Pankaj Bhowmik

Md. Abid Hasan Rafi, Mst. Fatematuj Johora, Pankaj Bhowmik

The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visualization in network slicing for next-generation networking systems. The dataset transforms multivariate Key Performance Indicator (KPI) vectors into visual representations through four distinct encoding methods: physically inspired mappings, Perlin noise, neural wallpapering, and fractal branching. For each encoding method, 30,000 samples are generated, each comprising a raw KPI vector and a corresponding RGB image at low-resolution pixels. The dataset simulates realistic and noisy network conditions to reflect operational uncertainties and measurement imperfections. SliceVision-F2I is suitable for tasks involving visual learning, network state classification, anomaly detection, and benchmarking of image-based machine learning techniques applied to network data. The dataset is publicly available and can be reused in various research contexts, including multivariate time series analysis, synthetic data generation, and feature-to-image transformations.

Md Jubaer Hossain Pantho, Joel Mandebi Mbongue, Pankaj Bhowmik et al.

In recent years, there has been a growing interest in realizing methodologies to integrate more and more computation at the level of the image sensor. The rising trend has seen an increased research interest in developing novel event cameras that can facilitate CNN computation directly in the sensor. However, event-based cameras are not generally available in the market, limiting performance exploration on high-level models and algorithms. This paper presents an event camera simulator that can be a potent tool for hardware design prototyping, parameter optimization, attention-based innovative algorithm development, and benchmarking. The proposed simulator implements a distributed computation model to identify relevant regions in an image frame. Our simulator's relevance computation model is realized as a collection of modules and performs computations in parallel. The distributed computation model is configurable, making it highly useful for design space exploration. The Rendering engine of the simulator samples frame-regions only when there is a new event. The simulator closely emulates an image processing pipeline similar to that of physical cameras. Our experimental results show that the simulator can effectively emulate event vision with low overheads.