This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.

We propose a new fine Doppler frequency estimator using two fast Fourier transform (FFT) samples for pulse Doppler radar that offers highly sensitive detection and a high resolution of velocity. The procedure of fine Doppler frequency estimation is completed through coarse frequency estimation (CFE) and fine frequency estimation (FFE) steps. During the CFE step, the integer part of the Doppler frequency is obtained by processing the FFT, after which, during the FFE step, the fractional part is estimated using the relationship between the FFT peak and its nearest resultant value. Our simulation results show that the proposed estimator has better accuracy than Candan's estimator in terms of bias. The root mean square error (RMSE) of the proposed estimator has more than 1.4 time better accuracy than Candan's estimator under a 1,024-point FFT and a signal-to-noise ratio (SNR) of 10 dB. In addition, when the FFT size is increased from 512 to 2,048, the RMSE characteristics of the proposed estimator improve by more than two-fold.

We present a multiband LTE SAW-less CMOS transmitter with source-follower-driven passive mixers, envelope-tracked RF-programmable gain amplifiers (RF-PGAs), and Marchand Baluns. A driver stage for passive mixers is realized by a source follower, which enables a quadrature modulator (QMOD) to achieve low noise performance at a 1.2 V supply and contributes to a small-area and low-power transmitter. An envelope-tracking technique is adopted to improve the linearity of RF-PGAs and obtain a better Evolved Universal Terrestrial Radio Access Adjacent Channel Leakage power Ratio (E-UTRA ACLR). The Marchand balun covers more frequency bands than a transformer and is more suitable for multiband operation. The proposed transmitter, which also includes digital-to-analog converters and a phase-locked loop, is implemented in a 65-nm CMOS process. The implemented transmitter achieves E-UTRA ACLR of less than -42 dBc and RX-band noise of less than -158 dBc/Hz in the frequency range of 700 MHz–2.6 GHz. These performances are good enough for multiband LTE and SAW-less operation.

This paper examines two-pass authenticated key exchange (AKE) protocols that are secure without the NAXOS technique under the gap Diffie-Hellman assumption in the random oracle model: FHMQV [18], KFU1 [21], SMEN- [13], and UP [17]. We introduce two protocol, biclique DH protocol and multiplied biclique DH protocol, to analyze the subject protocols, and show that the subject protocols use the multiplied biclique DH protocol as internal protocols. The biclique DH protocol is secure, however, the multiplied biclique DH protocol is insecure. We show the relations between the subject protocols from the viewpoint of how they overcome the insecurity of the multiplied biclique DH protocol: FHMQV virtually executes two multiplied biclique DH protocols in sequence with the same ephemeral key on two randomized static keys. KFU1 executes two multiplied biclique DH protocols in parallel with the same ephemeral key. UP is a version of KFU1 in which one of the static public keys is generated with a random oracle. SMEN- can be thought of as a combined execution of two multiplied biclique DH protocols. In addition, this paper provides ways to characterize the AKE protocols and defines two parameters: one consists of the number of static keys, the number of ephemeral keys, and the number of shared secrets, and the other is defined as the total sum of these numbers. When an AKE protocol is constructed based on some group, these two parameters indicate the number of elements in the group, i.e., they are related to the sizes of the storage and communication data.

Scalable Video Coding (SVC) is an extension of H.264/AVC, aiming to provide the ability to adapt to heterogeneous networks or requirements. It offers great flexibility for bitstream adaptation in multi-point applications such as videoconferencing. However, transcoding between SVC and AVC is necessary due to the existence of legacy AVC-based systems. The straightforward re-encoding method requires great computational cost, and delay-sensitive applications like videoconferencing require much faster transcoding scheme. This paper proposes an ultra-low-delay SVC-to-AVC MGS (Medium-Grain quality Scalability) transcoder for videoconferencing applications. Transcoding is performed in pure frequency domain with partial decoding/encoding in order to achieve significant speed-up. Three fast transcoding methods in frequency domain are proposed for macroblocks with different coding modes in non-KEY pictures. KEY pictures are transcoded by reusing the base layer motion data, and error propagation is constrained between KEY pictures. Simulation results show that proposed transcoder achieves averagely 38.5 times speed-up compared with the re-encoding method, while introducing merely 0.71 dB BDPSNR coding quality loss for videoconferencing sequences as compared with the re-encoding algorithm.

In this paper, we consider a multi-cell cognitive radio network (CRN), which overlays a multi-cell primary network. To manage the coexistence, a primary-willingness based coexistent architecture and a novel intra-cell spectrum overlay and inter-cell spectrum underlay sharing method are proposed. In the system, primary base stations will broadcast pilot signals and interference margins to assist the CRN for interference channel evaluation and power control. Subject to the interference margins imposed by the primary network, we define a utility (payoff) function that can represent the secondary system performance while taking into account the co-channel interference among secondary cells. A distributed resource allocation scheme is devised to guarantee the primary performance, and at the same time, maximize the secondary utility without any cooperation among cognitive base stations (CBS). Quality of Service among users is also considered by the scheme such that the instantaneous data rate for each secondary user is larger than a given minimum rate. The resource allocation problem can be decomposed into two subproblems: subchannel allocation and distributed power allocation game (DPAG). We prove that there exists a Nash equilibrium in the DPAG and the equilibrium is unique. Moreover, the DPAG is also Pareto optimal in some constrained environments, that is, no CBS can further improve its performance without impairing others. The proposed algorithm turns out to converge to an equilibrium within a small number of iterations.

This paper presents a digitally error-corrected pipeline analog-to-digital converter (ADC) using linearization of incomplete settling errors. A pre-charging technique is used for residue amplifiers in order to reduce the incomplete settling error itself and linearize the input signal dependency of the incomplete settling error. A technique with charge redistribution of divided capacitors is proposed for pre-charging capacitors without any additional reference sources. This linearized settling error is corrected by a first-order error approximation in digital domain with feasible complexity and cost. Simulation results show that the ADC achieves SNDR of 70 dB, SFDR of 79 dB at nyquist input frequency in a 65 nm CMOS process under 1.2 V power supply voltage for 1.2 Vp-p input signal swing. The estimated power consumption of the 12b 200 MS/s pipeline ADC using the proposed digital error correction of incomplete settling errors is 7.6 mW with a small FOM of 22 fJ/conv-step.

This paper presents the stability analysis for continuous-time Takagi-Sugeno fuzzy systems using a fuzzy Lyapunov function. The proposed fuzzy Lyapunov function involves the time derivatives of states to include new free matrices in the LMI stability conditions. These free matrices extend the solution space for Linear Matrix Inequalities (LMIs) problems. Numerical examples illustrate the effectiveness of the proposed methods.

Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.

In order to evaluate the goodness of frequency hopping (FH) sequence design, the periodic Hamming correlation function is used as an important measure. But aperiodic Hamming correlation of FH sequences matters in real applications, while it received little attraction in the literature compared with periodic Hamming correlation. In this paper, the new aperiodic Hamming correlation lower bounds for FH sequences, with respect to the size of the frequency slot set, the sequence length, the family size, the maximum aperiodic Hamming autocorrelation and the maximum aperiodic Hamming crosscorrelation are established. The new aperiodic bounds are tighter than the Peng-Fan bounds. In addition, the new bounds include the second powers of the maximum aperiodic Hamming autocorrelation and the maximum aperiodic Hamming crosscorrelation but the Peng-Fan bounds do not include them. For the given sequence length, the family size and the frequency slot set size, the values of the maximum aperiodic Hamming autocorrelation and the maximum aperiodic Hamming crosscorrelation are inside of an ellipse which is given by the new aperiodic bounds.

Due to the growing popularity of High Dynamic Range (HDR) images and HDR displays, a large amount of existing Low Dynamic Range (LDR) images are required to be converted to HDR format to benefit HDR advantages, which give rise to some LDR to HDR algorithms. Most of these algorithms especially tackle overexposed areas during expanding, which is the potential to make the image quality worse than that before processing and introduces artifacts. To dispel these problems, we present a new LDR to HDR approach, unlike the existing techniques, it focuses on avoiding sophisticated treatment to overexposed areas in dynamic range expansion step. Based on a separating principle, firstly, according to the familiar types of overexposure, the overexposed areas are classified into two categories which are removed and corrected respectively by two kinds of techniques. Secondly, for maintaining color consistency, color recovery is carried out to the preprocessed images. Finally, the LDR image is expanded to HDR. Experiments show that the proposed approach performs well and produced images become more favorable and suitable for applications. The image quality metric also illustrates that we can reveal more details without causing artifacts introduced by other algorithms.

In this letter, a novel adaptive detector that combines DFE and QRD-M is proposed for MIMO-OFDM system. QR decomposition (QRD) is commonly used in many MIMO detection algorithms. In particular, sorted QR decomposition (SQRD) is an advanced algorithm that improves MIMO detection performance. The proposed detector uses SQRD to achieve better performance. To reduce the computational complexity, the received layers of each subcarrier are ordered by using the post SNR and are detected by DFE and QRD-M detector based on the order. Therefore, the proposed detector structure is varied according to the channel state. In other words, the proposed detector achieves a good tradeoff between complexity and performance. A simulation confirms the substantial performance improvements of the proposed adaptive detector with only slightly greater complexity than the conventional detector.

Super resolution (SR) reconstruction is the process of fusing a sequence of low-resolution images into one high-resolution image. Many researchers have introduced various SR reconstruction methods. However, these traditional methods are limited in the extent to which they allow recovery of high-frequency information. Moreover, due to the self-similarity of face images, most of the facial SR algorithms are machine learning based. In this paper, we introduce a facial SR algorithm that combines learning-based and regularized SR image reconstruction algorithms. Our conception involves two main ideas. First, we employ separated frequency components to reconstruct high-resolution images. In addition, we separate the region of the training face image. These approaches can help to recover high-frequency information. In our experiments, we demonstrate the effectiveness of these ideas.

In this letter, we present a joint blind adaptive scheme to suppress inter-block interference and estimate a carrier frequency offset (CFO) in downlink OFDMA systems. The proposed scheme is a combination of a channel shortening method and a CFO estimator, both based on the carrier nulling criterion. Simulation results demonstrate the effectiveness of the proposed scheme.

In this paper, several spiral inductors with various ground clearance structures and turns were investigated to achieve noise suppression up to the fourth harmonic (3.2 GHz) regime of DDR3-1600. Their performances were characterized in terms of their capability to effectively suppress simultaneous switching noise (SSN) in the frequency region of interest. For a wider noise suppression bandwidth, a spiral inductor with large ground clearance, which provides a high self resonance frequency (SRF) as well as high inductances, was implemented. The proposed spiral inductor exhibited good noise suppression characteristics in the frequency domain and achieved 50% voltage fluctuation reduction in the time domain, compared to the identical 4-turn spiral without pattern ground structure.

MIMO two-way multi-hop networks are considered in which the radio resource is fully reused in all multi-hop links to increase spectrum efficiency while the adjacent interference signals are cancelled by MIMO processing. In addition, the nodes in the multi-hop network optimize their transmit powers to mitigate the remaining overreach interference. Our main contribution in this paper is to investigate an efficient relay placement method with power allocation in such networks. We present two formulations, namely QoS-constrained optimization and SINR balancing, and solve them using a sequential geometric programming method. The proposed algorithm takes advantage of convex optimization to find an efficient configuration. Simulation results show that relay placement has an important impact on the effectiveness of power allocation to mitigate the interference. Particularly, we found that an uniform relay location is optimal only in power-limited scenarios. With optimal relay locations, significant end-to-end rate gain and power consumption reduction are achieved by SINR balancing and QoS-constrained optimization, respectively. Furthermore, the optimal number of hops is investigated in power or interference-limited scenarios.

A method of calculating the exact top event probability of a fault tree with dynamic gates and repeated basic events is proposed. The top event probability of such a dynamic fault tree is obtained by converting the tree into an equivalent Markov model. However, the Markov-based method is not realistic for a complex system model because the number of states that should be considered in the Markov analysis increases explosively as the number of basic events in the model increases. To overcome this shortcoming, we propose an alternative method in this paper. It is a hybrid of a Bayesian network (BN) and an algebraic technique. First, modularization is applied to a dynamic fault tree. The detected modules are classified into two types: one satisfies the parental Markov condition and the other does not. The module without the parental Markov condition is replaced with an equivalent single event. The occurrence probability of this event is obtained as the sum of disjoint sequence probabilities. After the contraction of modules without parent Markov condition, the BN algorithm is applied to the dynamic fault tree. The conditional probability tables for dynamic gates are presented. The BN is a standard one and has hierarchical and modular features. Numerical example shows that our method works well for complex systems.

With the successful adoption of link analysis techniques such as PageRank and web spam filtering, current web search engines well support “navigational search”. However, due to the use of a simple conjunctive Boolean filter in addition to the inappropriateness of user queries, such an engine does not necessarily well support “informational search”. Informational search would be better handled by a web search engine using an informational retrieval model combined with enhancement techniques such as query expansion and relevance feedback. Moreover, the realization of such an engine requires a method to prosess the model efficiently. In this paper we propose a novel extension of an existing top-k query processing technique to improve search efficiency. We add to it the technique utilizing a simple data structure called a “term-document binary matrix,” resulting in more efficient evaluation of top-k queries even when the queries have been expanded. We show on the basis of experimental evaluation using the TREC GOV2 data set and expanded versions of the evaluation queries attached to this data set that the proposed method can speed up evaluation considerably compared with existing techniques especially when the number of query terms gets larger.

In this paper, we introduce generalized feed-forward shift registers (GF2SR) to apply them to secure and testable scan design. Previously, we introduced SR-equivalents and SR-quasi-equivalents which can be used in secure and testable scan design, and showed that inversion-inserted linear feed-forward shift registers (I2LF2SR) are useful circuits for the secure and testable scan design. GF2SR is an extension of I2LF2SR and the class is much wider than that of I2LF2SR. Since the cardinality of the class of GF2SR is much larger than that of I2LF2SR, the security level of scan design with GF2SR is much higher than that of I2LF2SR. We consider how to control/observe GF2SR to guarantee easy scan-in/out operations, i.e., state-justification and state-identification problems are considered. Both scan-in and scan-out operations can be overlapped in the same way as the conventional scan testing, and hence the test sequence for the proposed scan design is of the same length as the conventional scan design. A program called WAGSR (Web Application for Generalized feed-forward Shift Registers) is presented to solve those problems.

This paper presents a method for learning an overcomplete, nonnegative dictionary and for obtaining the corresponding coefficients so that a group of nonnegative signals can be sparsely represented by them. This is accomplished by posing the learning as a problem of nonnegative matrix factorization (NMF) with maximization of the incoherence of the dictionary and of the sparsity of coefficients. By incorporating a dictionary-incoherence penalty and a sparsity penalty in the NMF formulation and then adopting a hierarchically alternating optimization strategy, we show that the problem can be cast as two sequential optimal problems of quadratic functions. Each optimal problem can be solved explicitly so that the whole problem can be efficiently solved, which leads to the proposed algorithm, i.e., sparse hierarchical alternating least squares (SHALS). The SHALS algorithm is structured by iteratively solving the two optimal problems, corresponding to the learning process of the dictionary and to the estimating process of the coefficients for reconstructing the signals. Numerical experiments demonstrate that the new algorithm performs better than the nonnegative K-SVD (NN-KSVD) algorithm and several other famous algorithms, and its computational cost is remarkably lower than the compared algorithms.