Sparse Bayesian learning approach for discrete signal reconstruction

This study addresses the problem of discrete signal reconstruction from the perspective of sparse Bayesian learning (SBL). Generally, it is intractable to perform the Bayesian inference with the ideal discretization prior under the SBL framework. To overcome this challenge, we introduce a novel discretization enforcing prior to exploit the knowledge of the discrete nature of the signal-of-interest. By integrating the discretization enforcing prior into the SBL framework and applying the variational Bayesian inference (VBI) methodology, we devise an alternating optimization algorithm to jointly characterize the finite-alphabet feature and reconstruct the unknown signal. When the measurement matrix is i.i.d. Gaussian per component, we further embed the generalized approximate message passing (GAMP) into the VBI-based method, so as to directly adopt the ideal prior and significantly reduce the computational burden. Simulation results demonstrate substantial performance improvement of the two proposed methods over existing schemes. Moreover, the GAMP-based variant outperforms the VBI-based method with i.i.d. Gaussian measurement matrices but it fails to work for non i.i.d. Gaussian matrices.     

Electrical power free, low dead volume, pressure-driven pumping for microfluidic applications

This paper presents a simple-to-construct, low dead volume pump capable of generating a wide range of positive and negative pressures for microfluidic applications. The pump generates pressure or vacuum by changing the volume of air confined inside a syringe and is able to generate pressures between -95 and +300 kPa with a resolution as high as 1 Pa. Different from syringe pumps and electrokinetic pumping, which are capable of controlling flow rates only, our pump can be used to generate constant flow rates or constant pressures, which are required for certain applications such as the aspiration of biological cells for biophysical characterization. Compared to syringe pumps, the new pump has almost zero dead volume and does not exhibit pulsatile flows. Additionally, the system does not require electrical power and is cost effective (～$100). To demonstrate the capabilities of the pump, we used it to aspirate osteoblasts (MC3T3-E1 cells) and to determine Young's modulus of the cells, to generate a concentration gradient, and to produce variable-sized droplets in microchannels using hydrodynamic focusing.     

面向应用型人才的数字信号处理教学改革探索

数字信号处理课程是电子、通信类专业的专业必修课,针对该课程理论性强、内容抽象难懂的特点,再结合当前面向应用型人才的培养目标,本次教改对该课程进行了全面的改革。在理论教学上,针对不同专业的学习情况,调整教学内容,在课堂中,采用MATLAB和LABVIEW两个软件进行辅助教学。在实验教学环节中,完成基础性实验的基础上,新增项目化课程设计,培养学生的实践应用能力、分析解决实际问题的能力。     

低质量专利的识别方法及应用研究

随着专利申请授权数量的快速攀升,低质量专利也大量涌现,这给专利管理带来很大困扰;面对海量的专利数据,如何构建有效的低质量专利识别方法,筛选出其中的低质量专利是一项紧迫的工作。基于专利质量与专利维持的相关分析,研究提出低质量专利的识别方法和定量指标;以电通信领域的发明专利为研究样本,对低质量专利进行定量识别和评价分析。     

Switching signal design for exponential stability of discrete switched systems with interval time-varying delay

The switching signal design for global exponential stability of discrete switched systems with interval time-varying delay is considered in this paper. Some LMI conditions are proposed to design the switching signal and guarantee the global exponential stability of switched time-delay system. Some nonnegative inequalities are used to reduce the conservativeness of the systems. Finally, two numerical examples are illustrated to show the main result.     

Biorthogonal wavelet trees in the classification of embedded signal classes for intelligent sensors using machine learning applications

The paper deals with a method of constructing orthonormal bases of coordinates which maximize, through redundant dictionaries (frames) of biorthogonal bases, a class separability index or distances among classes. The method proposes an algorithm which consists of biorthogonal expansions over two redundant dictionaries. Embedded classes are often present in multiclassification problems. It is shown how the biorthogonality of the expansion can really help to construct a coordinate system which characterizes the classes. The algorithm is created for training wavelet networks in order to provide an efficient coordinate system maximizing the Cross Entropy function between two complementary classes. Sine and cosine wavelet packets are basis functions of the network. Thanks to their packet structure, once selected the depth of the tree, an adaptive number of basis functions is automatically chosen. The algorithm is also able to carry out centering and dilation of the basis functions in an adaptive way. The algorithm works with a preliminary extracted feature through shrinkage technique in order to reduce the dimensionality of the problem. In particular, our attention is pointed out for time-frequency monitoring, detection and classification of transients in rail vehicle systems and the outlier problem. In the former case the goal is to distinguish transients as inrush current and no inrush current and a further distinction between the two complementary classes: dangerous inrush current and no dangerous inrush current. The proposed algorithm is used on line in order to recognize the dangerous transients in real time and thus shut-down the vehicle. The algorithm can also be used in a general application of the outlier detection. A similar structure is used in developed algorithms which are currently integrated in the inferential modeling platform of the unit responsible for Advanced Control and Simulation Solutions within ABB's (Asea Brown Boveri) industry division. It is shown how impressive and rapid performances are achieved with a limited number of wavelets and few iterations. Real applications using real measured data are included to illustrate and analyze the effectiveness of the proposed method.     

Toward a modular,integrated, miniaturized,and portable microfluidic flow control architecture for organs-on-chips applications

Microfluidic organs-on-chips (OoCs) technology has emerged as the trend for in vitro functional modeling of organs in recent years. Simplifying the complexities of the human organs under controlled perfusion of required fluids paves the way for accurate prediction of human organ functionalities and their response to interventions like exposure to drugs. However, in the state-of-the-art OoC, the existing methods to control fluids use external bulky peripheral components and systems much larger than the chips used in experiments. A new generation of compact microfluidic flow control systems is needed to overcome this challenge. This study first presents a structured classification of OoC devices according to their types and microfluidic complexities. Next, we suggest three fundamental fluid flow control mechanisms and define component configurations for different levels of OoC complexity for each respective mechanism. Finally, we propose an architecture integrating modular microfluidic flow control components and OoC devices on a single platform. We emphasize the need for miniaturization of flow control components to achieve portability, minimize sample usage, minimize dead volume, improve the flowing time of fluids to the OoC cell chamber, and enable long-duration experiments.     

Finite-time non-fragile state estimation for discrete neural networks with sensor failures,time-varying delays and randomly occurring sensor nonlinearity

A finite-time non-fragile state estimation algorithm is discussed in this article for discrete delayed neural networks with sensor failures and randomly occurring sensor nonlinearity. First, by using augmented technology, such system is modeled as a kind of nonlinear stochastic singular delayed system. Then, a finite-time state estimator algorithm is provided to ensure that the singular error dynamic is regular, causal and stochastic finite-time stable. Moreover, the states and sensor failures can be estimated simultaneously. Next, in order to avoid the affection of estimator’s parameter perturbation, a finite-time non-fragile state estimation algorithm is given, and a simulation result demonstrates the usefulness of the proposed approach.     

Task analysis, calculation and approximation: The work of Stuart K. Card, 2007 Bower Laureate in Computer & Cognitive Science for Human-Centered Computing

In the early 1980s there was a large base of well-regarded cognitive theory—explaining human cognition. Additionally, personal technology was increasingly a factor in the workplace, but it was notoriously hard to use, leading to frustration rather than increased satisfaction and productivity [1], [2] and [3]. In 1974 Stuart Card accompanied Allen Newell from Carnegie Mellon University to Xerox’s Palo Alto Research Center (PARC) to work on the problem of human-computer interaction. Newell returned to CMU, but Card remained to become the founding manager of the User Interface Research group.Card’s first major work The Psychology of Human-Computer Interaction, was published in 1983. It was undertaken to address the problems of usability by closing the gap between theory and technology. It did this by providing a methodology for bringing theory to bear on application. The work served as a foundation for research and development in human-computer interaction for the following 15 years.