Timing Analysis Algorithm Research Articles

A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance.

Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays. This is not only due to the fact that screen-based SSVEP generally operates under static, stable conditions with simple and unvaried visual stimuli but also because conventional luminance-modulated stimuli can quickly induce visual fatigue. This study attempts to address these research gaps by designing SSVEP paradigms with stereo-related attributes and conducting a comparative analysis with the traditional 2D planar paradigm under the same VR environment. This study proposed two new paradigms: the 3D paradigm and the 3D-Blink paradigm. The 3D paradigm induces SSVEP by modulating the luminance of spherical targets, while the 3D-Blink paradigm employs modulation of the spheres' opacity instead. The results of offline 4-object selection experiments showed that the accuracy of 3D and 2D paradigm was 85.67 and 86.17% with canonical correlation analysis (CCA) and 86.17 and 91.73% with filter bank canonical correlation analysis (FBCCA), which is consistent with the reduction in the signal-to-noise ratio (SNR) of SSVEP harmonics for the 3D paradigm observed in the frequency-domain analysis. The 3D-Blink paradigm achieved 75.00% of detection accuracy and 27.02 bits/min of ITR with 0.8 seconds of stimulus time and task-related component analysis (TRCA) algorithm, demonstrating its effectiveness. These findings demonstrate that the 3D and 3D-Blink paradigms supported by VR can achieve improved user comfort and satisfactory performance, while further algorithmic optimization and feature analysis are required for the stereo-related paradigms. In conclusion, this study contributes to a deeper understanding of the impact of binocular stereoscopic vision mechanisms on SSVEP paradigms and promotes the application of SSVEP-BCI in diverse VR environments.

Path-Based Delay Variation Models for Parallel-Prefix Adders

State-of-the-art static timing analysis algorithms can evaluate worst-case delay in statistical terms. In this paper, a modeling framework is introduced for the evaluation of the maximum-delay Cumulative Density Function (CDF) of an ensemble of parallel-prefix adder topologies. For moderate variations and close-to-nominal supply voltages, the maximum delay of parallel-prefix adders is practically determined by the maximum of a set of near-critical-delay paths around the nominal maximum-delay path. These paths end to the most significant and neighboring bit positions. Matrix-based path delay formulations are derived for the particular set of paths. The introduced matrix formulations are exploited to assess the maximum-delay CDF by means of a multivariate Gaussian CDF. To validate the accuracy of the introduced models, a quantitative comparison of the proposed probabilistic delay models against Spice-level Monte-Carlo simulations is offered for certain parallel-prefix adders. Threshold-voltage variations summarize several process-dependent variation-inducing mechanisms and are modeled as Gaussian variations, introduced to BSIM-4 transistor models for a 16-nm technology node. For the nominal voltage case and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula> threshold-voltage variations, the introduced models estimate the 0.95 timing yield point with a mean absolute error below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1\%$</tex-math></inline-formula> compared to Spice-level simulations for the 16 bit-length case. Furthermore, an extension of the proposed approach to account for multiple end points is investigated that reduces the error for the estimation of maximum delay, demonstrated for a unit delay model and certain bit-lengths of Kogge-Stone adder.

DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors

Parallel hierarchical scheduling of multicore processors in avionics hypervisor is being studied. Parallel hierarchical scheduling utilizes modular reasoning about the temporal behavior of the upper Virtual Machine (VM) by partitioning CPU time. Directed Acyclic Graphs (DAGs) are used for modeling functional dependencies. However, the existing DAG scheduling algorithm wastes resources and is inaccurate. Decreasing the completion time (CT) of DAG and offering a tight and secure boundary makes use of joint-level parallelism and inter-joint dependency, which are two key factors of DAG topology. Firstly, Concurrent Parent and Child Model (CPCM) is researched, which accurately captures the above two factors and can be applied recursively when parsing DAG. Based on CPCM, the paper puts forward a hierarchical scheduling algorithm, which focuses on decreasing the maximum CT of joints. Secondly, the new Response Time Analysis (RTA) algorithm is proposed, which offers a general limit for other execution sequences of Noncritical joints (NC-joints) and a specific limit for a fixed execution sequence. Finally, research results show that the parallel hierarchical scheduling algorithm has higher performance than other algorithms.

Executable models of immune signaling pathways in HIV-associated atherosclerosis

Atherosclerosis (AS)-associated cardiovascular disease is an important cause of mortality in an aging population of people living with HIV (PLWH). This elevated risk has been attributed to viral infection, anti-retroviral therapy, chronic inflammation, and lifestyle factors. However, the rates at which PLWH develop AS vary even after controlling for length of infection, treatment duration, and for lifestyle factors. To investigate the molecular signaling underlying this variation, we sequenced 9368 peripheral blood mononuclear cells (PBMCs) from eight PLWH, four of whom have atherosclerosis (AS+). Additionally, a publicly available dataset of PBMCs from persons before and after HIV infection was used to investigate the effect of acute HIV infection. To characterize dysregulation of pathways rather than just measuring enrichment, we developed the single-cell Boolean Omics Network Invariant Time Analysis (scBONITA) algorithm. scBONITA infers executable dynamic pathway models and performs a perturbation analysis to identify high impact genes. These dynamic models are used for pathway analysis and to map sequenced cells to characteristic signaling states (attractor analysis). scBONITA revealed that lipid signaling regulates cell migration into the vascular endothelium in AS+ PLWH. Pathways implicated included AGE-RAGE and PI3K-AKT signaling in CD8+ T cells, and glucagon and cAMP signaling pathways in monocytes. Attractor analysis with scBONITA facilitated the pathway-based characterization of cellular states in CD8+ T cells and monocytes. In this manner, we identify critical cell-type specific molecular mechanisms underlying HIV-associated atherosclerosis using a novel computational method.

Timing analysis algorithm using a neural network under PVT variations

Process-voltage-temperature variations result in an increase in logic gate delays, which further lead to timing violations. Because of the computational complexity of the operators such as max and sum in statistical static timing analysis, this paper proposes a timing analysis algorithm using neural network to evaluate circuit timing under the PVT variations. In the proposed algorithm, a neural network method is applied to model the mean or standard deviation of the gate delay. Then, a viability analysis method is introduced to recognize the true critical path. The simulation results on ISCAS89 benchmarks illustrate that compared with SPICE Monte Carlo simulation, the error in the mean calculated by the proposed algorithm is less than 2%, and the error in the standard deviation is less than 3.64%. In addition, for s298, s344, s832, s1238 and s13207, the viability analysis algorithm achieves 1.04X, 1.01X, 2.77X, 1.62X and 4.48X speedup over full path search, respectively.

A Modified Endurance Time Analysis Algorithm to Correct Duration Effects for a Concrete Gravity Dam

A Modified Endurance Time Analysis Algorithm to Correct Duration Effects for a Concrete Gravity Dam

Comparison of different algorithms for time analysis for CPM schedule networks

Traditional Critical Path Method (CPM) and its different generalizations have become the prevailing technique in managing complex projects over recent decades. Among them, Precedence Diagramming Method (PDM), with its four precedence relationships, has become the most popular due to its increased flexibility in modeling complex project logic. Despite the four well-known precedence relationships, CPM/PDM still suffers from modeling shortcomings.Developments such as conditional logic, maximal relationships, point-to-point relationships, continuous relationships, and non-linear activities attempt to remedy these problems. However, these improvements require more complex and sometimes iterative time analysis.Monte Carlo (MC) simulation, which calculates the effects of risks on the project duration, also requires numerous repetitions of the deterministic time analysis. It is especially true if the network contains the developments mentioned above. In these cases, the time analysis speed is of great importance.The goal of this study is a) to implement different algorithms for time analysis that perform better than the traditional time analysis, b) to compare their speed on large-scale artificially created projects, c) to make suggestions regarding which algorithms are suitable in different cases d) to develop a database containing artificially created networks to serve as a basis for benchmarking for researchers and developers working in the field of project planning.Comparisons were based on networks with at least 1000 activities. Results show that, depending on the sample network's structure, different algorithms perform well. Therefore, different time analysis algorithms are necessary to implement into the scheduling tools, and they can decide which algorithms perform better.

Effect of A-scan rate and interscan interval on optical coherence angiography.

Optical coherence tomography angiography (OCTA) can provide rapid, volumetric, and noninvasive imaging of tissue microvasculature without the requirement of exogenous contrast agents. To investigate how A-scan rate and interscan time affected the contrast and dynamic range of OCTA, we developed a 1.06-µm swept-source OCT system enabling 100-kHz or 200-kHz OCT using two light sources. After system settings were carefully adjusted, almost the same detection sensitivity was achieved between the 100-kHz and 200-kHz modalities. OCTA of ear skin was performed on five mice. We used the variable interscan time analysis algorithm (VISTA) and the designated scanning protocol with OCTA images reconstructed through the correlation mapping method. With a relatively long interscan time (e.g., 12.5 ms vs. 6.25 ms for 200-kHz OCT), OCTA can identify more intricate microvascular networks. OCTA image sets with the same interscan time (e.g., 12.5 ms) were compared. OCTA images acquired with a 100-kHz A-scan rate showed finer microvasculature than did other imaging modalities. We performed quantitative analysis on the contrast from OCTA images reconstructed with different A-scan rates and interscan time intervals in terms of vessel area, total vessel length, and junction density.

Analyzing Relative Flow Speeds in Diabetic Retinopathy Using Variable Interscan Time Analysis OCT Angiography

Further insight into the flow characteristics of the vascular features associated with diabetic retinopathy (DR) may improve assessment and treatment of disease progression. The variable interscan time analysis (VISTA) algorithm is an extension of OCT angiography (OCTA) that detects relative blood flow speeds, which then can be depicted on a color-coded map. This study used VISTA to analyze relative blood flow speeds in the microvascular changes associated with DR. Cross-sectional study. Thirteen patients with varying severities of DR treated at New England Eye Center, Boston, Massachusetts. OCT angiography images centered at the fovea were obtained on a prototype swept-source OCT device, and the VISTA algorithm was applied to visualize relative blood flow speeds. Descriptive flow analysis of the retinal vascular features of DR was conducted on the VISTA-generated images. Twenty-six eyes were included in this study. Of these, 3 eyes had mild nonproliferative DR (NPDR), 6eyes had moderate NPDR, 4 eyes had severe NPDR, 9 eyes had proliferative DR, and 4 eyes were normal controls. Microaneurysms, intraretinal microvascular abnormalities (IRMAs), and neovascularization appeared to originate from areas of relatively slow blood flow speeds. Microaneurysms showed relatively slower flow, IRMAs showed turbulent, intermediate to slow flow, and venous beading and looping presented with relatively high flow speeds that tapered progressively. Neovascularization of venous origin demonstrated slower flow speeds, whereas that of arterial origin showed relatively high flow speeds. Additionally, increased disease severity was associated with globally slower flow speeds, with particularly slower flow around the foveal avascular zone. The VISTA algorithm seems to be a useful extension of OCTA that overcomes some of the limitations of normal gray-scale OCTA. It seems to have some potential in providing relevant insight into the pathogenesis of the microvascular changes associated with DR. These findings may assist in improving our understanding of the pathogenic changes that take place in DR.

Efficient routing in UASN during the thermohaline environment condition to improve the propagation delay and throughput

In underwater acoustic sensor network (UASN), the challenging issues are bandwidth, higher propagation delay and heavy packet loss during data transmission. The issues can be solved through efficient routing algorithms. The existing UASN routing algorithms have larger latency in the network link and high rate of packet loss because of the salinity and temperature in the water at different depths. The salinity and temperature changes according to the depth and called as thermohaline circulation. In this paper, convex directional flooding optimisation (CDFO) algorithm improves the latency, throughput and lifetime of the nodes in the network under thermohaline condition and longshore drift from longshore current, which consist of transportation of sediments. The CDFO combines the convex optimisation and directional flooding-based routing algorithm, convex optimisation helps in identification of the hidden nodes in the network and strong communication links are established through polynomial time and semantic analysis and directional flooding algorithm reduces the packet loss and increases the network throughput. The routing protocol has implemented in ns2-AquaSim simulator and test bed for measurement of the performance metrics of the UASN.

Survey of precedence relationships: Classification and algorithms

Abstract Precedence relationships used in project planning mean for most professionals the traditional Start-to-Start-z (SSz), Finish-to-Start-z (FSz), Finish-to-Finish-z (FFz) and Start-to-Finish-z (SFz) relationships where z stands for the minimal necessary duration between the defined endpoints (Start or Finish) of the activities. These relationships have been serving professionals for more than 50 years, and there is not much visible effort for further developing them, despite some well-established critiques on the modeling capability of the Precedence Diagramming Method (PDM). The purpose of this research is: a) gathering those well-known and lesser-known developments of logical relationships that can be used for modeling some, so far, un-modelable problems b) classifying them by using a classification scheme that has been developed for this purpose and c) developing algorithms for time analysis when missing. The following earlier developments are discussed: maximal precedence relationships, point-to-point precedence relationships, continuous precedence relationships, relationships with AND/OR logical switches and bidirectional precedence relations. The classification shows that 24 types of logical relationships exist, but that algorithms exist only in four types, and are missing in twenty cases. The missing algorithms are provided here. The main contributions to the Body of Knowledge are: a) providing a classification scheme for precedence relationships b) definition of 24 precedence relationships based on the classification categories c) developing the missing time analysis algorithms for twenty cases d) presenting a single unified algorithm that handles all the 24 types of precedence relationships.

Molecular imaging with neural training of identification algorithm (neural network localization identification).

Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising solutions for automated object identification, but usually rely on well represented training sets to learn object features. However, using a static training set can result in the learned identification algorithm making mistakes on data that is not well represented by the training set. Here, we present a method for training an artificial neural network without providing a training set in advance. This method uses the data to be analyzed, and the fitting algorithm to train an artificial neural network tailored to that data set. We show that the same artificial neural network can learn to identify at least two types of molecular emissions: the regular point spread functions (PSFs), and the astigmatism PSF. Simulations indicate that this method can be extremely reliable in extracting molecular emission signatures. Additionally, we implemented the artificial neural network calculation to be performed on a graphics processing unit (GPU) for massively parallelized calculation which drastically reduces the time required for the identification process. By implementing the neural identification on a GPU, we allow this method of identification to be used in a real time analysis algorithm. RESEARCH HIGHLIGHTS: Here, we present a machine learning algorithm for identifying point-spread functions without the need for an a priori training set. We show that this method can detect over 90% of molecules with less than 1% false positive identification in simulations. We further show that because this algorithm does not make assumptions of about the shape of molecular emission, it is compatible with models beyond the symmetric 2D Gaussian.

Timing analysis for embedded systems using non-preemptive EDF scheduling under bounded error arrivals

Embedded systems consist of one or more processing units which are completely encapsulated by the devices under their control, and they often have stringent timing constraints associated with their functional specification. Previous research has considered the performance of different types of task scheduling algorithm and developed associated timing analysis techniques for such systems. Although preemptive scheduling techniques have traditionally been favored, rapid increases in processor speeds combined with improved insights into the behavior of non-preemptive scheduling techniques have seen an increased interest in their use for real-time applications such as multimedia, automation and control. However when non-preemptive scheduling techniques are employed there is a potential lack of error confinement should any timing errors occur in individual software tasks. In this paper, the focus is upon adding fault tolerance in systems using non-preemptive deadline-driven scheduling. Schedulability conditions are derived for fault-tolerant periodic and sporadic task sets experiencing bounded error arrivals under non-preemptive deadline scheduling. A timing analysis algorithm is presented based upon these conditions and its run-time properties are studied. Computational experiments show it to be highly efficient in terms of run-time complexity and competitive ratio when compared to previous approaches.

Statistical Timing Analysis and Criticality Computation for Circuits With Post-Silicon Clock Tuning Elements

Post-silicon clock tuning elements are widely used in high-performance designs to mitigate the effects of process variations and aging. Located on clock paths to flip-flops, these tuning elements can be configured through the scan chain so that clock skews to these flip-flops can be adjusted after man- ufacturing. Owing to the delay compensation across consecutive register stages enabled by the clock tuning elements, higher yield and enhanced robustness can be achieved. These benefits are, nonetheless, attained by increasing die area due to the inserted clock tuning elements. For balancing performance improvement and area cost, an efficient timing analysis algorithm is needed to evaluate the performance of such a circuit. So far this evaluation is only possible by Monte Carlo simulation which is very timing- consuming. In this paper, we propose an alternative method using graph transformation, which computes a parametric minimum clock period and is more than 10 4 times faster than Monte Carlo simulation while maintaining a good accuracy. This method also identifies the gates that are critical to circuit performance, so that a fast analysis-optimization flow becomes possible.

Note: An improved 3D imaging system for electron-electron coincidence measurements.

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

A model-checking approach to schedulability analysis of global multiprocessor scheduling with fixed offsets

It is an active research topic to determine schedulability of a real-time sporadic or periodic taskset on a multicore processor with global scheduling policies such as global fixed-priority (FP) or earliest deadline first (EDF) algorithms. Analytical techniques such as utilisation bound tests and response time analysis algorithms generally suffer from excessive pessimism, and may cause low system utilisation. In this paper, we apply model-checking to address the restricted task model of periodic tasks with fixed release offsets and possible release jitter. We believe that this restricted task model is more realistic for current industry practice than the more general sporadic task model, since it can achieve higher CPU utilisation and better predicability. We present an approach to schedulability analysis of this restricted task model using the timed automata model-checker UPPAAL. This modelling framework is flexible and expressive, and can achieve reasonable scalability.

Statistical Timing Analysis for Latch-Controlled Circuits With Reduced Iterations and Graph Transformations

Level-sensitive latches are widely used in high- performance designs. For such circuits efficient statistical timing analysis algorithms are needed to take increasing process vari- ations into account. But existing methods solving this problem are still computationally expensive and can only provide the yield at a given clock period. In this paper we propose a method combining reduced iterations and graph transformations. The reduced iterations extract setup time constraints and identify a subgraph for the following graph transformations handling the constraints from nonpositive loops. The combined algorithms are very efficient, more than 10 times faster than other existing methods, and result in a parametric minimum clock period, which together with the hold time constraints can be used to compute the yield at any given clock period very easily.

Timing analysis of scenario‐based specifications using linear programming

SUMMARYScenario‐based specifications (SBSs), such as UML interaction models, offer an intuitive and visual way of describing design requirements, and are playing an increasingly important role in the design of software systems. This paper presents an approach to timing analysis of SBSs expressed by UML interaction models. The approach considers more general and expressive timing constraints in UML sequence diagrams (SDs), and gives a solution to the reachability analysis, constraint conformance analysis and bounded delay analysis problems, which reduces these problems into linear programs. With the synchronous interpretation of the SD compositions, the timing analysis algorithms in the approach form a decision procedure for a class of SBSs where any loop in any path is time‐independent of the other parts in the path. These algorithms are also a semi‐decision procedure for general SBSs with both the synchronous and asynchronous composition semantics. The approach also supports bounded timing analysis of SBSs, which investigates all the paths in the bound limit one by one, and performs the timing analysis for each finite path by linear programming. A tool prototype has been developed to support this approach. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Timing analysis of scenario-based specifications using linear programming

Scenario-based specifications (SBSs), such as UML interaction models, offer an intuitive and visual way of describing design requirements, and are playing an increasingly important role in the design of software systems. This paper presents an approach to timing analysis of SBSs expressed by UML interaction models. The approach considers more general and expressive timing constraints in UML sequence diagrams (SDs), and gives a solution to the reachability analysis, constraint conformance analysis and bounded delay analysis problems, which reduces these problems into linear programs. With the synchronous interpretation of the SD compositions, the timing analysis algorithms in the approach form a decision procedure for a class of SBSs where any loop in any path is time-independent of the other parts in the path. These algorithms are also a semi-decision procedure for general SBSs with both the synchronous and asynchronous composition semantics. The approach also supports bounded timing analysis of SBSs, which investigates all the paths in the bound limit one by one, and performs the timing analysis for each finite path by linear programming. A tool prototype has been developed to support this approach. Copyright © 2010 John Wiley & Sons, Ltd. (This paper presents a linear programming-based approach to timing analysis of scenario-based specifications (SBSs) expressed by UML interaction models. With more general and expressive timing constraints in UML sequence diagrams, the algorithms in the approach solve the problems of the reachability, constraint conformance and bounded delay analysis of SBSs. These algorithms form a decision procedure for the loop-unlimited SBSs where any loop in any path is time-independent of the other parts in the path, and a semi-decision procedure for general SBSs.)

The Local Time Step Iterated Timing Analysis Algorithm for Circuit Simulation

Relaxation-based algorithms are efficient in simulating large-scale circuits. One of the Relaxation-based algorithms, ITA (Iterated Timing Analysis), has even been widely used in industry. Hence we present accelerating techniques to fasten ITA’s speed. The basic idea is to let ITA utilize local time steps of subcircuits as much as possible. Our techniques include a heuristic approach, a method based on Strength of Signal Flow, and the method combining the previous two techniques. Proposed methods are quite advantageous, which have been justified by real experiments.