Moral Psychology and Degenerate Regimes in Plato\u27s Republic

The degenerate regimes and individuals have been a neglected topic in the literature on Plato’s moral psychology in the Republic. This thesis contributes to the currently limited literature on degradation, and explores the following issues in the interpretation of Plato: the validity of the city-soul analogy across all regimes, including both the just city and the unjust cities, the cause of degradation, and the bad-making feature in the degenerate regimes. In defense of my account of the badness of degradation, I also examine the hydraulic model of desire, and offer an interpretation that resolves an apparent tension between the model and Plato’s account of the tyrant

Motion analysis from a sequence of range images

The main goal of this work is to demonstrate the feasibility and potential of recovering motion from a sequence of range images as an alternate solution to the complex motion problem. The work presented in this thesis can be divided into two separate parts. The first part describes the long term process, and the second part discusses the short term process. The major problem for the long term process is to reliably find matching features in two or more successive images. An approach is proposed to establish the best match of point features between successive frames using a Hopfield neural network. A model is developed to convert the correspondence problem to the problem of minimizing an energy function, which occurs at the stable state of a Hopfield neural network. After establishing the feature matching, a $\delta$-bound matching concept is introduced to detect the reliable matching features, therefore increase the accuracy of the estimated motion parameters by removing the effect of mismatching features. In this way, the algorithm is tolerant to noise due to feature detection or occlusion. For the short term process, the case of a single rigid moving object is first studied. A simple, yet powerful, algorithm is proposed to estimate motion of a single rigid object. The motion problem is modeled as solving a set of linear equations. A weighted least squares technique has been found to provide the best performance among several other versions of least squares techniques. Theoretical analysis on the necessary and sufficients conditions for the unique interpretation of the motion parameters and on the sensitivity of the estimated motion parameters to noise provides further insight into the behavior of the algorithm. For more complicated motion such as nonrigid motion, the complete process can be viewed in two separate levels: low and high. In this thesis, attention has been paid to the low level processing. A 3D velocity field has been chosen to be the output of the low level stage. We first develop an algorithm which uniquely estimates 3D velocities of points on smooth surfaces by its first and second order partial derivatives, except at parabolic points. The algorithm is very fast and easy to implement in hardware or software. However, it does not provide reliable estimates of velocities near edge points. Hence we propose another algorithm, which is based on the correlation of the local structure of principal curvatures. The advantage of this correlation approach is that it can estimate velocities of both corner points as well as points on smooth curved surfaces, and vernier velocities of line edge points. The disadvantage is that it is computationally intensive compared with the approach for smooth surfaces. Therefore, we suggest that two algorithms should be combined together to give the best performance. Many experimental results on both synthetic and real images are presented in this thesis

A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet

Future integrated terrestrial-aerial-satellite networks will have to exhibit some unprecedented characteristics for the provision of both communications and computation services, and security for a tremendous number of devices with very broad and demanding requirements in an almost-ubiquitous manner. Although 3GPP introduced the concept of self-organization networks (SONs) in 4G and 5G documents to automate network management, even this progressive concept will face several challenges as it may not be sufficiently agile in coping with the immense levels of complexity, heterogeneity, and mobility in the envisioned beyond-5G integrated networks. In the presented vision, we discuss how future integrated networks can be intelligently and autonomously managed to efficiently utilize resources, reduce operational costs, and achieve the targeted Quality of Experience (QoE). We introduce the novel concept of self-evolving networks (SENs) framework, which utilizes artificial intelligence, enabled by machine learning (ML) algorithms, to make future integrated networks fully intelligent and automated with respect to the provision, adaptation, optimization, and management aspects of networking, communications, and computation. To envisage the concept of SEN in future integrated networks, we use the Intelligent Vertical Heterogeneous Network (I-VHetNet) architecture as our reference. The paper discusses five prominent communications and computation scenarios where SEN plays the main role in providing automated network management. Numerical results provide an insight on how the SEN framework improves the performance of future integrated networks. The paper presents the leading enablers and examines the challenges associated with the application of SEN concept in future integrated networks

HAPS for 6G Networks: Potential Use Cases, Open Challenges, and Possible Solutions

High altitude platform station (HAPS), which is deployed in the stratosphere at an altitude of 20-50 kilometres, has attracted much attention in recent years due to their large footprint, line-of-sight links, and fixed position relative to the Earth. Compared with existing network infrastructure, HAPS has a much larger coverage area than terrestrial base stations and is much closer than satellites to the ground users. Besides small-cells and macro-cells, a HAPS can offer one mega-cell, which can complement legacy networks in 6G and beyond wireless systems. This paper explores potential use cases and discusses relevant open challenges of integrating HAPS into legacy networks, while also suggesting some solutions to these challenges. The cumulative density functions of spectral efficiency of the integrated network and cell-edge users are studied and compared with terrestrial network. The results show the capacity gains achieved by the integrated network are beneficial to cell-edge users. Furthermore, the advantages of a HAPS for backhauling aerial base stations are demonstrated by the simulation results

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Uplink Contention Based SCMA for 5G Radio Access

Fifth generation (5G) wireless networks are expected to support very diverse applications and terminals. Massive connectivity with a large number of devices is an important requirement for 5G networks. Current LTE system is not able to efficiently support massive connectivity, especially on the uplink (UL). Among the issues arise due to massive connectivity is the cost of signaling overhead and latency. In this paper, an uplink contention-based sparse code multiple access (SCMA) design is proposed as a solution. First, the system design aspects of the proposed multiple-access scheme are described. The SCMA parameters can be adjusted to provide different levels of overloading, thus suitable to meet the diverse traffic connectivity requirements. In addition, the system-level evaluations of a small packet application scenario are provided for contention-based UL SCMA. SCMA is compared to OFDMA in terms of connectivity and drop rate under a tight latency requirement. The simulation results demonstrate that contention-based SCMA can provide around 2.8 times gain over contention-based OFDMA in terms of supported active users. The uplink contention-based SCMA scheme can be a promising technology for 5G wireless networks for data transmission with low signaling overhead, low delay, and support of massive connectivity.Comment: Submitted to Golobecom 5G workshop 201