Semantic classification of rural and urban images using learning vector quantization

One of the major hurdles in semantic image classification is that only low-level features can be reliably extracted from images as opposed to higher level features (objects present in the scene and their inter-relationships). The main challenge lies in grouping images into semantically meaningful categories based on the available low-level visual features of the images. It is important that we have a classification method that will handle a complex image dataset with not so well defined boundaries between clusters. Learning Vector Quantization (LVQ) neural networks offer a great deal of robustness in clustering complex datasets. This study presents a semantic image classification using LVQ neural network that uses low level texture, shape, and color features that are extracted from images from rural and urban domains using the Box Counting Dimension method (Peitgen et al. 1992), Fast Fourier Transformation and HSV color space. The performance measures precision and recall were calculated while using various ranges of input parameters such as learning rate, iterations, number of hidden neurons for the LVQ network. The study also tested for the feature robustness for image object orientation (rotation and position) and image size. Our method was compared against the method given in Prabhakar et al, 2002. The precision and recall while using various combination of texture, shape, and color features for our method was between .68 and .88, and 0.64 and .90 respectively compared against the precision and recall (for our image data set) of 0.59 and .62 for the method given by Prabhakar et al., 2002

Cyber Security Assessment of the Robot Operating System 2 for Aerial Networks

Best Student Paper, 2nd PlaceThe article of record as published may be found at https://doi.org/10.1109/SYSCON.2019.88368242019 IEEE International Systems Communications Conference (SYSCON)The Robot Operating System (ROS) is a widely adopted standard robotic middleware. However, its preliminary design is devoid of any network security features. Military grade unmanned systems must be guarded against network threats. ROS 2 is built upon the Data Distribution Service (DDS) standard and is designed to provide solutions to identified ROS 1 security vulnerabilities by incorporating authentication, encryption, and process profile features, which rely on public key infrastructure. The Department of Defense is looking to use ROS 2 for its military-centric robotics platform. This paper seeks to demonstrate that ROS 2 and its DDS security architecture can serve as a functional platform for use in military grade unmanned systems, particularly in unmanned Naval aerial swarms. In this paper, we focus on the viability of ROS 2 to safeguard communications between swarms and a ground control station (GCS). We test ROS 2’s ability to mitigate and withstand certain cyber threats, specifically that of rogue nodes injecting unauthorized data and accessing services that will disable parts of the UAV swarm. We use the Gazebo robotics simulator to target individual UAVs to ascertain the effectiveness of our attack vectors under specific conditions. We demonstrate the effectiveness of ROS 2 in mitigating the chosen attack vectors but observed a measurable operational delay within our simulations.This work was funded and sponsored by the Office of Naval Research via the Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) at NPS

Mapping Iterative Medical Imaging Algorithm on Cell Accelerator

Algebraic reconstruction techniques require about half the number of projections as that of Fourier backprojection methods, which makes these methods safer in terms of required radiation dose. Algebraic reconstruction technique (ART) and its variant OS-SART (ordered subset simultaneous ART) are techniques that provide faster convergence with comparatively good image quality. However, the prohibitively long processing time of these techniques prevents their adoption in commercial CT machines. Parallel computing is one solution to this problem. With the advent of heterogeneous multicore architectures that exploit data parallel applications, medical imaging algorithms such as OS-SART can be studied to produce increased performance. In this paper, we map OS-SART on cell broadband engine (Cell BE). We effectively use the architectural features of Cell BE to provide an efficient mapping. The Cell BE consists of one powerPC processor element (PPE) and eight SIMD coprocessors known as synergetic processor elements (SPEs). The limited memory storage on each of the SPEs makes the mapping challenging. Therefore, we present optimization techniques to efficiently map the algorithm on the Cell BE for improved performance over CPU version. We compare the performance of our proposed algorithm on Cell BE to that of Sun Fire ×4600, a shared memory machine. The Cell BE is five times faster than AMD Opteron dual-core processor. The speedup of the algorithm on Cell BE increases with the increase in the number of SPEs. We also experiment with various parameters, such as number of subsets, number of processing elements, and number of DMA transfers between main memory and local memory, that impact the performance of the algorithm

Implementation of Secure 6LoWPAN Communications for Tactical Wireless Sensor Networks

Naval Research Program ProjectNaval Research Program ArticleTactical wireless sensor networks (WSN) consist of power constrained devices spread throughout a region of interest to provide data extraction in real time. The main challenges to the deployment of tactical WSNs for mission-centric operations are limited nodal energy and information security. In this paper we develop security mechanisms to be implemented on a tactical WSN using the 6LoWPAN protocol for use by the United States Marine Corps (USMC). Specifically, we develop an architectural framework for tactical WSNs by studying security gaps and vulnerabilities within the 6LoWPAN security sublayer which is based on IEEE 802.15.4. We develop a key management scheme that is non-broadcast but that is also feasible in an operational scenario. In addition, we modify the 6LoWPAN packet structure to facilitate the newly developed keying mechanism. The tactical WSN architecture is designed to defend against a variety of network attacks that can potentially occur. Simulations will be conducted via MATLAB to show the effectiveness of the developed keying and communication mechanisms.Naval Research ProgramMarine Corps Systems Command (MCSC) in Quantico, VA, Grant Number NPS-N16-M296-BMarine Corps Systems Command (MCSC) in Quantico, VA, Grant Number NPS-N16-M296-

Involvement of Fatty Acid Binding Protein 5 and PPARβ/δ in Prostate Cancer Cell Growth

Fatty acid binding protein 5 (FABP5) delivers ligands from the cytosol directly to the nuclear receptor PPARβ/δ and thus facilitates the ligation and enhances the transcriptional activity of the receptor. We show here that expression levels of both FABP5 and PPARβ/δ are correlated with the tumorigenic potential of prostate cancer cell lines. We show further that FABP5 comprises a direct target gene for PPARβ/δ and thus the binding protein and its cognate receptor are engaged in a positive feedback loop. The observations demonstrate that, similarly to effects observed in mammary carcinomas, activation of the FABP5/PPARβ/δ pathway induces PPARβ/δ target genes involved in cell survival and growth and enhances cell proliferation and anchorage-independent growth in prostate cancer cells. Furthermore, the data show that downregulation of either FABP5 or PPARβ/δ inhibits the growth of the highly malignant prostate cancer PC3M cells. These studies suggest that the FABP5/PPARβ/δ pathway may play a general role in facilitating tumor progression and that inhibition of the pathway may comprise a novel strategy in treatment of cancer

Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards

This thesis presents a new reinforcement learning mechanism suitable to be employed in artificial spiking neural networks of leaky integrate-and-fire (LIF) or Izhikevich neurons. The proposed mechanism is upgraded from, and closely built upon the learning algorithm introduced by Florian, in which local synaptic plasticity is based on the relative spike-timing of the pre and post-synaptic neurons (STDP), and is modulated by a global reinforcement signal. This work introduces and deals with multiple challenges identified in existing reinforcement learning schemes, that includes the distal reward problem, the spatial credit assignment problem and the response numbness problem. A number of improvements, that are inspired either from the biological elements or from similar implementations in non-spiking neural networks, are suggested to handle these challenges, and are validated through biologically-inspired experiments. The notion and implementation of attentional feedback that handles the spatial credit assignment problem during synaptic reinforcement are introduced. The effects of attenuated rewards, which gate network learning after satisfactory reinforcement is achieved, are also demonstrated. This aids in the exploration of the agent to discover other rewardable behaviors during learning. A spike-rate based input encoding scheme termed as balanced-pair binary state (BPBS) encoding, and a corresponding methodology for response selection are also introduced to improve network stability and confidence in response selection. The proposed techniques are validated using multiple biologically-inspired single agent as well as multi-agent game-theoretic experimental tasks. The single-agent tasks include exclusive OR (XOR) function reproduction and a bot walking task. The multi-agent interactive and cooperative tasks demonstrated include the general-sum iterated prisoners' dilemma (IPD) game problem and the distributed SensorNetwork problem from the NIPS '05 reinforcement learning benchmarks. The results and findings discussed in this work validate that the proposed improvements to existing implementations of reinforcement learning could, in fact, lead to better brain-like learning and behavior in artificial agents

Resource Allocation in Relay Enhanced Broadband Wireless Access Networks

The use of relay nodes to improve the performance of broadband wireless access (BWA) networks has been the subject of intense research activities in recent years. Relay enhanced BWA networks are anticipated to support multimedia traffic (i.e., voice, video, and data traffic). In order to guarantee service to network users, efficient resource distribution is imperative. Wireless multihop networks are characterized by two inherent dynamic characteristics: 1) the existence of wireless interference and 2) mobility of user nodes. Both mobility and interference greatly influence the ability of users to obtain the necessary resources for service. In this dissertation we conduct a comprehensive research study on the topic of resource allocation in the presence of interference and mobility. Specifically, this dissertation investigates the impact interference and mobility have on various aspects of resource allocation, ranging from fairness to spectrum utilization. We study four important resource allocation algorithms for relay enhanced BWA networks. The problems and our research achievements are briefly outlined as follows. First, we propose an interference aware rate adaptive subcarrier and power allocation algorithm using maximum multicommodity flow optimization. We consider the impact of the wireless interference constraints using Signal to Interference Noise Ratio (SINR). We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating the impact of interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution. Second, we investigate the benefits of decoupled optimization of interference aware routing and scheduling using SINR and spatial reuse to improve the overall achievable throughput. We model the routing optimization problem as a linear program using maximum concurrent flows. We develop an optimization formulation to schedule the link traffic such that interference is mitigated and time slots are reused appropriately based on spatial TDMA (STDMA). The scheduling problem is shown to be NP-hard and is solved using the column generation technique. We compare our formulations to conventional counterparts in the literature and show that our approach guarantees higher throughput by mitigating the effect of interference effectively. Third, we investigate the problem of multipath flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI3M, considering the influence of interflow and intraflow interference. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M3F, is proposed where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare with various interference based routing metrics and interference aware bandwidth allocation algorithms established in the literature to show that RI3M and LMX:M3F succeed in improving network performance in terms of delay, packet loss ratio and bandwidth usage. Lastly, we develop a user mobility prediction model using the Hidden Markov Model(HMM) in which prediction control is transferred to the various fixed relay nodes in the network. Given the HMM prediction model, we develop a routing protocol which uses the location information of the mobile user to determine the interference level on links in its surrounding neighborhood. We use SINR as the routing metric to calculate the interference on a specific link (link cost). We minimize the total cost of routing as a cost function of SINR while guaranteeing that the load on each link does not exceed its capacity. The routing protocol is formulated and solved as a minimum cost flow optimization problem. We compare our SINR based routing algorithm with conventional counterparts in the literature and show that our algorithm reinforces routing paths with high link quality and low latency, therefore improving overall system throughput. The research solutions obtained in this dissertation improve the service reliability and QoS assurance of emerging BWA networks

Traffic Anomaly Detection and Analysis for 5G Enabled Autonomous Vehicle Systems

Seed Research Program 2023. A Quad, describing CRUSER Seed Research Program funded research.CRUSER Funded ResearchFY23 Funded Research ProposalConsortium for Robotics and Unmanned Systems Education and Research (CRUSER

Performance Study of the Robot Operating System 2 with QoS and Cyber Security Settings

2020 IEEE International Systems Conference (SysCon)Throughout the Department of Defense, there are ongoing efforts to increase cybersecurity and improve data transfer in unmanned robotic systems (UxS). This paper explores the performance of the Robot Operating System (ROS) 2, which is built with the Data Distribution Service (DDS) standard as a middleware. Based on how quality of service (QoS) parameters are defined in the robotic middleware interface, it is possible to implement strict delivery requirements to different nodes on a dynamic nodal network with multiple unmanned systems con- nected. Through this research, different scenarios with varying QoS settings were implemented and compared to baseline values to help illustrate the impact of latency and throughput on data flow. DDS security settings were also enabled to help understand the cost of overhead and performance when secured data is compared to plaintext baseline values. Our experiments were performed using a basic ROS 2 network consisting of two nodes (one publisher and one subscriber). Our experiments showed a measurable latency and throughput change between different QoS profiles and security settings. We analyze the trends and tradeoffs associated with varying QoS and security settings. This paper provides performance data points that can be used to help future researchers and developers make informative choices when using ROS 2 for UxS.This work was funded and sponsored by the Office of Naval Research via the Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) at NPS

Role of Heat Shock Proteins in Protein Synthesis, Folding, and Renaturation

Biochemistry and Molecular Bolog