A number of all-optical signal processing schemes based on nonlinear optical effects have been proposed and demonstrated for use in future photonic networks. Since various modulation formats have been developed for optical communication systems, all-optical converters between different modulation formats will be a key technology to connect networks transparently and efficiently. This paper reviews our recent works on all-optical modulation format conversion technologies in order to highlight the fundamental principles and applications in variety of all-optical signal processing schemes.

Nowadays the computing technology is going through a major paradigm shift. Local processing platforms are being replaced by physically out of reach yet more powerful and scalable environments such as the cloud computing platforms. Previously, we introduced the OJIT system as a novel approach for obfuscating remotely executed programs, making them difficult for adversaries to reverse-engineer. The system exploited the JIT compilation technology to randomly and dynamically transform the code, making it constantly changing, thereby complicating the execution state. This work aims to propose the new design iOJIT, as an enhanced approach that patches the old systems shortcomings, and potentially provides more effective obfuscation. Here, we present an analytic study of the obfuscation techniques on the generated code and the cost of applying such transformations in terms of execution time and performance overhead. Based upon this profiling study, we implemented a new algorithm to choose which obfuscation techniques would be better chosen for “efficient” obfuscation according to our metrics, i.e., less prone to security attacks. Another goal was to study the system performance with different applications. Therefore, we applied our system on a cloud platform running different standard benchmarks from SPEC suite.

In this paper, we consider a uniform bipartition problem in a population protocol model. The goal of the uniform bipartition problem is to divide a population into two groups of the same size. We study the problem under global fairness with various assumptions: 1) a population with or without a base station, 2) symmetric or asymmetric protocols, and 3) designated or arbitrary initial states. As a result, we completely clarify solvability of the uniform bipartition problem under global fairness and, if solvable, show the tight upper and lower bounds on the number of states.

Virtual Reality (VR) 360 degree video has ultra-high definition. Reducing the coding complexity becomes a key consideration in coding algorithm design. In this paper, a novel candidate mode pruning process is introduced between Rough Mode Decision and Most Probable Mode based on the statistical analysis of the intra-coding parameters used in VR 360 degree video coding under Cubemap projection (CMP) format. In addition, updated coding bits thresholds for VR 360 degree video are designed in the proposed algorithm. The experimental results show that the proposed algorithm brings 38.73% and 23.70% saving in average coding time at the cost of only 1.4% and 2.1% Bjontegaard delta rate increase in All-Intra mode and Randomaccess mode, respectively.

In this paper, we briefly review the concept of superconducting quantum computers and discuss their hardware architecture. We also describe the necessary technologies for the development of a medium-scale quantum computer with more than tens of thousands of quantum bits.

Unitary operator discrimination is a fundamental problem in quantum information theory. The basic version of this problem can be described as follows: Given a black box implementing a unitary operator U∈S:={U1, U2} under some probability distribution over S, the goal is to decide whether U=U1 or U=U2. In this paper, we consider the query complexity of this problem. We show that there exists a quantum algorithm that solves this problem with bounded error probability using $lceil{sqrt{6} heta_{ m cover}^{-1}} ceil$ queries to the black box in the worst case, i.e., under any probability distribution over S, where the parameter θcover, which is determined by the eigenvalues of $U_1^dagger {U_2}$, represents the “closeness” between U1 and U2. We also show that this upper bound is essentially tight: we prove that for every θcover > 0 there exist operators U1 and U2 such that any quantum algorithm solving this problem with bounded error probability requires at least $lceil{rac{2}{3 heta_{ m cover}}} ceil$ queries under uniform distribution over S.

This letter proposes a representation learning framework of convolutional neural networks (Convnets) that aims to rectify and improve the feature representations learned by existing transformation-invariant methods. The existing methods usually encode feature representations invariant to a wide range of spatial transformations by augmenting input images or transforming intermediate layers. Unfortunately, simply transforming the intermediate feature maps may lead to unpredictable representations that are ineffective in describing the transformed features of the inputs. The reason is that the operations of convolution and geometric transformation are not exchangeable in most cases and so exchanging the two operations will yield the transformation error. The error may potentially harm the performance of the classification networks. Motivated by the fractal statistics of natural images, this letter proposes a rectifying transformation operator to minimize the error. The proposed method is differentiable and can be inserted into the convolutional architecture without making any modification to the optimization algorithm. We show that the rectified feature representations result in better classification performance on two benchmarks.

Superconducting nanowire single-photon detector(SNSPD) has been one of the important ingredients for photonic quantum information processing (QIP). In order to see the potential of SNSPDs, I briefly review recent progresses of the photonic QIP with SNSPDs implemented for various purposes and present a possible direction for the development of SNSPDs.

Massive multiple-input multiple-output (MIMO) systems are a key promising technology for future broadband cellular networks. The propagation paths within massive MIMO radio channels are often sparse, both in the sub-6GHz frequency band and at millimeter wave frequencies. Herein, we propose a two-layer beamforming scheme for downlink transmission over massive multiuser MIMO sparse beam-space channels. The first layer employs a bipartite graph to dynamically group users in the beam-space domain; the aim is to minimize inter-user interference while significantly reducing the effective channel dimensionality. The second layer performs baseband linear MIMO precoding to maximize spatial multiplexing gain and system throughput. Simulation results demonstrate the proposed two-layer beamforming scheme outperforms other, more conventional algorithms.

Two-dimensional (2-D) maximally flat finite impulse response (FIR) digital filters have flat characteristics in both passband and stopband. 2-D maximally flat diamond-shaped half-band FIR digital filter can be designed very efficiently as a special case of 2-D half-band FIR filters. In some cases, this filter would require the reduction of the filter lengths for one of the axes while keeping the other axis unchanged. However, the conventional methods can realize such filters only if difference between each order is 2, 4 and 6. In this paper, we propose a closed-form frequency response of 2-D low-pass maximally flat diamond-shaped half-band FIR digital filters with arbitrary filter orders. The constraints to treat arbitrary filter orders are firstly proposed. Then, a closed-form transfer function is achieved by using Bernstein polynomial.

We propose a novel bit error rate (BER) analysis model of weighted-type fractional Fourier transform (WFRFT)-based systems with WFRFT order offset Δα. By using the traditional BPSK BER analysis method, we deduce the equivalent signal noise ratio (SNR), model the interference in the channel as a Gaussian noise with non-zero mean, and provide a theoretical BER expression of the proposed system. Simulation results show that its theoretical BER performance well matches the empirical performance, which demonstrates that the theoretical BER analysis proposed in this paper is reliable.

Spectral graph theory gives an algebraic approach to the analysis of the dynamics of a network by using the matrix that represents the network structure. However, it is not easy for social networks to apply the spectral graph theory because the matrix elements cannot be given exactly to represent the structure of a social network. The matrix element should be set on the basis of the relationship between persons, but the relationship cannot be quantified accurately from obtainable data (e.g., call history and chat history). To get around this problem, we utilize the universality of random matrices with the feature of social networks. As such a random matrix, we use the normalized Laplacian matrix for a network where link weights are randomly given. In this paper, we first clarify that the universality (i.e., the Wigner semicircle law) of the normalized Laplacian matrix appears in the eigenvalue frequency distribution regardless of the link weight distribution. Then, we analyze the information propagation speed by using the spectral graph theory and the universality of the normalized Laplacian matrix. As a result, we show that the worst-case speed of the information propagation changes up to twice if the structure (i.e., relationship among people) of a social network changes.

This paper proposes optimal beam patterns of analog beamforming for SU (Single User) massive MIMO (Multi-Input Multi-Output) transmission systems. For hybrid beamforming in SU massive MIMO systems, there are several design parameters such as beam patterns, the number of beams (streams), the shape of array antennas, and so on. In conventional hybrid beamforming, rectangular patch array antennas implemented on a planar surface with linear phase shift beam patterns have been used widely. However, it remains unclear whether existing configurations are optimal or not. Therefore, we propose a method using OBPB (Optimal Beam Projection Beamforming) for designing configuration parameters of the hybrid beamforming. By using the method, the optimal beam patterns are derived first, and are projected on the assumed surface to calculate the achievable number of streams and the resulting channel capacity. The results indicate OBPB with a spherical surface yields at least 3.5 times higher channel capacity than conventional configurations.

This paper presents a formal approach for generating train timetables in a mesoscopic level that is more concrete than the macroscopic level, where each station is simply expressed in a black-box, and more abstract than the microscopic level, where the infrastructure in each station-area is expressed in detail. The accuracy of generated timetable and the computational effort for the generation is a trade-off. In this paper, we design a formal mesoscopic modeling language by analyzing real railways, for example Tazawako-line as the first step of this work. Then, we define the constraint formulae for generating train timetables with the help of SMT (Satisfiability Module Theories)-Solver, and explain our tool RW-Solver that is an implementation of the constraint formulae. Finally, we demonstrate how RW-Solver with the help of SMT-Solver can be used for generating timetables in a case study of Tazawako-line.

Aimed at long-term monitoring of daily office conversations without recording the conversational content, a system is presented for estimating acoustic nonverbal information such as utterance duration, utterance frequency, and turn-taking. The system combines a sound localization technique based on the sound energy distribution with 16 beam-forming microphone-array modules mounted in the ceiling for reducing the influence of multiple sound reflection. Furthermore, human detection using a wide field of view camera is integrated to the system for more robust speaker estimation. The system estimates the speaker for each utterance and calculates nonverbal information based on it. An evaluation analyzing data collected over ten 12-hour workdays in an office with three assigned workers showed that the system had 72% speech segmentation detection accuracy and 86% speaker identification accuracy when utterances were correctly detected. Even with false voice detection and incorrect speaker identification and even in cases where the participants frequently made noise or where seven participants had gathered together for a discussion, the order of the amount of calculated acoustic nonverbal information uttered by the participants coincided with that based on human-coded acoustic nonverbal information. Continuous analysis of communication dynamics such as dominance and conversation participation roles through nonverbal information will reveal the dynamics of a group. The main contribution of this study is to demonstrate the feasibility of unconstrained long-term monitoring of daily office activity through acoustic nonverbal information.

We present a novel hybrid beamforming architecture for high speed 5G technologies. The architecture combines several new concepts to achieve significant hardware and cost reduction for large antenna arrays. Specifically, we employ an on-site code division multiplexing scheme to group several antenna elements into a single analog-to-digital converter (ADC). This approach significantly reduces analog hardware and power requirements by a factor of 8 to 32. Additionally, we employ a novel analog frequency independent beamforming scheme to eliminate phase shifters altogether and allow for coherent combining at the analog front-end. This approach avoids traditional phase-shifter-based approaches typically associated with bulky and inefficient components. Preliminary analysis shows that for an array of 800 elements, as much as 97% reduction in cost and power is achieved using the hybrid beamformer as compared to conventional beamformer systems.

MU-MIMO (Multi-User Multiple Input and Multiple Output) has been considered as a fundamental technology for simultaneous communications between a base station and multiple users. This is because it can generate a large virtual MIMO channel between a base station and multiple user terminals with effective utilization of wireless resources. As a method of implementing MU-MIMO downlink, Block Diagonalization (BD) was proposed in which the transmission weights are determined to cancel interference between multiple user terminals. On the other hand, Block Maximum Signal-to-Noise ratio (BMSN) was proposed which determines the transmission weights to enhance the gain for each user terminal in addition to the interference cancellation. As a feature, BMSN has a pseudo-noise for controlling the null depth to the interference. In this paper, to enhance further the BMSN performance, we propose the BMSN algorithm that has the pseudo-noise determined according to receiver SNR. As a result of computer simulation, it is confirmed that the proposed BMSN algorithm shows the significantly improved performance in evaluation of bit error rate (BER) and achievable bit rate (ABR).

With the rising importance of information security, the necessity of implementing better security measures in the physical layer as well as the upper layers is becoming increasing apparent. Given the development of more accurate and less expensive measurement devices, high-performance computers, and larger storage devices, the threat of advanced attacks at the physical level has expanded from the military and governmental spheres to commercial products. In this paper, we review the issue of information security degradation through electromagnetic (EM)-based compromising of security measures in the physical layer (i.e., EM information security). Owing to the invisibility of EM radiation, such attacks can be serious threats. We first introduce the mechanism of information leakage through EM radiation and interference and then present possible countermeasures. Finally, we explain the latest research and standardization trends related to EM information security.

Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.

A reconfigurable broadband linear power amplifier (PA) for long-range WLAN applications fabricated in a 180nm RF CMOS process is presented here. The proposed reconfigurable in/output matching network provides the PA with broadband capability at the two center frequencies of 0.5GHz and 0.85GHz. The output matching network is realized by a switchable transformer which shows maximum peak passive efficiencies of 65.03% and 73.45% at 0.45GHz and 0.725GHz, respectively. With continuous wave sources, a 1-dB bandwidth (BW1-dB) according to saturated output power is 0.4-1.2GHz, where it shows a minimum output power with a power added efficiency (PAE) of 25.62dBm at 19.65%. Using an adaptive power cell configuration at the common gate transistor, the measured PA under LTE 16-QAM 20MHz (40MHz) signals shows an average output power with a PAE exceeding 20.22 (20.15) dBm with 7.42 (7.35)% at an ACLRE-UTRA of -30dBc, within the BW1-dB.