Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Laryngeal preneoplastic lesions and cancer: challenging diagnosis. Qualitative literature review and meta-analysis

Background: The treatment of laryngeal cancer and its precursor lesions has a great impact on important laryngeal basic functions, thus, early detection and preoperative assessment are important for a curative and function-preserving therapy. Furthermore, delayed diagnosis, leads to loco-regional failure and a high incidence of second primary tumor, reasons for poor outcome. In this setting, there are two basic clinical problems in the management of premalignant and malignant laryngeal lesions. First, small and thin lesions are difficult to evaluate by the histopathologic examination and initial biopsies are often not sufficient for a conclusive diagnosis. Second, margins of the specimens from surgical excisions are difficult to evaluate due to tissue damage from the device, leaving us in doubt whether the excision is radical or not. From these observations, it is obvious that an instrument offering the possibility to detect pre-cancerous-early cancerous lesions, and satellite foci or second primaries would be the key to improving the survival rate in head and neck cancer. But, despite the high number of more advanced diagnostic techniques and methods, unfortunately, it is not uncommon for different clinicians to use different nomenclature or to identify different stage for the same laryngeal lesion. Object. Different modalities of diagnostic techniques of laryngeal lesions exist. Rather than difference between benign and obvious malignant diseases, more difficult is to detect the presence of precancerous epithelial alterations. Not all tests achieve the same diagnostic accuracy and that all tests must be considered against a gold standard, hence this meta-analysis of literature aimed to synthesise the validity of each single diagnostic technique in identifying and staging laryngeal diseases. Methods: A systematic review of literature was led searching for articles mentioning the following terms including their various combinations to maximize the yield: larynx, laryngeal cancer, white light (WL) endoscopy, contact endoscopy (CE), stroboscopy, autofluorescence (AF), ultrasound (US), narrow band imaging (NBI), computers assail tomography (CAT), magnetic resonance imaging (MRI), positron emission tomography (PET). A quantitative analysis was carried on for paper published after 2005 onward, reporting a minumun series of 10 patients each study, declaring sensitivity and specificity of each diagnostic system. Results: The search identified 7215 publications, of which 3616 published after 2005, with a final results of a total of 214 articles stratified and included by our selection criteria. 58 out of 214 articles were selected for quantitative synthesis. 35 out of 58 studies had a quality score of ≥ 6 (good), 15 presented a score between 4 and 5 (fair), the remaining 8 had a score between 2 and 3 (poor). While objections can be raised about the pooling of different diagnostic procedures under the same group and the high level of heterogeneity in the meta-analyses, the inclusion of over 2500 patients makes the results fairly robust. Conclusions: A comprehensive overview of the most recent advances in laryngeal imaging technology combined with all of the information needed to interpret findings and successfully manage patients with voice disorders can be found herein. With these data, clinicians can risk-stratify patients and select proper examination modalities in order to provide appropriate care. Moreover, study limitations, together with possible clinical and research implications have been counted, as well

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

Adsorption of organic Iodides on Reduced Silver Functionalized Silica Aerogel

In the nuclear waste reprocessing, radioactive iodine is released in both organic and inorganic forms into off-gas streams. Due to its properties of long half-life and accumulation in human bodies, the radioactive iodine is required to be removed by the Environmental Protection Agency (EPA) and Nuclear Regulatory Commission (NRC). In this presented work, the removal of organic iodides is studied particularly. Unlike that of inorganic iodine, the organic iodides were not well studied because of the low concentrations and the corresponding technical difficulties. Most of the studies on the organic iodides are semi-quantified, focusing on the performances of the adsorption columns of certain combinations of conditions and the quantified single-layer adsorptions were rarely reported. To provide the information and results supporting the adsorption columns design, single layer continuous-flow organic iodides adsorptions using silver-containing adsorbents were performed. The solid adsorption method was developed in replacement of the liquid scrubbing strategy for its low operational cost and simplicity of design. The most commonly used adsorbents include reduced silver exchanged mordenite (Ag0Z) and silver nitrate impregnated alumina (AgA), and they have been applied in multiple waste reprocessing plants around the world. In the 2010s, a novel silver-containing material, reduced silver functionalized silica aerogel (Ag0-Aerogel), was developed at the Pacific Northwest National Laboratory (PNNL), and has been considered as an outstanding material for its high silver content and adsorption rate. The efficiencies of the three materials were evaluated and the Ag0-Aerogel was identified to be the optimum adsorbent among Ag0-Aerogel, Ag0Z and AgA. Therefore, the CH3I adsorptions on Ag0-Aerogel were performed at various concentrations and temperatures. The data were analyzed using multiple models and the parameters were determined. The results indicated that the CH3I adsorption on Ag0-Aerogel is a surface reaction at the specified conditions and the adsorption rate increases with the adsorption temperature. Additionally, adsorptions of other iodoalkanes (C3H7I, C6H13I, C8H17I and C12H25I) were performed and the corresponding dependencies on temperatures, concentrations and the length of carbon chain were determined. The C6H13I and C12H25I adsorptions are likely to be zero-order adsorptions and the temperature dependencies may vary at different conditions. Moreover, the adsorption rates of C3H7I and C8H17I are higher than expected, accordingly, further studies are suggested. Using the parameters determined, the column adsorption modeling of organic iodides was conducted and the modeling results were comparable to the literature works. The model was also applied to predict the breakthrough of the column, and the outcomes indicate that a column of at least 15-20 cm is required to remove the organic iodides of up to approximately 100 ppbv. Based on the results of organic iodides adsorptions and the modeling works, the potential research objectives were recommended and the properties of the next generation materials and adsorption systems were also suggested

Biomarkers Used for the Diagnosis of Diseases

The detection and quantification of with high precision nucleic acid biomarkers and protein biomarkers in resource-limited settings is key to the early diagnosis of diseases and for monitoring the effects of treatments. As there is an enormous demand for high-quality biomarker detection platforms that are robust and highly applicable in resource-limited settings, this book is devoted to exploring methods for detection and quantification of biomarkers, focusing on the recent advances in this field

Alfvén waves underlying ionospheric destabilization: ground-based observations

During geomagnetic storms, terawatts of power in the million mile-per-hour solar wind pierce the Earth’s magnetosphere. Geomagnetic storms and substorms create transverse magnetic waves known as Alfvén waves. In the auroral acceleration region, Alfvén waves accelerate electrons up to one-tenth the speed of light via wave-particle interactions. These inertial Alfvén wave (IAW) accelerated electrons are imbued with sub-100 meter structure perpendicular to geomagnetic field B. The IAW electric field parallel to B accelerates electrons up to about 10 keV along B. The IAW dispersion relation quantifies the precipitating electron striation observed with high-speed cameras as spatiotemporally dynamic fine structured aurora. A network of tightly synchronized tomographic auroral observatories using model based iterative reconstruction (MBIR) techniques were developed in this dissertation. The TRANSCAR electron penetration model creates a basis set of monoenergetic electron beam eigenprofiles of auroral volume emission rate for the given location and ionospheric conditions. Each eigenprofile consists of nearly 200 broadband line spectra modulated by atmospheric attenuation, bandstop filter and imager quantum efficiency. The L-BFGS-B minimization routine combined with sub-pixel registered electron multiplying CCD video stream at order 10 ms cadence yields estimates of electron differential number flux at the top of the ionosphere. Our automatic data curation algorithm reduces one terabyte/camera/day into accurate MBIR-processed estimates of IAW-driven electron precipitation microstructure. This computer vision structured auroral discrimination algorithm was developed using a multiscale dual-camera system observing a 175 km and 14 km swath of sky simultaneously. This collective behavior algorithm exploits the “swarm” behavior of aurora, detectable even as video SNR approaches zero. A modified version of the algorithm is applied to topside ionospheric radar at Mars and broadcast FM passive radar. The fusion of data from coherent radar backscatter and optical data at order 10 ms cadence confirms and further quantifies the relation of strong Langmuir turbulence and streaming plasma upflows in the ionosphere with the finest spatiotemporal auroral dynamics associated with IAW acceleration. The software programs developed in this dissertation solve the century-old problem of automatically discriminating finely structured aurora from other forms and pushes the observational wave-particle science frontiers forward

Viking '75 spacecraft design and test summary. Volume 3: Engineering test summary

The engineering test program for the lander and the orbiter are presented. The engineering program was developed to achieve confidence that the design was adequate to survive the expected mission environments and to accomplish the mission objective

Enhancement of student learning in the lecture theatre by means of a radio frequency feedback system

The principal formal teaching mechanism of universities is the lecture - a cost efficient format where hundreds of students may be taught by a single lecturer. Lectures' learning outcome is less certain; the lecturer has little ability to appraise the nature of the audience's understanding. The introduction of an electronic communication path from students to the lecturer mitigates this disjuncture. This communication path has been introduced and was found to be extensively used during a series of lectures, with each student provided with a handset that is reliable, inexpensive and portable. Upon these handsets are buttons; the data is transmitted at radio frequencies to the lecturer, aggregated and displayed graphically for quick assimilation. It has been recognised that this communication path permits the direct measurement of student's educational behaviour without disturbing the lecture itself. Preliminary results indicating the value of this research methodology have been obtained. A significant correlation was identified between the percentage of questions during the lecture students answered correctly and their previous year's overall academic mark. A correlation was identified between the number of times students initiate communication with the lecturer and the number of questions during the lecture they answered correctly. Mixed evidence was found regarding a possible correlation between the evidence of satisfaction with their learning students provided using the system and the number of questions during the lecture they answered correctly. The introduction of an electronic communication path into lectures has proved to be an innovation deserving of further research and wider introduction into teaching practice

Applications of Nanobiotechnology

This book is dedicated to the applications of nanobiotechnology, i.e. the way that nanotechnology is used to create devices to study biological systems and phenomena. It includes seven chapters, organized in two sections. The first section (Chapters 1–5) covers a large spectrum of issues associated with nanoparticle synthesis, nanoparticle toxicity, and the role of nanotechnology in drug delivery, tissue engineering, agriculture, and biosensing. The second section (Chapters 6 and 7) is devoted to the properties of nanofluids and the medical and biological applications of computational fluid dymanics modeling