Power spectrum characterization of systematic coded UW-OFDM systems

Unique word (UW)-OFDM is a newly proposed multicarrier technique that has shown to outperform cyclic prefix (CP)-OFDM in fading channels. Until now, the spectrum of UW-OFDM is not thoroughly investigated. In this paper, we derive an analytical expression for the spectrum taking into account the DFT based implementation of the system. Simulations show that the proposed analytical results are very accurate. Compared to CP-OFDM, we show that UW-OFDM has much lower out-of-band (OOB) radiation, which makes it suitable for systems with strict spectral masks, as e. g. cognitive radios. Further, in this paper, we evaluate the effect of the redundant carrier placement on the spectrum

Barriers to Summer Food Service Program (SFSP) Implementation Before and After COVID-19: A Qualitative, Collective Case Study

Objectives: SFSP, as a federal food assistance program, could ensure that children have sufficient nutritious food during summer. According to a 2016 USDA report, only 0.4–0.6% of eligible children participated in this program in Nebraska, which makes for one of the lowest SFSP participation rates in the U.S. This study aims to better understand how the COVID-19 pandemic has impacted SFSP implementation in urban and rural settings across the state. Conclusions: Insights from this study may inform the development of policies that expand food access to vulnerable families. On the organizational level, this includes providing participating kids and their parents with both meal distribution options - meal sites and delivery options - during summer. On the policy level, this calls for revising the eligibility of SFSP by decreasing the threshold level from 50% to 30% to allow more schools in low-income neighborhoods to participate

Low-Resource Machine Learning Techniques for the Analysis of Online Social Media Textual Data

Low-resource and label-efficient machine learning methods can be described as the family of statistical and machine learning techniques that can achieve high performance without needing a substantial amount of labeled data. These methods include both unsupervised learning techniques, such as LDA, and supervised methods, such as active learning, each providing different benefits. Thus, this dissertation is devoted to the design and analysis of unsupervised and supervised techniques to provide solutions for the following problems: Unsupervised narrative summary extraction for social media content, Social media text classification with Active Learning (AL), Investigating restrictions and benefits of using Curriculum Learning (CL) for social media text classification. For the first problem, we present a framework that can identify the viral topics over time and provide a narrative summary for the identified topics in an unsupervised manner. Our framework can provide such information with varying time resolution. For the second problem, we present a strategy that conducts data sampling based on the local structures in the embedding space of a large pretrained language model. The data selection for annotation is conducted for the data samples that do not belong to a dominant set as these samples are less similar to the rest of the data points, and accordingly, are more challenging for the model. This criterion is a compelling technique that minimizes the need for large annotated datasets. Then for the third problem, we consider similar data difficulty notions to study the impacts of learning from such a curriculum to train models from easy samples first. This is opposite to the idea of active learning. However, instead of learning from a small number of data and disregarding a substantial amount of information, gradual training from easy samples leads to learning a trajectory to a better local minimum. Our study includes curricula based on both heuristics and model-derived

Fast and Effective Bag-of-Visual-Word Model to Pornographic Images Recognition Using the FREAK Descriptor

Recently, the Bag of Visual Word (BoVW) has gained enormous popularity between researchers to object recognition. Pornographic image recognition with respect to computational complexity, appropriate accuracy, and memory consumption is a major challenge in the applications with time constraints such as the internet pornography filtering. Most of the existing researches based on the Bow, using the very popular SIFT and SURF algorithms to description and match detected keypoints in the image. The main problem of these methods is high computational complexity due to constructing the high dimensional feature vectors. This research proposed a BoVW based model by adopting very fast and simple binary descriptor FREAK to speed-up pornographic recognition process. Meanwhile, the keypoints are detected in the ROI of images which improves the recognition speed due to eliminating many noise keypoints placed in the image background. Finally, in order to find the most representational visual-vocabulary, different vocabularies are generated from size 150 to 500 for BoVW. Compared with the similar works, the experimental results show that the proposed model has gained remarkable improvement in the terms of computational complexity

Urban Tree Species Classification Using Aerial Imagery

Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's aerial images and then investigates how state of the art deep Convolutional Neural Network models such as VGG and ResNet handle the classification problem of urban tree aerial images under different parameters. Experimental results show our best model achieves an average accuracy of 60% over 6 tree species

A Comparison of Embedded Deep Learning Methods for Person Detection

Recent advancements in parallel computing, GPU technology and deep learning provide a new platform for complex image processing tasks such as person detection to flourish. Person detection is fundamental preliminary operation for several high level computer vision tasks. One industry that can significantly benefit from person detection is retail. In recent years, various studies attempt to find an optimal solution for person detection using neural networks and deep learning. This study conducts a comparison among the state of the art deep learning base object detector with the focus on person detection performance in indoor environments. Performance of various implementations of YOLO, SSD, RCNN, R-FCN and SqueezeDet have been assessed using our in-house proprietary dataset which consists of over 10 thousands indoor images captured form shopping malls, retails and stores. Experimental results indicate that, Tiny YOLO-416 and SSD (VGG-300) are the fastest and Faster-RCNN (Inception ResNet-v2) and R-FCN (ResNet-101) are the most accurate detectors investigated in this study. Further analysis shows that YOLO v3-416 delivers relatively accurate result in a reasonable amount of time, which makes it an ideal model for person detection in embedded platforms