Time Graph Research Articles

Hierarchical Methodology Approach to SOC Design: A Comprehensive Look

With the process node (CMOS transistor size) becoming increasingly smaller, there is an ever-increasing need for the software to handle the large gate count with faster turnaround time and better accuracy. Reaching timing closure on multi-million gate VLSI chips using flat flow is infeasible due to hardware capacity limit, and excessive run time overheads. New flows and design implementations need to be introduced to manage the scalability of design sizes by scaling down the memory usage and run time. Traditionally, the flat implementation of designs was used for smaller designs with maximum accuracy. Then the hierarchical approach was introduced to better the run time and handle the millions of gates by partitioning and assembling. Through this paper, we are going to look at the various implementation flows and its artifacts viz. flat, traditional hierarchical, models, post assembly ECO, context views, feasibility flows . We are also going to look at a advances in the technology of timing graph reduction and physical data reduction that enables new flow implementations and can be applied to both hierarchical and flat methodologies.

Static Scheduling with Load Balancing for Solving Triangular Band Linear Systems on Multicore Processors

A new approach for solving triangular band linear systems is established in this study to balance the load and obtain a high degree of parallelism. Our investigation consists to attribute both adequate start time and processor to each task and eliminate the useless dependencies which are not used in the parallel solve stage. Thereby, processors execute in parallel their related tasks taking account of the considered precedence constraints. The theoretical lower bounds for parallel execution time and the number of processors required to carry out the task graph in the shortest time are determined. Experimentations are realized on a shared-memory multicore processor. The experimental results are fitted to the values derived from the determined mathematical formulas. The comparison of results obtained by our contribution with those from triangular systems resolution routine belonging to the library PLASMA, Parallel Linear Algebra Software for Multicore Architectures, confirms the efficiency of the proposed approach.

Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm

With the rapid development of Internet of Things technology, wireless sensor networks have been widely used in many places. This study mainly discusses the routing optimization strategy of the IoT perceptive layer based on the improved cat swarm algorithm. This study simulates a perceptive network with 100 nodes deployed randomly. As SDWSN for Internet of Things applications, in order to simulate the data transmission requirements of IoT communication and ensure the fairness of experimental comparison, this study uses the pseudo-random mechanism to generate the source address and destination address of data packets. A special SDN controller node is added to the network. The SDN controller node broadcasts information to each sensing node, and the common sensing node sends node information to the SDN controller. The SDN controller can survive the global time graph of the entire network according to the information of the common node. In order to avoid the problem of high energy consumption of cluster heads caused by long-distance data transmission, the cat algorithm protocol adopts multi-hop communication between cluster heads and BS and uses network overhead index to quantify link overhead as the basis for cluster heads to select the next hop node. When the inter-cluster multi-hop route is successfully established, the wireless sensor node begins to collect data and send it to BS node. Six monitoring nodes, two coordinators and one workstation were selected as the test objects. The data volume sent by each node was 2000, and the accuracy rate of test transmission information at different rates and transmission distances was determined. The group network coverage rate of cat swarm algorithm is always above 95%, and the average energy loss of nodes is the highest and less than 36%. The results show that the aggregate of energy consumption of cluster heads and the variance of energy consumption are the lowest in the improved cat cluster algorithm, which ensures the reliable transmission of node data.

Time2Graph+: Bridging Time Series and Graph Representation Learning via Multiple Attentions

Time series modeling has attracted great research interests in the last decades. Among the literature, shapelet-based models aim to extract representative subsequences, and could offer explanatory insights. In order to capture the shapelet dynamics and evolutions, we propose a novel framework of bridging time series representation learning and graph modeling, with two different implementations. We first formulate the process of extracting time-aware shapelets, then briefly introduce the key idea of transforming time series data into shapelet evolution graphs, to model the shapelet evolutionary patterns. A straightforward solution is to enumerate all possible shapelet transitions among adjacent time series segments, and apply a random-walk-based graph embedding algorithm to learn the time series representations (Time2Graph). We further extend Time2Graph by adopting graph attention mechanism to refine the procedure of modeling shapelet evolutions, namely Time2Graph+. Specifically, we transform each time series data into a unique and unweighted shapelet graph, and use GAT to automatically capture the correlations between shapelets. Experimental results show the significant improvements of Time2Graph+, and extensive observational analysis demonstrate the effectiveness and interpretability brought by attentions. Furthermore, the success of online deployment of Time2Graph+ model in State Grid of China validates the whole framework in the real-world application.

A Computational Comparison of Ether and Ester Electrolyte Stability on a Ca Metal Anode

Ca-ion batteries (CIBs) have the potential to provide inexpensive energy storage, but their realization is impeded by the lack of suitable electrolytes. Motivated by recent experimental progress, we perform ab initio molecular dynamics simulations to investigate early decomposition reactions at the anode-electrolyte interface. By examining different combinations of solvent—tetrahydrofuran (THF) or ethylene carbonate (EC)—and salt—Ca(BH 4 ) 2 , Ca(BF 4 ) 2 , Ca(BCl 4 ) 2 , and Ca(ClO 4 ) 2 —we identify a variety of behavioral trends between electrolyte solutions. Next, we perform a separate trajectory with pure THF and gradually increased negative charge; despite an addition of -32 e , no THF decomposition is detected. Charge analysis reveals that in a reductive environment, THF distributes excess charge evenly across its hydrocarbon backbone, while EC concentrates charge on its ester oxygens and carbonyl carbon, resulting in decomposition. Graphs of charge vs. time for both solvents reveal that EC decomposition products can be reduced by up to five electrons, while those of THF are limited to a single electron. Ultimately, we find Ca(BH 4 ) 2 and THF to be the most stable solution investigated herein, corroborating experimental evidence of its suitability as a CIB electrolyte.

Joint Vertex-Time Filtering on Graphs With Random Node-Asynchronous Updates

In graph signal processing signals are defined over a graph, and filters are designed to manipulate the variation of signals over the graph. On the other hand, time domain signal processing treats signals as time series, and digital filters are designed to manipulate the variation of signals in time. This study focuses on the notion of vertex-time filters, which manipulates the variation of a time-dependent graph signal both in the time domain and graph domain simultaneously. The key aspects of the proposed filtering operations are due to the random and asynchronous behavior of the nodes, in which they follow a collect-compute-broadcast scheme. For the analysis of the randomized vertex-time filtering operations, this study first considers the random asynchronous variant of linear discrete-time state-space models, in which each state variable gets updated randomly and independently (and asynchronously) in every iteration. Unlike previous studies that analyzed similar models under certain assumptions on the input signal, this study considers the model in the most general setting with arbitrary time-dependent input signals, which lay the foundations for the vertex-time graph filtering operations. This analysis shows that exponentials continue to be eigenfunctions in a statistical sense in spite of the random asynchronous nature of the model. This study also presents the necessary and sufficient condition for the mean-squared stability and shows that stability of the underlying state transition matrix is neither necessary nor sufficient for the mean-squared stability of the randomized asynchronous recursions. Then, the proposed filtering operations are proven to be mean-square stable if and only if the filter, the graph operator and the update probabilities satisfy a certain condition. The results show that some unstable vertex-time graph filters (in the synchronous case) can be implemented in a stable manner in the presence of randomized asynchronicity, which is also demonstrated by numerical examples.

Computing the weighted neighbor isolated tenacity of interval graphs in polynomial time

AbstractWeighted graphs in graph theory are created by weighing different values depending on the importance of connections or centers in a graph model. Networks can be modeled with graphs such that the devices and centers correspond to the vertices and connections correspond to the edges. In these networks, weight can be assigned to the vertices for the workload and importance of the devices and centers, so that planning such as security and cost can be made in advance in the design of the network. Network reliability and security is an important issue in the computing area. There are several parameters for vulnerability measurement values of these networks modeled with graphs. We recommend the weighted conversion of the neighbor isolated tenacity parameter for this topic. It is known that tenacity, which is the basis of this parameter, is NP‐hard. But polynomial solutions can be created in interval graphs, which is a special graph from the perfect graph class. In this article, polynomial time algorithm is given to calculate weighted neighbor isolated tenacity of the interval graphs.

NetKI: A kirchhoff index based statistical graph embedding in nearly linear time

Recent advancements in learning from graph-structured data have shown promising results on the graph classification task. However, due to their high time complexities, making them scalable on large graphs, with millions of nodes and edges, remains a challenge. In this paper, we propose NetKI, an algorithm to extract sparse representation from a given graph with n nodes and m edges in O(m∊-2log4n) time. Our approach follows the notion of Kirchhoff index that encodes the structure of the graph by estimating effective resistance - relying on this approach yields nearly linear time graph representation method that allows scalability on sufficiently large graphs. Through extensive experiments, we show that NetKI provides improved results in terms of running time on large networks and the classification accuracy is within range 2% from the state-of-the-art results.

Methodology of optimum unit commitment of energy systems with renewable energy sources

One of the promising ways to improve the reliability and efficiency of power supply for customers in the areas remote from central electrical grid is the use of hybrid power systems with renewable energy sources. The primary task of designing such systems is the unit commitment of the generating equipment that provides the optimal technical and economic indexes of the electric power system. The stochastic nature of generation and nonlinearity of the characteristics of power plants cause a high complexity of solving this problem, which, from a mathematical point of view, is formulated as an optimization problem. An accurate and reliable solution of this optimization problem increases the efficiency of design and operation of hybrid electric power systems with renewable energy sources. And it is a vital task of modern power industry. A probabilistic-statistical methods and models for the analysis of experimental data are used to construct climatic time series and graphs of electrical loads. In addition, to study the operating modes of the electric power system the MatLab system is used for the simulation and modeling, and an evolutionary particle swarm algorithm is used to solve the optimization problem. The original model of solar radiation is used as a part of this methodology. This model provides forecasting the key characteristics of solar radiation in any geographical point of Russia including the areas that have no results of routine actinometric observation. Weibull distribution function is used to forecast daily variations of wind speed. It enhances the validity of forecasting of electricity generation of wind-driven power plant at daily time interval. As a result of the research, a method of optimum unit commitment has been developed for the equipment of electric power systems based on renewable energy sources. The use of the particle swarm algorithm as a part of the methodology provides reliable and accurate determination of the extremum of the objective function, which increases the efficiency of design and operation of hybrid electric power systems with renewable energy sources. The method has been tested on practical examples of optimum unit commitment for the equipment of electric power systems of various configurations and has proven its effectiveness. The technique is implemented as a software application, which ensures the convenience of its practical application. The obtained results can be used by companies involved in the design and operation of electric power systems using renewable energy generating units.

CPG graphs: Some structural and hardness results

In this paper we continue the systematic study of Contact graphs of Paths on a Grid (CPG graphs) initiated in Deniz et al. (2018). A CPG graph is a graph for which there exists a collection of pairwise interiorly disjoint paths on a grid in one-to-one correspondence with its vertex set such that two vertices are adjacent if and only if the corresponding paths touch at a grid-point. If every such path has at most k bends for some k≥0, the graph is said to be Bk-CPG.We first show that, for any k≥0, the class of Bk-CPG graphs is strictly contained in the class of Bk+1-CPG graphs even within the class of planar graphs, thus implying that there exists no k≥0 such that every planar CPG graph is Bk-CPG. The main result of the paper is that recognizing CPG graphs and Bk-CPG graphs with k≥1 is NP-complete. Moreover, we show that the same remains true even within the class of planar graphs in the case k≥3. We then consider several graph problems restricted to CPG graphs and show, in particular, that Independent Set and Clique Cover remain NP-hard for B0-CPG graphs. Finally, we consider the related classes Bk-EPG of edge-intersection graphs of paths with at most k bends on a grid. Although it is possible to optimally color a B0-EPG graph in polynomial time, as this class coincides with that of interval graphs, we show that, in contrast, 3-Colorability is NP-complete for B1-EPG graphs.

Validation of Newton’s second law using Arduino: STEM teaching material

The present work offers an experimental technique specially designed and employed to comprehend and teach the Newton’s second law and to overcome certain instructional difficulties. The apparatus is mainly comprised of two specifically designed toy cars, two force sensors, one distance sensor, a pulley, two pieces of gut, an Arduino microprocessor and an ordinary computer. Newton’s second law is experimentally validated by measuring, using the Arduino system, the accelerations from the distance–time graphs and by directly measuring the tension forces. The experimental results are also compared with theoretical calculations, indicating a reasonably good agreement. The designed apparatus can be employed as a Science Technology Engineering Mathematics teaching material within physics laboratories, to achieve enhanced understanding of Newton’s second law and dynamics.

Analysis of Vibration Transmitted from Grinding Machine to Hand Arm System

From power hand tools to hand-arm systems, there are many occupational diseases associated with vibration transmission. People who have worked for a long time with the formal exposure of vibration companies suffer from trembling white fingers (VWF). These hazards are mainly due to the extreme exposure of vibrations spread from the grinding machine to the hand-held system. International standards have been developed to assess the risk of injury after exposure to these types of vibrations. The current standards recognize that aspects such as the effect of vibration transmission direction, arm posture, hand grip or force need to be investigated. The mathematical model of five DOF hand arm system was studied and simulation was done by using SIMULINK software. Displacement verses Time graphs were plotted to find at what frequency maximum amplitude existed and it was seen at frequency 10 Hz

Enhancing Blockchain security in cloud computing with IoT environment using ECIES and cryptography hash algorithm

Blockchain is one of the fast growing technologies that has significant role in the area of criminal investigation. The security is becoming a great threat to all the industries, Electronic Health Record(EHR), Banking, Smart Applications (SA), Supply Chain Management (SCM) and IoT environment in recent years. In this research, we have developed a novel framework that will monitor the activities that takes place on particular data evidence, We create a Cloud based Software Defined Network (SDN), its consists of 100 - mobile Nodes (IOT devices), open flow switch and Blockchain based controllers, cloud server, Authentication Server (AS) and investigator. Initially all users are registered with AS and obtain their secret key from AS based on the Harmony Search Optimization (HSO). In the mobile nodes the packets are encrypted by usingElliptic CurveIntegrated Encryption Scheme (ECIES) algorithm and transfer to the cloud server. SDN controller maintains blockchain to preserve evidences collected from data and signature of the users based on the SHA-256 Cryptographic Hash Algorithm. Authorized investigator performs following processes: identification, evidence collection, evidence analysis, and report generation based on the Logical Graph of Evidences (LGoE). we plot the resultant graph for Response time versus Number of users, Evidence insertion time versus number of users, Evidence verification time versus Number of users, Computational overhead versus number of users, Total change rate versus Number of users, Hash computation time versus number of users, Key generation time versus number of users, Encryption time versus number of users and Decryption time versus number of users. Finally, the investigator or other legal people can obtain the evidences from the controller and construct as a Logical Graph of Evidence (LGoE). The experimental analysis revealed the fact that the proposed system obtained better performance in terms of response time, accuracy, increasing throughput and total change security parameters.

Real Time Monitoring System Dari Active Solar Photovoltaic Tracker Berbasis Internet Of Things

Abstract: This monitoring system is used to monitor currents and voltages from Solar Power Plants (PLTS) with solar cell capacities of 50WP, 80WP, and 100WP. PLTS used is an active type of solar tracker, where solar cells used can find the maximum solar irradiation position by determining the upper and lower voltages as the reference voltage in the Arduino program. After PLTS can produce energy, then the energy can be used to turn on AC and DC electrical loads. From the energy used, here of course there are currents and voltages starting from the output of the solar cell, the accumulator output, the input at the AC load, and the input at the DC load. The monitoring results that have been carried out subsequently will be automatically sent to the Thingspeak application by using the SIM800L Module. This thingspeak application will send data in the form of numbers and graphs in real time. By comparing the value of the measurement results on Arduino and the thingspeak application, we get the size of the percentage of reading errors on the measuring instrument.

Weight Monitoring System for Tilanggit Dried Fish With SMS Notification

Weight Monitoring System for Tilanggit Dried fish with SMS Notification is a study about monitoring the weight and calculate the moisture level of the fish during the drying process. The system would read the data from the prototype and it would be viewed in the desktop application. The data gathered that will be viewed in the desktop application will automatically be calculated based on the formula in getting the moisture level of the fish. The system shows a real time graph of the data gathered. Fifty (50) respondents have been randomly selected to test the functionality, reliability and usability of the project which comprised by twenty (20) fishermen, twenty (20) consumer, five (5) faculty and five (5) businessmen. The functionality, reliability and usability of the system was evaluated and tested. The functionality of the system had the consolidated mean of 3.97 which means “Very Agree”. The reliability of the system had consolidated mean of 3.66 which means “Very Agree”, the usability of the system had the consolidated mean of 4.55 which means “Very Strongly Agree” and the summarized result had the consolidated mean of 4.10 which means “Very Agree”. Therefore, the users declared that the overall result based on the evaluation of the project that it was successfully passed with the project’s satisfactory performance. During the evaluation there were some recommendation that helped to improve the performance of the system and to satisfy the users. Such as to improve the design of the system and also study more to get the exact weight result.

Rancang Bangun Alat Kardiografi Berbasis Impedansi Listrik (Electrical Impedance Cardiography)

Electrocardiography is a process or method to observe and record the activity of the heart by using a graph of electrical voltage versus time. A standard plan to achieve this is by measuring the bioelectric potential or bioimpedance of the heart organ. In this research, an electrical impedance cardiography device will be designed and tested. The process is carried out by using four electrodes that are attached at the neck and the diaphragm level of the body, an electric current with a specific value and frequency, then injected on a pair of electrodes, and the voltage will be measured on other pair of electrodes. The device testing process is carried out on subjects in good health and having no medical abnormality in a sitting and upright position. Impedance data will be measured and recorded at each time; the data that has been obtained will then be processed to determine the impedance graph Z and the first derivative of Z (dZ/dt). The number of heartbeats will also be calculated each time with another method, which is by using a stopwatch to compare the validity of the heart rate readings from the impedance cardiography device. The result demonstrated that the electrical impedance cardiography device that has been made could be used to illustrate heart rate graphs as a result of cardiovascular activity

From text to graph: a general transition-based AMR parsing using neural network

Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing, which tells the inner meaning of language form. Recently, Abstract Meaning Representation (AMR) is attracted by many researchers for its semantic representation ability on an entire sentence. However, due to the non-projectivity and reentrancy properties of AMR graphs, they lose some important semantic information in parsing from sentences. In this paper, we propose a general AMR parsing model which utilizes a two-stack-based transition algorithm for both Chinese and English datasets. It can incrementally parse sentences to AMR graphs in linear time. Experimental results demonstrate that it is superior in recovering reentrancy and handling arcs while is competitive with other transition-based neural network models on both English and Chinese datasets.

Airborne LiDAR Assisted Obstacle Recognition and Intrusion Detection Towards Unmanned Aerial Vehicle: Architecture, Modeling and Evaluation

With the rapid development of wireless communication and flight control technologies, the unmanned aerial vehicles (UAVs) have been widely used in multiple application scenarios. A typical scenario is massive crowd management of the multi-millions annual Hajj Pilgrimage to Mecca where UAVs are widely utilized to conduct crowd monitoring by carrying sensory devices. The safe flight of a UAV is crucial for ensuring the successful execution of missions. With the aim to overcome the disadvantage caused by the ground station intrusion detection, the combination of UAV and airborne LiDAR has been widely studied in the field of UAV obstacle recognition. This article studies the UAV network architecture under a common scenario and proposes an obstacle recognition and intrusion detection algorithm for UAV based on an airborne LiDAR (ALORID). First, the preprocessing of the data obtained by a LiDAR, i.e., the coordinate conversion of LiDAR data in combination with UAV motion parameters, is completed. Then, the LiDAR data graph at the current moment is generated by the image noisy point filtering algorithm. After that, the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for image clustering of intrusions to obtain the LiDAR time-domain cumulative graph in a certain detection time. Finally, the motion recognition and location detection of each cluster are completed. The experiment results verify the effectiveness of the proposed algorithm in identifying the moving state of the intrusions.

Tropical Lexicographic Optimization: Synchronizing Timed Event Graphs

Tropical Algebra is used to model the dynamics of Timed Event Graphs (TEG), a particular class of Timed Discrete-Event System (TDES) in which we are interested only in synchronization and delay phenomena. Whenever this TEG has control inputs, we can use them to control the synchronization of the system to achieve some objective. Thus, this paper formulates a framework based on tropical algebra and lexicographic optimization to synchronize a TEG when dealing with many synchronization objectives that are ranked in previous priority order. We call this kind of problem the Tropical Lexicographic Synchronization Optimization (TLSO). This work develops a solution to this problem, based on Tropical Fractional Linear Programming (TFLP) and lexicographic optimization concepts. In this way, the basics of tropical algebra are determined, including essential terms to this paper, such as left and right residuations, and the following stages of the solution to the TLSO problem are explained. Therefore, this work presents a general framework based on structured algebraic models with application to TEG synchronization. By synchronization, we mean balancing and organizing events chronologically in order to achieve the desired goal. So, we are dealing with concepts closely related to symmetry ones. An illustrative numerical example is presented, which demonstrates the implementation of the proposed algorithms. The acquired results confirm the efficiency of the proposed methodology. Codes used for implementing the algorithms are listed in the appendix section of the article.

Characterization of a high-resolution breath acetone meter for ketosis monitoring.

BackgroundThe ketone bodies beta-hydroxybutyrate (BHB) and acetone are endogenous products of fatty acid metabolism. Although ketone levels can be monitored by measuring either blood BHB or breath acetone, determining the precise correlation between these two measurement methods has been challenging. The purpose of this study is to characterize the performance of a novel portable breath acetone meter (PBAM) developed by Readout, Inc., to compare single versus multiple daily ketone measurements, and to compare breath acetone (BrAce) and blood BHB measurements.MethodsWe conducted a 14-day prospective observational cohort study of 21 subjects attempting to follow either a low-carbohydrate/ketogenic or a standard diet. Subjects were asked to concurrently measure both blood BHB and BrAce five times per day and report the results using an online data entry system. We evaluated the utility of multiple daily measurements by calculating the coefficient of variation (CV) for each daily group of measurements. We calculated the correlation between coincident BrAce and blood BHB measurements using linear ordinary least squares regression analysis. We assessed the ability of the BrAce measurement to accurately predict blood BHB states using receiver operating characteristic (ROC) analysis. Finally, we calculated a daily ketone exposure (DKE) using the area under the curve (AUC) of a ketone concentration versus time graph and compared the DKE of BrAce and blood BHB using linear ordinary least squares regression.ResultsBrAce and blood BHB varied throughout the day by an average of 44% and 46%, respectively. The BrAce measurement accurately predicted whether blood BHB was greater than or less than the following thresholds: 0.3 mM (AUC = 0.898), 0.5 mM (AUC = 0.854), 1.0 mM (AUC = 0.887), and 1.5 mM (AUC = 0.935). Coincident BrAce and blood BHB measurements were moderately correlated with R2 = 0.57 (P < 0.0001), similar to literature reported values. However, daily ketone exposures, or areas under the curve, for BrAce and blood BHB were highly correlated with R2 = 0.80 (P < 0.0001).ConclusionsThe results validated the performance of the PBAM. The BrAce/BHB correlation was similar to literature values where BrAce was measured using highly accurate lab instruments. Additionally, BrAce measurements using the PBAM can be used to predict blood BHB states. The relatively high daily variability of ketone levels indicate that single blood or breath ketone measurements are often not sufficient to assess daily ketone exposure for most users. Finally, although single coincident blood and breath ketone measurements show only a moderate correlation, possibly due to the temporal lag between BrAce and blood BHB, daily ketone exposures for blood and breath are highly correlated.