A Generic Software Architecture for a Domain Specific Distributed Embedded System

In this paper, we have evolved a generic software architecture for a domain specific distributed embedded system. The system under consideration belongs to the Command, Control and Communication systems domain. The systems in such domain have very long operational lifetime. The quality attributes of these systems are equally important as the functional requirements. The main guiding principle followed in this paper for evolving the software architecture has been functional independence of the modules. The quality attributes considered most important for the system are maintainability and modifiability. Architectural styles best suited for the functionally independent modules are proposed with focus on these quality attributes. The software architecture for the system is envisioned as a collection of architecture styles of the functionally independent modules identified

Studies on arbuscular mycorrhiza (AM) in the Alentejo (Portugal) using pea mutants resistant to AM fungi as a control tool for field conditions

The utilization and management of arbuscular mycorrhiza (AM) symbiosis may improve production and sustainability of the cropping system. For this purpose, native AM fungi (AMF) were sought and tested for their efficiency to increase plant growth by enhanced P uptake and by alleviation of drought stress. Pot experiments with safflower (Carthamus tinctorius) and pea (Pisum sativum) in five soils (mostly sandy loamy Luvisols) and field experiments with peas were carried out during three years at four different sites. Host plants were grown in heated soils inoculated with AMF or the respective heat sterilized inoculum. In the case of peas, mutants resistant to AMF colonization were used as non-mycorrhizal controls. The mycorrhizal impact on yields and its components, transpiration, and P and N uptake was studied in several experiments, partly under varying P and N levels and water supply. Screening of native AMF by most probable number bioassays was not very meaningful. Soil monoliths were placed in the open to simulate field conditions. Inoculation with a native AMF mix improved grain yield, shoot and leaf growth variables as compared to control. Exposed to drought, higher soil water depletion of mycorrhizal plants resulted in a haying-off effect. The growth response to this inoculum could not be significantly reproduced in a subsequent open air pot experiment at two levels of irrigation and P fertilization, however, safflower grew better at higher P and water supply by multiples. The water use efficiency concerning biomass was improved by the AMF inoculum in the two experiments. Transpiration rates were not significantly affected by AM but as a tendency were higher in non-mycorrhizal safflower. A fundamental methodological problem in mycorrhiza field research is providing an appropriate (negative) control for the experimental factor arbuscular mycorrhiza. Soil sterilization or fungicide treatment have undesirable side effects in field and greenhouse settings. Furthermore, artificial rooting, temperature and light conditions in pot experiments may interfere with the interpretation of mycorrhiza effects. Therefore, the myc- pea mutant P2 was tested as a non-mycorrhizal control in a bioassay to evaluate AMF under field conditions in comparison to the symbiotic isogenetic wild type of var. FRISSON as a new integrative approach. However, mutant P2 is also of nod- phenotype and therefore unable to fix N2. A 3-factorial experiment was carried out in a climate chamber at high NPK fertilization to examine the two isolines under non-symbiotic and symbiotic conditions. P2 achieved the same (or higher) biomass as wild type both under good and poor water supply. However, inoculation with the AMF Glomus manihot did not improve plant growth. Differences of grain and straw yields in field trials were large (up to 80 per cent) between those isogenetic pea lines mainly due to higher P uptake under P and water limited conditions. The lacking N2 fixation in mutants was compensated for by high mineral N supply as indicated by the high N status of the pea mutant plants. This finding was corroborated by the results of a major field experiment at three sites with two levels of N fertilization. The higher N rate did not affect grain or straw yields of the non-fixing mutants. Very efficient AMF were detected in a Ferric Luvisol on pasture land as revealed by yield levels of the evaluation crop and by functional vital staining of highly colonized roots. Generally, levels of grain yield were low, at between 40 and 980 kg ha-1. An additional pot trial was carried out to elucidate the strong mycorrhizal effect in the Ferric Luvisol. A triplication of the plant equivalent field P fertilization was necessary to compensate for the mycorrhizal benefit which was with five times higher grain yield very similar to that found in the field experiment. However, the yield differences between the two isolines were not always plausible as the evaluation variable because they were also found in (small) field test trials with apparently sufficient P and N supply and in a soil of almost no AMF potential. This similarly occurred for pea lines of var. SPARKLE and its non-fixing mycorrhizal (E135) and non-symbiotic (R25) isomutants, which were tested in order to exclude experimentally undesirable benefits by N2 fixation. In contrast to var. FRISSON, SPARKLE was not a suitable variety for Mediterranean field conditions. This raises suspicion putative genetic defects other than symbiotic ones may be effective under field conditions, which would conflict with the concept of an appropriate control. It was concluded that AMF resistant plants may help to overcome fundamental problems of present research on arbuscular mycorrhiza, but may create new ones.

Global Optimization Algorithms and their Application to Distributed Systems

In this report, we discuss the application of global optimization and Evolutionary Computation to distributed systems. We therefore selected and classified many publications, giving an insight into the wide variety of optimization problems which arise in distributed systems. Some interesting approaches from different areas will be discussed in greater detail with the use of illustrative examples.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Effects of Organic and Inorganic Fertilizers on Growth and Yield of Banana (Musa AAA cv. Malindi) in Oman

The Sultanate of Oman is located on the south-eastern coast of the Arabian Peninsula, which lies on the south-western tip of the Asian continent. The strategic geographical locations of the Sultanate with its many maritime ports distributed on the Indian Ocean have historically made it one of the Arabian Peninsula leaders in the international maritime trade sector. Intensive trading relationships over long time periods have contributed to the high plant diversity seen in Oman where agricultural production depends entirely on irrigation from groundwater sources. As a consequence of the expansion of the irrigated area, groundwater depletion has increased, leading to the intrusion of seawater into freshwater aquifers. This phenomenon has caused water and soil salinity problems in large parts of the Al-Batinah governorate of Oman and threatens cultivated crops, including banana (Musa spp.). According to the Ministry of Agriculture and Fisheries, the majority of South Al-Batinah farms are affected by salinity (ECe > 4 dS m-1). As no alternative farmland is available, the reclamation of salt-affected soils using simple cultural practices is of paramount importance, but in Oman little scientific research has been conducted to develop such methods of reclamation. This doctoral study was initiated to help filling this research gap, particularly for bananas. A literature review of the banana cultivation history revealed that the banana germplasm on the Arabian Peninsula is probably introduced from Indonesia and India via maritime routes across the Indian Ocean and the Red Sea. In a second part of this dissertation, two experiments are described. A laboratory trial conducted at the University of Kassel, in Witzenhausen, Germany from June to July 2010. This incubation experiment was done to explore how C and N mineralization of composted dairy manure and date palm straw differed in alkaline non-saline and saline soils. Each soil was amended with four organic fertilizers: 1) composted dairy manure, 2) manure + 10% date palm straw, 3) manure + 30% date palm straw or 4) date palm straw alone, in addition to un-amended soils as control. The results showed that the saline soil had a lower soil organic C content and microbial biomass C than the non-saline soil. This led to lower mineralization rates of manure and date palm straw in the saline soil. In the non-saline soil, the application of manure and straw resulted in significant increases of CO2 emissions, equivalent to 2.5 and 30% of the added C, respectively. In the non-amended control treatment of the saline soil, the sum of CO2-C reached only 55% of the soil organic C in comparison with the non-saline soil. In which 66% of the added manure and 75% of the added straw were emitted, assuming that no interactions occurred between soil organic C, manure C and straw C during microbial decomposition. The application of straw always led to a net N immobilization compared to the control. Salinity had no specific effect on N mineralization as indicated by the CO2-C to Nmin ratio of soil organic matter and manure. However, N immobilization was markedly stronger in the saline soil. Date palm straw strongly promoted saprotrophic fungi in contrast to manure and the combined application of manure and date palm straw had synergistic positive effects on soil microorganisms. In the last week of incubation, net-N mineralization was observed in nearly all treatments. The strongest increase in microbial biomass C was observed in the manure + straw treatment. In both soils, manure had no effect on the fungi-specific membrane component ergosterol. In contrast, the application of straw resulted in strong increases of the ergosterol content. A field experiment was conducted on two adjacent fields at the Agricultural Research Station, Rumais (23°41’15” N, 57°59’1” E) in the South of Al-Batinah Plain in Oman from October 2007 to July 2009. In this experiment, the effects of 24 soil and fertilizer treatments on the growth and productivity of Musa AAA cv. 'Malindi' were evaluated. The treatments consisted of two soil types (saline and amended non-saline), two fertilizer application methods (mixed and ring applied), six fertilizer amendments (1: fresh dairy manure, 2: composted dairy manure, 3: composted dairy manure and 10% date palm straw, 4: composted dairy manure and 30% date palm straw, 5: only NPK, and 6: NPK and micronutrients). Sandy loam soil was imported from another part of Oman to amended the soil in the planting holes and create non-saline conditions in the root-zone. The results indicate that replacing the saline soil in the root zone by non-saline soil improved plant growth and yield more than fertilizer amendments or application methods. Particularly those plants on amended soil where NPK was applied using the ring method and which received micronutrients grew significantly faster to harvest (339 days), had a higher average bunch weight (9.5 kg/bunch) and were consequently more productive (10.6 tonnes/hectare/cycle) compared to the other treatments.

Evaluation of water retention capacity and flood control function of the forest catchment

This research quantitatively evaluates the water retention capacity and flood control function of the forest catchments by using hydrological data of the large flood events which happened after the serious droughts. The objective sites are the Oodo Dam and the Sameura Dam catchments in Japan. The kinematic wave model, which considers saturated and unsaturated sub-surface soil zones, is used for the rainfall-runoff analysis. The result shows that possible storage volume of the Oodo Dam catchment is 162.26 MCM in 2005, while that of Samerua is 102.83 MCM in 2005 and 102.64 MCM in 2007. Flood control function of the Oodo Dam catchment is 173 mm in water depth in 2005, while the Sameura Dam catchment 114 mm in 2005 and 126 mm in 2007. This indicates that the Oodo Dam catchment has more than twice as big water capacity as its capacity (78.4 mm), while the Sameura Dam catchment has about one-fifth of the its storage capacity (693 mm).

Efficient Characterisation and Optimal Control of Open Quantum Systems - Mathematical Foundations and Physical Applications

Since no physical system can ever be completely isolated from its environment, the study of open quantum systems is pivotal to reliably and accurately control complex quantum systems. In practice, reliability of the control field needs to be confirmed via certification of the target evolution while accuracy requires the derivation of high-fidelity control schemes in the presence of decoherence. In the first part of this thesis an algebraic framework is presented that allows to determine the minimal requirements on the unique characterisation of arbitrary unitary gates in open quantum systems, independent on the particular physical implementation of the employed quantum device. To this end, a set of theorems is devised that can be used to assess whether a given set of input states on a quantum channel is sufficient to judge whether a desired unitary gate is realised. This allows to determine the minimal input for such a task, which proves to be, quite remarkably, independent of system size. These results allow to elucidate the fundamental limits regarding certification and tomography of open quantum systems. The combination of these insights with state-of-the-art Monte Carlo process certification techniques permits a significant improvement of the scaling when certifying arbitrary unitary gates. This improvement is not only restricted to quantum information devices where the basic information carrier is the qubit but it also extends to systems where the fundamental informational entities can be of arbitary dimensionality, the so-called qudits. The second part of this thesis concerns the impact of these findings from the point of view of Optimal Control Theory (OCT). OCT for quantum systems utilises concepts from engineering such as feedback and optimisation to engineer constructive and destructive interferences in order to steer a physical process in a desired direction. It turns out that the aforementioned mathematical findings allow to deduce novel optimisation functionals that significantly reduce not only the required memory for numerical control algorithms but also the total CPU time required to obtain a certain fidelity for the optimised process. The thesis concludes by discussing two problems of fundamental interest in quantum information processing from the point of view of optimal control - the preparation of pure states and the implementation of unitary gates in open quantum systems. For both cases specific physical examples are considered: for the former the vibrational cooling of molecules via optical pumping and for the latter a superconducting phase qudit implementation. In particular, it is illustrated how features of the environment can be exploited to reach the desired targets.

ACTORS: A Model of Concurrent Computation in Distributed Systems

A foundational model of concurrency is developed in this thesis. We examine issues in the design of parallel systems and show why the actor model is suitable for exploiting large-scale parallelism. Concurrency in actors is constrained only by the availability of hardware resources and by the logical dependence inherent in the computation. Unlike dataflow and functional programming, however, actors are dynamically reconfigurable and can model shared resources with changing local state. Concurrency is spawned in actors using asynchronous message-passing, pipelining, and the dynamic creation of actors. This thesis deals with some central issues in distributed computing. Specifically, problems of divergence and deadlock are addressed. For example, actors permit dynamic deadlock detection and removal. The problem of divergence is contained because independent transactions can execute concurrently and potentially infinite processes are nevertheless available for interaction.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

Trajectory and Force Control of a Direct Drive Arm

Using the MIT Serial Link Direct Drive Arm as the main experimental device, various issues in trajectory and force control of manipulators were studied in this thesis. Since accurate modeling is important for any controller, issues of estimating the dynamic model of a manipulator and its load were addressed first. Practical and effective algorithms were developed fro the Newton-Euler equations to estimate the inertial parameters of manipulator rigid-body loads and links. Load estimation was implemented both on PUMA 600 robot and on the MIT Serial Link Direct Drive Arm. With the link estimation algorithm, the inertial parameters of the direct drive arm were obtained. For both load and link estimation results, the estimated parameters are good models of the actual system for control purposes since torques and forces can be predicted accurately from these estimated parameters. The estimated model of the direct drive arm was them used to evaluate trajectory following performance by feedforward and computed torque control algorithms. The experimental evaluations showed that the dynamic compensation can greatly improve trajectory following accuracy. Various stability issues of force control were studied next. It was determined that there are two types of instability in force control. Dynamic instability, present in all of the previous force control algorithms discussed in this thesis, is caused by the interaction of a manipulator with a stiff environment. Kinematics instability is present only in the hybrid control algorithm of Raibert and Craig, and is caused by the interaction of the inertia matrix with the Jacobian inverse coordinate transformation in the feedback path. Several methods were suggested and demonstrated experimentally to solve these stability problems. The result of the stability analyses were then incorporated in implementing a stable force/position controller on the direct drive arm by the modified resolved acceleration method using both joint torque and wrist force sensor feedbacks.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: the Infinite Horizon Case

We analyze an infinite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are identically distributed random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to maximize expected discounted, or expected average profit over the infinite planning horizon. We show that a stationary (s,S,p) policy is optimal for both the discounted and average profit models with general demand functions. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period.

Possible solution of some essential zero problems in compositional data analysis

One of the tantalising remaining problems in compositional data analysis lies in how to deal with data sets in which there are components which are essential zeros. By an essential zero we mean a component which is truly zero, not something recorded as zero simply because the experimental design or the measuring instrument has not been sufficiently sensitive to detect a trace of the part. Such essential zeros occur in many compositional situations, such as household budget patterns, time budgets, palaeontological zonation studies, ecological abundance studies. Devices such as nonzero replacement and amalgamation are almost invariably ad hoc and unsuccessful in such situations. From consideration of such examples it seems sensible to build up a model in two stages, the first determining where the zeros will occur and the second how the unit available is distributed among the non-zero parts. In this paper we suggest two such models, an independent binomial conditional logistic normal model and a hierarchical dependent binomial conditional logistic normal model. The compositional data in such modelling consist of an incidence matrix and a conditional compositional matrix. Interesting statistical problems arise, such as the question of estimability of parameters, the nature of the computational process for the estimation of both the incidence and compositional parameters caused by the complexity of the subcompositional structure, the formation of meaningful hypotheses, and the devising of suitable testing methodology within a lattice of such essential zero-compositional hypotheses. The methodology is illustrated by application to both simulated and real compositional data

An ATM distributed simulator for network management research

Due to the high cost of a large ATM network working up to full strength to apply our ideas about network management, i.e., dynamic virtual path (VP) management and fault restoration, we developed a distributed simulation platform for performing our experiments. This platform also had to be capable of other sorts of tests, such as connection admission control (CAC) algorithms, routing algorithms, and accounting and charging methods. The platform was posed as a very simple, event-oriented and scalable simulation. The main goal was the simulation of a working ATM backbone network with a potentially large number of nodes (hundreds). As research into control algorithms and low-level, or rather cell-level methods, was beyond the scope of this study, the simulation took place at a connection level, i.e., there was no real traffic of cells. The simulated network behaved like a real network accepting and rejecting SNMP ones, or experimental tools using the API node

Local model predictive control experiences with differential driven wheeled mobile robots

This work extends a previously developed research concerning about the use of local model predictive control in differential driven mobile robots. Hence, experimental results are presented as a way to improve the methodology by considering aspects as trajectory accuracy and time performance. In this sense, the cost function and the prediction horizon are important aspects to be considered. The aim of the present work is to test the control method by measuring trajectory tracking accuracy and time performance. Moreover, strategies for the integration with perception system and path planning are briefly introduced. In this sense, monocular image data can be used to plan safety trajectories by using goal attraction potential fields

Quantified set inversion with applications to control

This paper describes a new reliable method, based on modal interval analysis (MIA) and set inversion (SI) techniques, for the characterization of solution sets defined by quantified constraints satisfaction problems (QCSP) over continuous domains. The presented methodology, called quantified set inversion (QSI), can be used over a wide range of engineering problems involving uncertain nonlinear models. Finally, an application on parameter identification is presented