Personal e-healthcare service is growing significantly. A large number of personal e-health measuring and monitoring devices are now in the market. However, to achieve better health outcome, various devices or services need to work together. This coordination among services remains challenge, due to their variations and complexities. To address this issue, we have proposed an ontology-based framework for interactive self-assessment of RESTful e-health services. Unlike existing e-health service frameworks where they had tightly coupling between services, as well as their data schemas were difficult to change and extend in the future. In our work, the loosely coupling among services and flexibility of each service are achieved through the design and implementation based on HYDRA vocabulary and REST principles. We have implemented clinical knowledge through the combination of OWL-DL and SPARQL rules. All of these services evolve independently; their interfaces are based on REST principles, especially HATEOAS constraints. We have demonstrated how to apply our framework for interactive self-assessment in e-health applications. We have shown that it allows the medical knowledge to drive the system workflow according to the event-driven principles. New data schema can be maintained during run-time. This is the essential feature to support arriving of IoT (Internet of Things) based medical devices, which have their own data schema and evolve overtime.

In this letter, an effective acoustic feedback cancellation algorithm is proposed based on the normalized sub-band adaptive filter (NSAF). To improve the confliction between fast convergence rate and low misalignment in the NSAF algorithm, a variable step size is designed to automatically vary according to the update state of the filter. The update state of the filter is adaptively detected via the normalized distance between the long term average and the short term average of the tap-weight vector. Simulation results demonstrate that the proposed algorithm has superior performance in terms of convergence rate and misalignment.

We focus on the feature transform approach as one methodology for biometric template protection, where the template consists of the features extracted from the biometric trait. This study considers some properties of the unitary (including orthogonal) transform-based template protection in particular. It is known that the Euclidean distance between the templates protected by a unitary transform is the same as that between original (non-protected) ones as a property. In this study, moreover, it is shown that it provides the same results in l2-norm minimization problems as those of original templates. This means that there is no degradation of recognition performance in authentication systems using l2-norm minimization. Therefore, the protected templates can be reissued multiple times without original templates. In addition, a DFT-based template protection scheme is proposed as an unitary transform-based one. The proposed scheme enables to efficiently generate protected templates by the FFT, in addition to the useful properties. It is also applied to face recognition experiments to evaluate the effectiveness.

In PQCrypto 2013, Yasuda, Takagi and Sakurai proposed a new signature scheme as one of multivariate public key cryptosystems (MPKCs). This scheme (called YTS) is based on the fact that there are two isometry classes of non-degenerate quadratic forms on a vector space with a prescribed dimension. The advantage of YTS is its efficiency. In fact, its signature generation is eight or nine times faster than Rainbow of similar size. For the security, it is known that the direct attack, the IP attack and the min-rank attack are applicable on YTS, and the running times are exponential time for the first and the second attacks and sub-exponential time for the third attack. In the present paper, we give a new attack on YTS whose approach is to use the diagonalization of matrices. Our attack works in polynomial time and it actually recovers equivalent secret keys of YTS having 140-bits security against min-rank attack in around fifteen seconds.

This paper presents a design methodology for positioning sub-platform from the viewpoint of positioning for smartphone-based location-based services (LBS). To achieve this, we analyze a mechanism of positioning error generation including principles of positioning sub-systems and structure of smartphones. Specifically, we carry out the experiments of smartphone positioning performance evaluation by the smartphone basic API (Application Programming Interface) and by the wireless LAN in various environments. Then, we describe the importance of considering three layers as follows: 1) the lower layer that caused by positioning sub-systems, e.g., GPS, wireless LAN, mobile base stations, and so on; 2) the middle layer that caused by functions provided from the platform such as Android and iOS; 3) the upper layer that caused by operation algorithm of applications on the platform.

We deal with the scattering of a plane wave by the end-face of a waveguide system by the numerical method based on the sinc function and calculate the electric field on the end-face. It is shown that the results obtained analytically by the perturbation method are in relatively good agreement with the numerical results.

We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

The tree-based routing approach has been known as an efficient method for node mobility management and data packet transmission between two long-distance parties; however, its parameter adjustment must balance control overhead against the convergence speed of topology information according to node mobility. Meanwhile, location-based routing works more efficiently when the distance between the source and destination is relatively short. Therefore, this paper proposes a roadside unit (RSU) based hybrid routing protocol, called RSU-HRP that combines the strengths of both protocols while offsetting their weaknesses. In RSU-HRP, the tree construction is modified to take into account the link and route quality to construct a robust and reliable tree against high node mobility, and an optimized broadcast algorithm is developed to reduce control overhead induced by the advertisement message periodically sent from a roadside unit. In addition, the two routing methods are selectively used based on the computed distance in hops between a source and a destination. Simulation results show that RSU-HRP far outperforms TrafRoute in terms of packet delivery ratio, end-to-end delay, and control overhead in both Vehicle-to-Infrastructure and Vehicle-to-Vehicle communication models.

In many radio frequency identification (RFID) applications, the reader identifies the tags in its scope repeatedly. For these applications, many algorithms, such as an adaptive binary splitting algorithm (ABS), a single resolution blocking ABS (SRB), a pair resolution blocking ABS (PRB) and a dynamic blocking ABS (DBA) have been proposed. All these algorithms require the staying tags to reply with their IDs to be recognized by the reader. However, the IDs of the staying tags are stored in the reader in the last identification round. The reader can verify the existence of these tags when identifying them. Thus, we propose an anti-collision algorithm with short reply for RFID tag identification (ACSR). In ACSR, each staying tag emits a short reply to indicate its continued existence. Therefore, the data amount transmitted by staying tags is reduced significantly. The identification rate of ACSR is analyzed in this paper. Finally, simulation and analysis results show that ACSR greatly outperforms ABS, SRB and DBA in terms of the identification rate and average amount of data transmitted by a tag.

Superconducting single-flux-quantum (SFQ) device is an emerging device which can realize digital circuits with high switching speed and low power consumption. In SFQ digital circuits, voltage pulses are used for carrier of information, and the representation of logic values is different from that of CMOS circuits. Design methods exclusive to SFQ circuits have been developed. In this paper, we present timing analysis and functional verification methods for SFQ circuits based on new timing model which we call delay-based time frame model. Assuming that possible pulse arrival is periodic, the model defines comprehensive time frames and representation of logic values. In static timing analysis, expected pulse arrival time is checked based on the model, and the order among pulse arrival times is calculated for each logic gate. In functional verification, the circuit behavior is abstracted in a form similar to a synchronous sequential circuit using the order of pulse arrival times, and then the behavior is verified using formal verification tools. Using our proposed methods, we can verify the functional behavior of SFQ circuits with complex clocking scheme, which appear often in practical design but cannot be dealt with in existing verification method. Experimental results show that our method can be applied to practical designs.

In this letter, we propose a novel supervised pre-training technique for deep neural network (DNN)-hidden Markov model systems to achieve robust speech recognition in adverse environments. In the proposed approach, our aim is to initialize the DNN parameters such that they yield abstract features robust to acoustic environment variations. In order to achieve this, we first derive the abstract features from an early fine-tuned DNN model which is trained based on a clean speech database. By using the derived abstract features as the target values, the standard error back-propagation algorithm with the stochastic gradient descent method is performed to estimate the initial parameters of the DNN. The performance of the proposed algorithm was evaluated on Aurora-4 DB, and better results were observed compared to a number of conventional pre-training methods.

In this paper, we propose a patch-wise learning based approach to deal with the multiple-shot people re-identification task. In the proposed approach, re-identification is formulated as a patch-wise set-to-set matching problem, with each patch set being matched using a specifically learned Mahalanobis distance metric. The proposed approach has two advantages: (1) a patch-wise representation that moderates the ambiguousness of a non-rigid matching problem (of human body) to an approximate rigid one (of body parts); (2) a patch-wise learning algorithm that enables more constraints to be included in the learning process and results in distance metrics of high quality. We evaluate the proposed approach on popular benchmark datasets and confirm its competitive performance compared to the state-of-the-art methods.

Application prefetchers improve application launch performance on HDDs through either I/O reordering or I/O interleaving, but there has been no proposal to combine the two techniques. We present a new algorithm to combine both approaches, and demonstrate that it reduces cold start launch time by 50%.

This report describes an all-digital phase-locked loop (ADPLL) using a temperature compensated settling time reduction technique. The novelty of this work is autonomous oscillation control word estimation without a look-up table or memory circuits. The proposed ADPLL employs a multi-phase digitally controlled oscillator (DCO). In the proposed estimation method, the optimum oscillator tuning word (OTW) is estimated from the DCO frequency characteristic in the setup phase of ADPLL. The proposed ADPLL, which occupies 0.27×0.36mm2, is fabricated by a 65 nm CMOS process. The temperature compensation PLL controller (TCPC) is implemented using an FPGA. Although the proposed method has 20% area overhead, measurement results show that the 47% settling time is reduced. The average settling time at 25°C is 3µs. The average reduction energy is at least 42% from 0°C to 100°C.

Outsourcing IC design and fabrication is one of the effective solutions to reduce design cost but it may cause severe security risks. Particularly, malicious outside vendors may implement Hardware Trojans (HTs) on ICs. When we focus on IC design phase, we cannot assume an HT-free netlist or a Golden netlist and it is too difficult to identify whether a given netlist is HT-free or not. In this paper, we propose a score-based hardware-trojans identifying method at gate-level netlists without using a Golden netlist. Our proposed method does not directly detect HTs themselves in a gate-level netlist but it detects a net included in HTs, which is called Trojan net, instead. Firstly, we observe Trojan nets from several HT-inserted benchmarks and extract several their features. Secondly, we give scores to extracted Trojan net features and sum up them for each net in benchmarks. Then we can find out a score threshold to classify HT-free and HT-inserted netlists. Based on these scores, we can successfully classify HT-free and HT-inserted netlists in all the Trust-HUB gate-level benchmarks and ISCAS85 benchmarks as well as HT-free and HT-inserted AES gate-level netlists. Experimental results demonstrate that our method successfully identify all the HT-inserted gate-level benchmarks to be “HT-inserted” and all the HT-free gate-level benchmarks to be “HT-free” in approximately three hours for each benchmark.

In this paper, we propose SimCS (similarity based on contribution scores) to compute the similarity of scientific papers. For similarity computation, we exploit a notion of a contribution score that indicates how much a paper contributes to another paper citing it. Also, we consider the author dominance of papers in computing contribution scores. We perform extensive experiments with a real-world dataset to show the superiority of SimCS. In comparison with SimCC, the-state-of-the-art method, SimCS not only requires no extra parameter tuning but also shows higher accuracy in similarity computation.

The two-dimensional (2D) interference is one of the major impairments in bit-patterned media recording (BPMR) systems due to small bit and track pitches, especially at high recording densities. To alleviate this problem, we introduced a rate-4/5 constructive inter-track interference (CITI) coding scheme to prevent the destructive data patterns to be written onto a magnetic medium for an uncoded BPMR system, i.e., without error-correction codes. Because the CITI code produces only the hard decision, it cannot be employed in a coded BPMR system that uses a low-density parity-check (LDPC) code. To utilize it in an iterative decoding scheme, we propose a soft CITI coding scheme based on the log-likelihood ratio algebra implementation in Boolean logic mappings in order that the soft CITI coding scheme together with a modified 2D soft-output Viterbi algorithm (SOVA) detector and a LDPC decoder will jointly perform iterative decoding. Simulation results show that the proposed scheme provides a significant performance improvement, in particular when an areal density (AD) is high and/or the position jitter is large. Specifically, at a bit-error rate of 10-4 and no position jitter, the proposed system can provide approximately 1.8 and 3.5 dB gain over the conventional coded system without using the CITI code at the ADs of 2.5 and 3.0 Tera-bit per square inch (Tb/in2), respectively.

To analyze the structure of a set of high-dimensional perfect sequences over a composition algebra over R, we developed the theory of Fourier transforms of the set of such sequences. We define the discrete cosine transform and the discrete sine transform, and we show that there exists a relationship between these transforms and a convolution of sequences. By applying this property to a set of perfect sequences, we obtain a parameterization theorem. Using this theorem, we show the equivalence between the left perfectness and right perfectness of sequences. For sequences of real numbers, we obtain the parameterization without restrictions on the parameters.

Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.