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Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem.Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images.Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process.Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration.

Objective. Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems. Particularly, it is extremely challenging to decode multiclass MI EEG from the same upper limb. This research proposes a novel feature learning approach to address the classification problem of 6-class MI tasks, including imaginary elbow flexion/extension, wrist supination/pronation, and hand close/open within the unilateral upper limb. Approach. Instead of the traditional common spatial pattern (CSP) or filter-bank CSP (FBCSP) manner, the Riemannian geometry (RG) framework involving Riemannian distance and Riemannian mean was directly adopted to extract tangent space (TS) features from spatial covariance matrices of the MI EEG trials. Subsequently, to reduce the dimensionality of the TS features, the algorithm of partial least squares regression was applied to obtain more separable and compact feature representations. Main results. The performance of the learned RG feature representations was validated by a linear discriminative analysis and support vector machine classifier, with an average accuracy of 80.50% and 79.70% on EEG dataset collected from 12 participants, respectively. Significance. These results demonstrate that compared with CSP and FBCSP features, the proposed approach can significantly increase the decoding accuracy for multiclass MI tasks from the same upper limb. This approach is promising and could potentially be applied in the context of MI-based BMI control of a robotic arm or a neural prosthesis for motor disabled patients with highly impaired upper limb.

Abstract-In this paper, a class of nonholonomic chained systems is first converted into two subsystems, and then an explicit exponential decaying term is introduced into the input of the first subsystem to guarantee its controllability. After a state-scaling transformation, a model predictive control (MPC) scheme is proposed for the nonholonomic chained systems. The proposed MPC scheme employs a general projection neural network (GPN) to iteratively solve a quadratic programming (QP) problem over a finite receding horizon. The GPN employed in this paper is proved to be stable in the sense of Lyapunov, and its global convergence to the optimal solution is guaranteed for the reformulated QP. A simulation study is performed to show stable and convergent control performance under the proposed method, irrespective of whether the control input u 1 vanishes or not.Index Terms-General projection neural networks (GPNs), model predictive control (MPC), nonholonomic chained systems, scaling transformation.

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Safe separation between aircraft is the primary consideration in air traffic control. To achieve the required level of assurance for this safety-critical application, the Automated Airspace Concept (AAC) proposes three levels of conflict detection and resolution. Recently, a high-level operational concept was proposed to define the cooperation between components in the AAC. However, the proposed coordination protocol has not been formally studied. We use formal verification techniques to ensure there are no potentially catastrophic design flaws remaining in the AAC design before the next stage of production. We formalize the high-level operational concept, which was previously described only in natural language, in NuSMV and perform model validation by checking against LTL/CTL specifications we derive from the system description. We write LTL specifications describing safe system operations and use model checking for system verification. We employ specification debugging to ensure correctness of both sets of formal specifications and model abstraction to reduce model checking time and enable fast, design-time checking. We analyze two counterexamples revealing unexpected emergent behaviors in the operational concept that triggered design changes by system engineers to meet safety standards. Our experience report illuminates the application of formal methods in real safety-critical system development by detailing a complete end-to-end design-time verification process including all models and specifications.

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