This paper proposed an iterative soft interference canceller (IC) referred to as turbo equalizer for the self-coherent detection, and extrinsic information transfer (EXIT) chart based irregular low density parity check (LDPC) code optimization for the turbo equalizer in optical fiber short-reach transmissions. The self-coherent detection system is capable of linear demodulation by a single photodiode receiver. However, the self-coherent detection suffers from the interference induced by signal-signal beat components, and the suppression of the interference is a vital goal of self-coherent detection. For improving the error-free signal detection performance of the self-coherent detection, we proposed an iterative soft IC with the aid of forward error correction (FEC) decoder. Furthermore, typical FEC code is no longer appropriate for the iterative detection of the turbo equalizer. Therefore, we designed an appropriate LDPC code by using EXIT chart aided code design. The validity of the proposed turbo equalizer with the appropriate LDPC is confirmed by computer simulations.

Ring voltage-controlled-oscillators (VCOs) are increasingly being used to design ΔΣ ADCs. They have the merits of simple, highly digital and low-voltage tolerant, making them attractive alternatives for the classic scaling-unfriendly operational-amplifier-based methodology. This paper aims to provide a summary on the advancement of VCO-based ΔΣ ADCs. The scope of this paper includes the basics and motivations behind the VCO-based ADCs, followed by a survey covering a wide range of architectures and circuit techniques in both continuous-time (CT) and discrete-time (DT) implementation, and will discuss the key insights behind the contributions and drawbacks of these architectures.

This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.

A rapid single-flux-quantum (RSFQ) truncated multiplier based on bit-level processing is proposed. In the multiplier, two operands are transformed to two serialized patterns of bits (pulses), and the multiplication is carried out by processing those bits. The result is obtained by counting bits. By calculating in bit-level, the proposed multiplier can be implemented in small area. The gate level design of the multiplier is shown. The layout of the 4-bit multiplier was also designed.

Adaptive voltage scaling is a promising approach to overcome manufacturing variability, dynamic environmental fluctuation, and aging. This paper focuses on error prediction based adaptive voltage scaling (EP-AVS) and proposes a mean time to failure (MTTF) aware design methodology for EP-AVS circuits. Main contributions of this work include (1) optimization of both voltage-scaled circuit and voltage control logic, and (2) quantitative evaluation of power saving for practically long MTTF. Experimental results show that the proposed EP-AVS design methodology achieves 38.0% power saving while satisfying given target MTTF.

Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

In this paper, to make asynchronous circuit design easy, we propose a conversion method from synchronous Register Transfer Level (RTL) models to asynchronous RTL models with bundled-data implementation. The proposed method consists of the generation of an intermediate representation from a given synchronous RTL model and the generation of an asynchronous RTL model from the intermediate representation. This allows us to deal with different representation styles of synchronous RTL models. We use the eXtensible Markup Language (XML) as the intermediate representation. In addition to the asynchronous RTL model, the proposed method generates a simulation model when the target implementation is a Field Programmable Gate Array and a set of non-optimization constraints for the control circuit used in logic synthesis and layout synthesis. In the experiment, we demonstrate that the proposed method can convert synchronous RTL models specified manually and obtained by a high-level synthesis tool to asynchronous ones.

We have developed a novel array configuration based on the combination of sum and difference co-arrays. There have been many studies on array antenna configurations that enhance the degree of freedom (DOF) of an array, but the maximum DOF of the difference co-array configuration is often limited. With our proposed array configuration, called “sum and difference composite co-array”, we aim to further enhance the DOF by combining the concept of sum co-array and difference co-array. The performance of the proposed array configuration is evaluated through computer simulated beamforming*.

A physical-layer network coding (PNC) scheme is developed using serially concatenated continuous phase modulation (SCCPM) with symbol interleavers in a two-way relay channel (TWRC), i.e., SCCPM-PNC. The decoding structure of the relay is designed and the corresponding soft input soft output (SISO) iterative decoding algorithm is discussed. Simulation results show that the proposed SCCPM-PNC scheme performs good performance in bit error rate (BER) and considerable improvements can be achieved by increasing the interleaver size and number of iterations.

Body bias generators are useful circuits that can reduce variability and power dissipation in LSI circuits. However, the amplifier implemented into the body bias generator is difficult to design because of its complexity. To overcome the difficulty, this paper proposes a clearer cell-based design method of the amplifier than the existing cell-based design methods. The proposed method is based on a simple analytical model, which enables to easily design the amplifiers under various operating conditions. First, we introduce a small signal equivalent circuit of two-stage amplifiers by which we approximate a three-stage amplifier, and introduce a method for determining its design parameters based on the analytical model. Second, we propose a method of tuning parameters such as cell-based phase compensation elements and drive-strength of the output stage. Finally, based on the test chip measurement, we show the advantage of the body bias generator we designed in a cell-based flow over existing designs.

This paper discusses non-ideal issues in a fully passive noise shaping successive approximation register analog-to-digital converter. The fully passive noise shaping techniques are realized by switches and capacitors without operational amplifiers to be scalable and power efficient. However, some non-ideal issues, such as parasitic capacitance, comparator noise, thermal noise, will affect the performance of the noise shaping and then degrade the final achievable resolution. This paper analyzes the effects of the main non-ideal issues and provides the design reference for fully passive noise shaping techniques. The analysis is based on 2nd order fully passive noise shaping SAR ADC with an 8-bit architecture and an OSR of 4.

Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-the-art measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.

In this paper, we have investigated the etching selectivity of HfN encapsulating layer for high quality PtHf-alloy silicide (PtHfSi) formation with low contact resistivity on Si(100). The HfN(10 nm)/PtHf(20 nm)/p-Si(100) stacked layer was in-situ deposited by RF-magnetron sputtering at room temperature. Then, silicidation was carried out at 500°C/20 min in N2/4.9%H2 ambient. Next, the HfN encapsulating layer was etched for 1-10 min by buffered-HF (BHF) followed by the unreacted PtHf metal etching. We have found that the etching duration of the 10-nm-thick HfN encapsulating layer should be shorter than 6 min to maintain the PtHfSi crystallinity. This is probably because the PtHf-alloy silicide was gradually etched by BHF especially for the Hf atoms after the HfN was completely removed. The optimized etching process realized the ultra-low contact resistivity of PtHfSi to p+/n-Si(100) and n+/p-Si(100) such as 9.4×10-9Ωcm2 and 4.8×10-9Ωcm2, respectively, utilizing the dopant segregation process. The control of etching duration of HfN encapsulating layer is important to realize the high quality PtHfSi formation with low contact resistivity.

In this paper, we propose a new technique for the transient scattering problem of periodically arrayed dispersion media for the TE case by using a combination of the Fourier series expansion method (FSEM) and the fast inversion Laplace transform (FILT) method, and analyze the pulse response for various widths of the dispersion media. As a result, we clarified the influence of the dispersion media with an air region on the resulting waveform.

In order to optimize the performance of thermoelectric devices, we have fabricated and characterized the micrometer-scaled Si thermopile preserving the phonon-drag effect, where the Si thermopile consists of p- and n-type Si wire pairs. The measured Seebeck coefficient of the p-type Si wire was found to be higher than the theoretical value calculated only from the carrier transport, which indicates the contribution of phonon-drag part. Moreover, the measured Seebeck coefficient increased with increasing the width of Si wire. This fact is considered due to dependency of phonon-drag part on the wire width originating from the reduction of phonon-boundary scattering. These contributions were observed also in measured output voltage of Si-wire thermopile. Hence, the output voltage of Si-wire thermopile is expected can be enhanced by utilizing the phonon-drag effect in Si wire by optimizing its size and carrier concentration.

Network virtualization is viewed as a promising approach to facilitate the sharing of physical infrastructure among different kinds of users and applications. In this letter, we propose a topological consistency-based virtual network embedding (TC-VNE) over elastic optical networks (EONs). Based on the concept of topological consistency, we propose a new node ranking approach, named Sum-N-Rank, which contributes to the reduction of optical path length between preferred substrate nodes. In the simulation results, we found our work contributes to improve spectral efficiency and balance link load simultaneously without deteriorating blocking probability.

With the existing of coverage holes, the Quality of Service (such as event response, package delay, and the life time et al.) of a Wireless Sensor Network (WSN) may become weaker. In order to recover the holes, one can locate them by identifying the boundary nodes on their edges. Little effort has been made to distinguish the boundary nodes in a model where wireless sensors are randomly deployed on a three-dimensional surface. In this paper, we propose a distributed method which contains three steps in succession. It first projects the 1-hop neighborhood of a sensor to the plane. Then, it sorts the projected nodes according to their angles and finds out if there exists any ring formed by them. At last, the algorithm validates a circle to confirm that it is a ring surrounding the node. Our solution simulates the behavior of rotating a semicircle plate around a sensor under the guidance of its neighbors. Different from the existing results, our method transforms a three-dimensional problem into a two-dimensional one and maintaining its original topology, and it does not rely on any complex Hamiltonian Cycle finding to test the existence of a circle in the neighborhood of a sensor. Simulation results show our method outperforms others at the correctness and effectiveness in identifying the nodes on the edges of a three-dimensional WSN.

Data-driven untying of a recursive autoencoder (RAE) is proposed for utterance intent classification for spoken dialogue systems. Although an RAE expresses a nonlinear operation on two neighboring child nodes in a parse tree in the application of spoken language understanding (SLU) of spoken dialogue systems, the nonlinear operation is considered to be intrinsically different depending on the types of child nodes. To reduce the gap between the single nonlinear operation of an RAE and intrinsically different operations depending on the node types, a data-driven untying of autoencoders using part-of-speech (PoS) tags at leaf nodes is proposed. When using the proposed method, the experimental results on two corpora: ATIS English data set and Japanese data set of a smartphone-based spoken dialogue system showed improved accuracies compared to when using the tied RAE, as well as a reasonable difference in untying between two languages.