SPICE MODEL of OSWT5111A , White ,TA=25degree (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of OSWT5111A , White ,TA=25degree (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net.Japanese
Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of OSWT5111A , White ,TA=25degree (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of OSWT5111A , White ,TA=25degree (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net.Japanese
Version is http://www.spicepark.com by Bee Technologies.
Robot navigation in unknown environment with obstacle recognition using laser...IJECEIAES
Robot navigation in unknown and dynamic environments may result in aimless wandering, corner traps and repetitive path loops. To address these issues, this paper presents the solution by comparing the standard deviation of the distance ranges of the obstacles appeared in the robot navigation path. For the similar obstacles, The standard deviations of distance range vectors, obtained from the laser range finder sensor of the robot at similar pose, are very close to each other. Therefore, the measurements of odometer sensor are also combined with the standard deviation to recognize the location of the obstacles. A novel algorithm, with obstacle detection feature, is presented for robot navigation in unknown and dynamic environments. The algorithm checks the similarity of the distance range vectors of the obstacles in the path and uses this information in combination with the odometer measurements to identify the obstacles and their locations. The experimental work is carried out using Gazebo simulator.
Implementing 64-bit Maximally Equidistributed F2-Linear Generators with Merse...Shin Harase
Presentation slides for 64-bit maximally equidistributed F2-linear pseudorandom number generators MELG-64.
Article:
S. Harase and T. Kimoto, "Implementing 64-bit maximally equidistributed F2-linear generators with Mersenne prime period", ACM Transactions on Mathematical Software, Volume 44, Issue 3, April 2018, Article No. 30, 11 pp.
The code in C:
https://github.com/sharase/melg-64
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]KenjiKoide1
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2021), pp. 7708-7714, Prague, Czech Republic, Sep., 2021
https://staff.aist.go.jp/k.koide/
AN IMPROVED IRIS RECOGNITION SYSTEM BASED ON 2-D DCT AND HAMMING DISTANCE TEC...IJEEE
This paper proposes a new iris recognition system that implements Integro-Differential, Daugman Rubber Sheet Model, 2-D DCT, Hamming Distance to exact features from the iris and matching it with the sorted database.All these image-processing algorithms have been validated on noised real iris images & UBIRIS database
Robot navigation in unknown environment with obstacle recognition using laser...IJECEIAES
Robot navigation in unknown and dynamic environments may result in aimless wandering, corner traps and repetitive path loops. To address these issues, this paper presents the solution by comparing the standard deviation of the distance ranges of the obstacles appeared in the robot navigation path. For the similar obstacles, The standard deviations of distance range vectors, obtained from the laser range finder sensor of the robot at similar pose, are very close to each other. Therefore, the measurements of odometer sensor are also combined with the standard deviation to recognize the location of the obstacles. A novel algorithm, with obstacle detection feature, is presented for robot navigation in unknown and dynamic environments. The algorithm checks the similarity of the distance range vectors of the obstacles in the path and uses this information in combination with the odometer measurements to identify the obstacles and their locations. The experimental work is carried out using Gazebo simulator.
Implementing 64-bit Maximally Equidistributed F2-Linear Generators with Merse...Shin Harase
Presentation slides for 64-bit maximally equidistributed F2-linear pseudorandom number generators MELG-64.
Article:
S. Harase and T. Kimoto, "Implementing 64-bit maximally equidistributed F2-linear generators with Mersenne prime period", ACM Transactions on Mathematical Software, Volume 44, Issue 3, April 2018, Article No. 30, 11 pp.
The code in C:
https://github.com/sharase/melg-64
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]KenjiKoide1
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2021), pp. 7708-7714, Prague, Czech Republic, Sep., 2021
https://staff.aist.go.jp/k.koide/
AN IMPROVED IRIS RECOGNITION SYSTEM BASED ON 2-D DCT AND HAMMING DISTANCE TEC...IJEEE
This paper proposes a new iris recognition system that implements Integro-Differential, Daugman Rubber Sheet Model, 2-D DCT, Hamming Distance to exact features from the iris and matching it with the sorted database.All these image-processing algorithms have been validated on noised real iris images & UBIRIS database
Abstract— Security as far as transport turn out to be critical as there is huge increment in offers of vehicles all over the place. Each auto maker progressively discovering approaches to consume their business created by repairs. However a large portion of the most well-known repairs can without much of a stretch be stayed away from if proprietors had given careful consideration to the preventive upkeep prerequisites. Getting stranded in the remote region because of vehicle repair is experienced by practically everybody anytime of time. These can be forestalled or getting overhauled at a similar spot is the objective of our vehicle item. The real segments we are utilizing are Thermistor, fuel sensor, Ultrasonic sensor connected with flag molding circuit which is associated with microchip. Motor overheating can be determined to have temperature sensor, fuel level analyzed by fuel sensor and uneven tire is analyzed by utilizing ultrasonic sensor. These sensors will be valuable for keeping the support shortcomings.
Keywords— Arduino UNO, Fault Detection, Fuel Sensor, Thermistor, Ultrasonic Sensor.
Tenser Product of Representation for the Group CnIJERA Editor
The main objective of this paper is to compute the tenser product of representation for the group Cn. Also
algorithms designed and implemented in the construction of the main program designated for the determination
of the tenser product of representation for the group Cn including a flow-diagram of the main program. Some
algorithms are followed by simple examples for illustration.
CEM Workshop Lectures (8/11): Method of momentsAbhishek Jain
Above lecture can be downloaded from www.zeusnumerix.com
Computational Electromagnetics Workshop Lecture 8 of 11. The lecture provides a theoretical basis for the simulation of electrodynamics problems. The mathematical formulation for the method of moments is elaborated. These methods are in the family of frequency-domain methods. Method of moments is used for objects of lower electrical size.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
1. The new code for ESA meteoroid model.
Alexey Mints1 Valery Dikarev1 Gerhard Drolshagen2
1 University of Bielefeld, Germany
2 European Space Agency, Noordwijk, The Netherlands
18.07.2010
2. Problem
Outline
1 Problem
2 Code
3 GUI
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 2 / 22
3. Problem
Requirements
Input: Target trajectory and geometry, output content
specification;
Output: Estimated dust flux, number density and average
velocity. Dust distributions in mass, incidence
direction and velocity.
GUI: A tool to set input parameters and view output.
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 3 / 22
4. Problem
Orbital elements
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 4 / 22
5. Problem
Model (as of now)
5 populations, for each:
• Rectangular 3D grid in orbital space. Dimensions:
pericenter distance (0.05-6 a.u., 50 log-scale bins),
eccentricity (0-1, 100 bins) and inclination (0-180, 180
bins);
• Mass spectra (200 log-scale bins from 10−18 to 105
grams), independent of orbital elements;
Current model file size ∼28 Mbytes.
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 5 / 22
6. Problem
Grids
Figure: Possible grids: regular (left), irregular (right)
Old IMEM New IMEM
Regular (orthogonal) grid in Regular (spherical) grid in
orbital elements. incidence velocity.
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 6 / 22
7. Problem
Old and new approaches
Old IMEM New IMEM
Jacobian diverges, tricks are Symmetries cannot be used;
needed; No Jacobian needed;
Incidence direction has to be Incidence direction emerges
calculated: have to run over naturally from the grid;
the whole model range;
Reduced calculations for
Symmetries can be used; sensitivity;
Various scans can be easily
implemented;
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 7 / 22
8. Problem
Thresholds
C1 < mα V β < C2
• α = 1; β = 0 — mass threshold;
• α = 1; β = 1 — momentum threshold;
• α = 1; β = 2 — kinetic energy threshold;
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 8 / 22
9. Problem
Calculation grid
∗ grid ∗
Vdust = Vdust + Vtarget
Geometrical sensitivity can be calculated from ϕ;
grid
Knowing Vdust we can calculate mass densities as
C2 /V β
C1 /V β
mα dm;
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 9 / 22
10. Code
Outline
1 Problem
2 Code
3 GUI
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 10 / 22
11. Code
Application layout
Java Fortran
Interface Model Data module
Results Output
data datafiles
I/O routines Core
User Input
Task data
data datafiles
Global
settings
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 11 / 22
12. Code
Application composition
• Calculation engine — FORTRAN-95 program (∼4000
lines);
• GUI — Java graphical interface (∼11000 lines) developed
with NetBeans ;
• User documentation;
• Model file;
• Sample task and trajectory files;
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 12 / 22
13. Code
Engine input files
• Task file — over 30 parameters, defining task properties
and output content and format;
• Trajectory file — contains orbital parameters or
point-by-point trajectory;
• Sensitivity file — optional file containing sensitivity
function;
• Model file — dust orbital distribution model (binary file);
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 13 / 22
14. Code
Task file
D e s c r i p t i o n=
P o p u l a t i o n s=∗
M e t e o r o i d m o d e l=
T r a j e c t o r y f i l e =x . t r j
S e n s i t i v i t y f i l e =t . t s k s e n s
P l o t s e t t i n g s=t . t s k p l o t
R e s u l t=t . t s k r e s
S e n s i t i v i t y p r e s e t =0
P o p u l a t i o n s s t y l e =1
P o i n t s s t y l e =1
C o o r d i n a t e s s t y l e =1
E a r t h c o o r d i n a t e s =0
Scan mode=0
P h y s u n i t s =0
F l u x =1
N u m b e r d e n s i t y=0
A v e r a g e v e l o c i t y =1
T h r e s h o l d s=m, 1 . 0 0 e −18, 1 . 0 0 e+02
I n c i d e n c e r a n g e =0.00:180.000000
V e l o c i t y r a n g e =0.000000:100000.000000
T a r g e t t y p e =0
T a r g e t o r i e n t a t i o n =0
O r b i t k i n d =1
O r b i t s o l a r =0
O r b i t e c l i p t i c a l =0
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 14 / 22
15. Code
Engine output
#IMEM2 o u t p u t f i l e
#C r e a t e d =20/ 5/2010 2 0 : 2 7 : 3 5 . 1 6 0
#P h y s u n i t s=F
#Scan mode=0
#S c a n r e s o l u t i o n= 20
#P o p u l a t i o n s s t y l e =1
#P o i n t s s t y l e =1
#T h r e s h o l d s i n c o l u m n s=F
#C o o r d i n a t e s s t y l e =1
#E a r t h c o o r d i n a t e s=F
#N u m b e r d e n s i t y=F
#F l u x=T
#A v e r a g e v e l o c i t y=T
#T a r g e t t y p e =0
#T a r g e t o r i e n t a t i o n =0
#S e n s i t i v i t y p r e s e t =0
#T r a j e c t o r y t y p e =0
#T r a j e c t o r y t y p e= 0.: 180.
#M i s s i o n d u r a t i o n= 3.0
#M i s s i o n l a u n c h= 0.0
#P o i n t Thr | Time | asteroids collisions |...
# | | Flux | AvgV |...
# 1 | 2 | 3 | 4 | 5 |...
1 1 0 . 0 0 0 0 0 0 0 0 E+00 0 . 0 0 0 0 0 0 0 E+000 0 . 0 0 0 0 0 0 0 E+000 . . .
2 1 0 . 1 0 0 0 0 0 0 0 E+01 0 . 0 0 0 0 0 0 0 E+000 0 . 0 0 0 0 0 0 0 E+000 . . .
3 1 0 . 2 0 0 0 0 0 0 0 E+01 0 . 0 0 0 0 0 0 0 E+000 0 . 0 0 0 0 0 0 0 E+000 . . .
4 1 0 . 3 0 0 0 0 0 0 0 E+01 0 . 0 0 0 0 0 0 0 E+000 0 . 0 0 0 0 0 0 0 E+000 . . .
. A. .Mints, 18.07.2010
.. The new code for ESA meteoroid model. 15 / 22
16. GUI
Outline
1 Problem
2 Code
3 GUI
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 16 / 22
17. GUI
Main window
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 17 / 22
18. GUI
Output properties window
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 18 / 22
19. GUI
Progress window
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 19 / 22
20. GUI
Plots
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 20 / 22
21. GUI
Maps
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 21 / 22
22. GUI
Future plans
• Final release – September 2010;
• OpenMP and MPI extensions;
• Web-interface;
• More Engine features (for example, meteor flux for a
given location on Earth);
A. Mints, 18.07.2010 The new code for ESA meteoroid model. 22 / 22