DART—a High Level Software-Defined Radio Platform Model for Developing the Run-Time Controller

Novel cognitive radio platforms, such as IMEC’s COgnitive Baseband RAdio (COBRA), should ensure the feasibility of multiple streams and their reconfigurability and scalability during run-time. The control over these tasks should be dedicated to a run-time controller that (re)allocates the resources on the platform. E.g., when the channel conditions change requiring a switching to different modulation and coding scheme or a user starts a new stream. Current transaction level models are too detailed for rapid exploration of all run-time options and the high-level data-flow frameworks (such as Kahn process networks) lack the dynamism and reconfigurability that is essential for the exploration. In this paper we propose the DAtaflow for Run-Time (DART), the high-level dynamic data-flow platform model framework, suited for rapid run-time control development. We sketch how to use this framework to develop such a controller in the reactive and more challenging, proactive way. We derive the component timing based on Instruction Set Simulator (ISS) simulation and the reconfiguration timing based on Transaction Level Modeling (TLM) simulation. Finally, we verify results of our DART approach with full TLM simulation of our platform.     

Subnanometric stabilization of plasmon-enhanced optical microscopy

We have demonstrated subnanometric stabilization of tip-enhanced optical microscopy under ambient condition. Time-dependent thermal drift of a plasmonic metallic tip was optically sensed at subnanometer scale, and was compensated in real-time. In addition, mechanically induced displacement of the tip, which usually occurs when the amount of tip-applied force varies, was also compensated in situ. The stabilization of tip-enhanced optical microscopy enables us to perform long-time and robust measurement without any degradation of optical signal, resulting in true nanometric optical imaging with high reproducibility and high precision. The technique presented is applicable for AFM-based nanoindentation with subnanometric precision.     

Efficient test case generation for validation of UML activity diagrams

Unified Modeling Language (UML) is widely used as a system level specification language in embedded system design. Due to the increasing complexity of embedded systems, the analysis and validation of UML specifications is becoming a challenge. UML activity diagram is promising to modeling the overall system behavior. However, lack of techniques for automated test case generation is one major bottleneck in the UML activity diagram validation. This article presents a methodology for automatically generating test cases based on various model checking techniques. It makes three primary contributions: First, we propose coverage-driven mapping rules that can automatically translate activity diagram to formal models. Next, we present a procedure for automatic property generation according to error models. Finally, we apply various model checking based test case generation techniques to enable efficient test case generation. Our experimental results demonstrate that our approach can reduce the validation effort drastically by reducing both test case generation time and required number of test cases to achieve a functional coverage goal.     

ON SOME GENERALIZED DIFFERENCE PARANORMED SEQUENCE SPACES ASSOCIATED WITH MULTIPLIER SEQUENCE DEFINED BY MODULUS FUNCTION

In this article we introduce the paranormed sequence spaces(f,Λ,Δm,p), c0(f,Λ,Δ<,m>,p)and l<,∞>(f,Λ,Δ<,m>,p),associated with the multiplier sequence Λ=(λ<,k>),de- fined by a modulus function f.We study their different properties like solidness,sym- metricity,completeness etc.and prove some inclusion results.     

A Study of the von Neumann Stability Analysis of a Semi-implicit Numerical Method Applied Over the Radial Impurity Transport Equation in Tokamak Plasma

The radial impurity transport equation for tokamak plasma is a form of diffusion–convection–reaction equation. The impurity transport equation is solved to determine the distribution of impurity (non-fuel) ion species with different ionization states perpendicular to magnetic surfaces of tokamak plasma. The equation for each charge (ionization) state Z is a non-linear, second-order in space, first-order in time, parabolic partial differential equation coupled to the previous Z???1 and the next Z?+?1 charge states of the impurity species through its reaction term. The number of differential equations to be solved simultaneously is hence determined by the number of ionization states of the impurity species studied. The solution to the set of these coupled equations can be obtained using a semi-implicit numerical method applied on it. The present study describes the application of von Neumann stability analysis over the semi-implicit numerical method applied over the radial impurity transport equation and determines a generic stability criterion for the method. The stability analysis is further illustrated using the geometry of Aditya tokamak installed at the Institute for Plasma Research Gandhinagar, India as an example. The impurity species considered is oxygen (Atomic number?=?8). This leads to a set of eight coupled equations for charge states Z?=?1 to 8 over which von Neumann analysis is illustrated in present study.     

Learning-oriented Property Decomposition for Automated Generation of Directed Tests

SAT-based Bounded Model Checking (BMC) is promising for automated generation of directed tests. Due to the state space explosion problem, SAT-based BMC is unsuitable to handle complex properties with large SAT instances or large bounds. In this paper, we propose a framework to automatically scale down the SAT falsification complexity by utilizing the decision ordering based learning from decomposed sub-properties. Our framework makes three important contributions: i) it proposes learning-oriented decomposition techniques for complex property falsification, ii) it proposes an efficient approach to accelerate the complex property falsification using the learning from decomposed sub-properties, and iii) it combines the advantages of both property decomposition and property clustering to reduce the overall test generation time. The experimental results using both software and hardware benchmarks demonstrate the effectiveness of our framework.