RR Algorithm Research Articles

Performance Analysis of Hybrid RR Algorithm for Anomaly Detection in Streaming Data

Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the model. The proposed Hybrid Ensemble RR Model was tested on standard pedestrian datasets UCSDPed1, USDPed2. The data set has 70 videos in UCSD Ped1 and 28 videos in UCSD Ped2 with a total of 18560 frames. Since a real-time stream has strict memory constraints and storage issues, a simple computing machine does not suffice in performing analytics with stream data. Hence the proposed research is designed to work on a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit) supported framework. As shown in the experimental results section, recorded observations on frame-level EER (Equal Error Rate) and AUC (Area Under Curve) showed a 9% reduction in EER in UCSD Ped1, a 13% reduction in ERR in UCSD Ped2 and a 4% improvement in accuracy in both datasets.

Accurate respiratory rate determination using a novel insertable cardiac monitor algorithm: implications for diagnostic and monitoring potentials beyond heart rhythm disorders

Abstract Background Respiratory rate (RR) is a critical vital sign that is highly relevant in patients with cardiopulmonary disorders. The implantable cardiac monitor (ICM) provides useful data pertaining to heart rhythm, but little is known up to this point regarding its potential diagnostic value in the direct measurement of respiratory parameters. The addition of respiration information could improve understanding of overall health status of heart rhythm patients with ICM. Objective The primary objective of this study was to evaluate the accuracy of the RR detected by an existing implanted ICM as compared to gold-standard polysomnography (PSG) measurement of respiration. Methods This prospective single center study enrolled 25 patients (17 male, 62.7±12.2 yesrs) with an implanted ICM and suspected sleep-disordered breathing. The ICM was custom configured with research software to collect respiration data (Fig. 1). Simultaneous, time-synchronized PSG and ICM data were evaluated in two-minute epochs episodically during the night. The offline novel prototype RR algorithm was evaluated on episodes collected by the ICM and compared against expert manual adjudicated RR from PSG by two separate investigators. The interobserver agreement was assessed using intraclass correlation coefficient (ICC). The performance of the novel algorithm was assessed using Bland-Altman analysis with 95% limits of agreement (LOA). Results A total of 495 epochs were graded by two independent observers, with good ICC of 0.83 (95% C.I. 0.79–0.86). Epochs free of severe sleep disordered breathing/apnea (n=363) were included in this analysis with 106 of these containing periods of hypopnea. The development and validation datasets were comprised of 235 and 128 epochs, respectively. In the development data, the mean RR was 14.99±3 breaths per minute, and the mean RR was 13.44±2 breaths per minute in the test data. Using Bland-Altman analysis, the bias for the novel prototype algorithm was only −0.13 and +0.32 breaths per minute with 95% LOA of −2.24 to +1.98 and −2.56 to +3.19 breaths per minute (Fig. 2), in the in the development and test dataset respectively. Conclusion The novel prototype algorithm applied to the ICM data provided accurate determination of respiratory rate as compared to gold-standard PSG data. The capability to determine respiratory rate accurately from an existing ICM platform demonstrates the potential to extend the diagnostic power of ICMs beyond heart rhythm abnormalities to address a broad range of comorbidities including breathing disorders and heart failure. Funding Acknowledgement Type of funding sources: None.

Scheduling Algorithms for CPU

The process scheduling, is one of the most important tasks of the operating system. We present in this paper some types of scheduling algorithms of CPU. One of the most common scheduling algorithms used by the most operating systems is the Round Robin method in which, the ready processes waiting in ready queue, seize the processor for a short period of time known as the quantum (or time slice) circularly. But the most important issue in RR algorithm, which highly affects its efficiency. It determines the amount of the time quantum. Assigning very small and very large values for this parameter, will lead to performance decrease due to increasing context switching overhead, and degradation of the RR method to FCFS algorithm consequently. We will suggest a solution of allocated time quantum.

Performance Analysis of Arbitration Scheduling Algorithm Based on RR Optimization for Network-on-chips

The RR arbitration algorithm commonly used in the current Network-on-chips cannot meet the requirements for low power consumption and rapid response under high load conditions, with the continuous development of microelectronics technology. In order to solve this problem, based on the idea of non-competitive transmission and parallel optimization, this paper improves on the RR algorithm to obtain a MAC layer arbitration scheduling algorithm suitable for the Network-on-chips. By optimizing the service and query transfer process in parallel, the algorithm reduces the queue length of information grouping in the system and shortens the query period of the system. The algorithm model is established by using Markov process and probability generating function, mathematically analyze the first-order characteristics of the system, and obtain an accurate solution. The simulation model of the algorithm is established based on the simulation platform to verify the accuracy of the precise solution. After optimization, the system’s average queue length and average cycler have been greatly improved.

Minimizing Redundancy to Satisfy Reliability Requirement for a Parallel Application on Heterogeneous Service-Oriented Systems

Reliability is widely identified as an increasingly relevant issue in heterogeneous service-oriented systems because processor failure affects the quality of service to users. Replication-based fault-tolerance is a common approach to satisfy application's reliability requirement. This study solves the problem of minimizing redundancy to satisfy reliability requirement for a directed acyclic graph (DAG)-based parallel application on heterogeneous service-oriented systems. We first propose the enough replication for redundancy minimization (ERRM) algorithm to satisfy application's reliability requirement, and then propose heuristic replication for redundancy minimization (HRRM) to satisfy application's reliability requirement with low time complexity. Experimental results on real and randomly generated parallel applications at different scales, parallelism, and heterogeneity verify that ERRM can generate least redundancy followed by HRRM, and the state-of-the-art MaxRe and RR algorithm. In addition, HRRM implements approximate minimum redundancy with a short computation time.

Optimized dual path analysis using finite automata theory in wireless sensor networks

Optimized dual path analysis using finite automata theory in wireless sensor networks

A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment

In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.

Optimal Round Robin CPU Scheduling Algorithm Using Manhattan Distance

In Round Robin Scheduling the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. The performance of Round robin CPU scheduling algorithm is entirely dependent on the time quantum selected. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper a method using Manhattan distance has been proposed that decides a quantum value. The computation of the time quantum value is done by the distance or difference between the highest burst time and lowest burst time. The experimental analysis also shows that this algorithm performs better than RR algorithm and by reducing number of context switches, reducing average waiting time and also the average turna round time.

Modified RR Algorithm with Dynamic Time Quantum for Externally Prioritized Tasks

The objective of this paper is to modify Round Robin scheduling for soft real time systems. It introduces a variation of round robin algorithm which can schedule tasks considering their priorities in a round robin fashion. Simple Round Robin scheduling algorithm and Priority scheduling algorithm, both have their own drawbacks. The proposed algorithm removes the drawbacks of Round Robin scheduling and Priority scheduling. The proposed scheduling algorithm calculates different time slices for individual processes considering their priorities. Experimental analysis reveals that the proposed algorithm produces better average turnaround time, average waiting time and less number of context switches than some existing algorithms useful for interactive systems. KeywordsOperating System; Scheduling; Round Robin Algorithm; Context switch; Turnaround time; Average Waiting time __________________________________________________*****_________________________________________________

The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets

The GHG-CCI project is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The “ECV Greenhouse Gases” (ECV GHG) is the global distribution of important climate relevant gases – atmospheric CO2 and CH4 – with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 and XCH4, to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO2 from SCIAMACHY and TANSO and XCH4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as “Round Robin” (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the “breakthrough” single measurement precision requirement has been met for SCIAMACHY and TANSO XCO2 (<3ppm) and TANSO XCH4 (<17ppb). The achieved relative accuracy for XCH4 is 3–15ppb for SCIAMACHY and 2–8ppb for TANSO depending on algorithm and time period. Meeting the 0.5ppm systematic error requirement for XCO2 remains a challenge: approximately 1ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA).

FUZZY ROUND ROBIN CPU SCHEDULING ALGORITHM

One problem in Round Robin CPU Scheduling is that if the time required for the running process is slightly more than time quantum even by a fraction value, then process gets preempted and context switch occurs. This causes more waiting time for that process and more overheads due to unnecessary context switch. Another problem with RR scheduling is the value of time Quantum. If it is too large, RR algorithm degenerate to FCFS and if it is too short frequent context switches occurs which results into more overheads which in turn degrade the performance. In this work a Fuzzy Round Robin scheduling algorithm has been proposed that tries to remove these two problems using fuzzy technique. Simulation has been done to compare the performance of this algorithm with its non fuzzy counterpart.

Reliable workflow scheduling with less resource redundancy

We examine the problem of reliable workflow scheduling with less resource redundancy. As scheduling workflow applications in heterogeneous systems, either for optimizing the reliability or for minimizing the makespan, are NP-Complete problems, we alternatively find schedules for meeting specific reliability and deadline requirements. First, we analyze the reliability of a given schedule using two important definitions: Accumulated Processor Reliability (APR) and Accumulated Communication Reliability (ACR). Second, inspired by the reliability analysis, we present three scheduling algorithms: RR algorithm schedules least Resources to meet the Reliability requirement; DRR algorithm extends RR by further considering the Deadline requirement; and dynamic algorithm schedules tasks dynamically: It avoids the ''Chain effect'' caused by uncertainties on the task execution time estimates, and relieves the impact from the inaccuracy on failure estimation. Finally, the empirical evaluation shows that our algorithms can save a significant amount of computation and communication resources when performing a similar reliability compared to Fault-Tolerant-Scheduling-Algorithm (FTSA) algorithm.

Improved Round Robin Scheduling using Dynamic Time Quantum

Round Robin scheduling algorithm is the widely used scheduling algorithm in multitasking and real time environment. It is the most popular algorithm due to its fairness and starvation free nature towards the processes, which is achieved by using the time quantum. As the time quantum is static, it causes less context switching incase of high time quantum and high context switching incase of less time quantum. Increasing context switch leads to high avg. waiting time, high avg. turnaround time which is a overhead and degrades the system performance. So, the performance of the system solely depends upon the choice of optimal time quantum which is dynamic in nature. In this paper, we have proposed a new variant of RR scheduling algorithm known as Improved Round Robin (IRR) Scheduling algorithm, by arranging the processes according to their shortest burst time and assigning each of them with an optimal time quantum which is able to reduce all the above said disadvantages. Experimentally we have shown that our proposed algorithm performs better than the RR algorithm, by reducing context switching, average waiting and average turnaround time.

LTE系统下行MU-MIMO一种自适应调度算法

LTE系统是3G向4G演进的国际一致在研究的方向和系统，国内外对LTE的研究多方多面，但是对于资源调度方面一直都处于摸索发现阶段。当前较为成熟的算法包括正比公平算法，轮询算法等，虽然对系统的整体性能有所提升但是仍然将注意力过多的集中在SU-MIMO上，而没有深入的探究MU-MIMO的更高性能的提升。本文旨在通过探究下行系统一种MU-MIMO的自适应调度算法，进一步提高通信系统整体性能，采用用户配对以及CQI的重计算来实现MU-MIMO的自适应资源调度，并加以计算机仿真分析，从性能结果能看出该方法的易实现性以及优越性。 LTE system is the worldwide well known advanced version from 3Gto4G. However, in terms of all the research aspects, the resource scheduling is still in the discovery level. Nowadays, the more developed research achievements of scheduling are such like PF, RR algorithm, and so on. The shortage of these algorithms is that they still pay most their attention to the SU-MIMO system, rather the MU-MIMO. In this paper, the proposed method aims at the build of downlink resource scheduling which can improve the system performance a lot for MU-MIMO system. Through the simulation of computer, this paper proves the availability and excellence of the proposed method.

Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm(p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features.