An Open Architecture Framework for Electronic Warfare Based Approach to HLA Federate Development

A variety of electronic warfare models are developed in the Electronic Warfare Research Center. An Open Architecture Framework for Electronic Warfare (OAFEw) has been developed for reusability of various object models participating in the electronic warfare simulation and for extensibility of the electronic warfare simulator. OAFEw is a kind of component-based software (SW) lifecycle management support framework. This OAFEw is defined by six components and ten rules. The purpose of this study is to construct a Distributed Simulation Interface Model, according to the rules of OAFEw, and create Use Case Model of OAFEw Reference Conceptual Model version 1.0. This is embodied in the OAFEw-FOM (Federate Object Model) for High-Level Architecture (HLA) based distributed simulation. Therefore, we design and implement EW real-time distributed simulation that can work with a model in C++ and MATLAB API (Application Programming Interface). In addition, OAFEw-FOM, electronic component model, and scenario of the electronic warfare domain were designed through simple scenarios for verification, and real-time distributed simulation between C++ and MATLAB was performed through OAFEw-Distributed Simulation Interface

Carpal Tunnel Syndrome Caused by Space Occupying Lesions

PURPOSE: To evaluate the diagnosis and treatment of the carpal tunnel syndrome (CTS) due to space occupying lesions (SOL). MATERIALS and METHODS: Eleven patients and 12 cases that underwent surgery for CTS due to SOL were studied retrospectively. We excluded SOL caused by bony lesions, such as malunion of distal radius fracture, volar lunate dislocation, etc. the average age was 51 years. There were 3 men and 8 women. Follow-up period was 12 to 40 months with an average of 18 months. the diagnosis of CTS was made clinically and electrophysiologically. in patients with swelling or tenderness on the area of wrist flexion creases, magnetic resonance imaging (MRI) and/or computed tomogram (CT) were additionally taken as well as the carpal tunnel view. We performed conventional open transverse carpal ligament release and removal of SOL. RESULTS: the types of lesion confirmed by pathologic examination were; tuberculosis tenosynovitis in 3 cases, nonspecific tenosynovitis in 2 cases, and gout in one case. Other SOLs were tumorous condition in five cases, and abnormal palmaris longus hypertrophy in 1 case. Tumorous conditions were due to calcifying mass in 4 cases and ganglion in 1 case. Following surgery, all cases showed alleviation of symptom without recurrence or complications. CONCLUSION: in cases with swelling or tenderness on the area of wrist flexion creases, it is important to obtain a carpal tunnel view, and MRI and/or CT should be supplemented in order to rule out SOLs around the carpal tunnel, if necessary.ope

Progressive Fourier Neural Representation for Sequential Video Compilation

Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space and easily reconstruct it through a trainable mapping function. However, NIR methods assume a one-to-one mapping between the target data and representation models regardless of data relevancy or similarity. This results in poor generalization over multiple complex data and limits their efficiency and scalability. Motivated by continual learning, this work investigates how to accumulate and transfer neural implicit representations for multiple complex video data over sequential encoding sessions. To overcome the limitation of NIR, we propose a novel method, Progressive Fourier Neural Representation (PFNR), that aims to find an adaptive and compact sub-module in Fourier space to encode videos in each training session. This sparsified neural encoding allows the neural network to hold free weights, enabling an improved adaptation for future videos. In addition, when learning a representation for a new video, PFNR transfers the representation of previous videos with frozen weights. This design allows the model to continuously accumulate high-quality neural representations for multiple videos while ensuring lossless decoding that perfectly preserves the learned representations for previous videos. We validate our PFNR method on the UVG8/17 and DAVIS50 video sequence benchmarks and achieve impressive performance gains over strong continual learning baselines. The PFNR code is available at https://github.com/ihaeyong/PFNR.git