Characterization of interaction between the nucleus-encoded TBC2 protein with the 5'-untranslated region of psbC mRNA and associated factors in Chlamydomonas reinhardtii

This thesis has three main objectives, the main focus being a study of the TBC2 translational activation protein and its association with the psbC mRNA 5'-untranslated region. The translated TBC2 cDNA sequence had two paralogous protein sequences located within possible nucleotide open reading frames designated TBC2A and TBC2B , and all contained sequences resembling chloroplast transit peptides. TBC2Ap and TBC2Bp harbored the novel PPPEW repeat originally found in TBC2p. The second part of this thesis encompasses the theorized existence of a novel thylakoid biogenesis compartment in the C. reinhardtii chloroplast. Pulse-labeling experiments revealed equivalent synthesis of envelope-like membrane proteins in light versus dark growth conditions, providing no evidence supporting the transport theory through a novel thylakoid biogenesis compartment. The final topic of this thesis addresses the theory of chloroplast genome localization, paralleled to prior findings that the PEND protein in peas binds plastid nucleoids to the envelope membrane. A restriction digest determined that DNA found associated with low density membranes was only nuclea

Population adaptation for genetic algorithm-based cognitive radios

Abstract — Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle. I

A Systems Approach for Solving Inter-Policy Gaps in Dynamic Spectrum Access-Based Wireless Rural Broadband Networks

In this paper, we articulate the challenge of multiple intersecting policies for the realization of rural broadband networks employing dynamic spectrum access (DSA). Broadband connectivity has been identified as a critical component of economic development, especially during the COVID-19 pandemic, and rural communities have been significantly (and negatively) affected by the lack of this important resource. Although technologies exist that can deliver broadband connectivity, such as 4G LTE and 5G cellular networks, the challenges associated with efficiently deploying this infrastructure within a rural environment are multi-dimensional in terms of the different dependent policy decisions that need to be considered. To resolve this issue, we describe how systems engineering tools can be used for representing these intersecting policies such that system configurations can be optimized for efficient infrastructure deployment and operations. One technology requiring increased attention is DSA, where licensed and emerging wireless services can coexist together via spectrum sharing. However, implementation of this technology is challenging, where highly efficient Radio Access Technology (RAT), available spectrum, and user requirements need to be precisely aligned. All these elements to be configured are typically described by independent policies. While DSA is more complicated than previously used spectrum allocation schemes, inter-policy gaps occur that ultimately decrease the network\u27s efficiency. Consequently, a systems engineering framework has the potential to obtain the optimal solutions although the systems and wireless communities conceptualize and scope problems differently, which can impede collaboration. We present the use case where 4G LTE RAT technology employing DSA applied to digital terrestrial television (DTT) frequency bands can yield spectral efficiency loss when the different policy dimensions are not sufficiently accounted for within the use case. Computer simulations have shown that in an example rural scenario the availability of rural broadband can increase from 1% to 21% of locations if the inter-policy gaps are removed

The future of vehicular security and privacy [from the guest editors]

This special issue of IEEE Vehicular Technology Magazine presents the latest findings and perspectives on the emerging and important area of vehicular security and privacy. Five articles form this special issue, all of which introduce a breadth of new solutions that will help the community combat the growing threat of vehicular cyberattacks. These solutions include cryptographic methods, blockchain, and new architectural considerations for CAN to protect these transportation systems both from a wireless perspective as well as from inside the vehicle itself

Implementation of a Space Communications Cognitive Engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC)

Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station

Measurements and Analysis of Secondary User Device Effects on Digital Television Receivers

This is the published version. Copyright © 2009 Newman et al.This article presents results from a study of the potential effects of secondary users operating in unoccupied television spectrum. Television spectrum is known within the wireless communications community as being underutilized, making it a prime candidate for dynamic spectrum access. The proposed use of this open spectrum has prompted questions concerning the quantity of available channel space that could be used without negative impact on consumers who view digital television broadcasts and the viability of secondary use of open channels immediately adjacent to a digital television broadcast channel. In this work, we investigate secondary device operation in the channels directly adjacent to a desired television channel, and the effects upon a selection of consumer digital television (DTV) receivers. Our observations strongly suggest that secondary users could operate "White Space Devices" (WSDs) in unoccupied channel bandwidth directly adjacent to a desired digital television (DTV) channel, with no observable adverse impact upon the reception of the desired channel content