An End-to-End Multi-Task Learning Model for Image-based Table Recognition

Image-based table recognition is a challenging task due to the diversity of table styles and the complexity of table structures. Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each sub-problem independently using two separate systems. In this paper, we propose an end-to-end multi-task learning model for image-based table recognition. The proposed model consists of one shared encoder, one shared decoder, and three separate decoders which are used for learning three sub-tasks of table recognition: table structure recognition, cell detection, and cell-content recognition. The whole system can be easily trained and inferred in an end-to-end approach. In the experiments, we evaluate the performance of the proposed model on two large-scale datasets: FinTabNet and PubTabNet. The experiment results show that the proposed model outperforms the state-of-the-art methods in all benchmark datasets.Comment: 10 pages, VISAPP2023. arXiv admin note: substantial text overlap with arXiv:2303.0764

Architectures for block Toeplitz systems

In this paper efficient VLSI architectures of highly concurrent algorithms for the solution of block linear systems with Toeplitz or near-to-Toeplitz entries are presented. The main features of the proposed scheme are the use of scalar only operations, multiplications/divisions and additions, and the local communication which enables the development of wavefront array architecture. Both the mean squared error and the total squared error formulations are described and a variety of implementations are given

Training Strategies for Vision Transformers for Object Detection

Vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in evaluation section. These strategies brings down Vision Transformers detectors inference-time even less than traditional single-image based CNN detectors like FCOS. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.Comment: 9 pages, 2 figures, IEEE CVPR WAD'23 conferenc

Fault Secure Encoder and Decoder for NanoMemory Applications

Memory cells have been protected from soft errors for more than a decade; due to the increase in soft error rate in logic circuits, the encoder and decoder circuitry around the memory blocks have become susceptible to soft errors as well and must also be protected. We introduce a new approach to design fault-secure encoder and decoder circuitry for memory designs. The key novel contribution of this paper is identifying and defining a new class of error-correcting codes whose redundancy makes the design of fault-secure detectors (FSD) particularly simple. We further quantify the importance of protecting encoder and decoder circuitry against transient errors, illustrating a scenario where the system failure rate (FIT) is dominated by the failure rate of the encoder and decoder. We prove that Euclidean geometry low-density parity-check (EG-LDPC) codes have the fault-secure detector capability. Using some of the smaller EG-LDPC codes, we can tolerate bit or nanowire defect rates of 10% and fault rates of 10^(-18) upsets/device/cycle, achieving a FIT rate at or below one for the entire memory system and a memory density of 10^(11) bit/cm^2 with nanowire pitch of 10 nm for memory blocks of 10 Mb or larger. Larger EG-LDPC codes can achieve even higher reliability and lower area overhead

Digital television applications

Studying development of interactive services for digital television is a leading edge area of work as there is minimal research or precedent to guide their design. Published research is limited and therefore this thesis aims at establishing a set of computing methods using Java and XML technology for future set-top box interactive services. The main issues include middleware architecture, a Java user interface for digital television, content representation and return channel communications. The middleware architecture used was made up of an Application Manager, Application Programming Interface (API), a Java Virtual Machine, etc., which were arranged in a layered model to ensure the interoperability. The application manager was designed to control the lifecycle of Xlets; manage set-top box resources and remote control keys and to adapt the graphical device environment. The architecture of both application manager and Xlet forms the basic framework for running multiple interactive services simultaneously in future set-top box designs. User interface development is more complex for this type of platform (when compared to that for a desktop computer) as many constraints are set on the look and feel (e.g., TV-like and limited buttons). Various aspects of Java user interfaces were studied and my research in this area focused on creating a remote control event model and lightweight drawing components using the Java Abstract Window Toolkit (AWT) and Java Media Framework (JMF) together with Extensible Markup Language (XML). Applications were designed aimed at studying the data structure and efficiency of the XML language to define interactive content. Content parsing was designed as a lightweight software module based around two parsers (i.e., SAX parsing and DOM parsing). The still content (i.e., text, images, and graphics) and dynamic content (i.e., hyperlinked text, animations, and forms) can then be modeled and processed efficiently. This thesis also studies interactivity methods using Java APIs via a return channel. Various communication models are also discussed that meet the interactivity requirements for different interactive services. They include URL, Socket, Datagram, and SOAP models which applications can choose to use in order to establish a connection with the service or broadcaster in order to transfer data. This thesis is presented in two parts: The first section gives a general summary of the research and acts as a complement to the second section, which contains a series of related publications.reviewe