This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters used in the PSpice model. Simulation results show the input-output voltage differential is within 0.2% of measured, and ripple rejection ratio matches measured performance. The output characteristic under varying load and input conditions is also modeled within 0.2% accuracy.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters used in the PSpice model. Simulation results show the input-output voltage differential is within 0.2% of measured, and ripple rejection ratio matches measured performance. The output characteristic under varying load and input conditions is also modeled within 0.2% accuracy.
This document summarizes the modeling parameters and performance of the uPC78N24H voltage regulator. It includes:
1) A list of model parameters for the regulator including reference voltage, emission coefficient, and capacitance values.
2) Simulation results showing the input-output voltage differential is within 0.2% of measurements.
3) Ripple rejection ratio simulation matching measurements within 5%.
4) Output voltage simulation matching measurements to within 0.05% under varying load and input conditions.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and modeling parameters. It then provides simulation results and comparisons to measurements for key characteristics like input-output voltage differential, ripple rejection ratio, and output voltage. The simulations show good agreement with measurements within 1% error for most test cases.
This document summarizes the test results of a voltage regulator component. It describes the manufacturer, part number, and PSpice model parameters. It then provides the results of simulating the input-output voltage differential characteristic, ripple rejection ratio, and output characteristic. The simulation results match well with measurements, with less than 1% error in most cases.
This document summarizes the modeling parameters and performance of the uPC78N24H voltage regulator. It includes:
1) A list of model parameters for the regulator including reference voltage, emission coefficient, and capacitance values.
2) Simulation results showing the input-output voltage differential is within 0.2% of measurements.
3) Ripple rejection ratio simulation matching measurements within 5%.
4) Output voltage simulation matching measurements to within 0.05% under varying load and input conditions.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and modeling parameters. It then provides simulation results and comparisons to measurements for key characteristics like input-output voltage differential, ripple rejection ratio, and output voltage. The simulations show good agreement with measurements within 1% error for most test cases.
This document summarizes the test results of a voltage regulator component. It describes the manufacturer, part number, and PSpice model parameters. It then provides the results of simulating the input-output voltage differential characteristic, ripple rejection ratio, and output characteristic. The simulation results match well with measurements, with less than 1% error in most cases.
This document summarizes the modeling parameters and performance of the uPC78N08H voltage regulator. It includes:
1) A list of model parameters used in the PSpice model including reference voltage, emission coefficient, and capacitance values.
2) Simulation results showing the input-output voltage differential is within 0.008% of measurements.
3) Ripple rejection ratio simulation of 67.535dB is within -0.684% of measured value.
4) Output characteristic simulation of 7.9796V is within -0.255% of measured 8V output voltage.
SPICE MODEL of uPC24A15HF in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
Similar to SPICE MODEL of uPC78L10T in SPICE PARK (13)
Update 22 models(Schottky Rectifier ) in SPICE PARK(APR2024)Tsuyoshi Horigome
This document provides an inventory update of 6,747 parts at Spice Park as of April 2024. It lists the part numbers, manufacturers, and quantities of various semiconductor components, including 1,697 Schottky rectifier diodes from 29 different manufacturers. It also includes details on passive components, batteries, mechanical parts, motors, and lamps in the inventory.
The document provides an inventory update from April 2024 of the Spice Park collection which contains 6,747 electronic components. It includes tables listing the types of semiconductor components, passive parts, batteries, mechanical parts, motors, and lamps in the collection along with their manufacturer and quantities. One of the semiconductor components, the general purpose rectifier diode, is broken down into a more detailed table with 116 entries providing part numbers, manufacturers, thermal ratings, and remarks.
Update 31 models(Diode/General ) in SPICE PARK(MAR2024)Tsuyoshi Horigome
The document provides an inventory update from March 2024 of parts in the Spice Park warehouse. It lists 6,725 total parts across various categories including semiconductors, passive parts, batteries, mechanical parts, motors, and lamps. The semiconductor section lists 652 general purpose rectifier diodes from 18 different manufacturers with quantities ranging from 2 to 145 pieces.
This document provides an inventory list of parts at Spice Park as of March 2024. It contains 3 sections - Semiconductor parts (diodes, transistors, ICs etc.), Passive parts (capacitors, resistors etc.), and Battery parts. For Semiconductor parts, it lists 36 different part types and provides the quantity of each part. It then provides further details of Diode/General Purpose Rectifiers, listing the manufacturer and quantity of 652 individual part numbers.
Update 29 models(Solar cell) in SPICE PARK(FEB2024)Tsuyoshi Horigome
The document provides an inventory update from February 2024 of Spice Park, which contains 6,694 total pieces of electronic components and parts. It lists 36 categories of semiconductor devices, 11 categories of passive parts, 10 types of batteries, 5 mechanical parts, DC motors, lamps, and power supplies. It provides the most detailed listing for solar cells, with 1,003 total pieces from 51 manufacturers listed with part numbers.
The document provides an inventory update from February 2024 of Spice Park, which contains 6,694 electronic components. It lists the components by type (e.g. semiconductor), part number, manufacturer, thermal rating, and quantity on hand. For example, it shows that there are 621 general purpose rectifier diodes from manufacturers such as Fairchild, Fuji, Intersil, Rohm, Shindengen, and Toshiba. The detailed four-page section provides further information on the first item, general purpose rectifier diodes, including 152 individual part numbers and specifications.
This document discusses circuit simulations using LTspice. It describes driving a circuit simulation by inserting a 250 ohm resistor between the output terminals. It also describes simulating a 1 channel bridge circuit where the DUT1 and DUT2 resistors are both set to 100 ohms and the input voltage is set to either 1V or 5V.
This document discusses parametric sweeps of external and internal resistance values Rg for circuit simulation in LTspice. It also references outputting a waveform similar to a report on fall time characteristics for a device modeling report with customer Samsung.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Choosing The Best AWS Service For Your Website + API.pptx
SPICE MODEL of uPC78L10T in SPICE PARK
1. Device Modeling Report
COMPONENTS : VOLTAGE REGULATOR
PART NUMBER : uPC78L10T
MANUFACTURER : NEC Electronics Corporation
Panasonic
Bee Technologies Inc.
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004
2. MODEL PARAMETER
Pspice
model Model description
parameter
VREF Reference Voltage
N Emission Coefficient
BETA Tranconductance of JFET Transistor
VAF Early Voltage of Output Pass Transistor
CPZ Output Impedance Zero Capacitor
RB2 Base Resistance of Output Limit Voltage Source
ESC1 Coefficient of Current Limit Voltage Source
ESC2 Coefficient of Current Limit Voltage Source
EFB1 Coefficient of Foldback Current Voltage Source
EFB2 Coefficient of Foldback Current Voltage Source
EFB3 Coefficient of Foldback Current Voltage Source
EB Non-ideal Base-Collector Diode Saturation Current
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004
3. Input-Output Voltage Differential Characteristic
Evaluation Circuit
U1
1 IN OUT 3
GND UPC78L10T
2
V1 RL
17 Cout
250
0.1u
0
Simulation result
Input - Output
Input
Example
VIN - VOUT Measurement Simulation % Error
17 (V) – 10 (V) 7 (V) 7.0032 (V) 0.046
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004
4. Ripple Rejection (RR) Characteristic
Evaluation Circuit
U1
Vin Vout
1 IN OUT 3
GND UPC78L10T
2
D1 D2
S1VBA S1VBA C1 Cout RL
0.33u 0.1u 250
V1
D3 D4
VOFF = 0
VAMPL = 1
FREQ = 120 S1VBA S1VBA
V2
17
0
Simulation result
Output
Input
Comparison Table
Measurement Simulation % Error
Ripple rejection ratio
(dB)
69 66.021 -4.317
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004