Scale Invariant Feature Transform (SIFT) algorithm is a very excellent approach for feature detection. It is characterized by data intensive computation. The current studies of accelerating SIFT algorithm are mainly reflected in three aspects: optimizing the parallel parts of the algorithm based on general-purpose multi-core processors, designing the customized multi-core processor dedicated for SIFT, and implementing it based on the FPGA platform. The real-time performance of SIFT has been highly improved. However, the factors such as the input image size, the number of octaves and scale factors in the SIFT algorithm are restricted for some solutions, the flexibility that ensures the high execution performance under variable factors should be improved. This paper proposes a reconfigurable solution to solve this problem. We fully exploit the algorithm and adopt several techniques, such as full parallel execution, block computation and CORDIC transformation, etc., to improve the execution efficiency on a REconfigurable MUltimedia System called REMUS. Experimental results show that the execution performance of the SIFT is improved by 33%, 50% and 8 times comparing with that executed in the multi-core platform, FPGA and ASIC separately. The scheme of dynamic reconfiguration in this work can configure the circuits to meet the computation requirements under different input image size, different number of octaves and scale factors in the process of computing.

The application of information hiding to image compression is investigated to improve compression efficiency for JPEG color images. In the proposed method, entropy-coded DCT coefficients of chrominance components are embedded into DCT coefficients of the luminance component. To recover an image in the face of the degradation caused by compression and embedding, an image restoration method is also applied. Experiments show that the use of both information hiding and image restoration is most effective to improve compression efficiency.

Turbo equalization is an iterative equalization and decoding technique that can achieve impressive performance gains for communication systems. In this letter, we investigate the turbo equalization method for the decoding of the Davey-MacKay (DM) construction over the IDS-AWGN channels, which indicates a cascaded insertion, deletion, substitution (IDS) channel and an additive white Gaussian noise (AWGN) channel. The inner decoder for the DM construction can be seen as an maximum a-posteriori (MAP) detector. It receives the beliefs generated by the outer LDPC decoder when turbo equalization is used. Two decoding schemes with different kinds of inner decoders, namely hard-input inner decoder and soft-input inner decoder, are investigated. Simulation results show that significant performance gains are obtained for both decoders with respect to the insertion/deletion probability at different SNR values.

In forthcoming sensor networks, a multitude of sensor nodes deployed over a large geographical area for monitoring traffic, climate, etc. are expected to become an inevitable infrastructure. Clustering algorithms play an important role in aggregating a large volume of data that are produced continuously by the huge number of sensor nodes. In such networks, equal-sized multi-hop clusters which include an equal number of nodes are useful for efficiency and resiliency. In addition, scalability is important in such large-scale networks. In this paper, we mathematically design a decentralized equal-sized clustering algorithm using a partial differential equation based on the Fourier transform technique, and then design its protocol by discretizing the equation. We evaluated through simulations the equality of cluster sizes and the resiliency against packet loss and node failure in two-dimensional perturbed grid topologies.

The average Hamming correlation is an important performance indicator of frequency-hopping sequences (FHSs). In this letter, the average partial Hamming correlation (APHC) properties of FHSs are discussed. Firstly, the theoretical bound on the average partial Hamming correlation of FHSs is established. It works for any correlation window with length 1≤ω≤υ, where υ is the sequence period, and generalizes the bound developed by Peng et al which is valid only when ω=υ. A sufficient and necessary condition for a set of FHSs having optimal APHC for any correlation window is then given. Finally, sets of FHSs with optimal APHC are presented.

An instrumental variable (IV) based linear estimator is proposed for effective target localization in sensor network by using time-difference-of-arrival (TDOA) measurement. Although some linear estimation approaches have been proposed in much literature, the target localization based on TDOA measurement still has a room for improvement. Therefore, we analyze the estimation errors of existing localization estimators such as the well-known quadratic correction least squares (QCLS) and the robust least squares (RoLS), and demonstrate advantages of the proposition by comparing the estimation errors mathematically and showing localization results through simulation. In addition, a recursive form of the proposition is derived to consider a real time application.

A novel and energy-efficient algorithm with Quality-of-Service (QoS) guarantee is proposed for cooperative spectrum sensing (CSS) with soft information fusion and hard information fusion. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of secondary users (SUs), it is shown that the optimal number of SUs is related to the price of these QoS requirements.

IEEE802.11 Wireless Local Area Networks (WLANs) are becoming more and more pervasive due to their simple channel access mechanism, Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), but this mechanism provides all nodes including Access Point and other Stations with the same channel access probability. This characteristic does not suit the infrastructure mode which has so many downlink flows to be transmitted at the Access Point that congestion at the Access Point is more likely to occur. To resolve this asymmetry traffic problem, we develop an Optimal Contention Window Adjustment method assuming the condition of erroneous channels over WLANs. This method can be easily implemented and is compatible with the original CSMA/CA mechanism. It holds the ratio of downlink and uplink flows and at the same time achieves the maximum saturation throughput in the WLANs. We use the Markov Chain analytical model to analyze its performance and validate it through the simulations.

A modified modeling of residue effect on nano-electro-mechanical nonvolatile memory (NEMory) is presented for considering wet etching process. The effect of a residue under the cantilever is investigated for the optimization. The feasibility of the proposed model is investigated by finite element analysis simulations.

Reliable broadcasting in vehicular ad-hoc networks is challenging due to their unique characteristics including intermittent connectivity and various vehicular scenarios. Applications and services in intelligent transportation systems need an efficient, fast and reliable broadcasting protocol especially for safety and emergency applications. In our previous work, we have proposed DECA, a new reliable broadcasting protocol which suits such characteristics. To address the issue of various vehicular scenarios, our protocol performs beaconing to gather local density information of 1-hop neighbors and uses such information to adapt its broadcasting decision dynamically. Specifically, before broadcasting a message, a node selects a neighbor with the highest density and adds the selected neighbor's identifier to the message. Upon receiving of a broadcast message, each node checks whether or not it is the selected neighbor. If so, it is responsible for rebroadcasting the message immediately. This mechanism can raise the data dissemination speed of our protocol so that it is as fast as simple flooding. To address the issue of intermittent connectivity, identifiers of broadcast messages are added into beacons. This helps nodes to check if there are any broadcast messages that have not yet been received. In this paper, we propose DECA with a new beaconing algorithm and a new waiting timeout calculation, so-called DECA-bewa. Our new protocol can reduce redundant retransmissions and overall overhead in high density areas. The protocol performance is evaluated on the network simulator (NS-2). Simulation results show that DECA-bewa outperforms existing protocols in terms of reliability, overhead and speed of data dissemination.

In this paper, we consider the XPath satisfiability problem under restricted DTDs called “duplicate free”. For an XPath expression q and a DTD D, q is satisfiable under D if there exists an XML document t such that t is valid against D and that the answer of q on t is nonempty. Evaluating an unsatisfiable XPath expression is meaningless, since such an expression can always be replaced by an empty set without evaluating it. However, it is shown that the XPath satisfiability problem is intractable for a large number of XPath fragments. In this paper, we consider simple XPath fragments under two restrictions: (i) only a label can be specified as a node test and (ii) operators such as qualifier ([]) and path union (∪) are not allowed. We first show that, for some small XPath fragments under the above restrictions, the satisfiability problem is NP-complete under DTDs without any restriction. Then we show that there exist XPath fragments, containing the above small fragments, for which the satisfiability problem is in PTIME under duplicate-free DTDs.

Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.

Detailed tracking is required for many vision applications. A visual feature-based constraint underlies most conventional motion estimation methods. For example, optical flow methods assume that the brightness of each pixel is constant in two consecutive frames. However, it is difficult to realize accurate extraction and tracking using only visual feature information, because viewpoint changes and inconsistent illumination cause the visual features of some regions of objects to appear different in consecutive frames. A structure-based constraint of objects is also necessary for tracking. In the proposed method, both visual feature matching and structure matching are formulated as a linear assignment problem and then integrated.

First, this paper derives the prefix sum-of-products expression (PreSOP) and the number of products in a PreSOP for an interval function. Second, it derives Ψ(n,τp), the number of n-variable interval functions that can be represented with τp products. Finally, it shows that more than 99.9% of the n-variable interval functions can be represented with ⌈ n - 1 ⌉ products, when n is sufficiently large. These results are useful for a fast PreSOP generator and for estimating the size of ternary content addressable memories (TCAMs) for packet classification.

We investigated the effects of chemical treatments for removing native oxide layers on InAlN surfaces by X-ray photoelectron spectroscopy (XPS). The untreated surface of the air exposed InAlN layer was covered with the native oxide layer mainly composed of hydroxides. Hydrochloric acid treatment and ammonium hydroxide treatment were not efficient for removing the native oxide layer even after immersion for 15 min, while hydrofluoric acid (HF) treatment led to a removal in a short treatment time of 1 min. After the HF treatment, the surface was prevented from reoxidation in air for 1 h. We also found that the 5-min buffered HF treatment had almost the same effect as the 1-min HF treatment. Finally, an attempt was made to apply the HF-based treatment to the metal-InAlN contact to confirm the XPS results.

In this paper, negative bias temperature instability (NBTI) reliability of pFETs is analyzed under the post-fabrication SRAM self-improvement scheme that we have developed recently, where cell stability is self-improved by simply applying high stress voltage to supply voltage terminal (VDD) of SRAM cells. It is newly found that there is no significant difference in both threshold voltage and drain current degradation by NBTI stress between fresh PFETs and PFETs after self-improvement scheme application, indicating that the self-improvement scheme has no critical reliability problem.

High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.

A predictive spatio-temporal interval join finds all pairs of moving objects satisfying a join condition on future time interval and space. In this paper, we propose a method called PTJoin. PTJoin partitions the inner index into small sub-trees and performs the join process for each sub-tree to reduce the number of disk page accesses for each window search. Furthermore, to reduce the number of pages accessed by consecutive window searches, we partition the index so that overlapping index pages do not belong to the same partition. Our experiments show that PTJoin reduces the number of page accesses by up to an order of magnitude compared to Interval_STJoin [9], which is the state-of-the-art solution, when the buffer size is small.

In this paper, we suggest a novel pnp BJT structure to improve the matching characteristics of the bipolar junction transistor (BJT) which is fabricated using standard CMOS process. In the case of electrical characteristics, the collector current density Jc of the proposed structure (T2) is a little greater than the conventional structure (T1), which contributes to the greater current gain β of the proposed structure than the conventional structure. Although the matching characteristics of the collector current density of the proposed structure is almost similar to the conventional structure, that of the current gain of the proposed structure is better than the conventional structure about 14.81% due to the better matching characteristics of the base current density of the proposed structure about 59.34%. Therefore, the proposed BJT structure is desirable for high performance analog/digital mixed signal application.

In summary, we successfully fabricated the nonvolatile hybrid polymer 4F2 memory-cell. It was based on bistable state, which was observed in PS layer that is containing a Ni nanocrystals capped with NiO tunneling barrier sandwiched by Al electrodes. The current conduction mechanism for polymer memory-cell was demonstrated by fitting the I-V curves. The electrons were charged and discharged on Ni nanocrystals by tunneling through the NiO tunneling barrier. In addition, the memory-cell showed a good and reproducible nonvolatile memory-cell characteristic. Its memory margin is about 1.410. The retention-time is more than 105 seconds and the endurance cycles of program-and-erase is more than 250 cycles. Furthermore, Thefore, polymer memory-cell would be good candidates for nonvolatile 4F2 cross-bar memory-cell.