Advancing spaceborne tools for the characterization of planetary ionospheres and circumstellar environments

This work explores remote sensing of planetary atmospheres and their circumstellar surroundings. The terrestrial ionosphere is a highly variable space plasma embedded in the thermosphere. Generated by solar radiation and predominantly composed of oxygen ions at high altitudes, the ionosphere is dynamically and chemically coupled to the neutral atmosphere. Variations in ionospheric plasma density impact radio astronomy and communications. Inverting observations of 83.4 nm photons resonantly scattered by singly ionized oxygen holds promise for remotely sensing the ionospheric plasma density. This hypothesis was tested by comparing 83.4 nm limb profiles recorded by the Remote Atmospheric and Ionospheric Detection System aboard the International Space Station to a forward model driven by coincident plasma densities measured independently via ground-based incoherent scatter radar. A comparison study of two separate radar overflights with different limb profile morphologies found agreement between the forward model and measured limb profiles. A new implementation of Chapman parameter retrieval via Markov chain Monte Carlo techniques quantifies the precision of the plasma densities inferred from 83.4 nm emission profiles. This first study demonstrates the utility of 83.4 nm emission for ionospheric remote sensing. Future visible and ultraviolet spectroscopy will characterize the composition of exoplanet atmospheres; therefore, the second study advances technologies for the direct imaging and spectroscopy of exoplanets. Such spectroscopy requires the development of new technologies to separate relatively dim exoplanet light from parent star light. High-contrast observations at short wavelengths require spaceborne telescopes to circumvent atmospheric aberrations. The Planet Imaging Concept Testbed Using a Rocket Experiment (PICTURE) team designed a suborbital sounding rocket payload to demonstrate visible light high-contrast imaging with a visible nulling coronagraph. Laboratory operations of the PICTURE coronagraph achieved the high-contrast imaging sensitivity necessary to test for the predicted warm circumstellar belt around Epsilon Eridani. Interferometric wavefront measurements of calibration target Beta Orionis recorded during the second test flight in November 2015 demonstrate the first active wavefront sensing with a piezoelectric mirror stage and activation of a micromachine deformable mirror in space. These two studies advance our ``close-to-home'' knowledge of atmospheres and move exoplanetary studies closer to detailed measurements of atmospheres outside our solar system

Self-Organized Model Predictive Control for Air Traffic Management

In this paper a distributed model predictive control has been proposed for air traffic management problem in which aircraft use optimization to determine their own flight trajectories. The coordination approach of Self-organized Time Division Multiple Access is used to ensure no two aircraft re-plan their trajectories simultaneously. Unlike existing distributed predictive control, which needs a pre-organized optimizing sequence, this new approach requires no central coordination. By also terminating every trajectory with a loitering circle, recursive feasibility and constraint satisfaction, especially separation, can be guaranteed

Elastic Resource Management in Distributed Clouds

The ubiquitous nature of computing devices and their increasing reliance on remote resources have driven and shaped public cloud platforms into unprecedented large-scale, distributed data centers. Concurrently, a plethora of cloud-based applications are experiencing multi-dimensional workload dynamics---workload volumes that vary along both time and space axes and with higher frequency. The interplay of diverse workload characteristics and distributed clouds raises several key challenges for efficiently and dynamically managing server resources. First, current cloud platforms impose certain restrictions that might hinder some resource management tasks. Second, an application-agnostic approach might not entail appropriate performance goals, therefore, requires numerous specific methods. Third, provisioning resources outside LAN boundary might incur huge delay which would impact the desired agility. In this dissertation, I investigate the above challenges and present the design of automated systems that manage resources for various applications in distributed clouds. The intermediate goal of these automated systems is to fully exploit potential benefits such as reduced network latency offered by increasingly distributed server resources. The ultimate goal is to improve end-to-end user response time with novel resource management approaches, within a certain cost budget. Centered around these two goals, I first investigate how to optimize the location and performance of virtual machines in distributed clouds. I use virtual desktops, mostly serving a single user, as an example use case for developing a black-box approach that ranks virtual machines based on their dynamic latency requirements. Those with high latency sensitivities have a higher priority of being placed or migrated to a cloud location closest to their users. Next, I relax the assumption of well-provisioned virtual machines and look at how to provision enough resources for applications that exhibit both temporal and spatial workload fluctuations. I propose an application-agnostic queueing model that captures the resource utilization and server response time. Building upon this model, I present a geo-elastic provisioning approach---referred as geo-elasticity---for replicable multi-tier applications that can spin up an appropriate amount of server resources in any cloud locations. Last, I explore the benefits of providing geo-elasticity for database clouds, a popular platform for hosting application backends. Performing geo-elastic provisioning for backend database servers entails several challenges that are specific to database workload, and therefore requires tailored solutions. In addition, cloud platforms offer resources at various prices for different locations. Towards this end, I propose a cost-aware geo-elasticity that combines a regression-based workload model and a queueing network capacity model for database clouds. In summary, hosting a diverse set of applications in an increasingly distributed cloud makes it interesting and necessary to develop new, efficient and dynamic resource management approaches

Deep Learning-Based Attack Detection and Classification in Android Devices.

The increasing proliferation of Androidbased devices, which currently dominate the market with a staggering 72% global market share, has made them a prime target for attackers. Consequently, the detection of Android malware has emerged as a critical research area. Both academia and industry have explored various approaches to develop robust and efficient solutions for Android malware detection and classification, yet it remains an ongoing challenge. In this study, we present a supervised learning technique that demonstrates promising results in Android malware detection. The key to our approach lies in the creation of a comprehensive labeled dataset, comprising over 18,000 samples classified into five distinct categories: Adware, Banking, SMS, Riskware, and Benign applications. The effectiveness of our proposed model is validated using well-established datasets such as CICMalDroid2020, CICMalDroid2017, and CICAndMal2017. Comparing our results with state-of-the-art techniques in terms of precision, recall, efficiency, and other relevant factors, our approach outperforms other semi-supervised methods in specific parameters. However, we acknowledge that our model does not exhibit significant deviations when compared to alternative approaches concerning certain aspects. Overall, our research contributes to the ongoing efforts in the development of advanced techniques for Android malware detection and classification. We believe that our findings will inspire further investigations, leading to enhanced security measures and protection for Android devices in the face of evolving threats.Partial funding for open access charge: Universidad de Málag

Privacy in the Smart City - Applications, Technologies, Challenges and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities

Tree stem volume estimation from terrestrial lidar point cloud by unwrapping

Estimating the volume of standing trees is a fundamental concern in forestry and is typically accomplished using one or more measurements of stem diameter along with formulae that assume geometric primitives. In contrast, technologies such as terrestrial Light Detection And Ranging (LiDAR) can record very detailed spatial information on the actual surface of an object, such as a tree bole.We present a method using LiDAR that provides accurate volume estimates of tree stems, as well as 2D rasters that display details of stem surfaces, which we call the “unwrapping method.” This method combines the concepts of cylinder fitting, voxelization, and digital elevation models. The method is illustrated and tested using a sample of standing trees, whereby we are able to generate accurate volume estimates from the point cloud, as well as accurate visualization of the scanned stem sections. When compared to volume estimates derived from Huber’s, Smalian’s, and Newton’s formulae, the differences are consistent with previous studies comparing formula-derived volume estimates and water-displacement-derived volume estimates, suggesting the unwrapping method has comparable accuracy to water displacement

Integrating CSI Sensing in Wireless Networks: Challenges to Privacy and Countermeasures

The path toward 6G is still long and blurred, but a few key points seem to be already decided: integration of many different access networks; adoption of massive MIMO technologies; use of frequencies above current radio spectrum up to THz and beyond; and inclusion of artificial intelligence and machine learning in standard management and operations. One additional point that is less discussed, but seems key for success, is the advanced use of channel state information (CSI) for both equalization and decoding purposes as well as for sensing ones. CSI-based sensing promises a plethora of new applications and a quantum leap in service personalization and customer-centric network management. At the same time, CSI analysis, being based on the physical characteristics of the propagated signal, poses novel threats to people's privacy and security: No software-based solution or cryptographic method above the physical layer can prevent the analysis of CSI. CSI analysis can reveal people's position or activity, allow tracking them, and discover details on the environment that today can be seen only with cameras or radars. In this article, we discuss the current status of CSI-based sensing and present some technologies that can protect people's privacy and at the same time allow legitimate use of the information carried by the CSI to offer better services

A genetic screen probing for neural circuitry associated with hedonic behavior in Drosophila Melanogaster

The positive or negative affect that an animal has to an external stimulus is known as valence. In order to survive, an organism’s brain must appropriately encode pleasurable and unpleasurable stimuli and ascribe a specific valence to them in order that the animal correctly learn appetitive and aversive behaviors. In this study, optogenetic tools were employed to conduct a genetic screen using Drosophila Melanogaster. Several lines of flies that displayed unusual phenotypes were identified, suggesting that when given the choice, these flies preferred to remain on an otherwise aversive stimulus (noxious heat) if it meant either stimulating or inhibiting certain neurons. We hypothesized that along with dopamine, and octopamine, the regulation of serotonin in may have a fundamental influence on both valence (the extent to which an organism is in a state of pleasure) and also copulation duration. Once these phenotypes were established, we then explored how altering the parameters of their environment would change their valence. The findings suggest that flies may indeed have a neurological correlate for feeling pleasure or displeasure