Performance Of Real-time Applications Research Articles

High voltage gain multiphase interleaved DC-DC converter for DC micro grid application using ıntelligent control

This paper presents Multiphase Interleaved Boost Converter (MIBC) with Voltage Multiplier Cell (VMC) supplied for DC micro grid application. The main focus of this research is to increase the voltage gain and reduce the voltage stress across the switch. The improvement in voltage gain is acheievd by recycling the stored energy in the inductor, VMC and voltage lift capacitor. In the proposed MIBC the input current is divided among the three converters. Hence, the rating of devices used in the converter can be reduced, which in turn minimises the ohmic loss of the switch. In addition, the interleaved converter decreases the voltage stress across the switch. The MIBC is compared with existing converters for various parameters such as voltage gain, voltage stress of the main switch and diode and the number of components used. The hardware is implemented for the propoosed MIBC to validate performance in real time application.

Optimal Spectrum Utilization and Flow Controlling In Heterogeneous Network with Reconfigurable Devices

AbstractFairness provisioning in heterogeneous networks is a prime issue for high-rate data flow, wherein the inter-connectivity property among different communication devices provides higher throughput. In Hetnet, optimal resource utilization is required for efficient resource usage. Proper resource allocation in such a network led to higher data flow performance for real-time applications. In view of optimal resource allocation, a resource utilization approach for a reconfigurable cognitive device with spectrum sensing capability is proposed in this paper. The allocation of the data flow rate at device level is proposed for optimization of network fairness in a heterogeneous network. A dynamic approach of rate-inference optimization is proposed to provide fairness in dynamic data traffic conditions. The simulation results validate the improvement in offered quality in comparison to multi-attribute optimization.

Coordinated Volt/VAr control for real-time operation of smart distribution grids

This paper proposes a new approach to the development of coordinated Volt/VAr control for real-time operation of smart distribution grids. The methodology intended for the coordinated Volt/VAr control conveys the minimization of the voltage violations and also a better distribution of the number of commutations among the control equipments. The control algorithm developed centrally coordinates the equipment actions in the conditions where the local control does not act properly. The adjustments are performed in the voltage regulator, in the capacitors banks and in the reactive power injected by the inverters of the distributed generation. The coordinated control strategy uses the fuzzy logic combined with a heuristic algorithm, which has implementation simplicity and suitable performance for real-time applications. A numerical example is presented aiming to clarify the procedure of the proposed control algorithm. Furthermore, the algorithm performance is verified through a modified IEEE 34 node test feeder under distinct operating conditions.

Energy Efficient Algorithms for Real-Time Traffic Over Fading Wireless Channels

This paper studies the problem of using minimum power to provide satisfactory performance for real-time applications over unreliable and fading wireless channels. We demonstrate that this problem can be formulated as a linear programming problem. However, this formulation involves exponentially many constraints, and many parameters are either unavailable or difficult to compute, which makes it infeasible to employ standard techniques to solve the linear programming problem. Instead, we propose a simple online algorithm for this problem. We prove that our algorithm provides satisfactory performance to all real-time applications, and the total power consumption can be made arbitrarily close to the theoretical lower bound. Furthermore, our algorithm has very low complexity and does not require knowledge of many parameters in the linear programming problem, including the distributions of channel qualities. We further extend our algorithm to address systems where real-time applications and non-real-time ones coexist. We demonstrate that our algorithm achieves both low total power consumption and high utility for each non-real-time client while satisfying the performance requirements of real-time clients. Simulation results further provide some insights in setting important parameters of our algorithms, and demonstrate that our algorithm indeed achieves a significant reduction in power consumption.

Exploiting Static Non-Uniform Cache Architectures for Hard Real-Time Computing

High-performance processors using Non-Uniform Cache Architecture (NUCA) are increasingly used to deal with the growing wire delays in multicore/manycore processors. Due to the convergence of high-performance computing with embedded computing, NUCA caches are expected to benefit high-end embedded systems as well. However, for real-time systems that use multicore processors with NUCA caches, it is crucial to bound worst-case execution time (WCET) accurately and safely. In this paper, we developed a WCET analysis approach by considering the effect of static NUCA caches on WCET. We compared the WCET in real-time applications with different topologies of static NUCA caches. Our experimental results demonstrated that the static NUCA cache could improve the worst-case performance of realtime applications using multicore processor compared to the cache with uniform access time.

TOWARDS AUTOMATED REGRESSION ANALYSIS AND MANAGEMENT THROUGHOUT SOFTWARE LIFE-CYCLE

Software application performance tends to degrade over a period of time due to introduction of new features, feature enhancements and bugs-fixes as part of the software maintenance lifecycle. Critical applications cannot afford the performance degradation. However, teams engaged in software maintenance are not too effective in detecting potential performance impacting issues during the test and release phases of the software engineering process, since the run-time environments for individual customers are difficult to simulate. This challenge is exacerbated for applications with a large installed-base. This research paper proposes the concept of Application Baselining as a means to effectively detecting and containing software regression. It provides indication of real-time application performance by monitoring its critical parameters over long periods of time. By keeping track of the changes made to the application and its environment, their impact on application performance is correlated. The changes which adversely impact the application performance are then rolled-back to mitigate their effect. Early work towards development of such a framework is presented.

Comparison between Open CV and MATLAB Performance in Real Time Applications

Comparison between Open CV and MATLAB Performance in Real Time Applications

Simulation and Evaluation of Multiple Sporadic Servers Rescheduling

This paper deals with task scheduling in heterogeneous distributed embedded computer environments of renewable and timing variable resources. Real-time application performance can be considerably influenced by unexpected events in the environment. The predictive-reactive rescheduling procedure can enable significant improvements. The rescheduling procedure based on multiple server tasks enables usage of one's own resources, but also a fast connection to nodes with appropriate performance that will soften the influence of disturbances. We developed a tool that enables simulation of application execution conditions for a given environment and extraction of better scheduling parameter settings. Numerous scheduling parameters are improved with an increase in the number of nodes available for rescheduling. We also investigate cost-effectiveness of the method based on rescheduling time cost. The results demonstrate a possibility of achieving performance improvement by tuning cost parameters.

Real-time continuous intersection joins over large sets of moving objects using graphic processing units

The Multiple Time Bucket Join (MTB-join) algorithm is the state of the art for processing the continuous intersection join (CI-join) query over moving objects. It considerably outperforms alternatives, but still falls short of real-time application performance requirements for large sets of moving objects. In this paper, we achieve real-time performance for the CI-join query over large sets of moving objects by exploiting the computational power of commodity graphics processing units (GPUs). We first analyze how the main characteristics of the MTB-join algorithm make it ill suited to GPUs and identify key challenges in designing efficient GPU-based algorithms for the query. We then address these challenges by developing the multi-layered grid join (MLG-join) algorithm which has the following key features: (i) memory locality friendly indexing, (ii) no dynamic memory allocation, (iii) in-place object updates, (iv) lock-free concurrent updates, and (v) massive parallelism. These features unleash the full potential of the memory bandwidth and parallel processing of GPUs. Furthermore, we conduct a theoretical analysis which can predict the pruning power of the MLG-join algorithm given certain parameter values used in the algorithm. This allows us to select optimal parameter values. Through extensive experimental results, we show that our analysis accurately models the MLG-join algorithm's sensitivity to parameter values. The proposed MLG-join algorithm outperforms the MTB-join algorithm, and a GPU-based nested-loops join algorithm, by up to two orders of magnitude, and achieves real-time performance for CI-join queries on large sets of moving objects.

Flexible and highly stable electrospun nanofibrous membrane incorporating gold nanoclusters as an efficient probe for visual colorimetric detection of Hg(ii)

Here, we describe the visual colorimetric detection of Hg2+ based on a flexible fluorescent electrospun nanofibrous membrane (NFM).

A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers

This paper presents a stochastic multi-parameters divergence method for online parameter optimization of fractional-order proportional–integral–derivative (PID) controllers. The method is used for auto-tuning without the need for exact mathematical plant model and it is applicable to diverse plant transfer functions. The proposed controller tuning algorithm is capable of adaptively responding to parameter fluctuations and model uncertainties in real systems. Adaptation skill enhances controller performance for real-time applications. Simulations and experimental observations are carried on a prototype helicopter model to confirm the performance improvements obtained by the online auto-tuning of fractional-order PID structure in laboratory conditions.

Real-time interactive isosurfacing: a new method for improving marching isosurfacing algorithm output and efficiency

Efficient rendering of a changing volumetric data-set is central to the development of effective medical simulations that incorporate haptic feedback. A new method referred to as real-time interactive isosurfacing (RTII) is described in this paper. RTII is an algorithm that can be applied to output from Marching Cubes-like algorithms to improve performance for real-time applications. The approach minimises processing by re-evaluating the isosurface around changing sub-volumes resulting from user interactions. It includes innovations that significantly reduce mesh complexity and improve mesh quality as triangles are created from the Marching Tetrahedra isosurfacing algorithm. Rendering efficiency is further improved over other marching isosurfacing algorithm outputs by maintaining an indexed triangle representation of the mesh. The effectiveness of RTII is discussed within the context of an endoscopic sinus surgery simulation currently being developed by the authors.

A Real-Time Visualization Method of Objects Using Grid Coding Based on Surface Orientation and Structure

A systematic approach is introduced to infer surface structure of visible object surfaces using grid coding in this paper. The visualized object, which has surface of a terrain-like shape, is assumed to derive from data field of gird points defined by orthogonal division and data acquisition. Parametric surface is used to spatially represent the data for analysis. Central difference technology is presented to tessellate the curved surface and generate view-dependent multi-resolution models, which has excellent performance in real-time applications. Besides, an algorithm for data pre-processing is presented. The approach presented here also adapts to data acquired in realistic scene, which can be a virtual reality application. Comparing to previous methods, the method presented in this paper provides good flexibility in various applications. It works well in real-time graphic application while has good visual quality.

Performance Study of GPUs in Real-Time Trigger Applications for HEP Experiments

Graphical Processing Units (GPUs) have evolved into highly parallel, multi-threaded, multicore powerful processors with high memory bandwidth. GPUs are used in a variety of intensive computing applications. The combination of highly parallel architecture and high memory bandwidth makes GPUs a potentially promising technology for e_ective real-time processing for High Energy Physics (HEP) experiments. However, not much is known of their performance in real-time applications that require low latency, such as the trigger for HEP experiments. We describe an R&D project with the goal to study the performance of GPU technology for possible low latency applications, performing basic operations as well as some more advanced HEP lower-level trigger algorithms (such as fast tracking or jet finding). We present some preliminary results on timing measurements, comparing the performance of a CPU versus a GPU with NVIDIA's CUDA general-purpose parallel computing architecture, carried out at CDF's Level-2 trigger test stand. These studies will provide performance benchmarks for future studies to investigate the potential and limitations of GPUs for real-time applications in HEP experiments.

Improving System Predictability and Performance via Hardware Accelerated Data Structures

In Dynamic Data-Driven Application Systems, applications must dynamically adapt their behavior in response to objectives and conditions that change while deployed. One approach to achieve dynamic adaptation is to offer middleware that facilitates component migration between modalities in response to such dynamic changes. The triggering, planning, and cost evaluation of adaptation takes place within a scheduler. Scheduling overhead is a major limiting factor for implementing dynamic scheduling algorithms with high frequency timer-tick resolution in real time systems. In this paper, we present a scalable hardware scheduler architecture for real time systems that reduces processing overhead and improves timing predictability of the scheduler. A new hardware priority queue design is presented, which supports insertions in constant time, and removals in O(log n)time. The hardware scheduler supports three (Rate Monotonic Scheduling (RMS), Earliest Deadline First (EDF), and priority based) scheduling algorithms, which can be configured during run-time. The interface to the scheduler is provided through a set of custom instructions as an extension to the processors instruction set architecture. We also report on our experience migrating between two implementations of an ordered-set implementation, with the goal of providing predictable performance for real-time applications.

Three-dimensional path planning for unmanned aerial vehicle based on linear programming

SUMMARYIn this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. This resolution, defined to achieve the optimal velocity, deals with the optimization of a pair of orthogonal vectors. Some constraints, such as boundaries of the vehicle velocity, acceleration, sensor range, and flying height, are considered in this method. A number of simulations, under static and dynamic environments, are carried out to validate the performance of generating optimal trajectory in real time. Compared with ant colony optimization algorithm and genetic algorithm, our method has less parameters to tune and can achieve better performance in real-time application.

Achieving seamless handoffs via backhaul support in Wireless Mesh Networks

In Wireless Mesh Networks (WMNs), mobile clients may experience frequent handoffs due to the relatively small transmission range of the mesh routers. Each handoff may lead to packet delays and/or packet losses, which limits the performance of real-time applications over WMNs. In this work, we propose BASH--a Backhaul-Aided Seamless Handoff scheme. BASH takes advantage of the wireless backhaul feature of WMNs, and allows a mobile station to directly access the backhaul channel to probe the neighboring mesh routers. Our work shows that by utilizing the wireless backhaul, BASH (1) reduces the probing latency and, thus, the Layer-2 handoff latency; (2) allows partial overlap of the Layer-2 and Layer-3 handoffs, reducing the overall handoff latency; and (3) shortens the authentication latency by utilizing the transitivity of trust relationship. The experimental results show that BASH achieves an average Layer-2 handoff of 8.9 ms, which supports real-time applications during the handoff.

Loop-Based Instruction Prefetching to Reduce the Worst-Case Execution Time

Estimating and optimizing worst-case execution time (WCET) is critical for hard real-time systems to ensure that different tasks can meet their respective deadlines. Recent work has shown that simple prefetching techniques such as the Next-N-Line prefetching can enhance both the average-case and worst-case performance; however, the improvement on the worst-case execution time is rather limited and inefficient. This paper studies a loop-based instruction prefetching approach, which can exploit the program control-flow information to intelligently prefetch instructions that are most likely needed. Our evaluation indicates that the loop-based instruction prefetching outperforms the Next-N-Line prefetching in both the worst-case and the average-case performance for real-time applications.

Real-time 3D features reconstruction through monocular vision

A fast and interactive implementation for cam- era pose registration and 3D point reconstruction over a physical surface is described in this paper. The method (called SRE—Smart Reverse Engineering) extracts from a continuous image streaming, provided by a single cam- era moving around a real object, a point cloud and the camera's spatial trajectory. The whole per frame procedure follows three steps: camera calibration, camera registra- tion, bundle adjustment and 3D point calculation. Cam- era calibration task was performed using a traditional approach based on 2-D structured pattern, while the Opti- cal Flow approach and the Lucas-Kanade algorithm was adopted for feature detection and tracking. Camera regis- tration problem was then solved thanks to the Essential Matrix definition. Finally a fast Bundle Adjustment was performed through the Levenberg-Marquardt algorithm to achieve the best trade-off between 3D structure and cam- era variations. Exploiting a PC and a commercial web- cam, an experimental validation was done in order to verify precision in 3D data reconstruction and speed. Practical tests helped also to tune up several optimization parame- ters used to improve efficiency of most CPU time consum- ing algorithms, like Optical Flow and Bundle Adjustment. The method showed robust results in 3D reconstruction and very good performance in real-time applications.

Effective session key distribution for secure fast handover in mobile networks

In this paper, we introduce a session key distribution mechanism for fast secure handover in wireless mobile networks. The proposed mechanism is based on the stream control transmission protocol in where a mobile node actively changes its IP address without its connection loss. When a mobile node moves between different access routers, the required session key at a new access router is distributed previously through the tunnel established between the previous access router and the new access router. Due to the reduced key distribution time, the mobile node achieves its secure seamless handover. The provided performance analysis proves that the proposed mechanism provides the reduced signaling cost compared to existing mobility protocols. In addition, the proposed mechanism reduces the handover latency so that the reduced amount of packet loss provides the stable handover performance for real-time applications.