In this letter, we propose a blind adaptive algorithm for joint compensation of inter-block interference (IBI) and frequency-dependent IQ imbalance using a single time-domain equalizer. We combine the MERRY algorithm for IBI suppression with the differential constant modulus algorithm to compensate for IQ imbalance. The effectiveness of the proposed algorithm is shown through computer simulations.

In this paper, we propose an image adjustment method for multi-exposure images based on convolutional neural networks (CNNs). We call image regions without information due to saturation and object moving in multi-exposure images lacking areas in this paper. Lacking areas cause the ghosting artifact in fused images from sets of multi-exposure images by conventional fusion methods, which tackle the artifact. To avoid this problem, the proposed method estimates the information of lacking areas via adaptive inpainting. The proposed CNN consists of three networks, warp and refinement, detection, and inpainting networks. The second and third networks detect lacking areas and estimate their pixel values, respectively. In the experiments, it is observed that a simple fusion method with the proposed method outperforms state-of-the-art fusion methods in the peak signal-to-noise ratio. Moreover, the proposed method is applied for various fusion methods as pre-processing, and results show obviously reducing artifacts.

A novel method is presented for designing discrete coeffcient FIR linear phase filters using Hopfield neural networks. The proposed method is based on the minimization of the energy function of Hopfield neural networks. In the proposed method, the optimal solution for each filter gain factor is first searched for, then the optimal filter gain factor is selected. Therefore, a good solution in the specified criterion can be obtained. The feature of the proposed method is that it can be used to design FIR linear phase filters with different criterions simultaneously. A design example is presented to demonstrate The effectiveness of the proposed method.

R-regular Mth band filters are an important class of digital filters and are used in constructing Mth-band wavelet filter banks, where the regularity is essential. But this kind of filter has larger stopband peak errors compared with a minimax filter of the same length. In this paper, peak errors in stopband of R-regular 4th-band filters are reduced by means of superimposing two filters with successive regularities. Then the stopband peak errors in the resulting filters are compared with the original ones. The results show that the stopband peak errors are reduced significantly in the synthesized filter that has the same length as the longer one of the two original filters, at the cost of regularity.

The write operations on emerging Non-Volatile Memory (NVM), such as NAND Flash and Phase Change Memory (PCM), usually incur high access latency, and are required to be optimized. In this paper, we propose Asymmetric Read-Write (ARW) policies to minimize the write traffic sent to NVM. ARW policies exploit the asymmetry costs of read and write operations, and make adjustments on the insertion policy and hit-promotion policy of the replacement algorithm. ARW can reduce the write traffic to NVM by preventing dirty data blocks from frequent evictions. We evaluate ARW policies on systems with PCM as main memory and NAND Flash as disk. Simulation results on an 8-core multicore show that ARW adopted on the last-level cache (LLC) can reduce write traffic by more than 15% on average compared to LRU baseline. When used on both LLC and DRAM cache, ARW policies achieve an impressive reduction of 40% in write traffic without system performance degradation. When employed on the on-disk buffer of the Solid State Drive (SSD), ARW demonstrates significant reductions in both write traffic and overall access latency. Moreover, ARW policies are lightweight, easy to implement, and incur negligible storage and runtime overhead.

As the number of concurrently running applications on the chip multiprocessors (CMPs) is increasing, efficient management of the shared last-level cache (LLC) is crucial to guarantee overall performance. Recent studies have shown that cache partitioning can provide benefits in throughput, fairness and quality of service. Most prior arts apply true Least Recently Used (LRU) as the underlying cache replacement policy and rely on its stack property to work properly. However, in commodity processors, pseudo-LRU policies without stack property are commonly used instead of LRU for their simplicity and low storage overhead. Therefore, this study sets out to understand whether LRU-based cache partitioning techniques can be applied to commodity processors. In this work, we instead propose a cache partitioning mechanism for two popular pseudo-LRU policies: Not Recently Used (NRU) and Binary Tree (BT). Without the help of true LRU's stack property, we propose a profiling logic that applies curve approximation methods to derive the hit curve (hit counts under varied way allocations) for an application. We then propose a hybrid partitioning mechanism, which mitigates the gap between the predicted hit curve and the actual statistics. Simulation results demonstrate that our proposal can improve throughput by 15.3% on average and outperforms the stack-estimate proposal by 12.6% on average. Similar results can be achieved in weighted speedup. For the cache configurations under study, it requires less than 0.5% storage overhead compared to the last-level cache. In addition, we also show that profiling mechanism with only one true LRU ATD achieves comparable performance and can further reduce the hardware cost by nearly two thirds compared with the hybrid mechanism.

In this paper, we propose a novel design method of two channel critically sampled compactly supported biorthogonal graph wavelet filter banks with half-band kernels. First of all, we use the polynomial half-band kernels to construct a class of biorthogonal graph wavelet filter banks, which exactly satisfy the PR (perfect reconstruction) condition. We then present a design method of the polynomial half-band kernels with the specified degree of flatness. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which ensures that the half-band kernels have the specified zeros at λ=2. Therefore the constraints of flatness are satisfied at both of λ=0 and λ=2, and then the resulting graph wavelet filters have the flat spectral responses in passband and stopband. Furthermore, we apply the Remez exchange algorithm to minimize the spectral error of lowpass (highpass) filter in the band of interest by using the remaining degree of freedom. Finally, several examples are designed to demonstrate the effectiveness of the proposed design method.

In this paper, we discuss design of quadrature mirror filter (QMF) banks using digital allpass networks in the frequency domain. In the QMF banks composed of a parallel connection of two allpass networks, both aliasing error and amplitude distortion are always completely canceled. Therefore, we only need to design the analysis filters and eliminate phase distortion of the overall transfer function. We consider design of the QMF banks in two cases where phase responses of the filters are repuired or not required. In the case where the phase responses are not required, the design problem can be reduced to design of phase difference of two allpass networks. In the case where the phase responses are required, we present a procedure for designing the QMF banks with both equiripple magnitude and phase responses.

This paper proposes a new class of Hilbert pairs of almost symmetric orthogonal wavelet bases. For two wavelet bases to form a Hilbert pair, the corresponding scaling lowpass filters are required to satisfy the half-sample delay condition. In this paper, we design simultaneously two scaling lowpass filters with the arbitrarily specified flat group delay responses at ω=0, which satisfy the half-sample delay condition. In addition to specifying the number of vanishing moments, we apply the Remez exchange algorithm to minimize the difference of frequency responses between two scaling lowpass filters, in order to improve the analyticity of complex wavelets. The equiripple behavior of the error function can be obtained through a few iterations. Therefore, the resulting complex wavelets are orthogonal and almost symmetric, and have the improved analyticity. Finally, some examples are presented to demonstrate the effectiveness of the proposed design method.

As pseudorandom number generators for Monte Carlo simulations, inversive linear congruential generators (ICG) have some advantages compared with traditional linear congruential generators. It has been shown that a sequence generated by an ICG has a low discrepancy even if the length of the sequence is far shorter than its period. In this paper, we formulate fractional linear congruential generators (FCG), a generalized concept of the inversive linear congruential generators. It is shown that the sequence generated by an FCG is a geometrical shift of a sequence from an ICG and satisfies the same upper bounds of discrepancy. As an application of the general formulation, we show that under certain condition, "Leap-Frog technique," a way of splitting a random number sequence to parallel sequences, can be applied to the ICG or FCG with no extra cost on discrepancy.

It is known that an anticausal IIR filter can be realized in real time by using the time reversed section technique. When combined with a casual IIR filter, the overall transfer function can yield exact linear phase characteristic in theory. This paper presents a new method for designing complex IIR digital filters with exact linear phase. The design problem of IIR filters with exact linear phase can be reduced to magnitude-only filter design. The proposed procedure is based on the formulation of an eigenvalue problem by using Remez exchange algorithm. By solving the eigenvalue problem to compute the real maximum eigenvalue, the solution of the rational interpolation problem can be achieved. Therefore, the optimal filter coefficients are easily obtained through a few iterations. The proposed design algorithm not only retains the speed inherent in Remez exchange algorithm, but also Simplifies the interpolation step because is has been reduced to the computation of the real maximum eigenvalue. Several examples are presented to demonstrate the effectiveness of the proposed method.

This paper presents a new method for designing IIR Nyquist filters with zero intersymbol interference. It is shown that IIR Nyquist filters with zero intersymbol interference have some constraints on frequency response, i.e., both magnitude and phase error in passband are dependent on stopband error. Therefore, the frequency response is required to optimize only in stopband. The proposed procedure is based on the formulation of an eigenvalue problem by using Remez multiple exchange algorithm in stopband. Then, the filter coefficients can be computed by solving the eigenvalue problem, and the optimal solution with equiripple stopband response is easily obtained by applying an iteration procedure. The proposed procedure is more computationally efficient than the conventional methods.

This paper presents a new method for constructing orthonormal wavelet bases with vanishing moments based on general IIR filters. It is well-known that orthonormal wavelet bases can be generated by paraunitary filter banks. Then, synthesis of orthonormal wavelet bases can be reduced to design of paraunitary filter banks. From the orthonormality and regularity of wavelets, we derive some constraints to IIR filter banks, and investigate relations between the constrained filter coefficients and its zeros and poles. According to these relations, we can apply Remez exchange algorithm in stopband directly, and formulate the design problem in the form of an eigenvalue problem. Therefore, a set of filter coefficients can be easily computed by solving the eigenvalue problem, and the optimal filter coefficients with an equiripple response can be obtained after applying an iteration procedure. The proposed procedure is computationally efficient, and the number of vanishing moments can be arbitrarily specified.

Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

This paper will review the development of SiC power devices especially SiC power junction field-effect transistors (JFETs). Rationale and different approaches to the development of SiC power JFETs will be presented, focusing on normally-OFF power JFETs that can provide the highly desired fail-save feature for reliable power switching applications. New results for the first demonstration of SiC Power ICs will be presented and the potential for distributed DC-DC power converters at frequencies higher than 35 MHz will be discussed.

In this paper, a novel method is proposed for designing two channel biorthogonal filter banks with general IIR filters, which satisfy both the perfect reconstruction and causal stable conditions. Since the proposed filter banks are structurally perfect reconstruction implementation, the perfect reconstruction property is still preserved even when all filter coefficients are quantized. The proposed design method is based on the formulation of a generalized eigenvalue problem by using Remez multiple exchange algorithm. Then, the filter coefficients can be computed by solving the eigenvalue problem, and the optimal solution is easily obtained through a few iterations. One design example is presented to demonstrate the effectiveness of the proposed method.

Previous research illustrates that LRU replacement policy is not efficient when applications exhibit a distant re-reference interval. Recently RRIP policy is proposed to improve the performance for such kind of workloads. However, the lack of access recency information in RRIP confuses the replacement policy to make the accurate prediction. To enhance the robustness of RRIP for recency-friendly workloads, we propose an Dynamic Adaptive Insertion and Re-reference Prediction (DAI-RRP) policy which evicts data based on both re-reference prediction value and the access recency information. DAI-RRP makes adaptive adjustment on insertion position and prediction value for different access patterns, which makes the policy robust across different workloads and different phases. Simulation results show that DAI-RRP outperforms LRU and RRIP. For a single-core processor with a 1 MB 16-way set last-level cache (LLC), DAI-RRP reduces CPI over LRU and Dynamic RRIP by an average of 8.1% and 2.7% respectively. Evaluations on quad-core CMP with a 4 MB shared LLC show that DAI-RRP outperforms LRU and Dynamic RRIP (DRRIP) on the weighted speedup metric by an average of 8.1% and 15.7% respectively. Furthermore, compared to LRU, DAI-RRP consumes the similar hardware for 16-way cache, or even less hardware for high-associativity cache. In summary, the proposed policy is practical and can be easily integrated into existing hardware approximations of LRU.

To enable fine-grained delegations for proxy re-encryption systems, in AsiaCCS'09, Weng et al.'s introduced the concept of conditional proxy re-encryption (C-PRE), in which the proxy can convert a ciphertext only if a specified condition is satisfied. Weng et al. also proposed a C-PRE scheme, and claimed that their scheme is secure against chosen-ciphertext attack (CCA). In this paper, we show that their scheme is not CCA-secure under their defined security model.

In this study, the design and fabrication of a 110–140-GHz varistor mode frequency tripler made with four Schottky diodes pair are presented. Nonlinear simulations were performed to calculate the optimum diode embedding impedance and the required input power. A compact microstrip resonant cell (CMRC) filter was introduced for the first time in submillimeter multiplier, instead of the traditional low-and-high impedance microstrip filter. The shorter size and the wider stop band of the CMRC filter improved the performance of the tripler. The tripler exhibited the best conversion efficiency of 5.2% at 129 GHz and peak output power of 5.3 mW at 125 GHz. Furthermore, within the output bandwidth from 110 to 140 GHz, the conversion efficiency was greater than 1.5%.

The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.