A multi-phase crystal-less clock generator (MPCLCG) with a process-voltage-temperature (PVT) calibration circuit is proposed. It operates at 192 MHz with 8 phases outputs, and is implemented as a 0.18µm CMOS process for digital power management systems. A temperature calibrated circuit is proposed to align operational frequency under process and supply voltage variations. It occupies an area of 65µm × 75µm and consumes 1.1mW with the power supply of 1.8V. Temperature coefficient (TC) is 69.5ppm/°C from 0 to 100°C, and 2-point calibration is applied to calibrate PVT variation. The measured period jitter is a 4.58-ps RMS jitter and a 34.55-ps peak-to-peak jitter (P2P jitter) at 192MHz within 12.67k-hits. At 192MHz, it shows a 1-MHz-offset phase noise of -102dBc/Hz. Phase to phase errors and duty cycle errors are less than 5.5% and 4.3%, respectively.

Attacks against computer networks are evolving rapidly. Conventional intrusion detection system based on pattern matching and static signatures have a significant limitation since the signature database should be updated frequently. The unsupervised learning algorithm can overcome this limitation. Ant Clustering Algorithm (ACA) is a popular unsupervised learning algorithm to classify data into different categories. However, ACA needs to be complemented with other algorithms for the classification process. In this paper, we present a fuzzy anomaly detection system that works in two phases. In the first phase, the training phase, we propose ACA to determine clusters. In the second phase, the classification phase, we exploit a fuzzy approach by the combination of two distance-based methods to detect anomalies in new monitored data. We validate our hybrid approach using the KDD Cup'99 dataset. The results indicate that, compared to several traditional and new techniques, the proposed hybrid approach achieves higher detection rate and lower false positive rate.

Object detection is the first step in the object recognition. According to the detection results, its following works are affected. However, object detection has a heavy resource requirement in terms of, computing power and memory. If an image is enlarged, the computational load required for object detection is also increased. An-integral-image-based method guarantees fast object detection. Once an integral image is generated, the speed of the object detection procedure remains fixed, regardless of the pattern region size. However, this becomes an even greater issue if the image is enlarged. In this paper, we propose the use of directional integral image based object detection. A directional integral image gives direction to an integral image, which can then be calculated from various directions. Furthermore, many unnecessary calculations, which typically occur when a partial integral image is used for object detection, can be avoided. Therefore, the amount of computation is reduced, compared with methods using integral images. In experiments comparing methods, the proposed method required 40% fewer computations.

Multiple notch filters are used to suppress narrow-band or sinusoidal interferences in digital signals. In this paper, we propose a novel optimization design technique of an infinite impulse response (IIR) multiple notch filter. It is based on the Nelder-Mead simplex method. Firstly, the system function of the desired notch filter is constructed to form the objective function of the optimization technique. Secondly, the design parameters of the desired notch filter are optimized by Nelder-Mead simplex method. A weight function is also introduced to improve amplitude response of the notch filter. Thirdly, the convergence and amplitude response of the proposed technique are compared with other Nelder-Mead based design methods and the cascade-based design method. Finally, the practicability of the proposed notch filter design technique is demonstrated by some practical applications.

We deal with the scattering of a plane wave by the end-face of an ordered waveguide system composed of identical cores of equal space by the perturbation method and derive analytically the diffraction amplitude. It is shown that the results are in relatively good agreement with those obtained by the numerical method.

Wireless caching is one of the promising technologies to mitigate the traffic burden of cellular networks and the large cost of deploying a higher volume of wired backhaul by introducing caching storage. In the manner of “cutting” wired equipments, all types of vehicles can be readily leveraged as serving access points with caching storage, where their moving nature should be taken into account to improve latency and data throughput. In this paper, we consider a mobility-aware vehicular caching which has a role in offloading delay-tolerable contents from cellular networks. We first clarify the influence of mobility in cellular caching networks, then set the mobility-aware optimization problem of vehicular caching to carry on delay-tolerable contents. Trace-driven numerical results based on rural and urban topographies show that, in presence of individual demand for delay-tolerable contents, the proposed vehicular caching scheme enhances the quality-of-service (QoS) (maximally twofold) relying on the contents delivery being centrally or distributedly controlled.

By installing the various types of cells, imbalance in traffic load and excessive handover among cells in a heterogenous network can be prevalent. To deal with this problem, we propose a mobility-based cell association algorithm for load balancing in a heterogenous network. By defining a dynamic system load as a function of the mobility of mobile stations (MSs) and the transmit powers of cells, the proposed algorithm is designed such that it can optimize a utility function based on the fairness of the dynamic system load. Simulation results verify that the proposed algorithm improves the user perceived rate of MSs located at cell edges with slight increase in the number of handovers compared to a conventional cell association based on received signal strength.

We propose a novel improved characteristic basis function method (IP-CBFM) for accurately analysing the radar cross section (RCS). This new IP-CBFM incorporates the effect of higher-order multiple scattering and has major influences in analyzing monostatic RCS (MRCS) of single incidence and bistatic RCS (BRCS) problems. We calculated the RCS of two scatterers and could confirm that the proposed IP-CBFM provided higher accuracy than the conventional method while significantly reducing the number of CBF.

The diffraction by a thin material strip is analyzed for the H-polarized plane wave incidence using the Wiener-Hopf technique together with approximate boundary conditions. An asymptotic solution is obtained for the case where the thickness and the width of the strip are small and large compared with the wavelength, respectively. The scattered field is evaluated asymptotically based on the saddle point method and a far field expression is derived. Scattering characteristics are discussed in detail via numerical results of the radar cross section.

This paper proposes novel simplified maximum likelihood detection for XOR physical layer network coding (XOR-PNC) in bi-directional wireless relay systems with Quaternary phase shift keying (QPSK). The proposed detection applies unitary precoding to achieve superior performance without computationally prohibitive exhaustive search. The performance of the XOR employing the proposed simplified MLD with the precoding is analyzed in relay systems with orthogonal frequency division multiplexing (OFDM). The performance of the XOR-PNC with the proposed techniques is also evaluated by computer simulation. The XOR-PNC with the proposed techniques achieves about 7dB better performance than the amplify-and-forward physical layer network coding in the 5-path fading channel at BER=10-4. It is also shown that the XOR-PNC with the proposed techniques achieves better performance than that without precoding.

In this paper, we propose a technique to improve the gain of ultra wide-band (UWB) planar antennas by using low profile reflectors based on frequency selective surfaces (FSS). This technique not only enhances the gain of the planar UWB antennas but also guarantees a constant gain with weak variation across the entire UWB while keeping their attractive merits such as planar structure and easy fabrication. An UWB coplanar waveguide (CPW) fed antenna is installed above the proposed reflectors, to prove the effectiveness of the proposed technique. As a result, a constant gain is achieved across a very large bandwidth.

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.

In this paper, a new class of low-hit-zone (LHZ) frequency-hopping sequence sets (LHZ FHS sets) is constructed based upon the Cartesian product, and the periodic partial Hamming correlation within its LHZ are studied. Studies have shown that the new LHZ FHS sets are optimal according to the periodic partial Hamming correlation bounds of FHS set, and some known FHS sets are the special cases of this new construction.

In these days, we can see digital signages in many places, for example, inside stations or trains with the distribution of attractive promotional video clips. Users can easily get additional information related to such video clips via mobile devices such as smartphone by using some websites for retrieval. However, such retrieval is time-consuming and sometimes leads users to incorrect information. Therefore, it is desirable that the additional information can be directly obtained from the video clips. We implement a suitable digital watermarking method on smartphone to extract watermarks from video clips on signages in real-time. The experimental results show that the proposed method correctly extracts watermarks in a second on smartphone.

An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.

We introduce two cheater identifiable secret sharing (CISS) schemes with efficient reconstruction, tolerating t

Research on intrusion-tolerant systems (ITSs) is being conducted to protect critical systems which provide useful information services. To provide services reliably, these critical systems must not have even a single point of failure (SPOF). Therefore, most ITSs employ redundant components to eliminate the SPOF problem and improve system reliability. However, systems that include identical components have common vulnerabilities that can be exploited to attack the servers. Attackers prefer to exploit these common vulnerabilities rather than general vulnerabilities because the former might provide an opportunity to compromise several servers. In this study, we analyze software vulnerability data from the National Vulnerability Database (NVD). Based on the analysis results, we present a scheme that finds software combinations that minimize the risk of common vulnerabilities. We implement this scheme with CSIM20, and simulation results prove that the proposed scheme is appropriate for a recovery-based intrusion tolerant architecture.

We previously proposed an unsupervised model using the inclusion-exclusion principle to compute sentence information content. Though it can achieve desirable experimental results in sentence semantic similarity, the computational complexity is more than O(2n). In this paper, we propose an efficient method to calculate sentence information content, which employs the thinking of the difference set in hierarchical network. Impressively, experimental results show that the computational complexity decreases to O(n). We prove the algorithm in the form of theorems. Performance analysis and experiments are also provided.

After the East Japan great earthquake on March 11, 2011, many Japanese coastal resident areas were isolated from other because of destruction of information infrastructure, disconnection of communication network and excessive traffic congestion. The undelivered disaster information influenced the speed of evacuation, rescue of injured residents, and sending life-support materials to evacuation shelters. From the experience of such disaster, more robust and resilient networks are strongly required, particularly for preparation of large scale disasters. In this paper, in order to respond to those problems, we introduce Delay Tolerant Network (DTN) for disaster information transmission application in challenged network environment. Message delivery by transport vehicles such as cars between disaster-response headquarter and evacuation shelters in challenged network environment is considered. A improved message delivery method combined with DTN protocols and cognitive wireless network is explained. The computer simulation for the actual rural area in Japan is made to evaluate the performance and effectiveness of proposed method.

Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.