This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.

In this letter, we provide an asymptotic error rate performance evaluation of space-time block codes from coordinate interleaved orthogonal designs (STBCs-CIODs), especially in shadowed Rayleigh fading channels. By evaluating a simplified probability density function (PDF) of Rayleigh and Rayleigh-lognormal channels affecting the STBC-CIOD system, we derive an accurate closed-form approximation for the tight upper and lower bounds on the symbol error rate (SER). We show that shadowing asymptotically affects coding gain only, and conclude that an increase in diversity order under shadowing causes slower convergence to asymptotic bound due to the relatively larger loss of coding gain. By comparing the derived formulas and Monte-Carlo simulations, we validate the accuracy of the theoretical results.

Multimedia transactions between vehicles are expected to become a promising application in VANETs but security is a fundamental issue that must be resolved before such transactions can become practical and trusted. Existing certificate-based digital signature schemes are ineffective for ensuring the security of multimedia transactions in VANETs. This ineffectiveness exists because there is no guarantee that (1) vehicles can download the latest certificate revocation lists or that (2) vehicles can complete a multimedia transaction before leaving their communication range. These two problems result, respectively, from a lack of infrastructure and from the inconsistent connectivity inherent in VANETs. In this paper, we propose a digital signature approach that combines a certificateless signature scheme and short-lived public keys to alleviate these problems. We then propose a security protocol that uses the proposed signature approach for multimedia transactions between vehicles. The proposed protocol enables vehicles to trade in multimedia resources without an online trust authority. We provide an analytical approach to optimizing the security of the proposed protocol. The security and performance of our protocol are evaluated via simulation and theoretical analysis. Based on these evaluations, we contend that the proposed protocol is practical for multimedia transactions in VANETs in terms of security and performance.

A time-reversal (TR) approach with multiple signal classification (MUSIC) provides super-resolution for detection and localization using multistatic data collected from an array antenna system. The theory of TR-MUSIC assumes that the number of antenna elements is greater than that of scatterers (targets). Furthermore, it requires many sets of frequency-domain data (snapshots) in seriously noisy environments. Unfortunately, these conditions are not practical for real environments due to the restriction of a reasonable antenna structure as well as limited measurement time. We propose an approach that treats both noise reduction and relaxation of the transceiver restriction by using a time-domain gating technique accompanied with the Fourier transform before applying the TR-MUSIC imaging algorithm. Instead of utilizing the conventional multistatic data matrix (MDM), we employ a modified MDM obtained from the gating technique. The resulting imaging functions yield more reliable images with only a few snapshots regardless of the limitation of the antenna arrays.

In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.

A sensing efficiency optimization scheme based on two-stage spectrum sensing that maximizes the achievable throughput of the secondary network and minimizes the average sensing time is proposed in this paper. A selection method for the threshold is proposed and proved to ensure optimal sensing performance. An effective iterative algorithm is presented to solve the constructed efficiency optimization problem.

This work presents a real-time human detection system for VGA (Video Graphics Array, 640480) video, which well suits visual surveillance applications. To achieve high running speed and accuracy, firstly we design multiple fast scalar feature types on the gradient channels, and experimentally identify that NOGCF (Normalized Oriented Gradient Channel Feature) has better performance with Gentle AdaBoost in cascaded classifiers. A confidence measure for cascaded classifiers is developed and utilized in the subsequent tracking stage. Secondly, we propose to use speedup techniques including a detector pyramid for multi-scale detection and channel compression for integral channel calculation respectively. Thirdly, by integrating the detector's discrete detected humans and continuous detection confidence map, we employ a two-layer tracking by detection algorithm for further speedup and accuracy improvement. Compared with other methods, experiments show the system is significantly faster with 20 fps running speed in VGA video and has better accuracy as well.

More and more mobile devices adopt multi-battery and dynamic voltage scaling policy (DVS) to reduce the energy consumption and extend the battery runtime. However, since the nonlinear characteristics of the multi-battery are not considered, the practical efficiency is not good enough. In order to reduce the energy consumption and extend the battery runtime, this paper proposes an approach based on the battery characteristics to implement the co-optimization of the multi-battery scheduling and dynamic voltage scaling on multi-battery powered systems. In this work, considering the nonlinear discharging characteristics of the existing batteries, we use the Markov process to depict the multi-battery discharging behavior, and build a multi-objective optimal model to denote the energy consumption and battery states, then propose a binary tree based algorithm to solve this model. By means of this method, we get an optimal and applicable scheme about multi-battery scheduling and dynamic voltage scaling. Experimental results show that this approach achieves an average improvement in battery runtime of 17.5% over the current methods in physical implementation.

This paper presents a novel architecture design to optimize the reconfiguration process of a coarse-grained reconfigurable architecture (CGRA) called Reconfigurable Multimedia System II ( REMUS-II ). In REMUS-II, the tasks in multi-media applications are divided into two parts: computing-intensive tasks and control-intensive tasks. Two Reconfigurable Processor Units (RPUs) for accelerating computing-intensive tasks and a Micro-Processor Unit (µPU) for accelerating control-intensive tasks are contained in REMUS-II. As a large-scale CGRA, REMUS-II can provide satisfying solutions in terms of both efficiency and flexibility. This feature makes REMUS-II well-suited for video processing, where higher flexibility requirements are posed and a lot of computation tasks are involved. To meet the high requirement of the dynamic reconfiguration performance for multimedia applications, the reconfiguration architecture of REMUS-II should be well designed. To optimize the reconfiguration architecture of REMUS-II, a hierarchical configuration storage structure and a 3-stage reconfiguration processing structure are proposed. Furthermore, several optimization methods for configuration reusing are also introduced, to further improve the performance of reconfiguration process. The optimization methods include two aspects: the multi-target reconfiguration method and the configuration caching strategies. Experimental results showed that, with the reconfiguration architecture proposed, the performance of reconfiguration process will be improved by 4 times. Based on RTL simulation, REMUS-II can support the 1080p@32 fps of H.264 HiP@Level4 and 1080p@40 fps High-level MPEG-2 stream decoding at the clock frequency of 200 MHz. The proposed REMUS-II system has been implemented on a TSMC 65 nm process. The die size is 23.7 mm2 and the estimated on-chip dynamic power is 620 mW.

In the channel measurement and characterization, selecting a suitable excitation signal for a specified scenario is the primary task. This letter describes several selecting criteria of the excitation signal for channel sounding. And then the popular types of probing signals are addressed and through simulations their accuracy performances are compared in time-varying channels. The conclusion is the Constant Amplitude Zero Auto-Correlation (CAZAC) sequence yields better results in time-varying scenarios.

Mirrored serpentine microstrip lines are proposed for a parallel high speed digital signaling to reduce the peak far-end crosstalk (FEXT) voltage. Mirrored serpentine microstrip lines consist of two serpentine microstrip lines, each one equal to a conventional normal serpentine microstrip line. However, one serpentine microstrip line of the mirrored serpentine microstrip lines is flipped in the length direction, and thus, two serpentine microstrip lines face each other. Time domain reflectometry measurements show that the peak FEXT voltage of the mirrored serpentine microstrip lines is reduced by 56.4% of that of conventional microstrip lines and 30.0% of that of conventional normal serpentine microstrip lines.

In the work we introduce novel approach to remote sensing from space for the estimation of sea wave heights with a spaceborne high precision two-frequency radar altimeter with nadir synthesis antenna aperture. Experiments show considerable reduction of the decorrelation factor of the correlation coefficient and so significant enhancement of the sensitivity of the altimeter for the estimation for the sea wave status.

In this letter, we describe a low power current to time converter for wireless sensor networks. The proposed circuit has some advantages of high linearity and wide measurement range. From the evaluation using HSPICE with 0.18 µm CMOS device parameters, the output differential error for the input current variation is approximately 0.1 µs/nA under the condition that the current is varied from 100 nA to 500 nA. The idle power consumption is approximately zero.

The Direct Sampling Mixer (DSM) with a complex coefficient transfer function is demonstrated. The operation theory and the detail design methodology are discussed for the high order complex DSM, which can achieve large image rejection ratio by introducing the attenuation pole at the image frequency band. The proposed architecture was fabricated in a 65 nm CMOS process. The measured results agree well with the theoretical calculation, which proves the validity of the proposed architecture and the design methodology. By using the proposed design method, it will be possible for circuit designers to design the DSM with large image rejection ratio without repeated lengthy simulations.

Nowadays portable devices with multiple wireless interfaces and using multimedia services are becoming more popular on the Internet. This paper describes a family of multipath binomial congestion control algorithms for audio/video streaming, where a low variant of transmission rate is important. We extend the fluid model of binomial algorithms for single-path transmission to support the concurrent transmission of packets across multiple paths. We focus on the extension of two particular algorithms, SQRT and IIAD, for multiple paths, called MPSQRT and MPIIAD, respectively. Additionally, we apply the design technique (using the multipath fluid model) for multipath TCP (MPTCP) into the extension of SQRT and IIAD, called fbMPSQRT and fbMPIIAD, respectively. Both two approaches ensure that multipath binomial congestion control algorithms achieve load-balancing, throughput improvement, and fairness to single-path binomial algorithms at shared bottlenecks. Through the simulations and comparison with the uncoordinated protocols MPSQRT/MPIIAD, fbMPSQRT/fbMPIIAD and MPTCP, we find that our extended multipath transport protocols can preserve lower latency and transmission rate variance than MPTCP, fairly share with single-path SQRT/IIAD, MPTCP and TCP, and also can achieve throughput improvements and load-balancing equivalent to those of MPTCP under various scenarios and network conditions.

With the consideration of real-time stream processing technology, it's important to develop high availability mechanism to guarantee stream-based application not interfered by faults caused by potential anomalies. In this paper, we present a novel online prediction technique for predicting some anomalies which may occur in the near future. Concretely, we first present a value prediction which combines the Hidden Markov Model and the Mixture of Expert Model to predict the values of feature metrics in the near future. Then we employ the Support Vector Machine to do anomaly identification, which is a procedure to identify the kind of anomaly that we are about to alarm. The purpose of our approach is to achieve a tradeoff between fault penalty and resource cost. The experiment results show that our approach is of high accuracy for common anomaly prediction and low runtime overhead.

We propose a 2Nr MIMO ARQ scheme that uses multi-strata space-time codes composed of two layers. The phase and transmit power of each layer are assigned adaptively at each transmission round to mitigate the inter-layer interference and improve the block error rate by retransmission. Simulation results show that the proposed scheme achieves better performance than the conventional schemes in terms of the throughput and the block error rate.

In the framework of decentralized supervisory control of timed discrete event systems (TDESs), each local supervisor decides the set of events to be enabled to occur and the set of events to be forced to occur under its own local observation in order for a given specification to be satisfied. In this paper, we focus on fusion rules for the enforcement decisions and adopt the combined fusion rule using the AND rule and the OR rule. We first derive necessary and sufficient conditions for the existence of a decentralized supervisor under the combined fusion rule for a given partition of the set of forcible events. We next study how to find a suitable partition.

In order to improve the address discharge characteristics, we propose the modified selective reset waveform utilizing the address-bias voltage (Va-bias) during the ramp-up period. It is revealed that the proper Va-bias makes the weak discharge between the address and scan electrodes which plays a role in sufficiently removing the wall charge, thereby contributing to minimizing the wall-voltage variation during the address-period. As a result of adopting the Va-bias in the conventional selective reset driving waveform, it was found that the address discharge delay time can be shortened by approximately 40 ns and the address period of each subfield can be significantly reduced by about 43 µs.

Battery-powered wireless sensor networks are prone to premature failures because some nodes deplete their batteries more rapidly than others due to workload variations, the many-to-one traffic pattern, and heterogeneous hardware. Most previous sensor network lifetime enhancement techniques focused on balancing the power distribution, assuming the usage of the identical battery. This paper proposes a novel fine-grained cost-constrained lifetime-aware battery allocation solution for sensor networks with arbitrary topologies and heterogeneous power distributions. Based on an energy–cost battery pack model and optimal node partitioning algorithm, a rapid battery pack selection heuristic is developed and its deviation from optimality is quantified. Furthermore, we investigate the impacts of the power variations on the lifetime extension by battery allocation. We prove a theorem to show that power variations of nodes are more likely to reduce the lifetime than to increase it. Experimental results indicate that the proposed technique achieves network lifetime improvements ranging from 4–13 over the uniform battery allocation, with no more than 10 battery pack levels and 2-5 orders of magnitudes speedup compared with a standard integer nonlinear program solver (INLP).