A new matrix method for pulse train identification

As most of the key properties of a product are determined by its design concept, customer orientation must be ensured very early in the conceptual design phase. Hierarchical concept development can help to reduce the complexity of this strategically significant task. This paper examines the process of hierarchical concept design by means of the House of Concepts (HoC) approach, which was conceived specifically to support customer-oriented concept development. This approach, which builds upon Quality Function Deployment (QFD), is compact, illustrative, enhances comprehensibility and can be considered both a method and a guiding principle. Supporting the creation of solution concepts by analysing the dependencies between requirements, functions, structure and properties with matrix-based methods allows verifying the suitable product concepts. The HoC method promotes customer orientation, especially during conceptual design, and supports systematic concept development at different levels of abstraction. Thus, it enables step-by-step development of product concepts and helps to reduce the complexity of this important engineering task. The HoC approach can be used to ensure customer orientation in the development of product concepts, which leads to an increase in effectiveness in product development.

Globalized and localized matrix-pattern-oriented classification machine

The traditional matrix-based feature extraction methods that have been widely used in face recognition essentially work on the facial image matrixes only in one or two directions. For example, 2DPCA can be seen as the row-based PCA and only reflects the information in each row, and some structure information cannot be uncovered by it. In this paper, we propose the directional 2DPCA that can extract features from the matrixes in any direction. To effectively use all the features extracted by the D2DPCA, we combine a bank of D2DPCA performed in different directions to develop a matching score level fusion method named multi-directional 2DPCA for face recognition. The results of experiments on AR and FERET datasets show that the proposed method can obtain a higher accuracy than the previous matrix-based feature extraction methods.

Range, radial velocity, and acceleration MLE using frequency modulation coded LFM pulse train

Estimation of central pulse wave velocity from radial pulse wave analysis

Complex slopes are characterized by large numbers of failure modes, cut sets or link sets, or by statistical dependence between the failure modes. For such slopes, a systematic quantitative method, or matrix-based system reliability method, was described and improved for their reliability analysis. A construction formula of event vector cE was suggested to solve the difficulty of identifying any component E in sample space, and event vector c of system events can be calculated based on it, then the bounds of system failure probability can be obtained with the given probability information. The improved method was illustrated for four copper mine slopes with multiple failure modes, and the bounds of system failure probabilities were calculated by self-compiling program on the platform of the software MATLAB. Comparison in results from matrix-based system reliability method and two generic system methods suggests that identical accuracy could be obtained by all methods if there are only a few failure modes in slope system. Otherwise, the bounds by the Ditlevsen method or Cornell method are expanded obviously with the increase of failure modes and their precision can hardly satisfy the requirement of practical engineering while the results from the proposed method are still accurate enough.

Decentralized supervisory structure has drawn much attention in recent years to address the computational complexity in designing supervisory structures for large Petri net model. Many studies are reported in the paradigm of automata while few can be found in the Petri net paradigm. The decentralized supervisory structure can address the computational complexity, but it adds the structural complexity of supervisory structure. This paper proposed a new method of designing a global controller for decentralized systems of a large Petri net model for flexible manufacturing systems. The proposed method can both reduce the computational complexity by decomposition of large Petri net models into several subnets and structural complexity by designing a global supervisory structure that can greatly reduce the cost at the implementation stage. Two efficient algorithms are developed in the proposed method. Algorithm 1 is used to compute decentralized working zones from the given Petri net model for flexible manufacturing systems. Algorithm 2 is used to compute the global controller that enforces the liveness to the decentralized working zones. The ring assembling method is used to reconnect and controlled the working zones via a global controller. The proposed method can be applied to large Petri nets size and, in general, it has less computational and structural complexity. Experimental examples are presented to explore the applicability of the proposed method.

With a wide range of applications, single-image super-resolution (SISR) is a very important computer vision task. There are many SISR strategies which are based on the CNNs improvement, such as residual connections, deeper networks, and attention mechanisms. Although such kind of strategies often improve the SISR performance, they increase CNNs’ computational complexity and complex image texture areas still remain hard to be reconstructed. In particular, all the existing loss functions are minimized in the whole dynamic range, e.g. 0-255 in case of 8 bit image, which causes difficulty of CNNs’ learning in texture image regions above all. Developing new loss function provides a promising SISR solution, i.e. one should be beyond the existing regression loss functions, which encounter problem in reconstructing the image texture details. For such goal, this paper proposes a dynamic fine-grained loss function. It consists of both an existing regression loss function and a new pixel classification loss together with a dynamic regression range loss. Extensive experiments conducted on the benchmark SISR show that the proposed method can achieve better results and without extra computational cost.

This paper presents a new method for frame rate up‐conversion using sub‐sampled bilateral motion estimation, motion vector smoothing, simplified bidirectional motion compensated interpolation, and complex motion detection. The proposed method produces improved image quality and requires less computational complexity compared to conventional motion estimation. The average PSNRs of the proposed method for frame rate up‐conversion outperform those of the benchmark frame rate up‐conversion technique by up to 4.39 dB for the test sequences. Furthermore, the motion estimation of the proposed FRUC method reduces the required computational complexity to 12.5% of that required for full‐search block matching motion estimation.

With the increasing number of hypertension patients, the monitoring of blood pressure information becomes an important task. In this study, an end-to-end approach is proposed to estimate blood pressure from the pulse wave signal. In this approach, a normalized single pulse wave is the input of a neural network, which consists of the convolutional layers and the recurrent layers, then outputs the corresponding blood pressure. The convolutional layers consist of one-dimensional convolutional layers and depth-separable convolutional layers. The gated recurrent unit (GRU) is used in the recurrent layer. Finally, a dense layer is used to output estimated values of blood pressure. In comparison with previous approaches, the proposed method does not require complicated feature extraction. It is only necessary to input a single pulse wave into the neural network and blood pressure can be estimated. The proposed approach is tested in the multi-parameter intelligent monitoring in intensive care (MIMIC) dataset, and the average absolute error is 3.95 mmHg for systolic blood pressure and 2.14 mmHg for diastolic blood pressure. This result fulfills the international standard of blood pressure measurement, which shows the proposed approach is simple and effective. In practice, the proposed method is designed to obtain blood pressure information from pulse waves.

Matrix-based (2D) methods have advantages over vector-based (1D) methods. Matrix-based methods generally have less computational costs and higher recognition performances with respect to vector-based variants. In this work a two dimensional variation of Discriminative Common Vector Approach (2D-DCVA) is implemented. The performance of the method in single image problem is compared with the one dimensional Discriminative Common Vector Approach (1D-DCVA) and the two dimensional Fisher Linear Discriminant Analysis (2D-FLDA) on ORL, FERET, and YALE face databases. The best recognition performances are achieved in all databases with the proposed method.

A method is proposed for recognizing noises in pulse waves (PWs), which play an essential role in clinical pulse diagnosis. PWs have also been used in traditional Chinese medicine for thousands of years. We follow the principle that most noises appear as pairs of adjacent sampling points. These pairs satisfy five certain criteria. The principle and the criteria are based on observation and statistical analysis. Our proposed method has a noise recognition ratio of 82% and can help a lot for other physiological signals.

In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced and the system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) and minimum mean square error (MMSE) equalizers. Unfortunately, the direct ZF method requires the inversion of an NxN ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. As for the direct MMSE method, in addition to an NxN matrix inverse, it requires an extra NxN matrix multiplication, making the required computational complexity higher compared to the direct ZF method. In this dissertation, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and to apply Newton's iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O(N^3) to O(Nlog_2 N) . Also, the required number of the iteration is typically one or two. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-noise-ratio (SINR). For the MMSE method, we first reformulate the MMSE solution in a way that the extra matrix multiplication can be avoided. Similar to the ZF method, we then exploit the structure of the ICI matrix and apply Newton's iteration to reduce the complexity of the matrix inversion. For a multiple-input-multiple-output (MIMO) OFDM system, the required complexity of the ZF and MMSE methods becomes more intractable. We then manage to extend the proposed ZF and MMSE methods for SISO-OFDM systems to MIMO-OFDM systems. It turns out that the computational complexity can be reduced even more significantly. Simulation results show that the performance of the proposed methods is almost as good as that of the direct ZF and MMSE methods, while the required computational complexity is reduced dramatically. Finally, we explore the application of the proposed methods in mobility-induced ICI mitigation for OFDM multiple access (OFDMA) systems, and in carrier frequency offset (CFO) induced ICI mitigation for OFDMA uplink systems. As that in OFDM systems, the proposed methods can reduce the required computational complexity, effectively.

Previous computer-aided lung cancer image classification methods are all cost-blind, which assume that the misdiagnosis (categorizing a cancerous image as a normal one or categorizing a normal image as a cancerous one) costs are equal. In addition, previous methods usually require experienced pathologists to label a large amount of images as training samples. To this end, a novel transductive cost-sensitive method is proposed for lung cancer image classification on needle biopsies specimens, which only requires the pathologist to label a small amount of images. The proposed method analyzes lung cancer images in the following procedures: (i) an image capturing procedure to capture images from the needle biopsies specimens; (ii) a preprocessing procedure to segment the individual cells from the captured images; (iii) a feature extraction procedure to extract features (i.e. shape, color, texture and statistical information) from the obtained individual cells; (iv) a codebook learning procedure to learn a codebook on the extracted features by adopting k-means clustering, which aims to represent each image as a histogram over different codewords; (v) an image classification procedure to predict labels for testing images using the proposed multi-class cost-sensitive Laplacian regularized least squares (mCLRLS). We evaluate the proposed method on a real-image set provided by Bayi Hospital, which contains 271 images including normal ones and four types of cancerous ones (squamous carcinoma, adenocarcinoma, small cell cancer and nuclear atypia). The experimental results demonstrate that the proposed method achieves a lower cancer-misdiagnosis rate and lower total misdiagnosis costs comparing with previous methods, which includes the supervised learning approach (kNN, mcSVM and MCMI-AdaBoost), semi-supervised learning approach (LapRLS) and cost-sensitive approach (CS-SVM). Meanwhile, the experiments also disclose that both transductive and cost-sensitive settings are useful when only a small amount of training images are available.

The problem of image enhancement for low-contrast images with the small-size objects is considered. The histogram-based method for contrast enhancement of low-contrast images with the small-size objects on the basis of the estimation of parameters of contrast distribution at boundaries of image elements for the various definitions of contrast kernels is proposed. The proposed method is intended for the automatic preprocessing of low-contrast images with complex structure. The researches of the effectiveness for the proposed and the well-known histogram-based methods were conducted using the known no-reference contrast metrics.