We demonstrate a novel agentic control architecture for dynamic edge network reconfiguration, featuring a dual—Model Context Protocol (MCP) deployment: one instance at the gNodeB and another at the User Equipment (UE), coordinated via a shared client interface. A large language model (LLM) receives high-level user intent (e.g., "improve video call stability") and constructs a semantic action plan conditioned on real-time context and validated operational constraints. The system enables collaborative reasoning across the UE and RAN components, with human-in-the-loop authorization governing execution. Unlike conventional APIs or policy engines, MCP supports reversibility, semantic validation, and learning from feedback. This demonstration highlights the potential for intent-driven, context-aware, verifiable autonomy in edge wireless systems.

One of the important issues associated with real-life high-dimensional data analysis is how to extract significant and relevant features from multiview data. The multiset canonical correlation analysis (MCCA) is a well-known statistical method for multiview data integration. It finds a linear subspace that maximizes the correlations among different views. However, the existing methods to find the multiset canonical variables are computationally very expensive, which restricts the application of the MCCA in real-life big data analysis. The covariance matrix of each high-dimensional view may also suffer from the singularity problem due to the limited number of samples. Moreover, the MCCA-based existing feature extraction algorithms are, in general, unsupervised in nature. In this regard, a new supervised feature extraction algorithm is proposed, which integrates multimodal multidimensional data sets by solving maximal correlation problem of the MCCA. A new block matrix representation is introduced to reduce the computational complexity for computing the canonical variables of the MCCA. The analytical formulation enables efficient computation of the multiset canonical variables under supervised ridge regression optimization technique. It deals with the "curse of dimensionality" problem associated with high-dimensional data and facilitates the sequential generation of relevant features with significantly lower computational cost. The effectiveness of the proposed multiblock data integration algorithm, along with a comparison with other existing methods, is demonstrated on several benchmark and real-life cancer data.

One of the major problems in real-life multiblock dynamic data analysis is that all the available modalities may not be relevant. Some of them may provide noisy or even inconsistent information with respect to other modalities. So, it is necessary to evaluate the quality of a new modality before considering it for feature extraction. In this regard, the paper introduces a new multiset canonical correlation analysis (MCCA), termed as incremental MCCA (IMCCA). When a new modality is available for the analysis, the IMCCA generates the new canonical variables from that of the earlier modalities, without repeating the same procedure with the original data augmented by the new modality. The proposed IMCCA deals with the “curse of dimensionality” problem associated with multidimensional data sets, by using the ridge regression optimization technique. Using the proposed IMCCA model, a new feature extraction algorithm is introduced, which considers a new modality for the analysis if it has relevant and significant information with respect to existing modalities. The proposed algorithm starts with the two most relevant modalities, and the remaining modalities are added sequentially according to their relevance. The optimum regularization parameters for the proposed algorithm are estimated based on the supervised information of sample categories. The effectiveness of the proposed algorithm, along with a comparison with state-of-the-art multimodal data integration methods, is established on several real-life multiblock data sets.

One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features. To generate canonical variables sequentially, an analytical formulation is introduced to establish the relation between regularization parameters and CCA. The formulation enables the proposed method to extract required number of correlated features sequentially with lesser computational cost as compared to existing methods. To compute both significance and relevance measures of a feature, the concept of hypercuboid equivalence partition matrix of rough hypercuboid approach is used. It also provides an efficient way to find optimum regularization parameters employed in CCA. The efficacy of the proposed FaRoC algorithm, along with a comparison with other existing methods, is extensively established on several real life data sets.

One of the main problems in real life omics data analysis is how to extract relevant and non-redundant features from high dimensional multimodal data sets. In general, supervised regularized canonical correlation analysis (SRCCA) plays an important role in extracting new features from multimodal om ics data sets. However, the existing SRCCA optimizes regularization parameters based on the quality of first pair of canonical variables only using standard feature evaluation indices. In this regard, this paper introduces a new SRCCA algorithm, integrating judiciously the merits of SRCCA and rough hypercuboid approach, to extract relevant and non-redundant features in approximation spaces from multimodal omics data sets. The proposed method optimizes regularization parameters of the SRCCA based on the quality of a set of pairs of canonical variables using rough hypercuboid approach. While the rough hypercuboid approach provides an efficient way to calculate the degree of dependency of class labels on feature set in approximation spaces, the merit of SRCCA helps in extracting non-redundant features from multimodal data sets. The effectiveness of the proposed approach, along with a comparison with related existing approaches, is demonstrated on several real life data sets.