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A power-scalable RF front-end using quantized analog signal processing is presented. The front-end is based on a voltage-mode power-scalable approach which allows the power dissipation to be scaled upon the operative scenario and to perform an agile calibration for mismatch impairments. Power and input dynamic range can be scaled upon the desired 1-dB compression point (1dBCP) (from −15.3 to 0.5 dBm) while keeping the same sensitivity with 2.5-dB NF. Signal path power can vary between 3.3 and 6.4 mW while clock generation and distribution power can vary between 1.6 and 18.5 mW/GHz, with a phase noise as low as −171.2 dBc/Hz. After calibration, IM2 and IM3 improved up to 33 dB while 1dBCP improved by 1 dB, which resulted in achieving an IIP3 of 26.1 dBm and IIP2 of 71 dBm at 0-dBm 1dBCP.

The Bilateral Constant False Alarm Rate (BCFAR) detection algorithm calculates the spatial information of Synthetic Aperture Radar (SAR) image by the Gaussian kernel density estimator, and combines it with the intensity information of image to obtain the joint image for target detection. Compared with the classical CFAR detection algorithm which uses only intensity information for target detection, bilateral CFAR has better detection performance and robustness. However, with continuous high-intensity heterogeneous points (such as breakwater, azimuth ambiguity and phantom) in a complex environment, spatial information calculated by kernel density estimator will have more errors, which will lead to many false alarms in detection results. In addition, when it comes to a weak target with less similarity between adjacent pixels, it will miss detection. To effectively improve these problems, this paper designs an Improved Bilateral CFAR (IB-CFAR) algorithm in complex environment. The IB-CFAR proposed in this paper is mainly divided into three stages: intensity level division based on the nonuniform quantization method, intensity spatial domain information fusion and parameter estimation after clutter truncation. The intensity level division based on the nonuniform quantization method can improve the similarity and contrast information of weak targets, leading to improved ship detection rate. The information fusion of strength spatial domain is to fuse the spatial similarity, distance direction and strength information, which can further improve the detection rate and describe the ship structure information. Parameter estimation after clutter truncation can remove continuous high-intensity heterogeneous points in the background window and retain the real sea clutter samples to the maximum extent, which makes parameter estimation more accurate. Finally, according to the estimated parameters, an accurate sea clutter statistical model is established for CFAR detection. In this paper, the effectiveness and robustness of the proposed algorithm are verified by using GaoFen-3 and TerraSAR-X data.The experimental results show that the proposed algorithm performs well in the environment with more dense distribution of weak targets, and can obtain 97.85% detection rate and 3.52% false alarm rate in such environment. Compared with the existing detection algorithms, the detection rate increased by 5% and the false alarm rate reduced by 10%. However, when the number of weak targets is small and the background is very complex, few false alarms will appear.

Since polar codes are capacity-achieving codes, there have been various research works devoted to devising efficient implementation methods as well as improving error-correction performance. Since quantization is a critical implementation issue, in this paper, a nonuniform quantization method is proposed for the successive cancellation (SC) decoder of polar codes, which finds quantization boundary values based on the analysis of various quantization levels over the additive white Gaussian noise channel. Since low computational complexity, high reliability, and efficient memory management are required in the next-generation communication and memory systems, 2–4 bit precision levels are mainly considered in the proposed nonuniform quantization method. Depending on the presence of erasure, quantization levels are divided into three types, and the message alphabets and update rules are derived for each type. Also, a construction method of polar codes suitable for the proposed nonuniform-quantized SC decoder is proposed, which simultaneously determines the information set and the quantization boundary values based on the density evolution analysis and an upper bound of the block error probability. To determine quantization boundary values, a multivariate objective function is defined and an iterative coarse-to-fine search algorithm to minimize this objective function is proposed. In addition, a scaling method of quantizer output values is proposed when the number of quantization levels of quantizer is smaller than the number of quantization levels of decoder. Finally, simulation results confirm that the proposed nonuniform-quantized SC decoder shows better error-correction performance, lower decoding complexity, and higher memory efficiency compared to the best known uniform-quantized SC decoder.

The Euclidean projection involved in the decoding of low-density parity-check (LDPC) codes with the alternating direction method of multipliers (ADMM) can be simplified by jointly using uniform quantization and look-up tables (LUTs). However, the memory requirement for the original LUT-based ADMM decoding is comparatively large. In this letter, a nonuniform quantization method is proposed to save the memory cost by minimizing the mean square error of the outputs of Euclidean projections during quantization. Simulation results over two exemplified LDPC codes show that the proposed method can achieve similar error-rate performances when compared with the original LUT-based ADMM decoding by using significantly less memory units. [ABSTRACT FROM PUBLISHER]

In this paper high dynamic range nonuniform two-dimensional vector quantization model for Laplacean source was provided. Semilogarithmic A-law compression characteristic was used as radial scalar compression characteristic of two-dimensional vector quantization. Optimal number value of concentric quantization domains (amplitude levels) is expressed in the function of parameter A. Exact distortion analysis with obtained closed form expressions is provided. It has been shown that proposed model provides high SQNR values in wide range of variances, and overachieves quality obtained by scalar A-law quantization at same bit rate, so it can be used in various switching and adaptation implementations for realization of high quality signal compression.

In this paper, a power- and area-efficient, multirate, Quasi-cyclic, low-density, parity-check decoder is proposed. The proposed decoder design is based on a simplified adaptive normalized min-sum algorithm. The proposed algorithm effectively utilizes two correction factors for check-node and variable-node update processes. This corrects the channel errors at relatively low signal-to-noise ratio. In order to reduce the finite word length effects, a six-bit nonuniform quantization with the overlapped message passing scheme is used. In addition, an improved early termination scheme is also used to reduce the total number of decoding iterations. This reduces the overall power consumption of the decoder. The simulations have been carried out using Xilinx ISE 14.1 and implemented on Virtex 5 FPGA. The proposed decoder is synthesized using CADENCE with UMC 130 nm technology. With a core area of 1.16 mm $$^{2}$$ , the proposed decoder achieves a maximum throughput of 3.4 Gb/s for 15 decoding iterations with a power dissipation of 114.3 mW. [ABSTRACT FROM AUTHOR]

In this paper high dynamic range nonuniform two-dimensional vector quantization model for Laplacean source was provided. Semilogarithmic A-law compression characteristic was used as radial scalar compression characteristic of two-dimensional vector quantization. Optimal number value of concentric quantization domains (amplitude levels) is expressed in the function of parameter A. Exact distortion analysis with obtained closed form expressions is provided. It has been shown that proposed model provides high signal-to-quantization noise ratio (SQNR) values in wide range of variances, and overachieves quality obtained by scalar A-law quantization at the same bit rate, so it can be used in various switching and adaptation implementations for realization of high quality signal compression. [ABSTRACT FROM AUTHOR]

The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: 1) a constant output resolution associated with the entire input space and 2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. [ABSTRACT FROM AUTHOR]

Semi-fragile watermarking techniques aim at detecting malicious manipulations on an image, while allowing acceptable manipulations such as lossy compression. Although both of these manipulations are considered to be pixel value changes, semi-fragile watermarks should be sensitive to malicious manipulations but robust to the degradation introduced by lossy compression and other defined acceptable manipulations. In this paper, after studying the characteristics of both natural images and malicious manipulations, we propose two new semi-fragile authentication techniques robust against lossy compression, using random bias and nonuniform quantization, to improve the performance of the methods proposed by Lin and Chang. [ABSTRACT FROM PUBLISHER]