arXiv subjects

Vipin Chaudhary

Publications and source records attributed to Vipin Chaudhary.

78 records · Page 5Linked to original sources

arXiv2022

Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision

Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are compute-intensive because they indiscriminately compute many features on all pixels of the input image. We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task. For example, if the task is looking for cars, pixels in the sky are not very useful. Therefore, we propose that a CNN be modified to only operate on relevant pixels to save computation and energy. We propose a method to study three popular computer vision datasets, finding that 48% of pixels are irrelevant. We also propose the focused convolution to modify a CNN's convolutional layers to reject the pixels that are marked irrelevant. On an embedded device, we observe no loss in accuracy, while inference latency, energy consumption, and multiply-add count are all reduced by about 45%.

cs.CV↗

arXiv2022

Give me a knee radiograph, I will tell you where the knee joint area is: a deep convolutional neural network adventure

Knee pain is undoubtedly the most common musculoskeletal symptom that impairs quality of life, confines mobility and functionality across all ages. Knee pain is clinically evaluated by routine radiographs, where the widespread adoption of radiographic images and their availability at low cost, make them the principle component in the assessment of knee pain and knee pathologies, such as arthritis, trauma, and sport injuries. However, interpretation of the knee radiographs is still highly subjective, and overlapping structures within the radiographs and the large volume of images needing to be analyzed on a daily basis, make interpretation challenging for both naive and experienced practitioners. There is thus a need to implement an artificial intelligence strategy to objectively and automatically interpret knee radiographs, facilitating triage of abnormal radiographs in a timely fashion. The current work proposes an accurate and effective pipeline for autonomous detection, localization, and classification of knee joint area in plain radiographs combining the You Only Look Once (YOLO v3) deep convolutional neural network with a large and fully-annotated knee radiographs dataset. The present work is expected to stimulate more interest from the deep learning computer vision community to this pragmatic and clinical application.

eess.IV↗

arXiv2016

Quantified Spectrum Sharing: Motivation, Approach, and Benefits

A significant portion of the radio frequency spectrum remains underutilized with exclusive and static allocation of spectrum. The growing demand for spectrum has spurred a need for dynamic spectrum sharing paradigm. While the new dynamic spectrum sharing paradigm helps to improve utilization of the precious spectrum resource, there exist several obstacles on the technical, regulatory, and business fronts for the adoption of the new paradigm. In this paper, we investigate the limitations of the existing techniques and argue for quantified approach to dynamic spectrum sharing and management. We introduce a quantified approach to spectrum sharing based on defining and enforcing quantified spectrum-access rights. By discretizing the spectrum-space in the time, space, frequency dimensions, this approach enables quantifying the spectrum consumed by individual transceivers. It enables defining and enforcing a quantified spectrum-access policy in real-time. The proposed quantified approach brings in simplicity, precision and efficiency in terms of spectrum commerce and operations while addressing the key technical and regulatory challenges.

cs.NI↗

arXiv2015

MUSE: A Methodology for Characterizing and Quantifying the Use of Spectrum

Dynamic spectrum sharing paradigm is envisaged to meet the growing demand for the Radio Frequency (RF) spectrum. There exist several technical, regulatory, and business impediments for adopting the new paradigm. In this regard, we underscore the need of characterizing and quantifying the use of spectrum by each of the individual transmitters and receivers. We propose MUSE, a methodology to characterize and quantify the use of spectrum in the space, time, and frequency dimensions. MUSE characterizes the use of spectrum by a transmitter at a point in terms of the RF power occupied by the transmitter. It characterizes the use of spectrum by a receiver at a point in terms of the constraints on the RF-power that can be occupied by any of the transmitters in the system in order to ensure successful reception. It divides the spectrum-space into discrete unit-spectrum-spaces and quantifies the spectrum used by the individual transceivers in the discretized spectrum space. We characterize the performance of the spectrum management functions in the discretized spectrum-space and illustrate maximizing the use of spectrum. In order to address the challenges for the dynamic spectrum sharing paradigm, we emphasize on articulating, defining, and enforcing the spectrum-access rights in the discretized spectrum-space.

cs.NI↗

arXiv2014

Maximizing Spectrum Availability and Exploitation: How to Maximize Spectrum Sharing Benefits to the Incumbents?

A significant portion of the radio frequency spectrum remains underutilized due to exclusive and static allocation of spectrum. Provisioning secondary access to the underutilized spectrum could be beneficial to the incumbents if they could gain significant value out of the fallow spectrum while ensuring protection of their primary services. From an incumbent perspective, the spectrum sharing approach needs to be non-harmful as well as efficient. In order to make spectrum sharing efficient, it is necessary to maximize the spectrum available for secondary access as well as maximize its exploitation. We examine the impact of the conservative assumptions that lead to lesser availability of spectrum for secondary access. The problem of joint scheduling and spectrum-access footprint allocation is at the heart of maximizing the exploitation. This problem is NP-hard and we present a suboptimal approach based on the minimal spectrum consumption cost of a spectrum-access request. In order to improve the spectrum sharing potential, we investigate the impact of the various design choices for a spectrum access mechanism. The experiments demonstrate the significance of the active role of the incumbents, the benefits of fine granular spectrum access, and the need for transceiver standards for accomplishing efficient usage of the spectrum.

cs.CY↗

arXiv2014

Going Towards Discretized Spectrum Space: Quantification of Spectrum Consumption Spaces and a Quantified Spectrum Access Paradigm

Dynamic spectrum sharing approach is a paradigm shift from the conventional static and exclusive approach to spectrum allocation. The existing methodologies to define use of the spectrum and quantify its efficiency are based on the static spectrum assignment paradigm and not suitable for the dynamic spectrum sharing paradigm. There is a need to separately quantify the spectrum consumed by the individual transmitters and receivers when multiple heterogeneous wireless networks are sharing the spectrum in time, space, and frequency dimensions. By discretizing the spectrum dimensions, we define a methodology for quantifying the spectrum consumption spaces. This is an attempt to adopt the discretized signal processing principle and apply it to spectrum management functions that would bring in simplicity, flexibility, and precision among other advantages.

cs.NI↗