Let R=Z4 be the integer ring mod 4 and C be a linear code over R. The code C is called a triple cyclic code of length (r, s, t) over R if the set of its coordinates can be partitioned into three parts so that any cyclic shift of the coordinates of the three parts leaves the code invariant. These codes can be viewed as R[x]-submodules of R[x]/×R[x]/×R[x]/. In this paper, we determine the generator polynomials and the minimum generating sets of this kind of codes.

As three dimensional (3D) discrete wavelet transform (DWT) is widely used for high resolution volumetric data compression, and to further improve the performance of lossless coding, the adaptive directional lifting (ADL) structure based on non-separable 3D DWT with a (5,3) filter is proposed in this paper. The proposed 3D DWT has less lifting steps and better prediction performance compared to the existing separable 3D DWT with fixed filter coefficients. It also has compatibility with the conventional DWT defined by the JPEG2000 international standard. The proposed method shows comparable and better results with the non-separable 3D DWT and separable 3D DWT and it is effective for lossless coding of high resolution volumetric data.

In this paper, a self optimization beamforming null control (SOBNC) scheme is proposed. There is a need of maintaining signal to interference plus noise ratio (SINR) threshold to control modulation and coding schemes (MCS) in recent technologies like Wi-Fi, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A). Selection of MCS depends on the SINR threshold that allows maintaining key performance index (KPI) like block error rate (BLER), bit error rate (BER) and throughput at certain level. The SOBNC is used to control the antenna pattern for SINR estimation and improve the SINR performance of the wireless communication systems. The nulling comes with a price; if wider nulls are introduced, i.e. more number of nulls are used, the 3dB beam-width and peak side lobe level (SLL) in antenna pattern changes critically. This paper proposes a method which automatically controls the number of nulls in the antenna pattern as per the changing environment based on adaptive-network based fuzzy interference system (ANFIS) to maintain output SINR level higher or equal to the required threshold. Finally, simulation results show a performance superiority of the proposed SOBNC compared with minimum mean square error (MMSE) based adaptive nulling control algorithm and conventional fixed null scheme.

The random deployment of small cell base stations (BSs) causes the coverage areas of neighboring cells to overlap, which increases intercell interference and degrades the system capacity. This paper proposes a new intercell interference management (IIM) scheme to improve the system capacity in multiple-input multiple-output (MIMO) small cell networks. The proposed IIM scheme consists of both an interference cancellation (IC) technique on the receiver side, and a neural network (NN) based power control algorithm for intercell interference coordination (ICIC) on the transmitter side. In order to improve the system capacity, the NN power control optimizes downlink transmit power while IC eliminates interfering signals from received signals. Computer simulations compare the system capacity of the MIMO network with several ICIC algorithms: the NN, the greedy search, the belief propagation (BP), the distributed pricing (DP), and the maximum power, all of which can be combined with IC reception. Furthermore, this paper investigates the application of a multi-layered NN structure called deep learning and its pre-training scheme, into the mobile communication field. It is shown that the performance of NN is better than that of BP and very close to that of greedy search. The low complexity of the NN algorithm makes it suitable for IIM. It is also demonstrated that combining IC and sectorization of BSs acquires high capacity gain owing to reduced interference.

The likelihood-ratio based score level fusion (LR-based fusion) scheme has attracted much attention, since it maximizes accuracy if a log-likelihood ratio (LLR) is accurately estimated. In reality, it can happen that a user cannot input some query samples due to temporary physical conditions such as injuries and illness. It can also happen that some modalities tend to cause false rejection (i.e. the user is a “goat” for these modalities). The LR-based fusion scheme can handle these situations by setting LLRs corresponding to missing query samples to 0. In this paper, we refer to such a mode as a “modality selection mode”, and address an issue of accuracy in this mode. Specifically, we provide the following contributions: (1) We firstly propose a “modality selection attack”, in which an impostor inputs only query samples whose LLRs are more than 0 (i.e. takes an optimal strategy) to impersonate others. We also show that the impostor can perform this attack against the SPRT (Sequential Probability Ratio Test)-based fusion scheme, which is an extension of the LR-based fusion scheme to a sequential fusion scenario. (2) We secondly consider the case when both genuine users and impostors take this optimal strategy, and show that the overall accuracy in this case is “worse” than the case when they input all query samples. More specifically, we prove that the KL (Kullback-Leibler) divergence between a genuine distribution of integrated scores and an impostor's one, which can be compared with password entropy, is smaller in the former case. We also show to what extent the KL divergence losses for each modality. (3) We finally evaluate to what extent the overall accuracy becomes worse using the NIST BSSR1 Set 2 and Set 3 datasets, and discuss directions of multibiometric applications based on the experimental results.

The Virtual Machine Consolidation (VMC) algorithm is the core strategy of virtualization resource management software. In general, VMC efficiency dictates cloud datacenter efficiency to a great extent. However, all the current Virtual Machine (VM) consolidation strategies, including the Iterative Correlation Match Algorithm (ICMA), are not suitable for the dynamic VM consolidation of the level of physical servers in actual datacenter environments. In this paper, we propose two VM consolidation and placement strategies which are called standard Segmentation Iteration Correlation Combination (standard SICC) and Multi-level Segmentation Iteration Correlation Combination (multi-level SICC). The standard SICC is suitable for the single-size VM consolidation environment and is the cornerstone of multi-level SICC which is suitable for the multi-size VM consolidation environment. Numerical simulation results indicate that the numbers of remaining Consolidated VM (CVM), which are generated by standard SICC, are 20% less than the corresponding parameters of ICMA in the single-level VM environment with the given initial condition. The numbers of remaining CVMs of multi-level SICC are 14% less than the corresponding parameters of ICMA in the multi-level VM environment. Furthermore, the used physical servers of multi-level SICC are also 5% less than the used servers of ICMA under the given initial condition.

In this letter, we propose a novel texture descriptor that takes advantage of an anisotropic neighborhood. A brand new encoding scheme called Reflection and Rotation Invariant Uniform Patterns (rriu2) is proposed to explore local structures of textures. The proposed descriptor is called Oriented Local Binary Patterns (OLBP). OLBP may be incorporated into other varieties of Local Binary Patterns (LBP) to obtain more powerful texture descriptors. Experimental results on CUReT and Outex databases show that OLBP not only significantly outperforms LBP, but also demonstrates great robustness to rotation and illuminant changes.

Codebooks with good parameters are preferred in many practical applications, such as direct spread CDMA communications and compressed sensing. In this letter, an upper bound on the set size of a codebook is introduced by modifying the Levenstein bound on the maximum amplitudes of such a codebook. Based on an estimate of a class of character sums over a finite field by Katz, a family of codebooks nearly meeting the modified bound is proposed.

Mixed-signal integrated circuit design and simulation highly rely on behavioral models of circuit blocks. Such models are used for the validation of design specification, optimization of system topology, and behavioral synthesis using a description language, etc. However, automatic behavioral model generation is still in its early stages; in most scenarios designers are responsible for creating behavioral models manually, which is time-consuming and error prone. In this paper an automatic behavioral model generation method for switched-capacitor (SC) integrator is proposed. This technique is based on symbolic circuit modeling with approximation, by which parametric behavioral integrator model can be generated. Such parametric models can be used in circuit design subject to severe process variational. It is demonstrated that the automatically generated integrator models can accurately capture process variation effects on arbitrarily selected circuit elements; furthermore, they can be applied to behavioral simulation of SC Sigma-Delta modulators (SDMs) with acceptable accuracy and speedup. The generated models are compared to a recently proposed manually generated behavioral integrator model in several simulation settings.

A Field Programmable Sequencer and Memory (FPSM), which is a programmable unit exclusively optimized for peripherals on a micro controller unit, is proposed. The FPSM functions as not only the peripherals but also the standard built-in memory. The FPSM provides easier programmability with a smaller area overhead, especially when compared with the FPGA. The FPSM is implemented on the FPGA and the programmability and performance for basic peripherals such as the 8 bit counter and 8 bit accuracy Pulse Width Modulation are emulated on the FPGA. Furthermore, the FPSM core with a 4K bit SRAM is fabricated in 0.18µm 5 metal CMOS process technology. The FPSM is an half the area of FPGA, its power consumption is less than one-fifth.

System expandability becomes a major concern for highly parallel computers and data centers, because their number of nodes gradually increases year by year. In this context we propose a low-degree topology and its floor layout in which a cabinet or node set can be newly inserted by connecting short cables to a single existing cabinet. Our graph analysis shows that the proposed topology has low diameter, low average shortest path length and short average cable length comparable to existing topologies with the same degree. When incrementally adding nodes and cabinets to the proposed topology, its diameter and average shortest path length increase modestly. Our discrete-event simulation results show that the proposed topology provides a comparable performance to 2-D Torus for some parallel applications. The network cost and power consumption of DSN-F modestly increase when compared to the counterpart non-random topologies.

Because accurate position information plays an important role in wireless sensor networks (WSNs), target localization has attracted considerable attention in recent years. In this paper, based on target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem that can be solved by the compressed sensing (CS) technique. To satisfy the robust recovery condition called restricted isometry property (RIP) for CS theory requirement, an orthogonalization preprocessing method named LU (lower triangular matrix, unitary matrix) decomposition is utilized to ensure the observation matrix obeys the RIP. In addition, from the viewpoint of the positioning systems, taking advantage of the joint posterior distribution of model parameters that approximate the sparse prior knowledge of target, the sparse Bayesian learning (SBL) approach is utilized to improve the positioning performance. Simulation results illustrate that the proposed algorithm has higher positioning accuracy in multi-target scenarios than existing algorithms.

Ever-evolving malware makes it difficult to prevent it from infecting hosts. Botnets in particular are one of the most serious threats to cyber security, since they consist of a lot of malware-infected hosts. Many countermeasures against malware infection, such as generating network-based signatures or templates, have been investigated. Such templates are designed to introduce regular expressions to detect polymorphic attacks conducted by attackers. A potential problem with such templates, however, is that they sometimes falsely regard benign communications as malicious, resulting in false positives, due to an inherent aspect of regular expressions. Since the cost of responding to malware infection is quite high, the number of false positives should be kept to a minimum. Therefore, we propose a system to generate templates that cause fewer false positives than a conventional system in order to achieve more accurate detection of malware-infected hosts. We focused on the key idea that malicious infrastructures, such as malware samples or command and control, tend to be reused instead of created from scratch. Our research verifies this idea and proposes here a new system to profile the variability of substrings in HTTP requests, which makes it possible to identify invariable keywords based on the same malicious infrastructures and to generate more accurate templates. The results of implementing our system and validating it using real traffic data indicate that it reduced false positives by up to two-thirds compared to the conventional system and even increased the detection rate of infected hosts.

Radio over Fiber (RoF) is a promising solution for providing wireless access services. Heterogeneous radio signals are transferred via an optical fiber link using an analog transmission technique. When the RoF and the radio frequency (RF) devices have a nonlinear characteristic, these will create the intermodulation products (IMPs) in the system and generate the intermodulation distortion (IMD). In this paper, the IMD interference in the uplink RF signals from the coupling effect between the downlink and the uplink antennas has been addressed. We propose a method using the dynamic channel allocation (DCA) algorithm with the predistortion (PD) technique to improve the throughput performance of the multi-channel RoF system. The carrier to distortion plus noise power ratio (CDNR) is evaluated for all channel allocation combinations; then the best channel combination is assigned as a set of active channels to minimize the effect of IMD. The results show that the DCA with PD has the lowest IMD and obtains a better throughput performance.

Named Data Networking (NDN) is a proposed future Internet architecture that shifts the fundamental abstraction of the network from host-to-host communication to request-response for named, signed data-an information dissemination focused approach. This paper describes a general design for receiver-driven, real-time streaming data (RTSD) applications over the current NDN implementation that aims to take advantage of the architecture's unique affordances. It is based on experimental development and testing of running code for real-time video conferencing, a positional tracking system for interactive multimedia, and a distributed control system for live performance. The design includes initial approaches to minimizing latency, managing buffer size and Interest retransmission, and adapting retrieval to maximize bandwidth and control congestion. Initial implementations of these approaches are evaluated for functionality and performance results, and the potential for future research in this area, and improved performance as new features of the architecture become available, is discussed.

An application of laser annealing process, which is used to form the P-type Base junction for high-performance low-voltage power MOSFETs (Metal Oxide Semiconductor Field Effect Transistors), is proposed. An equivalent shallow-junction structure for P-Base junction with uniform impurity distribution is achieved by adopting green laser annealing of pulsed mode. Higher impurity activation for the shallow junction has been achieved by the laser annealing of melted phase than by conventional RTA (Rapid Thermal Annealing) of solid phase. The application of the laser annealing technology in the fabrication process of Low-Voltage U-MOSFET is also examined.

Single-carrier (SC) transmission with space-time block coded (STBC) transmit diversity can achieve good bit error rate (BER) performance. However, in a high mobility environment, the STBC codeword orthogonality is distorted and as consequence, the BER performance is degraded by the interference caused by the orthogonality distortion of STBC codeword. In this paper, we proposed a novel frequency-domain equalization (FDE) for SC-STBC transmit diversity in doubly selective fading channel. Multiple FDE weight matrices, each associated with a different code block, are jointly optimized based on the minimum mean square error (MMSE) criterion taking into account not only channel frequency variation but also channel time variation over the STBC codeword. Computer simulations confirm that the proposed robust FDE achieves BER performance superior to conventional FDE, which was designed based on the assumption of a quasi-static fading.

This paper proposes a new class of Hilbert pairs of almost symmetric orthogonal wavelet bases. For two wavelet bases to form a Hilbert pair, the corresponding scaling lowpass filters are required to satisfy the half-sample delay condition. In this paper, we design simultaneously two scaling lowpass filters with the arbitrarily specified flat group delay responses at ω=0, which satisfy the half-sample delay condition. In addition to specifying the number of vanishing moments, we apply the Remez exchange algorithm to minimize the difference of frequency responses between two scaling lowpass filters, in order to improve the analyticity of complex wavelets. The equiripple behavior of the error function can be obtained through a few iterations. Therefore, the resulting complex wavelets are orthogonal and almost symmetric, and have the improved analyticity. Finally, some examples are presented to demonstrate the effectiveness of the proposed design method.

There is a strong demand to enjoy broadband and stable Internet connectivity not only in office and the home but also in high-speed train. Several systems are providing high-speed train with Internet connectivity using various technologies such as leaky coaxial cable (LCX), Wi-Fi, and WiMAX. However, their actual throughputs are less than 2Mbps. We developed a free-space optical (FSO) communication transceiver called LaserTrainComm2014 that achieves the throughput of 1 Gbps between the ground and a train. LaserTrainComm2014 employs a high-speed image sensor for coarse tracking and a quadrant photo-diode (QPD) for accurate tracking. Since the image captured by the high-speed image sensor has several types of noise, image processing is necessary to detect the beacon light of the other LaserTrainComm2014. As a result of field experiments in a vehicle test course, LaserTrainComm2014 achieves handover time of 21 milliseconds (ms) in the link layer at the speed of 60km/h. Even if the network layer signaling takes time of 10 milliseconds, the total communication disruption time due to handover is short enough to provide passengers with Internet connectivity for live streaming Internet applications such as YouTube, Internet Radio, and Skype.

In this paper, we study the impact of imperfect channel information on an amplify-and-forward (AF)-based two-way relaying network (TWRN) with adaptive modulation which consists of two end-terminals and multiple relays. Specifically, we consider a single-relay selection scheme of the TWRN in the presence of outdated channel state information (CSI) and channel estimation errors. First, we choose the best relay based on outdated CSI, and perform adaptive modulation on both relaying paths with channel estimation errors. Then, we discuss the impact of the outdated CSI on the statistics of the signal-to-noise ratio (SNR) per hop. In addition, we formulate the end-to-end SNRs with channel estimation errors and offer statistic analyses in the presence of both the outdated CSI and channel estimation errors. Finally, we provide the performance analyses of the proposed TWRN with adaptive modulation in terms of average spectral efficiency, average bit error rate, and outage probability. Numerical examples are given to verify our obtained analytical results for various system conditions.