This paper presents a novel delta-sigma modulator that uses a switched-capacitor (SC) integrator with the structure of a finite impulse response (FIR) filter in a loop filter configuration. The delta-sigma analog-to-digital converter (ΔΣADC) is used in various conversion systems to enable low-power, high-accuracy conversion using oversampling and noise shaping. Increasing the gain coefficient of the integrator in the loop filter configuration of the ΔΣADC suppresses the quantization noise that occurs in the signal band. However, there is a trade-off relationship between the integrator gain coefficient and system stability. The SC integrator, which contains an FIR filter, can suppress quantization noise in the signal band without requiring an additional operational amplifier. Additionally, it can realize a higher signal-to-quantization noise ratio. In addition, the poles that are added by the FIR filter structure can improve the system's stability. It is also possible to improve the flexibility of the pole placement in the system. Therefore, a noise transfer function that does not contain a large gain peak is realized. This results in a stable system operation. This paper presents the essential design aspects of a ΔΣADC with an FIR filter. Two types of simulation results are examined for the proposed first- and second-order, and these results confirm the effectiveness of the proposed architecture.

This paper presents a 6th-order quadrature bandpass delta sigma AD modulator (QBPDSM) with 2nd-order image rejection using dynamic amplifier and noise coupling (NC) SAR quantizer embedded by passive adder for the application of wireless communication system. A novel complex integrator using dynamic amplifier is proposed to improve the energy efficiency of the QBPDSM. The NC SAR quantizer can realize an additional 2nd-order noise shaping and 2nd-order image rejection by the digital domain noise coupling technique. As a result, the 6th-order QBPDSM with 2nd-order image rejection is realized by two complex integrators using dynamic amplifier and the NC SAR quantizer. The SPICE simulation results demonstrate the feasibility of the proposed QBPDSM in 90nm CMOS technology. Simulated SNDR of 76.30dB is realized while a sinusoid -3.25dBFS input is sampled at 33.3MS/s and the bandwidth of 2.083MHz (OSR=8) is achieved. The total power consumption in the modulator is 6.74mW while the supply voltage is 1.2V.

Two-dimensional (2-D) maximally flat finite impulse response (FIR) digital filters have flat characteristics in both passband and stopband. 2-D maximally flat diamond-shaped half-band FIR digital filter can be designed very efficiently as a special case of 2-D half-band FIR filters. In some cases, this filter would require the reduction of the filter lengths for one of the axes while keeping the other axis unchanged. However, the conventional methods can realize such filters only if difference between each order is 2, 4 and 6. In this paper, we propose a closed-form frequency response of 2-D low-pass maximally flat diamond-shaped half-band FIR digital filters with arbitrary filter orders. The constraints to treat arbitrary filter orders are firstly proposed. Then, a closed-form transfer function is achieved by using Bernstein polynomial.

It is well-known that chaos synchronization in coupled chaotic systems arises from conditions with specific coupling, such as complete, phase, and generalized synchronization. Recently, several methods for controlling this chaos synchronization using a nonlinear feedback controller have been proposed. In this study, we applied a proposed reducing range of orbit feedback method to coupled cubic maps in order to control synchronization of chaos-chaos intermittency. By evaluating the system's behavior and its dependence on the feedback and coupling strength, we confirmed that synchronization of chaos-chaos intermittency could be induced using this nonlinear feedback controller, despite the fact that the asynchronous state within a unilateral attractor is maintained. In particular, the degree of synchronization is high at the edge between the chaos-chaos intermittency parameter region for feedback strength and the non-chaos-chaos intermittency region. These characteristics are largely maintained on large-scale coupled cubic maps.

Many applications, such as image signal processing, has an inherent tolerance for insignificant inaccuracies. Multiplication is a key arithmetic function for many applications. Approximate multipliers are considered an efficient technique to trade off energy relative to performance and accuracy for the error-tolerant applications. Here, we design and analyze four approximate multipliers that demonstrate lower power consumption and shorter critical path delay than the conventional multiplier. They employ an approximate tree compressor that halves the height of the partial product tree and generates a vector to compensate accuracy. Compared with the conventional Wallace tree multiplier, one of the evaluated 8-bit approximate multipliers reduces power consumption and critical path delay by 36.9% and 38.9%, respectively. With a 0.25% normalized mean error distance, the silicon area required to implement the multiplier is reduced by 50.3%. Our multipliers outperform the previously proposed approximate multipliers relative to power consumption, critical path delay, and design area. Results from two image processing applications also demonstrate that the qualities of the images processed by our multipliers are sufficiently accurate for such error-tolerant applications.

A diversity strategy is efficient to reduce the fluctuation of communication quality caused by fading. In order to further maintain the communication quality and improve the communication capacity, this paper proposes a two-dimensional diversity approach by serially-concatenating spectral precoding and power normalized-differential space time block coding (PN-DSTBC). Spectral precoding is able to take benefit from a frequency diversity effect without loss in spectral efficiency. In addition, PN-DSTBC is robust against serious phase noise in an extremely high frequency (EHF) band by exploiting a spatial diversity effect. However, there is a problem that a naive concatenation degrades the performance due to the imbalance of equivalent noise variances over transmit frequencies. Thus, we examine an equalized PN-DSTBC decoder as a modified approach to uniform equivalent noise variances over frequencies. The performance evaluation using computer simulations shows that the proposed modified approach yields the performance improvement at any modulation schemes and at any number of transmit frequencies. Furthermore, in the case of 64QAM and two transmit frequencies, the performance gain of the modified approach is 4dB larger than that of PN-DSTBC only at uncoded BER=10^-4.

We formulate the design of an optimal quantizer as an optimization problem that finds the quantization indices that minimize quantization error. As a solution of the optimization problem, an approach based on dynamic programming, which is called DP quantization, is proposed. It is observed that quantized signals do not always contain all kinds of signal values which can be represented with given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to quantizer design. Signal values with zero frequency do not impact quantization error, so there is the potential to reduce the complexity of the optimal quantizer by not computing signal values that have zero frequency. However, conventional methods for DP quantization were not designed to consider amplitude sparseness, and so fail to reduce complexity. The proposed algorithm offers a reduced complexity optimal quantizer that minimizes quantization error while addressing amplitude sparseness. Experimental results show that the proposed algorithm can achieve complexity reduction over conventional DP quantization by 82.9 to 84.2% on average.

Lane detection or road detection is one of the key features of autonomous driving. In computer vision area, it is still a very challenging target since there are various types of road scenarios which require a very high robustness of the algorithm. And considering the rather high speed of the vehicles, high efficiency is also a very important requirement for practicable application of autonomous driving. In this paper, we propose a deep convolution neural network based lane detection method, which consider the lane detection task as a pixel level segmentation of the lane markings. We also propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiment proves that our method can achieve high accuracy for various road scenes in real-time.

A class of multimodulus algorithms (MMA(p)) optimized by an optimal step-size (OS) for blind equalization are firstly investigated in this letter. The multimodulus (MM) criterion is essentially a split cost function that separately implements the real and imaginary part of the signal, hence the phase can be recovered jointly with equalization. More importantly, the step-size leading to the minimum of the MM criterion along the search direction can be obtained algebraically among the roots of a higher-order polynomial at each iteration, thus a robust optimal step-size multimodulus algorithm (OS-MMA(p)) is developed. Experimental results demonstrate improved performance of the proposed algorithm in mitigating the inter-symbol interference (ISI) compared with the OS constant modulus algorithm (OS-CMA). Besides, the computational complexity can be reduced by the proposed OS-MMA(2) algorithm.

A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.

This paper deals with the minimum and maximum value distributions based on the n-variate FGM copula with one dependence parameter. The ranges of dependence parameters are theoretically determined so that the probability density function always takes a non-negative value. However, the closed-form conditions of the ranges for the dependence parameters have not been known in the literature. In this paper, we newly provide the necessary conditions of the ranges of the dependence parameters for the minimum and maximum value distributions which are derived from FGM copula, and show the asymptotic properties of the ranges.

In this letter, as a generalization of Heng's constructions in the paper [9], a construction of codebooks, which meets the Welch bound asymptotically, is proposed. The parameters of codebooks presented in this paper are new in some cases.

We propose a recursive algorithm to reduce the computational complexity of the r-order nonlinearity of n-variable Boolean functions. Applying the algorithm and using the sufficient and necessary condition put forward by [1] to cut the vast majority of useless search branches, we show that the covering radius of the Reed-Muller Code R(3, 7) in R(5, 7) is 20.

We investigate the uplink channel selection problem of unmanned aerial vehicle (UAV)-aided data collection system in delay-sensitive sensor networks. In the studied model, the fixed-wing UAV is dispatched to gather sensing information from terrestrial sensor nodes (SNs) and they contend for uplink channels for transmission. With the goal of minimizing the system-wide delay, we formulate a resource allocation problem. Encountered with the challenge that the flight trajectory of UAV is unknown to SNs and the wireless channel is time-varying, we solve the problem by stochastic game approach and further propose a fully distributed channel selection algorithm which is proved to converge to a pure strategy Nash Equilibrium (NE). Simulation results are presented to show that our proposed algorithm has good performance.

One of essential requirements for the next generation communications is to support higher spectral efficiency (SE) and energy efficiency (EE) than the existing communication system. For increasing the SE, carrier aggregation (CA) has received great attention. In this paper, we propose an energy efficient smart crest factor reduction (E2S-CFR) method for increasing the EE while satisfying the required SE when the CA is applied. The proposed E2S-CFR exploits different weights on each carrier according to the required error vector magnitude (EVM), and efficiently reduces the peak to average power ratio (PAR). Consequently, we can reduce the bias voltage of a power amplifier, and it leads to save total consumed energy. Through performance evaluation, we demonstrate that the proposed E2S-CFR improves the EE by 11.76% compared to the existing schemes.

As an emerging and promising technique, device-free localization (DFL) has drawn considerable attention in recent years. By exploiting the inherent spatial sparsity of target localization, the compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements. In practical scenarios, a prior knowledge about target locations is usually available, which can be obtained by coarse localization or tracking techniques. Among existing CS-based DFL approaches, however, few works consider the utilization of prior knowledge. To make use of the prior knowledge that is partly or erroneous, this paper proposes a novel faulty prior knowledge aided multi-target device-free localization (FPK-DFL) method. It first incorporates the faulty prior knowledge into a three-layer hierarchical prior model. Then, it estimates location vector and learns model parameters under a variational Bayesian inference (VBI) framework. Simulation results show that the proposed method can improve the localization accuracy by taking advantage of the faulty prior knowledge.

For any positive integer n, define an iterated function $f(n)=\left\{ \begin{array}{ll} n/2, & \mbox{ $n$ even, } 3n+1, & \mbox{ $n$ odd. } \end{array} \right.$ Suppose k (if it exists) is the lowest number such that f^k(n)<n, and the operation of “multiplying by 3 and adding one” occurs O(n) times and that of “dividing by 2” occurs E(n) times from n to f^k(n). We conjecture that 2^E(n)-1<3^O(n)<2^E(n). This conjecture is similar to the conjecture proposed by Terras in 1976, and we also give an upper bound for the residual term of n.