There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.

The hardness in solving the shortest vector problem (SVP) is a fundamental assumption for the security of lattice-based cryptographic algorithms. In 2010, Micciancio and Voulgaris proposed an algorithm named the Gauss Sieve, which is a fast and heuristic algorithm for solving the SVP. Schneider presented another algorithm named the Ideal Gauss Sieve in 2011, which is applicable to a special class of lattices, called ideal lattices. The Ideal Gauss Sieve speeds up the Gauss Sieve by using some properties of the ideal lattices. However, the algorithm is applicable only if the dimension of the ideal lattice n is a power of two or n+1 is a prime. Ishiguro et al. proposed an extension to the Ideal Gauss Sieve algorithm in 2014, which is applicable only if the prime factor of n is 2 or 3. In this paper, we first generalize the dimensions that can be applied to the ideal lattice properties to when the prime factor of n is derived from 2, p or q for two primes p and q. To the best of our knowledge, no algorithm using ideal lattice properties has been proposed so far with dimensions such as: 20, 44, 80, 84, and 92. Then we present an algorithm that speeds up the Gauss Sieve for these dimensions. Our experiments show that our proposed algorithm is 10 times faster than the original Gauss Sieve in solving an 80-dimensional SVP problem. Moreover, we propose a rotation-based Gauss Sieve that is approximately 1.5 times faster than the Ideal Gauss Sieve.

This article proposes a dynamic linkage service which is a specific service model of integrated broadcast — broadband services based ATSC 3.0. The dynamic linkage service is useful to the viewer who wants to continue watching programs using TV or their personal devices, even after the terrestrial broadcast ends due to the start of the next regular programming. In addition, we verify the feasibility of the proposed extended dynamic linkage service through developed emulation system based on ATSC 3.0. In consideration of the personal network capabilities of the viewer environment, the service was tested with 4K/2K Ultra HD and receiving the service was finished within 4 second over intranet.

We propose an optimal power allocation scheme that maximizes the transmission rate of device-to-device (D2D) communications underlaying a cellular system based on orthogonal frequency division multiplexing (OFDM). The proposed algorithm first calculates the maximum allowed transmission power of a D2D transmitter to restrict the interference caused to a cellular link that share the same OFDM subchannels with the D2D link. Then, with a constraint on the maximum transmit power, an optimization of water-filling type is performed to find the optimal transmit power allocation across subchannels and within each subchannel. The performance of the proposed power allocation scheme is evaluated in terms of the average achievable rate of the D2D link.

We propose a new design approach to improve the computational efficiency of an optimal design of optical waveguide devices utilizing coupled mode theory (CMT) and a neural network (NN). Recently, the NN has begun to be used for efficient optimal design of optical devices. In this paper, the eigenmode analysis required in the CMT is skipped by using the NN, and optimization with an evolutionary algorithm can be efficiently carried out. To verify usefulness of our approach, optimal design examples of a wavelength insensitive 3dB coupler, a 1 : 2 power splitter, and a wavelength demultiplexer are shown and their transmission properties obtained by the CMT with the NN (NN-CMT) are verified by comparing with those calculated by a finite element beam propagation method (FE-BPM).

The communications quality of content delivery networks (CDNs), which are geographically distributed networks that have been optimized for content delivery, deteriorates when interflow congestion conditions are severe. Herein, we propose an adaptive server and path switching scheme that is based on the estimated acquisition throughput of each path. We also provide simulation results that show our proposed method can provide higher throughput performance levels than existing methods.

This paper proposes a voice conversion (VC) method based on a model that links linguistic and acoustic representations via latent phonological distinctive features. Our method, called speech chain VC, is inspired by the concept of the speech chain, where speech communication consists of a chain of events linking the speaker's brain with the listener's brain. We assume that speaker identity information, which appears in the acoustic level, is embedded in two steps — where phonological information is encoded into articulatory movements (linguistic to physiological) and where articulatory movements generate sound waves (physiological to acoustic). Speech chain VC represents these event links by using an adaptive restricted Boltzmann machine (ARBM) introducing phoneme labels and acoustic features as two classes of visible units and latent phonological distinctive features associated with articulatory movements as hidden units. Subjective evaluation experiments showed that intelligibility of the converted speech significantly improved compared with the conventional ARBM-based method. The speaker-identity conversion quality of the proposed method was comparable to that of a Gaussian mixture model (GMM)-based method. Analyses on the representations of the hidden layer of the speech chain VC model supported that some of the hidden units actually correspond to phonological distinctive features. Final part of this paper proposes approaches to achieve one-shot VC by using the speech chain VC model. Subjective evaluation experiments showed that when a target speaker is the same gender as a source speaker, the proposed methods can achieve one-shot VC based on each single source and target speaker's utterance.

A long-reach coexisting PON system (1G/10G-EPON, video, and TWDM-PON) that uses the Wavelength Selective-Asymmetrical optical SPlitter (WS-ASP) without any active devices like optical amplifiers is proposed. The proposal can take into account the subscriber distribution in an access network and provide specific services in specific areas by varying the splitting ratios and the branch structure in the optical splitter. Simulations confirm the key features of WS-ASP, its novel process for deriving the splitting-ratios and greater transmission distance than possible with symmetrical splitters. Experiments on a prototype system demonstrate how wavelengths can be assigned to specific areas and optical link budget enhancement. For 1G-EPON systems, the prototype system with splitting-ratio of 60% attains the optical link budget enhancement of 4.2dB compared with conventional symmetrical optical splitters. The same prototype offers the optical link budget enhancement of 4.0dB at the bit rate of 10G-EPON systems. The values measured in the experiment agree well with the simulation results with respect to the transmission distance.

This paper presents a method for many-to-one voice conversion using phonetic posteriorgrams (PPGs) based on an adversarial training of deep neural networks (DNNs). A conventional method for many-to-one VC can learn a mapping function from input acoustic features to target acoustic features through separately trained DNN-based speech recognition and synthesis models. However, 1) the differences among speakers observed in PPGs and 2) an over-smoothing effect of generated acoustic features degrade the converted speech quality. Our method performs a domain-adversarial training of the recognition model for reducing the PPG differences. In addition, it incorporates a generative adversarial network into the training of the synthesis model for alleviating the over-smoothing effect. Unlike the conventional method, ours jointly trains the recognition and synthesis models so that they are optimized for many-to-one VC. Experimental evaluation demonstrates that the proposed method significantly improves the converted speech quality compared with conventional VC methods.

We propose a deep learning-based model for classifying pathological voices using a convolutional neural network and a feedforward neural network. The model uses combinations of heterogeneous parameters, including mel-frequency cepstral coefficients, linear predictive cepstral coefficients and higher-order statistics. We validate the accuracy of this model using the Massachusetts Eye and Ear Infirmary (MEEI) voice disorder database and the Saarbruecken Voice Database (SVD). Our model achieved an accuracy of 99.3% for MEEI and 75.18% for SVD. This model achieved an accuracy that is 7.18% higher than that of competitive models in previous studies.

We address the security issue of RSA with implicitly related keys in this paper. Informally, we investigate under what condition is it possible to efficiently factorize RSA moduli in polynomial time given implicit relation of the related private keys that certain portions of bit pattern are the same. We formulate concrete attack scenarios and propose lattice-based cryptanalysis by using lattice reduction algorithms. A subtle lattice technique is adapted to represent an unknown private key with the help of known implicit relation. We analyze a simple case when given two RSA instances with the known amount of shared most significant bits (MSBs) and least significant bits (LSBs) of the private keys. We further extend to a generic lattice-based attack for given more RSA instances with implicitly related keys. Our theoretical results indicate that RSA with implicitly related keys is more insecure and better asymptotic results can be achieved as the number of RSA instances increases. Furthermore, we conduct numerical experiments to verify the validity of the proposed attacks.

In this paper, we propose an interactive system for controlling the pressure while cutting paper with a knife. The purpose is to improve the cutting skill of novices learning the art of paper-cutting. Our system supports skill improvement for novices by measuring and evaluating their cutting pressure in real-time. In this study, we use a knife with a blade attached to a stylus with a pressure sensor, which can measure the pressure, coordinates, and cutting time. We have developed a similar support system using a stylus and a tablet device. This system allows the user to experience the pressure of experts through tracing. Paper-cutting is created by cutting paper with a knife. The practice system in this paper provides practice in an environment more akin to the production of paper cutting. In the first experiment, we observed differences in cutting ability by comparing cutting pressures between novices and experts. As a result, we confirmed that novices cut paper at a higher pressure than experts. We developed a practice system that guides the novices on controlling the pressure by providing information on the cutting pressure values of experts. This system shows the difference in pressure between novices and experts using a synchronous display of color and sound. Using these functions, novices learn to adjust their cutting pressure according to that of experts. Determining the right cutting pressure is a critical skill in the art of paper-cutting, and we aim to improve the same with our system. In the second experiment, we tested the effect of the practice system on the knife device. We compared the changes in cutting pressure with and without our system, the practice methods used in the workshop, and the previously developed stylus-based support system. As a result, we confirmed that practicing with the knife device had a better effect on the novice's skill in controlling cutting pressure than other practice methods.

This paper proposes a novel flexible receiver with virtual channels for overloaded multiple-input multiple-output (MIMO) channels. The receiver applies extended rotation matrices proposed in the paper for the flexibility. In addition, adaptive selection of the extended rotation matrices is proposed for further performance improvement. We propose two techniques to reduce the computational complexity of the adaptive selection. As a result, the proposed receiver gives us an option to reduce the complexity with a slight decrease in the transmission performance by changing receiver configuration parameters. A computer simulation reveals that the adaptive selection attains a gain of about 3dB at the BER of 10-3.

This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of multiple Gaussian mixture models (GMM). In voice conversion studies, realization of conversion from/to an arbitrary speaker's voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC) based on an eigenvoice GMM (EV-GMM) was proposed. In the EVC, a speaker space is constructed based on GMM supervectors which are high-dimensional vectors derived by concatenating the mean vectors of each of the speaker GMMs. In the speaker space, each speaker is represented by a small number of weight parameters of eigen-supervectors. In this paper, we revisit construction of the speaker space by introducing the tensor factor analysis of training data set. In our approach, each speaker is represented as a matrix of which the row and the column respectively correspond to the dimension of the mean vector and the Gaussian component. The speaker space is derived by the tensor factor analysis of the set of the matrices. Our approach can solve an inherent problem of supervector representation, and it improves the performance of voice conversion. In addition, in this paper, effects of speaker adaptive training before factorization are also investigated. Experimental results of one-to-many voice conversion demonstrate the effectiveness of the proposed approach.

Wireless medical telemetry service (WMTS) is an important wireless communication system in healthcare facilities. Recently, the potential for electromagnetic interference by noise emitted by switching regulators installed in light-emitting diode (LED) lamps has been a serious problem. In this study, we evaluated the characteristics of the electromagnetic noise emitted from LED lamps and its effect on WMTS. Switching regulators generally emit wide band impulsive noise whose bandwidth reaches 400MHz in some instances owing to the switching operation, but this impulsive nature is difficult to identify in the reception of WMTS because the bandwidth of WMTS is much narrower than that of electromagnetic noise. Gaussian approximation (GA) can be adopted for band-limited electromagnetic noise whose characteristics have no repetitive variation. On the other hand, GA with the impulsive correction factor (ICF) can be adopted for band-limited electromagnetic noise that has repetitive variation. We investigate the minimum receiver sensitivity of WMTS for it to be affected by electromagnetic noise emitted from LED lamps. The required carrier-to-noise power ratio (CNR) of Gaussian noise and electromagnetic noise for which GA can be adopted was approximately 15dB, but the electromagnetic noise for which GA with the ICF can be adopted was 3 to 4dB worse. Moreover, the spatial distribution of electromagnetic noise surrounding an LED lamp installation was measured. Finally, we roughly estimated the offset distance between the receiving antenna of WMTS and LED lamps when a WMTS signal of a certain level was added in a clinical setting using our experimental result for the required CNR.

A method for estimating circuit performance variation caused by packaging-induced mechanical stress is proposed. The developed method is based on the stress distribution chart for the target integrated circuit (IC) and the stress sensitivity characteristics of individual devices. This information is experimentally obtained using a specially designed test chip and a cantilever bending calibration system. A post-packaging analysis and simulation tool, called Stress Netlist Generator (SNG), is developed for conducting the proposed method. Based on the stress distribution chart and the stress sensitivity characteristics, SNG modifies the SPICE model parameters in the target netlist according to the impact of the packaging-induced stress. The netlist generated by SNG is used to estimate packaging-induced performance variation with high accuracy. The developed method is remarkably effective even for small-scale ICs with chip sizes of roughly 1 mm2, such as power management ICs, which require higher precision.

Simultaneous multithreading (SMT) technology improves CPU throughput, but also causes unpredictable performance fluctuations for co-running workloads. Although recent major SMT processors have adopted some techniques to promote hardware support for quality-of-service (QoS), achieving both precise performance guarantees and high throughput on SMT architectures is still a challenging open problem. In this paper, we demonstrate through some comprehensive investigations on a cycle-accurate simulator that not only almost all in-core resources suffer from severe contention as workloads vary but also there is a non-linear relationship between performance and available quotas of resources. We consider these observations as the fundamental reason leading to the challenging problem above. Thus, we introduce QoSMT, a novel hardware scheme that leverages a closed-loop controlling mechanism consisting of detection, prediction and adjustment to enforce precise performance guarantees for specific targets, e.g. achieving 85%, 90% or 95% of the performance of a workload running alone respectively. We implement a prototype on GEM5 simulator. Experimental results show that the average control error is only 1.4%, 0.5% and 3.6%.

To improve the likability of speech, we propose a voice conversion algorithm by controlling the fundamental frequency (F0) and the spectral envelope and carry out a subjective evaluation. The subjects can manipulate these two speech parameters. From the result, the subjects preferred speech with a parameter related to higher brightness.

Massive open online course (MOOC) is an online course aimed at unlimited participation and open access via the web. Although there are many MOOC providers, they typically focus on the online course providing and typically do not link with traditional education and business sector requirements. This paper presents a MOOC service framework that focuses on adopting MOOC to provide additional services to support students in traditional education and to provide credit bank consisting of student academic credentials for business sector demand. Particularly, it extends typical MOOC to support academic/ credential record and transcript issuance. The MOOC service framework consists of five layers: authentication, resources, learning, assessment and credential layers. We discuss the adoption of the framework in Thai MOOC, the national MOOC system for Thai universities. Several main issues related to the framework adoption are discussed, including the service strategy and model as well as infrastructure design for large-scale MOOC service.

Media Access Control (MAC) is critical to guarantee different Quality of Service (QoS) requirements for Unmanned Aerial Vehicle (UAV) networks, such as high reliability for safety packets and high throughput for service packets. Meanwhile, due to their ability to provide lower delay and higher data rates, more UAVs are using frequently directional antennas. However, it is challenging to support different QoS in UAV networks with directional antennas, because of the high mobility of UAV which causes serious channel resource loss. In this paper, we propose CU-MAC which is a MAC protocol for Centralized UAV networks with directional antennas. First, we design a mobility prediction based time-frame optimization scheme to provide reliable broadcast service for safety packets. Then, a traffic prediction based channel allocation scheme is proposed to guarantee the priority of video packets which are the most common service packets nowadays. Simulation results show that compared with other representative protocols, CU-MAC achieves higher reliability for safety packets and improves the throughput of service packets, especially video packets.