Usage of Moving Average
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1. Signals & Systems - Noise Cancellation 2. Operating Systems - Process Scheduling 3. Computer Networks - TCP Timeout
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This document provides an overview of the User Datagram Protocol (UDP). It discusses several key points:
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- UDP packets, called user datagrams, have a fixed 8-byte header containing source/destination port numbers, length, and checksum fields. Port numbers identify the sending/receiving processes and range from 0-65,535.
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TCP uses three mechanisms to trigger the transmission of data segments:
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This document provides an overview of the User Datagram Protocol (UDP). It discusses several key points:
- UDP is a connectionless, unreliable transport protocol that performs limited error checking and uses minimal overhead. It is suitable for applications that need simple request-response communication and do not require reliability.
- UDP packets, called user datagrams, have a fixed 8-byte header containing source/destination port numbers, length, and checksum fields. Port numbers identify the sending/receiving processes and range from 0-65,535.
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The document also outlines the basic building blocks of digital filters, including adders, multipliers, and delay elements. It introduces linear time-invariant digital filters and explains their input-output relationship using difference equations and the z-transform.
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Parallel computing uses multiple processors or computers simultaneously to solve problems faster than a single processor. While n processors could theoretically provide an n times speedup, in reality various factors limit speedup. Parallel computing aims to solve "grand challenge" problems like modeling large DNA structures or global weather forecasting that would take too long on today's computers. It works by dividing a large problem into sub-problems that can be solved concurrently. The maximum theoretical speedup is limited by the fraction of a problem that must be solved sequentially. In practice, speedup depends on how effectively a problem can be divided into parallelizable parts.
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The document discusses digital filters and their design process. It explains that the design process involves four main steps: approximation, realization, studying arithmetic errors, and implementation.
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The document also outlines the basic building blocks of digital filters, including adders, multipliers, and delay elements. It introduces linear time-invariant digital filters and explains their input-output relationship using difference equations and the z-transform.
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1. The student designed a network for the Goethe-Institut in Dhaka to simulate their networking needs. The network included servers, PCs, wireless access points, switches, routers, IP phones, and other devices across three floors.
2. Physical and logical network diagrams were created showing the layout and connections between devices. Key features of the design included separate networks for each floor, wireless connectivity, remote device management, and security features.
3. A cost analysis was conducted calculating the total price of network devices, coming to a total of over 13 million BDT. Common network protocols like DNS, FTP, and SMTP were configured. The project taught the student about network concepts and skills in areas
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2) The speedup formula is: Speedup = 1 / (B + (1-B)/Number of Processors). This shows diminishing returns as more processors are added.
3) A speedup curve based on Amdahl's Law will always be below the ideal linear speedup (S=N) line, showing the limits on parallelization from the serial components of a program.
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This document describes a distributed radar tracking simulation using MATLAB and the Parallel Computing Toolbox. It divides a Monte Carlo simulation of tracking an aircraft's path across multiple radar station tasks. The tasks are created and submitted to a cluster, and the results are retrieved and concatenated upon completion. Plotting the standard deviation of estimation errors over time allows comparing the distributed computation time to a sequential simulation.
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Project_Objectives
The objective is to design a Java application to visualize traffic data from sensor records on a map. The application should display real-time traffic flow and average speed information with different colors, and allow toggling between the two views over time. It should also mark areas identified by an algorithm as having traffic anomalies. The application will use an existing dataset of traffic sensor records containing location, time, flow and speed data to populate the map visualization.
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This document discusses timing and synchronization in digital communication systems. It explains that digital signals are transmitted as pulses or clocks, and receivers must detect these signals accurately by synchronizing to the transmitter's clock. Five methods of clock exchange are described to achieve synchronization between machines: free running, line-timed, loop-timed, external, and through-timed. Maintaining accurate synchronization is important to avoid errors that can reduce throughput or cause audible clicks in signals like voice and video.
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The document discusses cost optimal parallel algorithms. It defines the cost of an algorithm as the product of its parallel time complexity and number of processors used. A cost optimal algorithm has the same complexity class as an optimal sequential algorithm. Several common problems like prefix sum and list ranking are shown to not have cost optimal parallel solutions. The document then discusses parallel reduction algorithms, proving using Brent's Theorem that a cost optimal parallel reduction algorithm exists with time complexity of log(n) and n/log(n) processors. An example of summing n numbers optimally in parallel is provided.
Limited Sensing
The document discusses modifying the First-Come First-Served (FCFS) algorithm to allow for limited sensing by users. It proposes three modifications: 1) users monitor the channel only when they have packets, 2) after N-1 consecutive idle slots a collision is forced, and 3) new users are served first using a Last-Come First-Served approach. With these modifications, new users can synchronize within N slots and deadlocks are avoided while maintaining high throughput that approaches FCFS as N increases.
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Similar to Usage of Moving Average
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3) Many industrial automation companies are adopting IEEE 1588 to enable real-time deterministic applications that require highly synchronized networked devices.
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Synchronization and determinism are important considerations when selecting an industrial control system and the associated fieldbus. Additionally, it’s important for field devices to have network-wide interrupts for activating outputs, capturing input data, oversampling or latching events. These are all significant facets in the overall network synchronization scheme.
This webinar on Tuesday, Oct. 23 at 2 PM EST will explain how the Distributed Clock mechanism in EtherCAT works to meet all of these functions using properties inherent to the protocol. This can be done using a standard Ethernet network adaptor, all without the overhead of IEEE 1588.
Attend this webinar to learn:
How Distributed Clocks (DCs) in EtherCAT facilitate measurement of propagation delay throughout the system and synchronize network devices to a single time value
What EtherCAT slave devices can do to facilitate temporal behavior for outputs and inputs as well as implementing data oversampling
More about some of the concepts that enable EtherCAT to have a high scan rate as well as high levels of synchronization
Computer arithmetic in computer architecture
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Analytical Research of TCP Variants in Terms of Maximum Throughput
This paper is comparative, throughput analysis, for
the TCP variants as for New Reno, Westwood & High Speed,
and it analyzes the outcomes in simulated environment for NS -3
(version 3.25) simulator with reference to multiple varying
network parameters that includes network simulation time,
router bandwidth, varying traffic source counts to observe which
is one of the best TCP variant in different scenarios. Analysis
was done using dumbbell topology to figure out the comparative
maximum throughput of TCP variants. The analysis gives result
as TCP Variant “NewReno” is good when low bandwidth is used,
while TCP Variant “HighS peed” is good in terms of using large
bandwidths in comparison to Westwood. Network traffic flow
was observed in NetAnim tool.
Stream Processing Overview
This document provides an overview of stream processing. It discusses how stream processing systems are used to process large volumes of real-time data continuously and produce actionable information. Examples of applications discussed include traffic monitoring, network monitoring, smart grids, and sensor networks. Key concepts of stream processing covered include data streams, operators, windows, programming models, fault tolerance, and platforms like Storm and Spark Streaming.
13 tm adv
This document provides an overview of advanced transaction management techniques. It discusses mixing heterogeneous transaction managers, high availability commit and transfer of commit protocols, optimizing commit processes, and disaster protection through data and application replication. Specific topics covered include system pairing, logical logging, session takeover, and using replication for fault tolerance and high availability.
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div, grad, curl, and all that - a review
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This document provides an overview of Gaussian processes. It begins with definitions of normal distributions and their properties. It then discusses the central limit theorem and how it relates to multivariate Gaussian distributions. Different visualizations of Gaussian processes are presented, showing how they can model non-linear regression by representing distributions over functions. The document concludes by discussing how Gaussian processes are mathematically equivalent to infinitely wide neural networks, and how this connection has been extended to deeper networks.
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JFEF encoding
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Usage of Moving Average
- 2. Outline ❏ Definition of Moving Average ❏ Example 1. EEE2153 ❏ Example 2. CSE4070 ❏ Example 3. CSE4175
- 3. Just a tiny bit of math… A moving average (rolling average or running average) is a calculation to analyze data points by creating series of averages of different subsets of the full data set.
- 10. Summary Simple Moving Average : Nearest n points + Same significance Cumulative Moving Average : All the points + Same significance Exponential Moving Average : All the points + Different significance
- 14. A little more bit of math… Every signal can be converted into sum of cosines. Fourier Transform
- 15. Noise: Unwanted Signals Typical noise have high frequencies.
- 16. Fourier transform of Moving average Simple moving average works as a low-pass filter.
- 19. CPU bursts vs. I/O bursts
- 21. Multiple Processes’ CPU bursts
- 22. Gantt Chart Process Burst Time Arrival Time P1 8 0 P2 4 1 P3 9 2 P4 5 3 P1 P2 P4 P1 P3 0 1 5 10 17 26
- 23. Process Scheduling P1 P2 P4 P1 P3 0 1 5 10 17 26 Shortest-Job-First Algorithm is optimal. Average Waiting Time ( P1( 10 - 1 ) + P2( 1 - 1 ) + P3( 17 - 2 ) + P4( 5 - 3 ) ) / 4 = 26 / 4 = 6.5
- 24. Pseudo-SJF Algorithm P1 P2 P4 P1 P3 0 1 5 10 17 26 OS needs to predict burst length of each process. Recent activity should be more important.
- 25. Pseudo-SJF Algorithm Use Exponential Moving Average to estimate burst length.
- 27. TCP Timeout
- 28. OSI Model The Open Systems Interconnection model (OSI model) is a conceptual model that characterizes and standardizes the communication functions without regard to their underlying internal structure and technology.
- 29. OSI Model
- 30. Transmission Control Protocol TCP provides reliable, ordered, and error-checked delivery between apps running on hosts communicating by an IP network. Major Internet applications such as the WWW, email, and file transfer rely on TCP.
- 31. Transmission Control Protocol Packet can be lost; but TCP must ensure connection.
- 32. Packet Retransmission Timer too long → Performance degradation Timer too short → Unnecessary retransmission
- 33. Round Trip Time Estimation TCP needs to estimate with a safety margin.
- 34. Real-world Formulas for RTT Estimation EstimatedRTT := (1-α) * EstimatedRTT + α * SampleRTT DevRTT := (1-β) * DevRTT + β * |SampleRTT - EstimatedRTT| TimeoutInterval := EstimatedRTT + 4*DevRTT (Typically, α = 0.125, β = 0.25)
- 35. Summary ❏ Moving Average ❏ Noise Cancellation ❏ Process Scheduling ❏ TCP Timeout