This paper proposes the use of on-chip monitor circuits to detect process shift and process spread for post-silicon diagnosis and model-hardware correlation. The amounts of shift and spread allow test engineers to decide the correct test strategy. Monitor structures suitable for detection of process shift and process spread are discussed. Test chips targeting a nominal process corner as well as 4 other corners of “slow-slow”, “fast-fast”, “slow-fast” and “fast-slow” are fabricated in a 65nm process. The monitor structures correctly detects the location of each chip in the process space. The outputs of the monitor structures are further analyzed and decomposed into the process variations in threshold voltage and gate length for model-hardware correlation. Path delay predictions match closely with the silicon values using the extracted parameter shifts. On-chip monitors capable of detecting process shift and process spread are helpful for performance prediction of digital and analog circuits, adaptive delay testing and post-silicon statistical analysis.

In this paper, we present an approach for 3D face recognition based on Multi-level Partition of Unity (MPU) Implicits under pose and expression variations. The MPU Implicits are used for reconstructing 3D face surface in a hierarchical way. Three landmarks, nose, left eyehole and right eyehole, can be automatically detected with the analysis of curvature features at lower levels of reconstruted face. Thus, the 3D faces are initially registered to a common coordinate system based on the three landmarks. A variant of Iterative Closest Point (ICP) algorithm is proposed for matching the point surface of a given probe face to the implicits face surface in the gallery. To evaluate the performance of our approach for 3D face recognition, we perform an experiment on GavabDB face database. The results of the experiment show that our method based on MPU Implicits and Adaptive ICP has great capability for 3D face recognition under pose and expression variations.

This paper proposes an enhancement to a previously reported adaptive peak-to-average power ratio (PAPR) reduction method based on clipping and filtering (CF) for eigenmode multiple-input multiple-output (MIMO) — orthogonal frequency division multiplexing (OFDM) signals. We enhance the method to accommodate the case with adaptive modulation and channel coding (AMC). Since the PAPR reduction process degrades the signal-to-interference and noise power ratio (SINR), the AMC should take into account this degradation before PAPR reduction to select accurately the modulation scheme and coding rate (MCS) for each spatial stream. We use the lookup table-based prediction of SINR after PAPR reduction, in which the interference caused by the PAPR reduction is obtained as a function of the stream index, frequency block index, clipping threshold for PAPR reduction, and input backoff (IBO) of the power amplifier. Simulation results show that the proposed PAPR reduction method increases the average throughput compared to the conventional CF method for a given adjacent channel leakage power ratio (ACLR) when we assume practical AMC.

Based on sample-pair refinement and local optimization, this paper proposes a high-accuracy and quick matting algorithm. First, in order to gather foreground/background samples effectively, we shoot rays in hybrid (gradient and uniform) directions. This strategy utilizes the prior knowledge to adjust the directions for effective searching. Second, we refine sample-pairs of pixels by taking into account neighbors'. Both high confidence sample-pairs and usable foreground/background components are utilized and thus more accurate and smoother matting results are achieved. Third, to reduce the computational cost of sample-pair selection in coarse matting, this paper proposes an adaptive sample clustering approach. Most redundant samples are eliminated adaptively, where the computational cost decreases significantly. Finally, we convert fine matting into a de-noising problem, which is optimized by minimizing the observation and state errors iteratively and locally. This leads to less space and time complexity compared with global optimization. Experiments demonstrate that we outperform other state-of-the-art methods in local matting both on accuracy and efficiency.

We implement external memory-free deep pipelined FPGA implementation including HOG feature extraction and AdaBoost classification. To construct our design by compact FPGA, we introduce some simplifications of the algorithm and aggressive use of stream oriented architectures. We present comparison results between our simplified fixed-point scheme and an original floating-point scheme in terms of quality of results, and the results suggest the negative impact of the simplified scheme for hardware implementation is limited. We empirically show that, our system is able to detect human from 640480 VGA images at up to 112 FPS on a Xilinx Virtex-5 XC5VLX50 FPGA.

In order to execute coherent Doppler processing in a high range-rate scenario, whether it is for detection, estimation or imaging, range walk embedded in target return should be compensated first. In case of a bistatic radar geometry where a transmitter, a receiver and a target can be all moving, the extent of range walk depends on their relative positions and velocities. This paper presents a coherent Doppler processing algorithm to achieve target detection and Doppler frequency estimation of a target under a bistatic radar geometry. This algorithm is based on the assumption that a target has constant Doppler frequency during a coherent processing interval (CPI). Thus, we first show under what condition the assumption could be valid. We next develop an algorithm, along with its implementation procedures where the region of range walk, called a window, is manipulated. Finally, the performance of a proposed algorithm is examined through simulations.

This paper describes a unified VLSI architecture which can be applied to various types of transforms used in MPEG-2/4, H.264, VC-1, AVS and the emerging new video coding standard named HEVC (High Efficiency Video Coding). A novel design named configurable butterfly array (CBA) is also proposed to support both the forward transform and the inverse transform in this unified architecture. Hadamard transform or 4/8-point DCT/IDCT are used in traditional video coding standards while 16/32-point DCT/IDCT are newly introduced in HEVC. The proposed architecture can support all these transform types in a unified architecture. Two levels (architecture level and block level) of hardware sharing are adopted in this design. In the architecture level, the forward transform can share the hardware resource with the inverse transform. In the block level, the hardware for smaller size transform can be recursively reused by larger size transform. The multiplications of 4 or 8-point transform are implemented with Multiplierless MCM (Multiple Constant Multiplication). In order to reduce the hardware overhead, the multiplications of 16/32 point DCT are implemented with ICM (input-muxed constant multipliers) instead of MCM or regular multipliers. The proposed design is 51% more area efficient than previous work. To the author's knowledge, this is the first published work to support both forward and inverse 4/8/16/32-point integer transform for HEVC standard in a unified architecture.

This paper presents a novel adaptable 4-moduli set {2n + k, 2n+1, 2n-1, 22n+1}. It offers diverse dynamic ranges (DRs) from 25n-2n to 25n + k-2n + k that are used to conquer the over-range issue in RNS-application hardware designs. The proposed adaptable set possesses the coarse parameter n and fine parameter k. It not only has better parallelism and larger dynamic range (DR) than the existing adaptive 3-moduli sets, but also holds more sizable and flexible than the general 4-moduli sets with single parameter. For the adaptable R-to-B conversion, this paper first derives a fast reverse converting algorithm based on Chinese Remainder Theorem (CRT) and then presents the efficient converter architecture. From the experimental results, the proposed adaptable converter achieves better hardware performance in various DRs. Based on TSMC 0.18 µm CMOS technology, the proposed converter design is implemented and its results get at least 20.93% saving of Area-Delay-Power (ADP) products on average when comparing with the latest converter works.

Ultra-wideband pulse radar is a promising technology for the imaging sensors of rescue robots operating in disaster scenarios, where optical sensors are not applicable because of thick smog or high-density gas. For the above application, while one promising ultra-wideband radar imaging algorithm for a target with arbitrary motion has already been proposed with a compact observation model, it is based on an ellipsoidal approximation of the target boundary, and is difficult to apply to complex target shapes. To tackle the above problem, this paper proposes a non-parametric and robust imaging algorithm for a target with arbitrary motion including rotation and translation being observed by multi-static radar, which is based on the matching of target boundary points obtained by the range points migration (RPM) algorithm extended to the multi-static radar model. To enhance the imaging accuracy in situations having lower signal-to-noise ratios, the proposed method also adopts an integration scheme for the obtained range points, the antenna location part of which is correctly compensated for the estimated target motion. Results from numerical simulations show that the proposed method accurately extracts the surface of a moving target, and estimates the motion of the target, without any target or motion model.

This paper investigates a preprocessing technique for a multiuser MIMO downlink system. An efficient joint precoder design with adaptive power allocation is proposed by adopting the channel-diagonalization technique and the minimum mean square error (MMSE) criterion. By exploiting an MMSE-based decoder, we propose an iterative algorithm to design the precoder with further derived closed-form solutions for implementing adaptive power allocation. Simulation results verify the effectiveness of our proposed approach. Compared with conventional benchmark schemes, they show that our proposal matches the performance but with reduced computational complexity.

The optimization of polarimetric contract enhancement (OPCE) is one of the important problems in radar polarimetry since it provides a substantial benefit for target enhancement. Considering different scattering mechanisms between the desired targets and the undesired targets, Yang et al. extended the OPCE model to the generalized OPCE (GOPCE) problem. Based on a modified GOPCE model and the linear discriminant analysis, a ship detector is proposed in this paper to improve the detection performance for polarimetric Synthetic Aperture Radar (SAR) imagery. In the proposed method, we modify the combination form of the three polarimetric parameters (i.e., the plane scattering similarity parameter, the diplane scattering similarity parameter and the Cloude entropy), then use an optimization function resembling the classical Fisher criterion to optimize the optimal polarization states corresponding to the radar received power and the fusion vector corresponding to the polarimetric parameters. The principle of the optimization detailed in this paper lies in maximizing the difference between the desired targets and sea clutter, and minimizing the clutter variance at the same time. RADARSAT-2 polarimetric SAR data acquired over Tanggu Port (Tianjin, China) on June 23, 2011 are used for validation. The experimental results show that the proposed method improves the contrast of the targets and sea clutter and meanwhile reduces the clutter variance. In comparison to another GOPCE based ship detector and the classical polarimetric whitening filter (PWF), the proposed method shows a better performance for weak targets. In addition, we also use the RADARSAT-2 data acquired over San-Francisco on April 9, 2008 to further demonstrate the improvement of this method for target contrast.

Parasitic antenna elements with tunable terminations can be used for interference suppression in multi-antenna systems without using the degrees of freedom. The authors have proposed a fast non-iterative algorithm for optimizing the termination conditions. However, this method cannot be used for suppressing the interference from unknown systems since it requires the channel state information. In this paper, a fast non-iterative algorithm based on the correlation matrix, which can be obtained even from unknown interference sources, is proposed for the multi-antenna system with parasitic antenna elements. The correlation matrix including both receiving and parasitic antennas can be estimated from a few observations of the signals even without receiving signals at the parasitic antenna. By using this correlation matrix, the power of the interference with the arbitrary termination conditions can be easily estimated. Therefore, the termination condition, which minimizes the interference power, can be calculated without knowledge of the channel state information or additional estimations. The results of a numerical analysis indicate that proposed method works well in suppressing the interference without the perfect channel state information.

In LTE-Advanced heterogeneous networks, a typical cell layout to enhance frequency utilization is to incorporate picocells and femtocells in a macrocell. However, the co-channel interference between the marcocell and picocell/femtocell is an important issue when the same frequency band is used between these systems. We have already clarified how the interference from the femto(macro) cell affects on the macro(femto) cell. In this paper, we evaluate the interference rejection characteristics by an adaptive array with user equipment (UE). The characteristics are evaluated based on the K-factor used in the Nakagami-Race Fading model and the spatial correlation that is obtained in an actual outdoor environment. It is shown that a two-element adaptive array at the macro UE (M-UE) can sufficiently reduce the interference from the femto base station (F-BS) to the M-UE even if the number of total signals exceeds the degrees of freedom of the array.

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.

I propose an acoustic model adaptation method using bases constructed through the sparse principal component analysis (SPCA) of acoustic models trained in a clean environment. I perform experiments on adaptation to a new speaker and noise. The SPCA-based method outperforms the PCA-based method in the presence of babble noise.

In this paper, we propose a pedestrian detection algorithm based on both appearance and motion features to achieve high detection accuracy when applied to complex scenes. Here, a pedestrian's appearance is described by a histogram of oriented spatial gradients, and his/her motion is represented by another histogram of temporal gradients computed from successive frames. Since pedestrians typically exhibit not only their human shapes but also unique human movements generated by their arms and legs, the proposed algorithm is particularly powerful in discriminating a pedestrian from a cluttered situation, where some background regions may appear to have human shapes, but their motion differs from human movement. Unlike the algorithm based on a co-occurrence feature descriptor where significant generalization errors may arise owing to the lack of extensive training samples to cover feature variations, the proposed algorithm describes the shape and motion as unique features. These features enable us to train a pedestrian detector in the form of a spatio-temporal histogram of oriented gradients using the AdaBoost algorithm with a relatively small training dataset, while still achieving excellent detection performance. We have confirmed the effectiveness of the proposed algorithm through experiments on several public datasets.

Cyclic Redundancy Check (CRC) is a well known error detection scheme used to detect corruption of digital content in digital networks and storage devices. Since it is a compute-intensive process which adversely affects performance, hardware acceleration using FPGAs has been tried and satisfactory performance has been achieved. However, recent extended usage of networks and storage systems require various correction capabilities for various CRC standards. Traditional hardware designs based on the LFSR (Linear Feedback Shift Register) tend to have fixed structure without such flexibility. Here, fully-adaptable CRC accelerator based on a table-based algorithm is proposed. The table-based algorithm is a flexible method commonly used in software implementations. It has been rarely implemented with the hardware, since it is believed that the operational speed is not enough. However, by using pipelined structure and efficient use of memory modules in FPGAs, it appeared that the table-based fixed CRC accelerators achieved better performance than traditional implementation. Based on the implementation, fully-adaptable CRC accelerator which eliminate the need for many non-adaptable CRC implementations is proposed. The accelerator has ability to process arbitrary number of input data and generates CRC for any known CRC standard, up to 65 bits of generator polynomial, during run-time. Further, we modify Table generation algorithm in order to decrease its space complexity from O(nm) to O(n). On Xilinx Virtex 6 LX550T board, the fully-adaptable accelerators occupy between 1 to 2% area to produce maximum of 289.8 Gbps at 283.1 MHz if BRAM is deployed, or between 1.6 - 14% of area for 418 Gbps at 408.9 MHz if tables are implemented in logic. Proposed architecture enables further expansion of throughput by increasing a number of input bits M processed at a time.

A method for the specification of weighting functions for a spaceborne/airborne interferometric synthetic aperture radar (SAR) sensor for Earth observation and environment monitoring is introduced. This method is based on designing an optimum mismatched filter which minimizes the total power in sidelobes located out of a specified range region around the peak value point of the system point-target response, i.e. impulse response function under the constraint imposed on the peak value. It is shown that this method allows achieving appreciable improvement in accuracy performance without degradation in the range resolution.

Ultra-wideband (UWB) pulse radar has high range resolution and permeability in a dielectric medium, and has great potential for the non-destructive inspection or early-stage detection of breast cancer. As an accurate and high-resolution imaging method for targets embedded in a dielectric medium, extended range points migration (RPM) has been developed. Although this method offers an accurate internal target image in a homogeneous media, it assumes the permittivity of the dielectric medium is given, which is not practical for general applications. Although there are various permittivity estimation methods, they have essential problems that are not suitable for clear, dielectric boundaries like walls, or is not applicable to an unknown and arbitrary shape of dielectric medium. To overcome the above drawbacks, we newly propose a permittivity estimation method suitable for various shapes of dielectric media with a clear boundary, where the dielectric boundary points and their normal vectors are accurately determined by the original RPM method. In addition, our method iteratively compensates for the scattered waveform deformation using a finite-difference time domain (FDTD) method to enhance the accuracy of the permittivity estimation. Results from a numerical simulation demonstrate that our method achieves accurate permittivity estimation even for a dielectric medium of wavelength size.