Conventional image set methods typically learn from image sets stored in a single location. However, in real-world applications, image sets are often distributed across different locations. Learning from such distributed sets using deep neural networks poses challenges for efficient image set classification and retrieval. To address this, we propose Distributed Cascade Manifold Hashing Network (DCMHN) for compact image set representation. DCMHN represents each image set using an SPD manifold and utilizes a manifold hashing network to generate hash codes, enabling efficient classification and retrieval. The network is trained in a cascaded manner, where the bilinear mapping in the BiMap layer is learned first, followed by joint learning of the hash function and classifier in the hash layer. DCMHN enforces local consistency on global variables across neighboring nodes, allowing parallel optimization. Extensive experiments on three benchmark image set datasets demonstrate that the proposed DCMHN achieves competitive accuracies in distributed settings, and outperforms state-of-the-arts in terms of computation and storage efficiency.

Visual Concept Implantation (VCI) is essential in text-to-image fields. While VCI methods in diffusion models have matured, Visual Concept Disentanglement (VCD) remains an unexplored area. VCD involves analyzing Prompt Spaces trained in VCI to produce disentangled SubPrompts or SubCones for exploring interpretability. However, challenges arise due to Prompt Space design complexity and feature information extraction. We propose Picasso, a unified framework for VCD in diffusion models (DM). Our contributions include: for performance evaluation: Transforming VCD in DM into a regular clustering task by Visualization based on SubCones (VbSC); for unified framework design: Picasso processes diverse Prompt Spaces using spatially clusterable features; for Prompt Space exploration: Introducing a temporal-spatial SOTA Prompt Design subset based on temporal features. Our method provides a feasible mechanism for VCD in DM. Through functional and interpretability validation methods, we will comprehensively evaluate the effectiveness of our proposed method in visual concept implantation tasks and verify the correctness of the parameter space design principles. Picasso will be released at https://haoyu-cai.github.io/our_picasso .

A Wireless Power Transfer (WPT) system with a simplified Receiver (RX) is proposed for Implantable Medical Devices (IMDs) in this paper. This system utilizes LCC-S compensation of the coupling coils for rough voltage regulation and global control for fine voltage regulation and high efficiency. To further reduce the cost, data transmission is realized based on the coupling coils. This method minimizes the components in the receiver, reducing the receiver volume and making it very suitable to wirelessly power the IMDs. A prototype is built up and the board-level experimental results validate the effectiveness of the proposed method. With the modulation signal based on Load Shift Keying (LSK), data communication through the energy link and adjustment of transmitting power in primary side, the system can adjust output voltage automatically. From the measurement results, the system power efficiency can achieve 49% at 5.1 V output voltage.