Prospects for using integrated atom-photon junctions for quantum information processing

We investigate the use of integrated, microfabricated photonic-atomic junctions for quantum information processing applications. The coupling between atoms and light is enhanced by using microscopic optics without the need for cavity enhancement. Qubits that are collectively encoded in hyperfine states of small ensembles of optically trapped atoms, coupled via the Rydberg blockade mechanism, seem a particularly promising implementation. Fast and high-fidelity gate operations, efficient readout and long coherence times are all possible. Large numbers of qubits can be achieved because of the intrinsic scalability of the microfabricated optics.     

Non-stationary power signal processing for pattern recognition using HS-transform

A new approach to time-frequency transform and pattern recognition of non-stationary power signals is presented in this paper. In the proposed work visual localization, detection and classification of non-stationary power signals are achieved using hyperbolic S-transform known as HS-transform and automatic pattern recognition is carried out using GA based Fuzzy C-means algorithm. Time-frequency analysis and feature extraction from the non-stationary power signals are done by HS-transform. Various non-stationary power signal waveforms are processed through HS-transform with hyperbolic window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using Fuzzy C-means algorithm and finally the algorithm is optimized using genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95.75% using Fuzzy C-means and genetic based Fuzzy C-means algorithm, respectively.     

Detection thresholds for tracking in clutter--A connection between estimation and signal processing

In the Kalman-Bucy filter and other trackers, the dependence of tracking performance upon the quality of the measurement data is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the values of the measurement inputs. The measurement noise and process noise covariances determine, via the Riccati equation, the state estimation error covariance. When the origin of the measurements is also uncertain, one has the widely studied problem of data association (or data correlation), and tracking performance depends critically on signal processing parameters, primarily the probabilities of detection and false alarm. In this paper we derive a modified Riccati equation that quantifies (approximately) the dependence of the state error covariance on these parameters. We also show how to use a receiver operating characteristic (ROC) curve in conjunction with the above relationship to determine the detection threshold in the signal processing system that provides measurements to the tracker so as to minimize tracking errors. The approach presented in this paper provides a feedback mechanism from the information processing (tracking) subsystem to the signal processing subsystem so as to optimize the overall performance in clutter.     

用MAX7000A实现多普勒信号数字信号处理的分析与线路设计

本文以连续波多普勒无线电引信为研究背景,建立目标信号数学模型,针对目前炸高控制方法及存在问题,提出了一种新的多普勒脉宽技术测高方法,在此基础上增加一个近表面炸的性能。并介绍了采用可编程逻辑器件实现的方法以及计算机仿真。实践证明,对Ｄｏｐｐｌｅｒ信号的数字信息处理,用一片ＣＰＬＤ实现,提高引信信号处理的速度,并极大提高了系统的可靠性。     

Integrated CMOS photodetectors and signal processing for very low-level chemical sensing with the bioluminescent bioreporter integrated circuit

We report an integrated CMOS microluminometer optimized for the detection of low-level bioluminescence as part of the bioluminescent bioreporter integrated circuit (BBIC). This microluminometer improves on previous devices through careful management of the sub-femtoampere currents, both signal and leakage, that flow in the front-end processing circuitry. In particular, the photodiode is operated with a reverse bias of only a few mV, requiring special attention to the reset circuitry of the current-to-frequency converter (CFC) that forms the front-end circuit. We report a sub-femtoampere leakage current and a minimum detectable signal (MDS) of 0.15 fA (1510 s integration time) using a room temperature 1.47 mm2 CMOS photodiode. This microluminometer can detect luminescence from as few as 5000 fully induced Pseudomonas fluorescens 5RL bacterial cells.     

整合多维不确定投入产出关系的效率评价新方法

为还原多维不确定投入产出关系在效率评价理念、理论与评价方法方面存在的创新需求,从评价目标导向、评价要素构成和要素关联不确定三个维度,刻画一般效率评价情景中的要素间不确定关联呈现特征。在此基础上,针对现有方法在提取投入产出指标多维关联、刻画多类型效率涌现路径、促成多元效率内涵横向比较等方面存在的不足,创新性融合ANP方法与DEA方法构建了能够应对复杂情景下多维效率内涵提取、转换与融合需求的新方法。案例应用结果表明,以上所提出的理论及方法有效、可行,对延伸及拓展DEA方法的相对权比较理念实现复杂投入产出关联的跨层次比较,具有一定借鉴价值。     

Computer-aided quasi-static analysis of coplanar transmission lines for microwave integrated circuits using the finite difference method

The finite difference method (FDM) is applied to the analysis of microwave integrated circuit (MIC) components that are based on the coplanar waveguide geometry. Effective dielectric constant, attenuation due to dielectric loss, and line impedance are calculated for open and shielded MICs as functions of transmission line dimensions and material parameters. The FDM is shown to be a practical engineering CAD tool for detailed examination of MICs having an arbitrary number of conductors and lossy dielectric layers, including field effect transistors.     

集成电路的电磁兼容性分析与设计研究

基于电磁兼容的基本理论,分析研究了集成电路的电磁兼容问题,详细描述了集成电路电磁兼容性设计应遵循的一些原则、方法。     

A survey on design and synthesis techniques for photonic integrated circuits

The Journal of Supercomputing - In recent years, silicon photonics (Si-photonics) have received significant attention among researchers due to complementary metal-oxide semiconductor compatibility,...     

系统控制和信号处理中的Delta 算子方法

对Delta算子方法在高速信号处理与控制领域的最新进展进行综述，其内容包括Delta算子系统的鲁棒性与H∞控制，基于Delta算子的PIP控制与GPC控制，信号处理中的Delta算子方法，基于有限字长的控制器和滤波器的实现等。最后介绍了Delta算子方法的若干应用成果。     

Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE-based feature selection

Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low-cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K-Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.     

An adaptive speech signal processing for COVID-19 detection using deep learning approach

International Journal of Speech Technology - Researchers and scientists have been conducting plenty of research on COVID-19 since its outbreak. Healthcare professionals, laboratory technicians, and...     

Deep learning for multimedia signal processing and applications

Multimedia Tools and Applications -     

Space-time coding and signal processing for MIMO communications

Rapid growth in mobile computing and other wireless multimedia services is inspiring many research and development activities on high-speed wireless communication systems.Main challenges in this area include the development of efficient coding and modulation signal processing techniques for improving the quality and spectral efficiency of wireless systems.The recently emerged space-time coding and signal processing techniques for wireless communication systems employing multiple transmit and receive antennas offer a powerful paradigm for meeting these challenges.This paper provides an overview on the recent development in space-time coding and signal processing techniques for multiple-input multiple-output (MIMO) communication systems.We first review the information theoretic results on the capacities of wireless systems employing multiple transmit and receive antennas.We then describe two representative categoriesof space-time systems,namely,the BLAST system and the space-time block coding system,both of which have been proposed for next-generation high-speed wireless system.Signal processing techniques for channel estimation and decoding in space-time systems are also discussed. Finally,some other coding and signal processing techniques for wireless systems employing multiple transmit and receive antennas that are currently under intensive research are also briefly touched upon.     

Sparse representations and sphere decoding for array signal processing

Array processing algorithms are used in many applications for source localization and signal waveform estimation. When the number of snapshots is small and/or the signal-to-noise ratio (SNR) is low, it becomes a challenge to discriminate closely-spaced sources. In this paper, two new array processing algorithms exploiting sparsity are proposed to overcome this problem. The first proposed method combines a well-known sparsity preserving algorithm, namely the least absolute shrinkage and selection operator (LASSO), with the Bayesian information criterion (BIC) to eliminate user parameters. The second proposed algorithm extends the sphere decoding algorithm, which is widely used in communication applications for the recovery of signals belonging to a finite integer dictionary, to promote the sparsity of the solution. The proposed algorithms are compared with several existing sparse signal estimation techniques. Simulations involving uncorrelated and coherent sources demonstrate that the proposed algorithms, especially the algorithm based on sphere decoding, show better performance than the existing methods. Moreover, the proposed algorithms are shown to be more practical than the existing methods due to the easiness in selecting their user parameters.     

Multiplication and convolution of distributions for signal processing theory

In the theory of signal processing, signals are usually classified either by determining whether their time domain is discrete or continuous, or by determining whether they are periodic. However, no comprehensive definitions of multiplication and convolution exist that are consistent with the theories behind all classes, although some important theorems in signal processing involve multiplication and convolution. In order to unite the theories behind these classifications, we will consider tempered distributions. In this paper, we propose an approach to the multiplication and convolution of distributions that is appropriate to signal processing theory, and prove a well-known theorem regarding the impulse response of continuous linear time-invariant systems of tempered distributions in the context of this new approach.     

Power processing circuits for electromagnetic, electrostatic and piezoelectric inertial energy scavengers

Inertial energy scavengers are self-contained devices which generate power from ambient motion, by electrically damping the internal motion of a suspended proof mass. There are significant challenges in converting the power generated from such devices to useable form, particularly in micro-engineered variants. This paper examines this power conversion requirement for each of the cases of electromagnetic, electrostatic and piezo-electric transduction, and presents new circuit approaches for the first two of these.     

Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing

The purpose of this research was to represent the new laboratory test procedure that could be applicable in the field condition. Therefore, the performance of a pneumatic planter was investigated under laboratory conditions for maize, castor, fababean, sorghum, sugar beet, watermelon and cucumber seeds. The effect of operational speed [(1) 2.5–4 km/h and (2) 6–8.5 km/h] and vacuum pressure was evaluated by examining the quality of feed index, precision in spacing (coefficient of variation), miss index and multiple index. The most perfect operating parameter values for maize, castor, sorghum and sugar beet seeds were obtained at the first level of operating speed and 4.0 kPa pressure; for watermelon seed: second level of speed and 4.5 kPa pressure; and for cucumber seed: first level of speed and 4.5 kPa pressure. Furthermore, in order to determine the relationship between most important operating parameters affecting the performance of the pneumatic metering device and seed physical properties, regression models were developed using genetic programming (GP) algorithm. According to the results, the developed model using GP encompasses all physical properties of seeds as well as operational parameters. The model strongly describes the effect of investigated factors on seed spacing uniformity with values of the coefficient of determination R ² of 0.938, RMSE of 3.01 and MAE of 3.362087. Furthermore, the associated P value of 2.9851e−17 represents that the model is statistically significant. Model obtained from GP approach not only has a higher value of the coefficient of determination compared to regression model but is able to present the relationship between two operating parameters affecting the performance of row crop pneumatic metering device and seed physical properties, as well.

     

Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing

Digital game-based learning (DGBL) has become a viable instructional option in recent years due to its support of learning motivation. Recent studies have mostly focused on identifying motivational factors in digital games (e.g., curiosity, rules, control) that support intrinsic motivation. These findings, however, are limited in two fronts. First, they did not depict the interactive nature of the motivational processing in DGBL. Second, they excluded the outcome processing (learners’ final effort versus performance evaluation) as a possible motivation component to sustain the iterative game playing cycle. To address these problems, situated in the integrative theory of Motivation, Volition, and Performance (MVP), this study examined the relationship between motivational processing and outcome processing in an online instructional game. The study surveyed 264 undergraduate students after playing the Trade Ruler online game. Based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). The finding preliminarily suggests that both motivational processing and outcome processing need to be considered when designing DGBL. Furthermore, the finding implies a potential relationship between intrinsic motives and extrinsic rewards in DGBL.