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In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively. [ABSTRACT FROM AUTHOR]

This note deals with the problem of observer-based fast rate fault detection for a class of multirate sampled-data (MSD) systems. Applying a lifting technique, a linear time-invariant (LTI) representation with slow sampling period is firstly obtained for the MSD systems and, based on this, an observer-based fault detection filter is considered as a residual generator. Then an optimization fault detection approach for LTI systems is modified to the residual generation for the MSD systems and, by solving a discrete-time Algebraic Riccati equation, a family of optimal solutions with causality constraint can be obtained. An inverse lifting operation on the generated residual implements its fast rate. The residual evaluation problem is also considered. A numerical example is finally given to illustrate the effectiveness of the proposed design techniques. Index Terms--Fast rate, fault detection, multirate, observer-based, sampled-data system.

Zhang et al. presented a new multiresolution multisensor data fusion scheme for dynamic systems to be observed by multisensors of different resolutions. State space projection equation is introduced to associate the states of a system at each resolution with others. A discrete model of the system is built by using Haar wavelet or general compactly supported wavelet as a linear projection operator to approximate the state space projection. In fact, different wavelet corresponds to different sensor structure. For the multiresolution multisensor system with unknown sensor structure, the projection operator should be estimated online. Up to now, this problem has never been investigated. In this brief, the projection operator is estimated by using least-square estimation algorithm and recursive least-square estimation algorithm. The research on projection operator estimation lays a foundation for the popularization and application of the multiresolution multisensor system estimation algorithm in the real system. Index Terms--Estimation, multiresolution multisensor data fusion, projection operator, wavelet.

In the traditional parity relation based fault detection approach, a lower order parity vector means a simpler online realization but a poorer performance index, while a higher order parity vector brings a better performance index but leads to a higher computational load and a higher rate of misdetection. In this note, by introducing the so-called stationary wavelet transform into the residual signal, a new kind of parity relation based residual generator and the corresponding optimization approach are proposed. The residual generator of the new approach can ensure a good performance index, a satisfactory low misdetection rate and a suitable response speed to faults with a low order parity vector and a simple online implementation form. The method is compared with three existing parity relation based approaches. Index Terms--Fault detection, parity space approach, robustness, wavelet transform.

Problems related to the integrated design of robust fault detection (FD) systems are studied in this note. Firstly, it is revealed that due to the time window introduced to realize the 2-norm based evaluation function, an optimal design of a FD system with the 2-norm based evaluation function may not ensure the expected optimal performance when the system is realized in real applications. To solve this problem, an integrated design method of FD systems using the absolute value of residual signal as evaluation function is then proposed. It leads to a residual generator which is much easier to be realized. Different from the usual 2-norm based approaches whose mathematical basis is the relationship between the energy of the output and input signals of a dynamic system, a relationship between the instant power of the output signal and the energy of the past input signal of a dynamic system is established and further used for FD system design. On the other hand, since this new approach leads to, like the well known [H.sub.2] optimal solution, a narrow band filter and thus may lead to a high rate of miss detection and a large delay in detecting faults, another new kind of evaluation function based on the absolute value of Wavelet Transform (WT) of residual signal and the corresponding integrated design approach for FD systems are further proposed. Due to the good time and frequency properties of WT, the FD system with WT-based evaluation function is simple in its online implementation, ensures an early fault detection and moreover a low miss detection ratio. Index Terms--Fault detection, observer based approach, residual generation and evaluation, robustness, wavelet transform.