Computationally highly efficient mixture of adaptive filters

We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and propose unconstrained, affinely constrained and convexly constrained combination weight configurations. Here, we achieve better trade-off in terms of the transient and steady-state convergence performance while providing significant computational reduction. Hence, through the introduced approaches, we can greatly enhance the convergence performance of the constituent filters with a slight increase in the computational load. In this sense, our approaches are suitable for big data applications where the data should be processed in streams with highly efficient algorithms. In the numerical examples, we demonstrate the superior performance of the proposed approaches over the state of the art using the well-known datasets in the machine learning literature.     

Linear-time prediction mode reduction method for intracoding in H.264/AVC

We present a linear-time elimination method to adaptively reduce the nine intraprediction modes in H.264/AVC to two, three, five, or nine modes while preserving a satisfactory reconstructed video quality. Resemblance between prediction modes and appearing frequency for each prediction mode are first determined in an off-line way from the training video sequences. Prediction modes are modeled as vertices and resemblance between two prediction modes as the weight of the connecting edge in a complete graph. Then, find the Hamilton cycle with the minimum sum of weights. When encoding each intrablock, prediction modes corresponding to the adjacent vertices along the Hamilton cycle are examined for similarity. The prediction mode which appears less frequently in a pair of similar prediction modes is declared as redundant and eliminated. When compared with Laroche et al.’s method which aims to reduce the bitrate, experimental results demonstrate that the proposed elimination method can substantially reduce the execution time while preserving the peak signal-to-noise ratio and bitrate performance.     

A joint-optimization NSAF algorithm based on the first-order Markov model

Recently, the normalized subband adaptive filter (NSAF) algorithm has attracted much attention for handling colored input signals. Based on the first-order Markov model of the optimal weight vector, this paper provides some insights for the convergence of the standard NSAF. Following these insights, both the step size and the regularization parameter in the NSAF are jointly optimized by minimizing the mean-square deviation. The resulting joint-optimization step size and regularization parameter algorithm achieves a good tradeoff between fast convergence rate and low steady-state error. Simulation results in the context of acoustic echo cancelation demonstrate good features of the proposed algorithm.     

Design of oversampled nonuniform filter banks with arbitrary rational frequency partitioning

Among the theory and design of oversampled nonuniform filter banks (NUFBs), most of the existing works only consider the cases with integer decimators, and up to now the issue with rational sampling factors has not been discussed yet. In this paper, we generalize the partial modulation technique to realize arbitrary rational frequency partitioning of oversampled NUFBs with highly desired linear-phase (LP) property. Further for the subbands with sampling factors violating the guard band restriction, a phase-modification scheme is derived to avoid uneliminable large aliasing and meanwhile preserving the LP characteristics of shifted analysis/synthesis filters. By using the proposed method, the design issue of LP oversampled NUFBs can be reduced to that of several prototypes, decreasing the design complexity largely. As illustrated by examples, the proposed algorithm is more general in terms of arbitrary rational decimation and thus has broad application prospects.     

Structural sparse coding seeds–active appearance model for object tracking

In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance model. The proposed object tracking scheme involves three stages: training, detection and tracking. In the training stage, an incremental SVM model that directly measures the candidates samples and target difference. The proposed mixture generate–discriminative method can well separate two highly correlated positive candidates images. In the detection stage, the trained weighted vector is used to separate the target object in positive candidates images with respect to the seeds images. In the tracking stage, we employ the particle filter to track the object through an appearance adaptive updating algorithm with seeds–active constrained sparse representation. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the current literature.     

Photonic Side-Channel Analysis of Arbiter PUFs

As intended by its name, physically unclonable functions (PUFs) are considered as an ultimate solution to deal with insecure storage, hardware counterfeiting, and many other security problems. However, many different successful attacks have already revealed vulnerabilities of certain digital intrinsic PUFs. This paper demonstrates that legacy arbiter PUF and its popular extended versions (i.e., feed-forward and XOR-enhanced) can be completely and linearly characterized by means of photonic emission analysis. Our experimental setup is capable of measuring every PUF internal delay with a resolution of 6 ps. Due to this resolution, we indeed require only the theoretical minimum number of linear independent equations (i.e., physical measurements) to directly solve the underlying inhomogeneous linear system. Moreover, it is not required to know the actual PUF responses for our physical delay extraction. We present our practical results for an arbiter PUF implementation on a complex programmable logic device manufactured with a 180 nm process. Finally, we give an insight into photonic emission analysis of arbiter PUF on smaller chip architectures by performing experiments on a field programmable gate array manufactured with a 60 nm process.     

Complete parameter measurement for MIMO bistatic radar without synchronization

In order to measure the range, angle, and Doppler frequency of the target without any synchronization in the bistatic radar, a novel complete parameter estimation method based on separability of a pair of Linear Frequency Modulation (LFM) signal is presented. The Doppler frequency is measured by the time difference between two peak positions corresponding to the positive and the negative LFM return signal respectively. Direction Of Departures (DODs) and Direction Of Arrivals (DOAs) of the target are estimated by constructing a special eigenmatrix in which the estimated angles can be extracted from the eigenvalue or the eigenvector. The target position can be located in the presence of the estimated DODs, DOAs and the signal delay difference between the echo and the directive wave signal in Multiple Input Multiple Output (MIMO) bistatic radar without any synchronization. The correctness and effectiveness of the proposed method are verified by the computer simulation.     

A Study of Plasma-Cleaned Ag-Plated Cu Leadframe Surfaces

In this study, an Ar plasma was employed to remove the antitarnish layer on Ag-plated Cu leadframe surfaces using various process powers and times. Measurements of the contact angle, field-emission scanning electron microscopy, x-ray photoelectron spectroscopy, and time-of-flight secondary-ion mass spectrometry were employed to characterize both the Ag and Cu surfaces before and after plasma cleaning. The antitarnish layer on the leadframe surfaces was determined to be benzotriazole and/or its derivatives. Low-power and short-duration plasma treatments did not significantly change the Ag and Cu surfaces in terms of their surface morphologies, and such treatments were not able to totally remove the antitarnish layer. In contrast, a high-power and long-duration plasma treatment made the surfaces slightly smoother and was able to remove the antitarnish layer on both Ag and Cu surfaces. However, cross-contaminations were also detected between the Ag and Cu surfaces during the plasma cleaning process. This cross-contamination is due to redeposition of Ag and Cu atoms during the plasma treatment.     

ACAR: Adaptive Connectivity Aware Routing for Vehicular Ad Hoc Networks in City Scenarios

Multi-hop vehicle-to-vehicle communication is useful for supporting many vehicular applications that provide drivers with safety and convenience. Developing multi-hop communication in vehicular ad hoc networks (VANET) is a challenging problem due to the rapidly changing topology and frequent network disconnections, which cause failure or inefficiency in traditional ad hoc routing protocols. We propose an adaptive connectivity aware routing (ACAR) protocol that addresses these problems by adaptively selecting an optimal route with the best network transmission quality based on statistical and real-time density data that are gathered through an on-the-fly density collection process. The protocol consists of two parts: 1) select an optimal route, consisting of road segments, with the best estimated transmission quality, and 2) in each road segment of the chosen route, select the most efficient multi-hop path that will improve the delivery ratio and throughput. The optimal route is selected using our transmission quality model that takes into account vehicle densities and traffic light periods to estimate the probability of network connectivity and data delivery ratio for transmitting packets. Our simulation results show that the proposed ACAR protocol outperforms existing VANET routing protocols in terms of data delivery ratio, throughput and data packet delay. Since the proposed model is not constrained by network densities, the ACAR protocol is suitable for both daytime and nighttime city VANET scenarios.     

Cooperative and Opportunistic Channel Access for Vehicle to Roadside (V2R) Communications

This paper focuses on vehicle to roadside (V2R) communications in vehicular networks based on the IEEE 802.11 DCF MAC protocol. In vehicular networks, roadside units (RSUs) are typically spaced apart along the road and each vehicle can be connected to an RSU only when the vehicle is within its transmission range. Due to the high relative speed between a moving vehicle and a stationary RSU, the residence time of the vehicle within the coverage of each RSU is very short. Thus it is hard for the system to reach a steady state. With multi-hop forwarding, in which a vehicle may be connected to an RSU through relaying over other vehicles, the connection time of each V2R access may be extended. But this is at the expense of introducing wireless interference among vehicles, which may dramatically degrade the system performance. To tackle these challenges, we propose a new mechanism called Proxy-based Vehicle to RSU access (PVR) for V2R communications. This protocol is designed to exploit cooperative and opportunistic forwarding between any two distant RSUs and to emulate back-to-back transmissions within the coverage of an RSU. As a result, it can shorten the access delay by taking advantage of opportunistic forwarding and mitigate the interference problem during the short residence time within the coverage of an RSU. The simulation results show that PVR achieves excellent performance and outperforms all existing solutions for V2R communications in vehicular networks.