Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.

Synthesis Methods of Multiple-valued Structures of Language Systems

The basic construction concepts of many-valued intellectual systems, which are adequate to primal problems of person activity and using hybrid tools with many-valued of coding are considered. The many-valued intellectual systems being two-place, but simulating neuron processes of space toting which are different on a level of actions, inertial and threshold of properties of neurons diaphragms, and also modification of frequency of following of the transmitted messages are created. All enumerated properties and functions in point of fact are essential not only are discrete on time, but also many-valued.

Robot Control Using Inductive, Deductive and Case Based Reasoning

The paper deals with a problem of intelligent system’s design for complex environments. There is discussed a possibility to integrate several technologies into one basic structure that could form a kernel of an autonomous intelligent robotic system. One alternative structure is proposed in order to form a basis of an intelligent system that would be able to operate in complex environments. The proposed structure is very flexible because of features that allow adapting via learning and adjustment of the used knowledge. Therefore, the proposed structure may be used in environments with stochastic features such as hardly predictable events or elements. The basic elements of the proposed structure have found their implementation in software system and experimental robotic system. The software system as well as the robotic system has been used for experimentation in order to validate the proposed structure - its functionality, flexibility and reliability. Both of them are presented in the paper. The basic features of each system are presented as well. The most important results of experiments are outlined and discussed at the end of the paper. Some possible directions of further research are also sketched at the end of the paper.

Um sistema inteligente de teleoftalmologia para o auxílio no diagnóstico de catarata

The increasing in world population, with higher proportion of elderly, leads to an increase in the number of individuals with vision loss and cataracts are one of the leading causes of blindness worldwide. Cataract is an eye disease that is the partial or total opacity of the crystalline lens (natural lens of the eye) or its capsule. It can be triggered by several factors such as trauma, age, diabetes mellitus, and medications, among others. It is known that the attendance by ophthalmologists in rural and poor areas in Brazil is less than needed and many patients with treatable diseases such as cataracts are undiagnosed and therefore untreated. In this context, this project presents the development of OPTICA, a system of teleophthalmology using smartphones for ophthalmic emergencies detection, providing a diagnostic aid for cataract using specialists systems and image processing techniques. The images are captured by a cellphone camera and along with a questionnaire filled with patient information are transmitted securely via the platform Mobile SANA to a online server that has an intelligent system available to assist in the diagnosis of cataract and provides ophthalmologists who analyze the information and write back the patient’s report. Thus, the OPTICA provides eye care to the poorest and least favored population, improving the screening of critically ill patients and increasing access to diagnosis and treatment.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

CLARITY and PACT-based imaging of adult zebrafish and mouse for whole-animal analysis of infections.

Visualization of infection and the associated host response has been challenging in adult vertebrates. Owing to their transparency, zebrafish larvae have been used to directly observe infection in vivo; however, such larvae have not yet developed a functional adaptive immune system. Cells involved in adaptive immunity mature later and have therefore been difficult to access optically in intact animals. Thus, the study of many aspects of vertebrate infection requires dissection of adult organs or ex vivo isolation of immune cells. Recently, CLARITY and PACT (passive clarity technique) methodologies have enabled clearing and direct visualization of dissected organs. Here, we show that these techniques can be applied to image host-pathogen interactions directly in whole animals. CLARITY and PACT-based clearing of whole adult zebrafish and Mycobacterium tuberculosis-infected mouse lungs enables imaging of mycobacterial granulomas deep within tissue to a depth of more than 1 mm. Using established transgenic lines, we were able to image normal and pathogenic structures and their surrounding host context at high resolution. We identified the three-dimensional organization of granuloma-associated angiogenesis, an important feature of mycobacterial infection, and characterized the induction of the cytokine tumor necrosis factor (TNF) within the granuloma using an established fluorescent reporter line. We observed heterogeneity in TNF induction within granuloma macrophages, consistent with an evolving view of the tuberculous granuloma as a non-uniform, heterogeneous structure. Broad application of this technique will enable new understanding of host-pathogen interactions in situ.

Molecular Insights of CD4+ T Cell Differentiation, Effector Formation and Helper Function

CD4+ T cells play a crucial in the adaptive immune system. They function as the central hub to orchestrate the rest of immunity: CD4+ T cells are essential governing machinery in antibacterial and antiviral responses by facilitating B cell affinity maturation and coordinating the innate and adaptive immune systems to boost the overall immune outcome; on the contrary, hyperactivation of the inflammatory lineages of CD4+ T cells, as well as the impairments of suppressive CD4+ regulatory T cells, are the etiology of various autoimmunity and inflammatory diseases. The broad role of CD4+ T cells in both physiological and pathological contexts prompted me to explore the modulation of CD4+ T cells on the molecular level.

microRNAs (miRNAs) are small RNA molecules capable of regulating gene expression post-transcriptionally. miRNAs have been shown to exert substantial regulatory effects on CD4+ T cell activation, differentiation and helper function. Specifically, my lab has previously established the function of the miR-17-92 cluster in Th1 differentiation and anti-tumor responses. Here, I further analyzed the role of this miRNA cluster in Th17 differentiation, specifically, in the context of autoimmune diseases. Using both gain- and loss-of-function approaches, I demonstrated that miRNAs in miR-17-92, specifically, miR-17 and miR-19b in this cluster, is a crucial promoter of Th17 differentiation. Consequently, loss of miR-17-92 expression in T cells mitigated the progression of experimental autoimmune encephalomyelitis and T cell-induced colitis. In combination with my previous data, the molecular dissection of this cluster establishes that miR-19b and miR-17 play a comprehensive role in promoting multiple aspects of inflammatory T cell responses, which underscore them as potential targets for oligonucleotide-based therapy in treating autoimmune diseases.

To systematically study miRNA regulation in effector CD4+ T cells, I devised a large-scale miRNAome profiling to track in vivo miRNA changes in antigen-specific CD4+ T cells activated by Listeria challenge. From this screening, I identified that miR-23a expression tightly correlates with CD4+ effector expansion. Ectopic expression and genetic deletion strategies validated that miR-23a was required for antigen-stimulated effector CD4+ T cell survival in vitro and in vivo. I further determined that miR-23a targets Ppif, a gatekeeper of mitochondrial reactive oxygen species (ROS) release that protects CD4+ T cells from necrosis. Necrosis is a type of cell death that provokes inflammation, and it is prominently triggered by ROS release and its consequent oxidative stress. My finding that miR-23a curbs ROS-mediated necrosis highlights the essential role of this miRNA in maintaining immune homeostasis.

A key feature of miRNAs is their ability to modulate different biological aspects in different cell populations. Previously, my lab found that miR-23a potently suppresses CD8+ T cell cytotoxicity by restricting BLIMP1 expression. Since BLIMP1 has been found to inhibit T follicular helper (Tfh) differentiation by antagonizing the master transcription factor BCL6, I investigated whether miR-23a is also involved in Tfh differentiation. However, I found that miR-23a does not target BLIMP1 in CD4+ T cells and loss of miR-23a even fostered Tfh differentiation. This data indicate that miR-23a may target other pathways in CD4+ T cells regarding the Tfh differentiation pathway.

Although the lineage identity and regulatory networks for Tfh cells have been defined, the differentiation path of Tfh cells remains elusive. Two models have been proposed to explain the differentiation process of Tfh cells: in the parallel differentiation model, the Tfh lineage is segregated from other effector lineages at the early stage of antigen activation; alternatively, the sequential differentiation model suggests that naïve CD4+ T cells first differentiate into various effector lineages, then further program into Tfh cells. To address this question, I developed a novel in vitro co-culture system that employed antigen-specific CD4+ T cells, naïve B cells presenting cognate T cell antigen and BAFF-producing feeder cells to mimic germinal center. Using this system, I were able to robustly generate GC-like B cells. Notably, well-differentiated Th1 or Th2 effector cells also quickly acquired Tfh phenotype and function during in vitro co-culture, which suggested a sequential differentiation path for Tfh cells. To examine this path in vivo, under conditions of classical Th1- or Th2-type immunizations, I employed a TCRβ repertoire sequencing technique to track the clonotype origin of Tfh cells. Under both Th1- and Th2- immunization conditions, I observed profound repertoire overlaps between the Teff and Tfh populations, which strongly supports the proposed sequential differentiation model. Therefore, my studies establish a new platform to conveniently study Tfh-GC B cell interactions and provide insights into Tfh differentiation processes.

Optimização dos consumos de água na irrigação de campos golfe

A presente dissertação propõe o desenvolvimento de um sistema de Irrigação de baixo custo para campos de Golfe. Este sistema é capaz de recolher a previsão meteorológica e ainda medir um conjunto de valores (temperatura, humidade, velocidade do vento) que determina quando e quanto regar. Os campos de Golfe consumem diariamente elevadas quantidades de água, sendo esta a principal crítica feita pelas organizações ambientais. Esta dissertação incorpora uma comunicação sem fios de baixo custo, que dispensa a cablagem que é necessária para haver comunicação entre os diversos equipamentos, que estão distribuídos pelo campo de Golfe. O sistema desenvolvido pretende reduzir os desperdícios dos recursos hídricos na rega, pois é um sistema inteligente que poderá ser adquirido não só por gestores de campos de Golfe, mas também por jardins residenciais e municipais. Com o objetivo de criar um sistema de baixo custo foi elaborado um algoritmo de reencaminhamento de mensagens, que permite utilizar equipamentos de comunicação sem fios de baixo custo. Todo o sistema de Irrigação é controlado e monitorizado através de uma interface, desenvolvida em Microsoft Visual Basic.

Toll-like receptor 9 in breast cancer

The innate immune system recognizes microbial features leading to the activation of the adaptive immune system. The role of Toll-like receptor 9 (TLR9) is to recognize microbial DNA. In addition to immune cells, TLR9 is widely expressed in breast cancer in addition to other cancers. Breast cancer is the most common cancer in women, affecting approximately one in eight in industrialized countries. In the clinical setting, breast cancer is divided into three clinical subtypes with type-specific treatments. These subtypes are estrogen receptor (ER)-positive, HER2-positive and triple-negative (TNBC) breast cancer. TNBC is the most aggressive subtype that can be further divided into several subtypes. TNBC tumors lack ER, progesterone receptor and HER2 receptor. Therefore, the current clinically used targeted therapies are not suitable for TNBC treatment as TNBC is a collection of diseases rather than one entity. Some TNBC patients are cured with standard chemotherapy, while others rapidly die due to the disease. There are no clinically used iomarkers which would help in predicting which patients respond to chemotherapy. During this thesis project, we discovered a novel good-prognosis TNBC subtype. These tumors have high TLR9 expression levels. Our findings suggest that TLR9 screening in TNBC patient populations might help to identify the patients that are at the highest risk regarding a relapse. To gain better understanding on the role of TLR9 in TNBC, we developed an animal model which mimicks this disease. We discovered that suppression of TLR9 expression in TNBC cells increases their invasive properties in hypoxia. In line with the clinical findings, TNBC cells with low TLR9 expression also formed more aggressive tumors in vivo. TLR9 expression did not, however, affect TNBC tumor responses to doxorubicin. Our results suggest that tumor TLR9 expression may affect chemotherapyrelated immune responses, however, this requires further investigation. Our other findings revealed that DNA released by chemotherapy-killed cells induces TLR9-mediated invasion in living cancer cells. Normally, extracellular self-DNA is degraded by enzymes, but during massive cell death, for example during chemotherapy, the degradation machinery may be exhausted and self-DNA is taken up into living cells activating TLR9. We also discovered that the malaria drug chloroquine, an inhibitor of autophagy and TLR9 signalling does not inhibit TNBC growth in vivo, independently of the TLR9 status. Finally, we found that ERα as well as the sex hormones estrogen and testosterone regulate TLR9 expression and activity in breast cancer cells in vitro. As a conclusion, we suggest that TLR9 is a potential biomarker in TNBC. ------- Sisäsyntyisen immuniteetin tehtävä on tunnistaa mikrobien molekyylirakenteita, mikä saa aikaan adaptiivisen immuunijärjestelmän aktivoitumisen. Tollin kaltainen reseptori 9 (TLR9) on dna:ta tunnistava sisäsyntyisen immuniteetin reseptori, jota ilmennetään myös useissa syövissä, kuten rintasyövässä. Rintasyöpä on naisten yleisin syöpä, johon joka kahdeksas nainen sairastuu elämänsä aikana. Kliinisesti rintasyöpä jaotellaan kolmeen alatyyppiin, joista kolmoisnegatiivinen rintasyöpä on aggressiivisin. Tämän tyypin syövät eivät ilmennä hormonireseptoreja (estrogeeni- ja progesteronireseptori) tai HER2-reseptoria. Tästä johtuen kolmoisnegatiivisten potilaiden hoitoon ei voida käyttää rintasyövän nykyisten hoitosuositusten mukaisia täsmähoitoja. Kolmoisnegatiivinen rintasyöpä ei kuitenkaan ole yksi sairaus, koska molekyylitasolla sen on osoitettu koostuvan lukuisista, biologialtaan erilaisista syöpämuodoista. Tällä hetkellä kliinisessä käytössä ei ole biomarkkeria, jonka avulla kolmoisnegatiivisen rintasyövän alatyypit voisi erottaa toisistaan. Löysimme uuden kolmoisnegatiivisen syövän alatyypin, joka ilmentää vain vähän TLR9-proteiinia. Tällä alatyypillä on erittäin huono ennuste ja tulostemme perusteella TRL9-tason selvittäminen voisi seuloa huonoennusteiset syövät kolmoisnegatiivisten syöpien joukosta. Kehitimme eläinmallin, jolla voidaan tutkia matalan ja korkean TLR9-tason vaikutuksia kolmoisnegatiivisten kasvainten hoitovasteeseen. Toinen löytömme oli, että kemoterapialla tapettujen syöpäsolujen dna saa aikaan elävien syöpäsolujen TLR9-välitteistä invaasiota. Normaalisti entsyymit hajoittavat yksilön oman solunulkoisen dna:n. Erikoistilanteissa, kuten syöpähoitojen yhteydessä, jolloin solukuolema on massiivista, elimistön oma koneisto ei ehdi tuhoamaan solunulkoista dna:ta ja sitä voi kertyä eläviin soluihin, joissa se aktivoi TLR9:n. Kolmanneksi havaitsimme, että malarialääke klorokiini, joka estää TLR9:n toimintaa ja jolla on syövänvastaisia vaikutuksia soluviljelyolosuhteissa, ei estänyt TLR9-positiivisten tai TLR9-negatiivisten kasvainten kasvua käyttämässämme eläinmallissa. Neljänneksi soluviljelykokeittemme tulokset osoittivat, että sukupuolihormonit estrogeeni ja testosteroni sekä estrogeenireseptori osallistuvat TLR9:n ilmentymisen ja aktiivisuuden säätelyyn. Tuloksemme osoittavat, että TLR9 potentiaalinen biomarkkeri kolmoisnegatiivisessa rintasyövässä.

Doença enxerto-versus hospedeiro: determinação da expressão de genes e alterações de vias associadas ao sistema imunológico usando metodos da biofísica

O transplante de medula óssea (TMO) é um procedimento terapêutico importante em casos relacionados à pacientes com leucemia ou linfoma. Em decorrência desse processo, uma reação conhecida como doença enxerto-versus-hospedeiro (GVHD) pode ocorrer em pacientes susceptíveis como conseqüência da presença de células imunocompetentes do doador. Entretanto, não existe um modelo para descrever completamente as ações relacionadas ao mecanismo imunológico da GVHD desde a fase que inicializa a doença até a fase efetora. O Objetivo geral deste estudo é a investigação da resposta imunológica considerando-se o sistema HLA (antígenos leucocitários humano) em pacientes que desenvolveram a GVHD em decorrência do TMO. O National Cancer Institute (NCI) – Pathway interaction Database e Reactome foram usados como bases de dados com o objetivo de se estudar a expressão de genes e vias relacionados às Classes I e II do sistema HLA (antígenos leucocitários humano). O estudo considerou a mudança de expressão de genes relacionados às 17 vias do sistema imunológico com potencialidade para se expressar em pacientes que desenvolveram a GVHD associada à TMO. Dados referentes aos transcriptomas foram obtidos utilizando-se a plataforma GPL570 Affymetrix Genoma Humano U133 Plus. A atividade relativa foi usada para determinar as alterações das vias em amostras de GVHD em relação ao controle. As análises foram realizadas utilizando-se o software Via Complex e Bioconductor. Observou-se aumento significativo da expressão de genes ralacionados às vias do sistema imune adaptativo, antígenos associados às Classe I e II do HLA, fosforilação de CD3 e CD247, sinalização dos receptores de células T em CD4+ nativas e ativação de NF-kapa β nas células B. Também observou-se alterações significativas na mudança de expressão dos genes associados às vias relacionadas à super família de moléculas B7:CD28\CTLA-4 quando comparadas ao controle. Isso pode indicar a necessidade de geração de um segundo sinal co-estimulador em GVHD, acionado pelas moléculas dessa super família. O aumento da expressão do gene CD69 nas amostras experimentais caracteriza a ativação celular e, portanto, a sinalização de estímulos em GVHD. Os achados obtidos neste estudo contribuem para melhor elucidar o mecanismo imunopatogênico associado à GVHD. P

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

'Artificial Immune Systems Tutorial'

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.

Laser beam control, combining, and propagation in atmospheric turbulence

This dissertation is concerned with the control, combining, and propagation of laser beams through a turbulent atmosphere. In the first part we consider adaptive optics: the process of controlling the beam based on information of the current state of the turbulence. If the target is cooperative and provides a coherent return beam, the phase measured near the beam transmitter and adaptive optics can, in principle, correct these fluctuations. However, for many applications, the target is uncooperative. In this case, we show that an incoherent return from the target can be used instead. Using the principle of reciprocity, we derive a novel relation between the field at the target and the scattered field at a detector. We then demonstrate through simulation that an adaptive optics system can utilize this relation to focus a beam through atmospheric turbulence onto a rough surface. In the second part we consider beam combining. To achieve the power levels needed for directed energy applications it is necessary to combine a large number of lasers into a single beam. The large linewidths inherent in high-power fiber and slab lasers cause random phase and intensity fluctuations occurring on sub-nanosecond time scales. We demonstrate that this presents a challenging problem when attempting to phase-lock high-power lasers. Furthermore, we show that even if instruments are developed that can precisely control the phase of high-power lasers; coherent combining is problematic for DE applications. The dephasing effects of atmospheric turbulence typically encountered in DE applications will degrade the coherent properties of the beam before it reaches the target. Finally, we investigate the propagation of Bessel and Airy beams through atmospheric turbulence. It has been proposed that these quasi-non-diffracting beams could be resistant to the effects of atmospheric turbulence. However, we find that atmospheric turbulence disrupts the quasi-non-diffracting nature of Bessel and Airy beams when the transverse coherence length nears the initial aperture diameter or diagonal respectively. The turbulence induced transverse phase distortion limits the effectiveness of Bessel and Airy beams for applications requiring propagation over long distances in the turbulent atmosphere.

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is successful at detecting port scans.