Analysis Framework for Opportunistic Spectrum OFDMA and its Application to the IEEE 802.22 Standard

We present an analytical model that enables throughput evaluation of Opportunistic Spectrum Orthogonal Frequency Division Multiple Access (OS-OFDMA) networks. The core feature of the model, based on a discrete time Markov chain, is the consideration of different channel and subchannel allocation strategies under different Primary and Secondary user types, traffic and priority levels. The analytical model also assesses the impact of different spectrum sensing strategies on the throughput of OS-OFDMA network. The analysis applies to the IEEE 802.22 standard, to evaluate the impact of two-stage spectrum sensing strategy and varying temporal activity of wireless microphones on the IEEE 802.22 throughput. Our study suggests that OS-OFDMA with subchannel notching and channel bonding could provide almost ten times higher throughput compared with the design without those options, when the activity and density of wireless microphones is very high. Furthermore, we confirm that OS-OFDMA implementation without subchannel notching, used in the IEEE 802.22, is able to support real-time and non-real-time quality of service classes, provided that wireless microphones temporal activity is moderate (with approximately one wireless microphone per 3,000 inhabitants with light urban population density and short duty cycles). Finally, two-stage spectrum sensing option improves OS-OFDMA throughput, provided that the length of spectrum sensing at every stage is optimized using our model

Hybrid-Fusion Transformer for Multisequence MRI

Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer. However, the different characteristics of the modality have not been fully integrated into Transformer for medical segmentation. In this work, we propose the novel hybrid fusion Transformer (HFTrans) for multisequence MRI image segmentation. We take advantage of the differences among multimodal MRI sequences and utilize the Transformer layers to integrate the features extracted from each modality as well as the features of the early fused modalities. We validate the effectiveness of our hybrid-fusion method in three-dimensional (3D) medical segmentation. Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the proposed method outperforms previous state-of-the-art methods on the task of brain tumor segmentation and brain structure segmentation.Comment: 10 pages, 4 figure

Founding SAP Student User Group (SUG) at Southern Illinois University Carbondale - SIU SUG

Enterprise Computing has quickly become of paramount importance for businesses vying to survive in today’s unlimited competition market. SAP software has become a front runner and leader of business and technical innovation in the enterprise computing industry. Major fortune 500 companies are using SAP software as their main operating software. As the need for individuals knowledgeable on SAP has increased dramatically, learning SAP is becoming important for the market of employmen

A cultural political economy of South Korea's development model in variegated capitalism

This thesis investigates the Park Chung Hee model (PCHM). This term refers to a South Korean variant of the East Asian model of capitalism—particularly, the historical model that guided the rapid and sustained growth of the economy since the mid-1960s. This historical investigation is theoretically informed by a cultural political economy of variegated capitalism (VarCap-CPE) that enables a differential and integral exploration of both historical and contemporary capitalism. In this context, my contribution is twofold. The first is theoretical. While a theoretically informed historical investigation into East Asian capitalism requires an approach to (post-)colonialism, imperialism and hegemony as a prerequisite, VarCap-CPE has still not fully integrated such an approach into its analytical framework. So, my first aim is to improve this paradigm by drawing on Marx’s insights into colonialism, the world market, and international hegemony and propose how they might be put in their place, provisionally, in a VarCap-CPE analysis. My second goal is empirical. Based on the enhanced version of the VarCap-CPE, I aim to give a better account of the PCHM than previous literature in political economy. Specifically, I show how the model was informed by two contradictory state strategies: (1) the fascist and autarkic state strategies of Imperial Japan; and (2) the liberal and free trade-oriented developmentalism, based on W.W. Rostow’s modernization theory. I thereby demonstrate that the PCHM was self-contradictory and, in this context, present it as a “chimerical” model that combines in a contradictory manner the DNA of two rival species. On this basis, I provide an integral account of its seemingly miraculous performance as well as the dilemmas, contradictions and crisis-proneness that beset it. In addition, unlike much of the extant literature on the Park model, my analysis permits theoretically consistent further research into its crisis and subsequent neoliberalisation

SplitAMC: Split Learning for Robust Automatic Modulation Classification

Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency.Comment: to be presented at IEEE VTC2023-Sprin