Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.
Charity organizations struggle to attract donors due to poor UI and varying donor perspectives, resulting in them investing resources in marketing rather than addressing people's needs. Existing funding platforms lack HCI-based solutions, making it difficult for donors to effectively interact with recipient organizations. This donor-recipient relationship is crucial in modern times with advanced technology, but donors often face obstacles while donating or lack options based on demographics, audience insight. The research proposes a new prototype model, called charity360 onwards, that enhances the UI/UX and HCI approach for all existing charity platforms. The model is scalable and applicable to various funding platforms, and visualizes the donor-recipient relationship effectively. The charity360 model utilizes demographic data to establish a trust-based relationship between donors and recipients. Its unique synaptic mapping feature allows for global recipients, giving donors unprecedented control over their charitable contributions. The model includes audience and demographic insights that have not been previously available, providing an easier donation process for both tech-savvy and non-tech-savvy donors. The charity360 model has the potential to amplify donation processes in any society and strengthen integrated welfare efforts among global organizations, making it a valuable addition to the field of charity.
WiMAX networks experience sporadic congestion on uplink when applications running at subscriber stations need more bandwidth to transmit than allocated. With the fast proliferation of mobile Internet, the wireless community has been looking for a framework that can address the issue of impediment on uplink. Due to asymmetric behavior of Internet applications downlink sub-frame is expected to have longer duration as compared to uplink. According to IEEE 806.16 standard for WiMAX the segmentation of TDD frame between uplink and downlink can be dynamically redefined even at runtime. Research contributions so far lack in addressing an optimal strategy for readjustment of uplink and downlink sub-frame boundaries; based on traffic statistics. In this paper, we introduce a mechanism that allows uplink sub-frame to grow, borrowing resources from the downlink sub-frame, if the uplink utilization is high and the downlink is being underutilized. We present here, a framework to dynamically demarcate the TDD frame-duration between uplink and downlink. Proposed algorithm takes into account the present utilization of downlink and reallocates a certain quantum of free resources to uplink. This occurs when uplink observes shortage of bandwidth to transmit. We simulate some test scenarios using OPNET Modeler with and without dynamic reallocation capability. The results of our simulation confirm the effectiveness of proposed algorithm which observes a remarkable decrease in end-to-end packet delay. Also, we observe an improvement in throughput at uplink such that, the performance of downlink remains unaffected.
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