In this paper, we propose a new structure for a compact matched filter bank for a mutually orthogonal zero-correlation zone (MO-ZCZ) sequence set consisting of ternary sequence pairs obtained by Hadamard and binary ZCZ sequence sets; this construction reduces the number of two-input adders and delay elements. The matched filter banks are implemented on a field-programmable gate array (FPGA) with 51,840 logic elements (LEs). The proposed matched filter bank for an MO-ZCZ sequence set of length 160 can be constructed by a circuit size that is about 8.6% that of a conventional matched filter bank.

High efficiency video coding (HEVC) is the new generation video compression standard. Sample adaptive offset (SAO) is a new compression tool adopted in HEVC which reduces the distortion between original samples and reconstructed samples. SAO estimation is the process of determining SAO parameters in video encoding. It is divided into two phases: statistic collection and parameters determination. There are two difficulties for VLSI implementation of SAO estimation. The first is that there are huge amount of samples to deal with in statistic collection phase. The other is that the complexity of Rate Distortion Optimization (RDO) in parameters determination phase is very high. In this article, a fast SAO estimation algorithm and its corresponding VLSI architecture are proposed. For the first difficulty, we use bitmaps to collect statistics of all the 16 samples in one 4×4 block simultaneously. For the second difficulty, we simplify a series of complicated procedures in HM to balance the algorithms complexity and BD-rate performance. Experimental results show that the proposed algorithm maintains the picture quality improvement. The VLSI design based on this algorithm can be implemented using 156.32K gates, 8,832bits single port RAM for 8bits depth case. It can be synthesized to 400MHz @ 65nm technology and is capable of 8K×4K @ 120fps encoding.

This paper presents an adaptive feedback canceller (AFC) based on a pseudo affine projection (PAP) algorithm that can provide fast and stable adaptation to the time-varying environment. The proposed algorithm utilizes the adaptive linear prediction (LP) to obtain the LP coefficients of input signal model and the inverse gain filter (IGF) to alleviate the effect of compensation gain. As a result, when the input is model as an AR signal, the proposed algorithm satisfies the condition for having an almost unbiased estimatie of the feedback path and then its performance is relatively independent of the gain setting of hearing aids. Simulation results showed that the proposed algorithm is capable of obtaining unbaised feedback path estimates and high speech quality.

An asymmetric zero correlation zone (A-ZCZ) sequence set is a type of ZCZ sequence set and consists of multiple sequence subsets. It is the most important property that is the cross-correlation function between arbitrary sequences belonging to different sequence subsets has quite a large zero-cross-correlation zone (ZCCZ). Our proposed A-ZCZ sequence sets can be constructed based on interleaved technique and orthogonality-preserving transformation by any perfect sequence of length P=Nq(2k+1) and Hadamard matrices of order T≥2, where N≥1, q≥1 and k≥1. If q=1, the novel sequence set is optimal ZCZ sequence set, which has parameters (TP,TN,2k+1) for all positive integers P=N(2k+1). The proposed A-ZCZ sequence sets have much larger ZCCZ, which are expected to be useful for designing spreading sequences for QS-CDMA systems.

A code shift keying (CSK) using pseudo-noise (PN) codes for optical wireless communications with intensity/modulation and direct detection (IM/DD) is considered. Since CSK has several PN codes, the data transmission rate and the bit error rate (BER) performance can be improved by increasing the number of PN codes. However, the conventional optical PN codes are not suitable for optical CSK with IM/DD because the ratio of the number of PN codes and the code length of PN code, M/L is smaller than 1/√L. In this paper, an optical CSK with a new PN code, which combines the generalized modified prime sequence code (GMPSC) and Hadamard code is analyzed. The new PN code can achieve M/L=1. Moreover, the BER performance and the data transmission rate of the CSK system with the new PN code are evaluated through theoretical analysis by taking the scintillation, background-noise, avalanche photodiode (APD) noise, thermal noise, and signal dependent noise into account. It is found that the CSK system with the new PN code outperforms the conventional optical CSK system.

We present a dynamic key generation method, KeyQ, for establishing shared secret keys in EPCglobal Generation 2 (Gen2) compliant systems. Widespread adoption of Gen2 technologies has increased the need for protecting communications in these systems. The highly constrained resources on Gen2 tags limit the usability of traditional key distribution techniques. Dynamic key generation provides a secure method to protect communications with limited key distribution requirements. Our KeyQ method dynamically generates fresh secret keys based on the Gen2 adaptive Q algorithm. We show that the KeyQ method generates fresh and unique secret keys that cannot be predicted with probability greater than 10-250 when the number of tags exceeds 100.

Radar systems using ultra-wideband (UWB) signals have definitive advantages in high range resolution. These are suitable for accurate 3-dimensional (3-D) sensing by rescue robots operating in disaster zone settings, where optical sensing is not applicable because of thick smog or high-density gas. For such applications, where no a priori information of target shape and position is given, an accurate method for 3-D imaging and motion estimation is strongly required for effective target recognition. In addressing this issue, we have already proposed a non-parametric 2-dimensional (2-D) imaging method for a target with arbitrary target shape and motion including rotation and translation being tracked using a multi-static radar system. This is based on matching target boundary points obtained using the range points migration (RPM) method extended to the multi-static radar system. Whereas this method accomplishes accurate imaging and motion estimation for single targets, accuracy is degraded severely for multiple targets, due to interference effects. For a solution of this difficulty, this paper proposes a method based on a novel matching scheme using not only target points but also normal vectors on the target boundary estimated by the Envelope method; interference effects are effectively suppressed when incorporating the RPM approach. Results from numerical simulations for both 2-D and 3-D models show that the proposed method simultaneously achieves accurate target imaging and motion tracking, even for multiple moving targets.

The physical optics (PO) approximation is one of the widely-used techniques to calculate scattering fields with a reasonable accuracy in the high frequency region. The computational load of PO radiation integral dramatically increases at higher frequencies since it is proportional to the electrical size of scatterer. In order to suppress this load, a variety of techniques, such as the asymptotic evaluation by the stationary phase method (SP), the equivalent edge currents (EECs), the low-order polynomial expansion method and the fast physical optics (FPO), have been proposed and developed. The adaptive sampling method (ASM) proposed by Burkholder is also one of the techniques where the sampling points in radiation integral should be adaptively determined based upon the phase change of integrand. We proposed a quite different approach named ``Localization of the radiation integrals.'' This localization method suggests that only the small portions of the integration with a slow phase change contribute to the scattering field. In this paper, we newly introduce the ASM in the localization method and applied the proposed method into the radar cross section (RCS) analysis of 2-dimensional strip and cylinder. We have confirmed that the proposed method provides the frequency-independent number of division in the radiation integrals and computational time and accuracy. As the starting point for extension to 3-D case, the application of the proposed method for a reflection from an infinite PEC plane and a part of sphere was also examined.

Razor Flip-Flop (FF) is a good combination for the dynamic voltage scaling (DVS) technique to achieve high energy efficiency. We previously proposed a RazorProtector scheme, which uses, under a very high IR-drop zone, a redundant data-path to provide a very fast recovery for a Razor-FF based processor. In this paper, we propose a dynamic method to adjust the redundancy level to fine-grained fit both the program behaviors and processor manufacturing variations so as to achieve an optimal power saving. We design an online turning method to adjust the redundancy level according to the most related parameters, ILP (Instruction Level Parallelism) and DCF (Delay Criticality Factor). Our simulation results show that under a workload suite with different behaviors, the adaptive redundancy can achieve better Energy Delay Product (EDP) reduction than any static controls. Compared to the traditional application of Razor-FF and DVS, our proposed dynamic control achieves an EDP reduction of 56% in average for the workloads we studied.

In this paper, we propose distributed medium access control (MAC) protocols based on an adaptive sensing period adjustment scheme for low-cost multiple secondary users in interweave-type cognitive radio (CR) networks. The proposed MAC protocols adjust the sensing period of each secondary user based on both primary sensing and secondary data channels in distributed manner. Then, the secondary user with the shortest sensing period accesses the medium using request-to-send (RTS) and clear-to-send (CTS) message exchange. Three components affect the length of each user's sensing period: sensing channel quality from the primary system, data channel quality to the secondary receiver, and collision probability among multiple secondary transmitters. We propose two sensing period adjustment (SPA) schemes to efficiently improve achievable rate considering the three components, which are logarithmic SPA (LSPA) and exponential SPA (ESPA). We evaluate the performance of the proposed schemes in terms of the achievable rate and other factors affecting it, such as collision probability, false alarm probability, and average sensing period.

One of the major applications of contrast-enhanced ultrasound (CEUS) is lesion classification. After contrast agents are administered, it is possible to identify a lesion type from its enhancement pattern. However, CEUS image reading is not easy because there are various types of enhancement patterns even for the same type of lesion, and clear classification criteria have not yet been defined. Some studies have used conventional time intensity curves (TICs), which show the vessel dynamics of a lesion. It is possible to predict lesion type from the TIC parameters, such as the coefficients obtained by curve fitting, peak intensity, flow rate and time to peak. However, these parameters are not always provide sufficient accuracy. In this paper, we prepare 1D Haar-like features which describe intensity changes in a TIC and adopt the Adaboost machine learning technique, which eases understanding of which features are useful. Hyperparameters of weak classifiers, e.g., the step size of a Haar-like filter length and threshold for output of the filter, are optimized by searching for those parameters that give the best accuracy. We evaluate the proposed method using 36 focal splenic lesions in canines 16 of which were benign and 20 malignant. The accuracies were 91.7% (33/36) when inspected by an experienced veterinarian, 75.0% (27/36) by linear discriminant analysis (LDA) using conventional three TIC parameters: time to peak, area under curve and peak intensity, and 91.7% (33/36) using our proposed method. McNemar testing shows the p-value to be less than 0.05 between the proposed method and LDA. This result shows the statistical significance of differences between the proposed method and the conventional TIC analysis method using LDA.

In this paper, we design an Unequal Error Protection (UEP) rateless code with special coding graph and apply it to propose a novel HTTP adaptive streaming based on UEP rateless code (HASUR). Our designed UEP rateless code provides high diversity on decoding probability and priority for data in different important level with overhead smaller than 0.27. By adopting this UEP rateless channel coding and scalable video source coding, our HASUR ensures symbols with basic quality to be decoded first to guarantee fluent playback experience. Besides, it also provides multiple layers to ensure the most suitable quality for fluctuant bandwidth and packet loss rate (PLR) without estimating them in advance. We evaluate our HASUR against the alternative solutions. Simulation results show that HASUR provides higher video quality and more adapts to bandwidth and PLR than other two commercial schemes under End-to-End transmission.

In this letter, we formally present the definition of KDM-CCA1 security in public key setting, which falls in between the existing KDM-CPA and KDM-CCA2 security. We also prove that if a public key encryption scheme is CCA1 secure and has the properties of secret-key multiplication (or addition) homomorphism, and conditioned plaintext-restorability, then it is KDM-CCA1 secure w.r.t. two ensembles of functions that had been used in [15],[17], respectively. For concrete scheme, we show that the (tailored) Damgård's Elgamal scheme achieves this KDM-CCA1 security based on different assumptions.

The combined linear frequency modulation continuous wave (LFMCW) and inverse synthetic aperture radar (ISAR) can be used for imaging long-distance targets because of its long-distance and high resolution imaging abilities. In this paper, we find and study the dechirp distortion phenomenon (DDP) for imaging long-distance targets by a dechirp-on-receive LFMCW radar. If the targets are very far from the radar, the maximum delay-time is not much smaller than a single sweep duration, and the dechirp distortion is triggered since the distance of the target is unknown in a LFMCW-ISAR system. DDP cannot be ignored in long-distance imaging because double images of a target appear in the frequency domain, which reduces resolution and degrades image quality. A novel LFMCW-ISAR signal model is established to analyze DDP and its negative effects on long-distance target imaging. Using the proportionately distributed energy of double images, the authors propose a method to correct dechirp distortion. In addition, the applicable scope of the proposed method is also discussed. Simulation results validate the theoretical analysis and the effectiveness of the proposed method.

There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.

We present a low-complexity complementary pair affine projection (CP-AP) adaptive filter which employs the intermittent update of the filter coefficients. To achieve both a fast convergence rate and a small residual error, we use a scheme combining fast and slow AP filters, while significantly reducing the computational complexity. By employing an evolutionary method which automatically determines the update intervals, the update frequencies of the two constituent filters are significantly decreased. Experimental results show that the proposed CP-AP adaptive filter has an advantage over conventional adaptive filters with a parallel structure in that it has a similar convergence performance with a substantial reduction in the total number of updates.

Soil moisture retrieval from polarimetric synthetic aperture radar (SAR) imagery over forested terrain is quite a challenging problem, because the radar backscatter is affected by not only the moisture content, but also by large vegetation structures such as the trunks and branches. Although a large number of algorithms which exploit radar backscatter to infer soil moisture have been developed, most of them are limited to the case of bare soil or little vegetation cover that an incident wave can easily reach the soil surface without serious disturbance. However, natural land surfaces are rarely free from vegetation, and the disturbance in radar backscatter must be properly compensated to achieve accurate soil moisture measurement in a diversity of terrain surfaces. In this paper, a simple polarimetric parameter, co-polarized backscattering ratio, is shown to be a criterion to infer moisture content of forested terrain, from both a theoretical forest scattering simulation and an appropriate experimental validation under well-controlled condition. Though modeling of forested terrain requires a number of scattering mechanisms to be taken into account, it is essential to isolate them one by one to better understand how soil moisture affects a specific and principal scattering component. For this purpose, we consider a simplified microwave scattering model for forested terrain, which consists of a cloud of dielectric cylinders as a representative of trunks, vertically stood on a flat dielectric soil surface. This simplified model can be considered a simple boreal forest model, and it is revealed that the co-polarization ratio in the ground-trunk double-bounce backscattering can be an useful index to monitor the relative variation in the moisture content of the boreal forest.

We propose a new algorithm to suppress both stationary background noise and nonstationary directional interference noise in a speech enhancement system that employs the generalized sidelobe canceller. Our approach builds on advances in generalized sidelobe canceller design involving the transfer function ratio. Our system is composed of three stages. The first stage estimates the transfer function ratio on the acoustic path, from the nonstationary directional interference noise source to the microphones, and the powers of the stationary background noise components. Secondly, the estimated powers of the stationary background noise components are used to execute spectral subtraction with respect to input signals. Finally, the estimated transfer function ratio is used for speech enhancement on the primary channel, and an adaptive filter reduces the residual correlated noise components of the signal. These algorithmic improvements give consistently better performance than the transfer function generalized sidelobe canceller when input signal-to-noise ratio is 10 dB or lower.

Recently, radar resource management of multifunction radar is a challenging issue in electronically scanned array radar technology. This paper deals with radar beam scheduling, which is a core issue of radar resource management. This paper proposed stochastic scheduler algorithm using Simulated Annealing (SA) and Hybrid scheduler algorithm which automatically selects two different types of schedulers according to the radar load: Rule based scheduler using modified Butler algorithm for underload situations and SA based scheduler for overload situations. The proposed algorithms are evaluated in terms of scheduling latency, the number of scheduled tasks, and time complexity. The simulation results show that the performance of rule based scheduler is seriously degraded in overload situation. However, SA based scheduler and Hybrid scheduler have graceful performance degradation in overload situation. Compared with rule based scheduler, SA based scheduler and Hybrid scheduler can schedule many more tasks on time for the same operation duration in the overload situation. Even though their time complex is relatively high, it can be applied to real applications if the parameters are properly controlled. Especially, Hybrid scheduler has an advantage of low time complexity with good performance.

In this paper, we introduced a novel Kernel-Reliability-based K-Means (KRKM) clustering algorithm for categorizing an unknown dataset under noisy condition. Compared with the conventional clustering algorithms, the proposed KRKM algorithm will measure both the reliability and the similarity for classifying data into its neighbor clusters by the dynamic kernel functions, where the noisy data will be rejected by being given low reliability. The reliability for classifying data is measured by a dynamic kernel function whose window size will be determined by the triangular relationship from this data to its two nearest clusters. The similarity from a data item to its neighbor clusters is measured by another adaptive kernel function which takes into account not only the similarity from data to clusters but also that between its two nearest clusters. The main contribution of this work lies in introducing the dynamic kernel functions to evaluate both the reliability and similarity for clustering, which makes the proposed algorithm more efficient in dealing with very strong noisy data. Through various experiments, the efficiency and effectiveness of proposed algorithm have been confirmed.