantial interest in the research community as one of the most promising future Internet architectures. The Named Data Networking (NDN) approach, which is one of the most recent instantiations of the ICN approach, would be a good choice for multimedia services, because NDN utilizes in-network storage embedded in NDN routers by caching recently or frequently requested contents. It is important to determine which data to cache at which NDN routers in order to achieve high performance, by considering not only the popularity of contents but also the inter-chunk popularity of a content item. This paper presents a chunk-block-based incremental caching scheme that considers both content and inter-chunk popularity. Our proposed scheme employs an incremental cache populating mechanism, which utilizes not only core-side but also edge-side NDN routers according to the request rate of the content item. Through simulations, we show that the proposed scheme achieves less delay, reduced redundant network traffic, and a higher cache hit ratio than legacy schemes.

The privacy of users' data has become a big issue for cloud service. This research focuses on image cloud database and the function of similarity search. To enhance security for such database, we propose a framework of privacy-enhanced search scheme, while all the images in the database are encrypted, and similarity image search is still supported.

This paper proposes a Bayesian approach to image recognition based on separable lattice hidden Markov models (SL-HMMs). The geometric variations of the object to be recognized, e.g., size, location, and rotation, are an essential problem in image recognition. SL-HMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. This makes it possible to model not only invariances to the size and location of the object but also nonlinear warping in both dimensions. The maximum likelihood (ML) method has been used in training SL-HMMs. However, in some image recognition tasks, it is difficult to acquire sufficient training data, and the ML method suffers from the over-fitting problem when there is insufficient training data. This study aims to accurately estimate SL-HMMs using the maximum a posteriori (MAP) and variational Bayesian (VB) methods. The MAP and VB methods can utilize prior distributions representing useful prior information, and the VB method is expected to obtain high generalization ability by marginalization of model parameters. Furthermore, to overcome the local maximum problem in the MAP and VB methods, the deterministic annealing expectation maximization algorithm is applied for training SL-HMMs. Face recognition experiments performed on the XM2VTS database indicated that the proposed method offers significantly improved image recognition performance. Additionally, comparative experiment results showed that the proposed method was more robust to geometric variations than convolutional neural networks.

Sound localization systems are widely studied and have several potential applications, including hearing aid devices, surveillance and robotics. However, few proposed solutions target portable systems, such as wearable devices, which require a small unnoticeable platform, or unmanned aerial vehicles, in which weight and low power consumption are critical aspects. The main objective of this research is to achieve real-time sound localization capability in a small, self-contained device, without having to rely on large shaped platforms or complex microphone arrays. The proposed device has two surface-mount microphones spaced only 20 mm apart. Such reduced dimensions present challenges for the implementation, as differences in level and spectra become negligible, and only time-difference of arrival (TDoA) can be used as a localization cue. Three main issues have to be addressed in order to accomplish these objectives. To achieve real-time processing, the TDoA is calculated using zero-crossing spikes applied to the hardware-friendly Jeffers model. In order to make up for the reduction in resolution due to the small dimensions, the signal is upsampled several-fold within the system. Finally, a coherence-based spectral masking is used to select only frequency components with relevant TDoA information. The proposed system was implemented on a field-programmable gate array (FPGA) based platform, due to the large amount of concurrent and independent tasks, which can be efficiently parallelized in reconfigurable hardware devices. Experimental results with white-noise and environmental sounds show high accuracies for both anechoic and reverberant conditions.

Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.

As one of the techniques enabling individual singers to produce the varieties of voice timbre beyond their own physical constraints, a statistical voice timbre control technique based on the perceived age has been developed. In this technique, the perceived age of a singing voice, which is the age of the singer as perceived by the listener, is used as one of the intuitively understandable measures to describe voice characteristics of the singing voice. The use of statistical voice conversion (SVC) with a singer-dependent multiple-regression Gaussian mixture model (MR-GMM), which effectively models the voice timbre variations caused by a change of the perceived age, makes it possible for individual singers to manipulate the perceived ages of their own singing voices while retaining their own singer identities. However, there still remain several issues; e.g., 1) a controllable range of the perceived age is limited; 2) quality of the converted singing voice is significantly degraded compared to that of a natural singing voice; and 3) each singer needs to sing the same phrase set as sung by a reference singer to develop the singer-dependent MR-GMM. To address these issues, we propose the following three methods; 1) a method using gender-dependent modeling to expand the controllable range of the perceived age; 2) a method using direct waveform modification based on spectrum differential to improve quality of the converted singing voice; and 3) a rapid unsupervised adaptation method based on maximum a posteriori (MAP) estimation to easily develop the singer-dependent MR-GMM. The experimental results show that the proposed methods achieve a wider controllable range of the perceived age, a significant quality improvement of the converted singing voice, and the development of the singer-dependnet MR-GMM using only a few arbitrary phrases as adaptation data.

This paper describes a novel harmonic-based robust voice activity detection (H-RVAD) method with harmonic spectral local peak (HSLP) feature. HSLP is extracted by spectral amplitude analysis between the adjacent formants, and such characteristic can be used to identify and verify audio stream containing meaningful human speech accurately in low SNR environment. And, an enhanced low SNR noisy speech recognition system framework with wakeup module, speech recognition module and confirmation module is proposed. Users can determine or reject the system feedback while a recognition result was given in the framework, to prevent any chance that the voiced noise misleads the recognition result. The H-RVAD method is evaluated by the AURORA2 corpus in eight types of noise and three SNR levels and increased overall average performance from 4% to 20%. In home noise, the performance of H-RVAD method can be performed from 4% to 14% sentence recognition rate in average.