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This paper proposes a novel one-port passive circuit topology consisting of a two-dimensional network of resistors and capacitors, which can be used as a fault-tolerant building block for analog circuit design. Through an analytical procedure, the network is shown to follow simple first-order admittance dynamics. A Monte Carlo method is employed to describe the effect of simultaneous faults (short or open circuit) in random network elements in terms of confidence bounds in the frequency-domain admittance profile. Faults in 10% of the elements resulted in only minor changes of the frequency response (up to 3.9 dB in magnitude and 12.5 ∘ in phase in 95% of the cases). An example is presented to illustrate the use of the proposed RC network in the faulttolerant design of a low-pass filter.

concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)\ud of breast tissue are discussed. The algorithms are based on recent advances in multidimensional\ud signal processing and aim to advance current state‐of‐the‐art computer‐aided detection\ud and analysis of breast tumours when these are observed at various states of development. The topics\ud discussed include image feature extraction, information fusion using radiomics, multi‐parametric\ud computer‐aided classification and diagnosis using information fusion of tensorial datasets as well\ud as Clifford algebra based classification approaches and convolutional neural network deep learning\ud methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised\ud strategies as well as generative adversarial networks and algorithms using generated\ud confrontational learning approaches. In order to address the problem of weakly labelled tumour\ud images, generative adversarial deep learning strategies are considered for the classification of\ud different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence\ud (AI) based framework for more robust image registration that can potentially advance the early\ud identification of heterogeneous tumour types, even when the associated imaged organs are\ud registered as separate entities embedded in more complex geometric spaces. Finally, the general\ud structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task\ud detection and learning is proposed as a way forward. The proposed algorithm makes use of novel\ud loss functions that form the building blocks for a generated confrontation learning methodology\ud that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on\ud time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The\ud proposed framework can potentially reduce the costs associated with the interpretation of medical\ud images by providing automated, faster and more consistent diagnosis.

A fractional-order internal model control technique is applied to a three-dimensional resistive-capacitive network to enforce desired closed loop\ud dynamics of first order. In order to handle model mismatch issues resulting from the random allocation of the components within the network, the control law is augmented with a model-reference adaptive strategy in an external loop. By imposing a control law on the system to obey first order dynamics, a calibrated transient response is ensured. The methodology enables feedback control of complex\ud systems with emergent responses and is robust in the presence of measurement noise or under conditions of poor model identification. Furthermore, it is also applicable to systems that exhibit higher order fractional dynamics. Examples of feedback-controlled transduction include cantilever positioning in atomic force microscopy or the control of complex de-excitation lifetimes encountered in\ud many types of spectroscopies, e.g., nuclear magnetic, electron-spin, microwave, multiphoton fluorescence, Förster resonance, etc. The proposed solution should also find important applications in more complex electronic, microwave, and photonic lock-in problems. Finally, there are further applications across the broader measurement science and instrumentation community when designing complex feedback systems at the system level, e.g., ensuring the adaptive control of distributed physiological processes through the use of biomedical implants.