Abstract: The swirl gripper is an electrically activated noncontact handling device that uses swirling airflow to generate a lifting force. This force can be used to pick up a workpiece placed underneath the swirl gripper without any contact. It is applicable, for example, in the semiconductor wafer production line, where contact must be avoided during the handling and moving of a workpiece to minimize damage. When a workpiece levitates underneath a swirl gripper, the gap height between them is crucial for safe handling. Therefore, in this paper, we propose a method to estimate the levitation gap height by detecting pressure at two points. The method is based on theoretical model of the swirl gripper, and has been experimentally verified. Furthermore, the force between the gripper and the workpiece can also be estimated using the detected pressure. As a result, the nonlinear relationship between the force and gap height can be linearized by adjusting the rotating speed of the fan in the swirl gripper according to the estimated force and gap height. The linearized relationship is expected to enhance handling stability of the workpiece.
Abstract: Polypropylene (PP) is one of the most commonly used plastics because of its low density, outstanding mechanical properties, and low cost. However, its drawbacks such as low surface energy, poor dyeability, lack of chemical functionalities, and poor compatibility with polar polymers and inorganic materials, have restricted the application of PP. To expand its application in biomedical materials, functionalization is considered to be the most effective way. In this study, PP was functionalized with a chiral monomer, (S)-1-acryloylpyrrolidine-2-carboxylic acid ((S)-APCA), by free-radical grafting in the solid phase. The grafting degree of PP-g-APCA was determined by chemical titration method, and the chemical structure of functionalized PP was characterized by FTIR spectroscopy, which confirmed that the chiral monomer (S)-APCA was successfully grafted onto PP. Static water contact angle results suggested that the surface hydrophilicity of PP was significantly improved by solid phase grafting and assistance of surface water treatment. Protein adsorption and platelet adhesion results showed that hemocompatibility of PP was greatly improved by grafting the chiral monomer.
Abstract: Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.
Abstract: A digital baseband Application-Specific Integrated Circuit (ASIC) (yclic Redundancy Checkis developed for a microchip transponder to transmit signals and temperature levels from biomedical monitoring devices. The transmission protocol is adapted from the ISO/IEC 11784/85 standard. The module has a decimation filter that employs only a single adder-subtractor in its datapath. The filtered output is coded with cyclic redundancy check and transmitted through backscattering Load Shift Keying (LSK) modulation to a reader. Fabricated using the 0.18-μm CMOS technology, the module occupies 0.116 mm2 in chip area (digital baseband: 0.060 mm2, decimation filter: 0.056 mm2), and consumes a total of less than 0.9 μW of power (digital baseband: 0.75 μW, decimation filter: 0.14 μW).
Abstract: This paper addresses the robust stability problem of a class of delayed neutral Lur’e systems. Combined with the property of convex function and double integral Jensen inequality, a new tripe integral Lyapunov functional is constructed to derive some new stability criteria. Compared with some related results, the new criteria established in this paper are less conservative. Finally, two numerical examples are presented to illustrate the validity of the main results.
Abstract: Discrete wavelet transform (DWT) has been widely adopted in biomedical signal processing for denoising, compression
and so on. Choosing a suitable decomposition level (DL) in DWT is of paramount importance to its performance. In this paper, we propose to exploit sparseness of the transformed signals to determine the appropriate DL. Simulation results have shown that the sparseness of transformed signals after DWT increases with the increasing DLs. Additional Monte-Carlo simulation results have verified the effectiveness of sparseness measure in determining the DL.
Abstract: In this paper, a low-power digital controller for DC-DC power conversion was presented. The controller generates the pulse-width modulated (PWM) signal from digital inputs provided by analog-to-digital converter (ADC). An efficient and simple design scheme to develop the control unit was discussed. This method allows minimization of the consumed resources of the chip and it is based on direct digital design approach. In this application, with the proposed scheme, nearly half area and two-third of the power consumption was saved compared to the conventional schemes. This work illustrates the possibility of implementing low-power and area-efficient power management circuit using direct digital design based approach.
Abstract: In this paper, a novel multipurpose audio watermarking
algorithm is proposed based on Vector Quantization (VQ) in Discrete
Cosine Transform (DCT) domain using the codeword labeling and
index-bit constrained method. By using this algorithm, it can fulfill the
requirements of both the copyright protection and content integrity
authentication at the same time for the multimedia artworks. The
robust watermark is embedded in the middle frequency coefficients of
the DCT transform during the labeled codeword vector quantization
procedure. The fragile watermark is embedded into the indices of the
high frequency coefficients of the DCT transform by using the
constrained index vector quantization method for the purpose of
integrity authentication of the original audio signals. Both the robust
and the fragile watermarks can be extracted without the original audio
signals, and the simulation results show that our algorithm is effective
with regard to the transparency, robustness and the authentication
requirements
Abstract: Cooperative visual modeling is more and more
necessary in our complicated world. A collaborative environment
which supports interactive operation and communication is required
to increase work efficiency. We present a collaborative visual
modeling framework which collaborative platform could be built on.
On this platform, cooperation and communication is available for
designers from different regions. This framework, which is different
from other collaborative frameworks, contains a uniform message
format, a message handling mechanism and other functions such as
message pretreatment and Role-Communication-Token Access
Control (RCTAC). We also show our implementation of this
framework called Orchestra Designer, which support BPLE
workflow modeling cooperatively online.
Abstract: In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with timevarying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI). Compared with some recent results in literature, the conservatism of the new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.
Abstract: This paper studies the pth moment exponential synchronization of a class of stochastic neural networks with mixed delays. Based on Lyapunov stability theory, by establishing a new integrodifferential inequality with mixed delays, several sufficient conditions have been derived to ensure the pth moment exponential stability for the error system. The criteria extend and improve some earlier results. One numerical example is presented to illustrate the validity of the main results.
Abstract: A digital system is proposed for low power 100-
channel neural recording system in this paper, which consists of 100
amplifiers, 100 analog-to-digital converters (ADC), digital controller
and baseband, transceiver for data link and RF command link. The
proposed system is designed in a 0.18 μm CMOS process and 65 nm
CMOS process.
Abstract: The bonding configuration and the heat of adsorption
of a furfural molecule on the Pd(111) surface were determined by ab
initio density-functional-theory calculations. The dynamics of pure
liquid water, the liquid-solid interface formed by liquid water and the
Pd(111) surface, as well as furfural at the water-Pd interface, were
investigated by ab initio molecular dynamics simulations at finite
temperatures. Calculations and simulations suggest that the bonding
configurations at the water-Pd interface promote decarbonylation of
furfural.
Abstract: The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.
Abstract: This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.
Abstract: This paper studies the mean square exponential synchronization problem of a class of stochastic neutral type chaotic neural networks with mixed delay. On the Basis of Lyapunov stability theory, some sufficient conditions ensuring the mean square exponential synchronization of two identical chaotic neural networks are obtained by using stochastic analysis and inequality technique. These conditions are expressed in the form of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. The feedback controller used in this paper is more general than those used in previous literatures. One simulation example is presented to demonstrate the effectiveness of the derived results.
Abstract: In this Letter, a class of impulsive switched cellular neural networks with time-varying delays is investigated. At the same time, parametric uncertainties assumed to be norm bounded are considered. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions guaranteeing exponential stability for all admissible parametric uncertainties are derived via constructing appropriate Lyapunov functional. One numerical example is provided to illustrate the validity of the main results obtained in this paper.
Abstract: In this paper, the issue of pth moment stability of a class of stochastic neural networks with mixed delays is investigated. By establishing two integro-differential inequalities, some new sufficient conditions ensuring pth moment exponential stability are obtained. Compared with some previous publications, our results generalize some earlier works reported in the literature, and remove some strict constraints of time delays and kernel functions. Two numerical examples are presented to illustrate the validity of the main results.
Abstract: The discrete-time uncertain system with time delay is investigated for bounded input bounded output (BIBO). By constructing an augmented Lyapunov function, three different sufficient conditions are established for BIBO stabilization. These conditions are expressed in the form of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Two numerical examples are provided to demonstrate the effectiveness of the derived results.
Abstract: In this paper, the robust exponential stability problem of discrete-time uncertain stochastic neural networks with timevarying delays is investigated. By introducing a new augmented Lyapunov function, some delay-dependent stable results are obtained in terms of linear matrix inequality (LMI) technique. Compared with some existing results in the literature, the conservatism of the new criteria is reduced notably. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.