This paper considers the design of an observer-based iterative learning control law for discrete linear systems using repetitive process stability theory The resulting design produces a stabilizing feedback controller...
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Algae are very important to the life of human as a bio-indicator of water pollution. The observation of algae vitality can give information of the environment changes of the algae, which are shown by the changes of pa...
Algae are very important to the life of human as a bio-indicator of water pollution. The observation of algae vitality can give information of the environment changes of the algae, which are shown by the changes of parameter in its environment. This study is aimed to observe the influence of toxicity of herbicide 2,4-D dimethylamine 865 SL on the vitality of algae species Chlorella Kessleri in producing dissolved oxygen as a result of photosynthetic mechanism. A 50 µL herbicide 2,4-D dimethylamine 865 SL with a concentration of 10 % was added into immobilized algae in the Biochip-C from cellasys GmbH and stimulated with artificial light of 400-700 nm for photosynthetic process. The effect of herbicide 2,4-D dimethylamine 865 SL was observed and shows the increase of the basal potential after 1500 s, which indicates the dissolved oxygen reduction in the environment of the algae. This effect is reversible and a restoration of the photosynthetic activity take place after the substance removal. The use of this toxin is systemic, which slowly kill the living cells.
In-line monitoring of 3D printed parts is vital if quality is to be maintained with this new manufacturing modality. Specifically, the reliable detection of pores in printed parts is vital if the finished products are...
In-line monitoring of 3D printed parts is vital if quality is to be maintained with this new manufacturing modality. Specifically, the reliable detection of pores in printed parts is vital if the finished products are to have the desired strength characteristics. In this work, we utilize COMSOL(Burlington, MA) to numerically compare a new detection method where the interferometer and laser-generated ultrasound are focused at the same spatial location. The changes in the surface response to defects in the near-field of the induced ultrasound wave are then assessed as a function of defect size and depth. Our numerical results demonstrated that the impact of defects was easier to visualize when quantifying the surface velocity as opposed to surface displacement. The amplitude of the difference is comparable to that observed when utilizing scattering of the Rayleigh wave in the detection. However, the new approach does not require a 1 mm separation between the laser-generating ultrasound spot and the interferometer improving the spatial resolution of the detection.
The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such ...
The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.
Data-driven artificial intelligence technologies have made much progress in medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotati...
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Today’s automobiles are equipped with an increasing variety of safety features, yet the use of acoustic methods for automobile crash prevention and detection has been somewhat limited even though the acoustic waves g...
Today’s automobiles are equipped with an increasing variety of safety features, yet the use of acoustic methods for automobile crash prevention and detection has been somewhat limited even though the acoustic waves generated during such events can offer valuable information. For example, the high-pitched squealing caused by tire skidding can provide advance warning especially if it is caused by an adjacent car. During car collisions, the elastic waves traveling along the steel car frame are 17 times faster than the speed of sound in air, which can signal a crash more promptly than center-mounted acceleration sensors. To make full use of the high-speed acoustic signals, a wavelet-based algorithm implementable in real-time has been developed to isolate and detect specific pre-crash and crash events such as honking, tire skidding and collision in multi-channel acoustic datasets. The proposed algorithm offers distinct advantages in sudden onset detection, temporal localization accuracy, and computational cost over existing time- and frequency-domain methods. Results demonstrated on a crash scenario are indicative of a substantial enhancement in automobile pre-crash and crash detection performance by acoustic methods.
The method of Wolf, basically provides the calculation of the maximum exponent of Lyapunov by the evolution of the distance between two points closes from two nearby paths. This enables the quantification of the chaos...
The method of Wolf, basically provides the calculation of the maximum exponent of Lyapunov by the evolution of the distance between two points closes from two nearby paths. This enables the quantification of the chaos through the sign of the exponent, is also an important tool as an indicator of the predictability of a time series. The algorithm presented by Alan Wolf is not very efficient because of a search performed by brute force, therefore, the execution times turn out to be very high, here is where lies the importance of the parallelization of this method. In the present work, two parallel implementations of the method of Wolf shared memory platforms, described the first using the API second based for GPU, and OpenMP using NVIDIA CUDA platform. The results obtained in terms of execution time decreased approximately above 90%, for all patients in study for parallel OpenMP program. In the case of the parallel program on GPU, achieved a decrease of runtimes between approximately 15% and 65% (depending on the case study), in comparison to the parallel OpenMP version; and even more, a considerable decrease of time in comparison to the sequential version.
Due to the impact of network technology, all information are transmitted in digital mode and the security of information is ever more important. In order to ensure the secret messages are not been stolen when transmit...
Due to the impact of network technology, all information are transmitted in digital mode and the security of information is ever more important. In order to ensure the secret messages are not been stolen when transmitting, it will be a good countermeasure to encrypt the secret message before transmitting. The level of security of information is based on the number of participants. Security is definite when a single person is involved. In the scenario, if many people are involved, security may be ensured if secrets are kept in. This lead the direction for researchers to develop new cryptographic scheme in the past two decades. This paper proposes techniques for transmission of secret messages between two parties and also sharing of messages between multiple parties. These methods uses well known public key cryptography algorithm RSA and Hilbert matrix for authentication and encryption. The proposed method overcome the issues addressed by the existing scheme and ensures secure transmission of text messages with less computational Complexity and no additional code book. The proposed (N, N) Secret sharing Scheme also reduces the overhead of generation of keys for each pair of parties.
With the rapid development of machine vision technology, more and more attention has been paid to the visual-aided inertial navigation system. It is important that to extract and track the line features at the dynamic...
With the rapid development of machine vision technology, more and more attention has been paid to the visual-aided inertial navigation system. It is important that to extract and track the line features at the dynamic situation in the visual-aided inertial navigation system which is based on visual line feature information to compensate attitude errors. A novel line feature description is proposed that use the SURF points to mark the LSD lines. Then, through coarse matching and fine matching, the function of continuously tracking the one line features in different images was realized. These line feature description and tracking method are applied in the visual-aided inertial navigation system, and its effectiveness is verified by the vehicle experiment.
In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performan...
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In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.
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