Month: July 2024
Public Facing Copilot trained on Sharepoint Document Library
Hello all,
Is it possible to have a public facing copilot that is able to read documents in a SharePoint library? I know we can require authentication, but the end users are not going to be logging in. The chatbot is to be embedded in our website for software/hardware troubleshooting. Is it going to be better to set up the knowledgebase a different way?
Thank you,
-Jason
Hello all, Is it possible to have a public facing copilot that is able to read documents in a SharePoint library? I know we can require authentication, but the end users are not going to be logging in. The chatbot is to be embedded in our website for software/hardware troubleshooting. Is it going to be better to set up the knowledgebase a different way? Thank you, -Jason Read More
Can’t add more than 60 performance counters for IIS at once!
Hi,
I have a lot of sites under IIS installation (4000-5000 sites). Don’t tell me why :
I’m trying to add all of them at once in Performance counter to see which one is consuming high traffic but the windows mmc is unable to add them at once, I can pick up maximum 60 sites. Adding them this way is just nightmare.
Can anybody advice me how to add all of them at once using powershell script?
Thanks
Hi, I have a lot of sites under IIS installation (4000-5000 sites). Don’t tell me why :I’m trying to add all of them at once in Performance counter to see which one is consuming high traffic but the windows mmc is unable to add them at once, I can pick up maximum 60 sites. Adding them this way is just nightmare. Can anybody advice me how to add all of them at once using powershell script? Thanks Read More
Empowering.Cloud Community Update – August 2024
Check out the latest community briefings, Teams Insider Podcast and Operator Connect Updates.
Community Briefings
Microsoft Mesh: The Future of Virtual Collaboration
https://app.empowering.cloud/briefings/370/microsoft-mesh-the-future-of-virtual-collaboration
In our latest community briefing, MVPs Miguel Tabera and Ángel Carrillo discuss the vision for the future of collaboration within Microsoft Teams and how Microsoft Mesh is enabling collaboration like never before.
Key features of Microsoft Mesh: Avatars, Immersive Spaces and Customer Immersive Spaces
Microsoft Mesh experiences in Microsoft Teams including hybrid meetings and use cases
Customization of immersive spaces: no code and pro code (Mesh toolkit) options
Future development of Microsoft Mesh with Copilot
Microsoft Teams Monthly Update July 2024
https://app.empowering.cloud/briefings/371/Microsoft-Teams-Monthly-Update-July-2024
MVP Tom Arbuthnot runs through the latest Microsoft news and updates you need to know this month.
Empowering.Cloud 2nd Anniversary
EU issues ‘Statement of Objections’ to Microsoft – what does this mean for Teams?
Important Microsoft Teams Rooms and Phone update (MC804766)
Dynamics 365 Contact Center Generally Available
Copilot for Sales Teams plugin
Microsoft Teams Bookable Desks Generally Available
Microsoft Teams Webinar Integration with HubSpot
New Optimized Microsoft Teams VDI
Changes for creating and managing Teams Phone (TP) resource accounts
Classic Teams end of support
Skype for Business Server Subscription Edition
Microsoft Teams Insider Podcast
Truly Working from Anywhere with Teams Phone Mobile
Robert Skyrme, BT/EE Technical Pre-Sales Consultant and Tom Arbuthnot explore everything Teams Phone Mobile, including its benefits and real-world use cases for field workers, sales teams and engineering teams.
Making your Microsoft Teams Meeting Room Intelligent
Chris Fitzsimmons, Senior Solutions Architect at AVI-SPL and MVP Tom Arbuthnot explore Copilot and AI in Microsoft Teams Rooms and BYOD, what is available today, what is coming, and the value it brings.
Enhancing Efficiency at Century Communities with Microsoft Teams Phone
Craig Cothern, National Director of IT Delivery at Century Communities, a top 10 home builder in the US, and Oli Lifely, Head of Sales at Luware UK, discuss Century Communities’ Microsoft Teams journey and how Luware Nimbus addressed their unique communication challenges.
The Evolution of Microsoft Teams Rooms and Devices with Craig Durr
Craig Durr, Chief Analyst and Founder of The Collab Collective explores the evolution of meeting room technology, AI’s impact and multi-camera solutions.
Microsoft Teams Operator Connect Updates
The race to 100 countries or 100 operators is on – there’s not too much to report here this month, although providers continue to be extending their Microsoft Teams Operator Connect coverage to more countries.
Country Changes:
GlobalConnect – Norway
CallTower – Australia
Tata Communicaitons – Kenya and Ukraine
Telnyx – Colombia, Costa Rica, Dominican Republic, El Salvador, Panama, Peru
Check out our full Power BI report of all the Operators here:
https://members.empowering.cloud/microsoft-teams-operator-connect-providers
Upcoming Community Events
Teams Fireside Chat with Miguel Corteguera – 8th August| 16:00 BST | Virtual
Join other Empowering.Cloud community members as we chat with Miguel Corteguera, Technical Strategy – Global Customer Success at Microsoft at 16:00 BST on Thursday 8th August.
Microsoft Teams Devices Ask Me Anything
EMEA/NA – 19th August | 16:00 BST| Virtual
Hosts: MVP Graham Walsh, Michael Tressler, Jimmy Vaughan
APAC – 20th August | 15:30 AEST | Virtual
Hosts: MVP Graham Walsh, Phil Clapham, Andrew Higgs, Justin O’Meara
Microsoft Teams Devices Ask Me Anything is a monthly community which gives you all an update on the important and Microsoft Teams devices news, as well as the chance to ask questions and get them answered by the experts. We have 2 sessions to cover different time zones, so there’s really no excuse not to come along to at least one!
Microsoft Tech Community – Latest Blogs –Read More
Microsoft Loop | Live, Five-Part Learning Series
Are you ready to learn more about Microsoft Loop? We have five upcoming Loop events for you – numerous opportunities to see Microsoft Loop in action, to learn about upcoming roadmap items and Copilot/AI innovations, ask questions, get time-saving tips and tricks, and provide direct feedback to the Loop product team.
Microsoft Loop – Five-part learning series: https://aka.ms/LoopLearningSeries.
We’re all eyes and ears each week for five weeks – starting August 14th, 2024. Review each weekly, online learning opportunity below. Register to learn and share all things Loop – there it is! If you can’t make it to one, each part will be made available for on-demand viewing as soon as possible; we’ll keep this blog up to date throughout the series.
Part 1 | “Meet the makers: The story behind Loop”
This is part 1 of 5 in the Loop community learning series.
Co-presenters: Ron Pessner, Michelle Holtmann, and Karuana Gatimu
Date/time: Wednesday, August 14th, 2024, from 10:00am – 11:00am PDT
Abstract: In our ‘Meet the Makers’ virtual event on August 14th, leaders from the Microsoft Loop product team kickoff our learning series to share insights about the future of creating together with ease – no matter where you are or what tools you use. This opening session focuses on the strategy and vision for how Loop unlocks the power of shared thinking – with the intelligence of Microsoft Copilot – into a simple, familiar experience to get more done, right where you are, coordinated across your apps. And there will be experts live, active in the comments to address questions and feedback based on what’s shared.
Register for this session and put it on your calendar today! [same link to watch live]
Part 2 | “Almost everything you need to know to start with Microsoft Loop”
This is part 2 of 5 in the Loop community learning series.
Co-presenters: Rebecca Keys and Tamine Mokdissi
Date/time: Wednesday, August 21st, 2024, from 10:00am – 10:30am PDT.
Abstract: Ever wondered how to get started with Loop? Whether you’re new to Loop or need a refresher, we’ve got you covered in this engaging session. We’ll show you both what’s coming soon and how to get started with Loop. We’ll highlight Loop components across various Microsoft 365 apps – creating and customizing your pages and workspaces within your favorite apps. We will share many tips along the way so you, too, can seamlessly and collaboratively whip up your projects to the next level.
Register for this session and put it on your calendar today! [same link to watch live]
Part 3 | “Level up your project management with Loop”
This is part 3 of 5 in the Loop community learning series.
Speakers: Esha Mathur and Jessica Mariscal Quintana
Date/time: Wednesday, August 28th, 2024, from 10:00am – 10:30am PDT.
Abstract: Turn your projects into progress! With Loop, project management gets better – from set up, to onboarding your team, to working in collaborative workspace – all you need in one place. We’ll demo a variety of project-oriented tasks: Creating and sharing tasks lists, setting automation rules, and monitoring your milestones with automatically generated Workspace status. And we’ll have experts online to address your project questions and feedback.
Register for this session and put it on your calendar today! [same link to watch live]
Part 4 | “Meet Copilot in Loop“
This is part 4 of 5 in the Loop community learning series.
Speakers: Jenna Hong and Oby Omu
Date/time: Wednesday, September 4th, 2024, from 10:00am – 10:30am PDT.
Abstract: Take productivity one step further with Copilot in Loop. Discover the value of starting projects with content created by AI, stay in sync with a Loop-powered assistant, and make the most of your Microsoft Teams meetings. Copilot in Loop saves you time when creating page summaries, recap changes, and quickly get answers to make progress. We have a lot to share and are eager to hear what you think.
Register for this session and put it on your calendar today! [same link to watch live]
Part 5 | “AMA: Live Video Q&A with the Loop team”
This is part 5 of 5 in the Loop community learning series.
Co-presenters: Derek Liddell, Dan Costenaro, and Manon Knoertzer
Date/time: Wednesday, September 11th, 2024, from 10:00am – 11:30am PDT.
Abstract: The Loop AMA (Ask Microsoft Anything) is a chance to ask open questions and provide feedback about what you’ve learned about Microsoft Loop throughout this learning series. The AMA is a 90-minute opportunity to connect live with Loop experts who will be live on video to answer your questions and listen to feedback. Note: If you are unable to attend the live AMA hour, you can ask your question at any time on the event page below in comments and the team will work to address it during the event – either on video or in direct written response in the comments; so, check back for sure.
Register for this session and put it on your calendar today! [same link to watch live]
Stay connected
Microsoft Loop is a transformative co-creation experience that brings together teams, content and tasks across your tools and devices. Loop combines a powerful and flexible canvas with portable components that move freely and stay in sync across applications — enabling teams to think, plan, and create together.
We are excited to share more about the productivity experience we love. Our goal is to bring you into the Loop and keep you there – both by gaining knowledge and helping you discover the power of working together in new ways. My team and I couldn’t be more excited and ready to engage with the best community in tech. See you every week once the series kicks off.
Note: Additional Microsoft Loop links and resources
Try it today! loop.cloud.microsoft to get Loop’ed in!
Microsoft Loop adoption center
Get started with Loop (support.microsoft.com)
Follow @MicrosoftLoop on Twitter.
Join our LinkedIn group! LinkedIn
Continue the conversation by joining us in the Microsoft 365 community. Want to share best practices or join community events? Become a member by “joining” the Microsoft 365 community. For tips & tricks or to stay up to date on the latest news and announcements directly from the product teams, make sure to Follow or Subscribe to the Microsoft 365 community blog (review all Loop posts).
Thanks! Ron Pessner (Microsoft CVP – Loop)
Microsoft Tech Community – Latest Blogs –Read More
How to adjust font size in Matlab editor
I want to adjust the font size in the Matlab editor (R2024a running on Mac, M3 chip, OS Sonoma 14.5).
Matlab>Settings>Fonts gives me the option of specifying a font for "Command Window, Command History, Editor". I can change the font (I choose a larger font) and it is used by Command window and Command History, but not by the Editor. The default font for the Editor is too small to read comfortably.
Thanks.I want to adjust the font size in the Matlab editor (R2024a running on Mac, M3 chip, OS Sonoma 14.5).
Matlab>Settings>Fonts gives me the option of specifying a font for "Command Window, Command History, Editor". I can change the font (I choose a larger font) and it is used by Command window and Command History, but not by the Editor. The default font for the Editor is too small to read comfortably.
Thanks. I want to adjust the font size in the Matlab editor (R2024a running on Mac, M3 chip, OS Sonoma 14.5).
Matlab>Settings>Fonts gives me the option of specifying a font for "Command Window, Command History, Editor". I can change the font (I choose a larger font) and it is used by Command window and Command History, but not by the Editor. The default font for the Editor is too small to read comfortably.
Thanks. matlab editor, font size MATLAB Answers — New Questions
how can I add new property to a platform, Unrecognized property ‘Height’ for class ‘matlabshared.satellitescenario.Platform’.
%Load the Aircraft Trajectory
aircraft = load("aircraftTT.mat");
aircraft1 = load("aircraftTT1.mat");
aircraft2 = load("aircraftTT2.mat");
aircraft3 = load("aircraftTT3.mat");
aircraft4 = load("aircraftTT4.mat");
geoplot(aircraft.aircraftTT.AircraftLLA(:,1), aircraft.aircraftTT.AircraftLLA(:,2), "b–",…
aircraft1.aircraftTT1.AircraftLLA1(:,1), aircraft1.aircraftTT1.AircraftLLA1(:,2), "r–",…
aircraft2.aircraftTT2.AircraftLLA2(:,1), aircraft2.aircraftTT2.AircraftLLA2(:,2), "k–",…
aircraft3.aircraftTT3.AircraftLLA3(:,1), aircraft3.aircraftTT3.AircraftLLA3(:,2), "g–",…
aircraft4.aircraftTT4.AircraftLLA4(:,1), aircraft4.aircraftTT4.AircraftLLA4(:,2), "c–"…
)
title("Aircrafts Trajectories")
%legend(‘show’);
geobasemap streets
geolimits([38.0887 38.1099],[140.6330 140.6660])
%Load the Vehicles path
gx = geoaxes;
geolimits(gx,[38.0887,38.1099],[140.6330,140.6660]);
title("Vehicles Path")
geobasemap(gx,"streets");
useManualInput = true;
if useManualInput
waypoints = zeros(15,3); %#ok<*UNRCH>
numWaypoints = size(waypoints,1);
[lat,lon] = ginput(numWaypoints);
waypoints = [lat lon ones(numWaypoints,1)];
else
waypoints = […
42.265387594440746, -71.566584651695194, 1; …
42.263702708358274, -71.569614091115028, 1; …
42.262575723421115, -71.573098554161504, 1; …
42.260479485544565, -71.575579488911472, 1; …
42.258318030927740, -71.578013745650068, 1; …
42.256140084405871, -71.580417734892066, 1; …
42.253864954613888, -71.582655636609843, 1; …
42.251556291741792, -71.584827877421873, 1; …
42.249201618438420, -71.586910025508118, 1; …
42.246834381495290, -71.588965321234468, 1; …
42.244433745609328, -71.590949233154006, 1; …
42.242264173597064, -71.593357390593781, 1; …
42.240253137371795, -71.596002172990822, 1; …
42.238444180204169, -71.598914079082988, 1; …
42.236661606027504, -71.601851940116248, 1; …
42.235163293921218, -71.605069451220544, 1; …
42.233664981814933, -71.608286962324840, 1; …
42.232221634012099, -71.611549664950445, 1; …
42.230785995268782, -71.614818705946718, 1; …
42.229491349524331, -71.618178093198480, 1; …
42.228523450114722, -71.621746854731867, 1; …
42.227591252458026, -71.625329339092573, 1; …
42.227021657836168, -71.629051198649620, 1; …
42.226488781649429, -71.632774933477108, 1; …
42.226848543969822, -71.636544256810438, 1; …
42.227208306290223, -71.640313580143768, 1; …
42.227475917842995, -71.644096605079170, 1; …
42.227730436282329, -71.647881576787640, 1; …
42.227949419806208, -71.651670397035346, 1; …
42.228142838835581, -71.655461985995458, 1; …
42.228336257864946, -71.659253574955557, 1];
lat = waypoints(:,1);
lon = waypoints(:,2);
end
geoplot(gx,lat,lon,’r-‘)
title("Vehicles Path")
% Find the distance in statute miles between each waypoint
distInDeg = distance(waypoints(1:end-1,1:2),waypoints(2:end,1:2)); % deg
distInSM = deg2sm(distInDeg); % statute miles
carSpeed = 70 / 3600; % miles per second
timeOfArrival = [0; cumsum(distInSM) / carSpeed]; % seconds
carPath = geoTrajectory(waypoints,timeOfArrival);
%Define Mission Start Date and Duration"Creating the scenario"
mission.StartDate = datetime(2024,7,24,02,42,30,TimeZone="UTC");
mission.StopTime = mission.StartDate + hours(2) + minutes(2);
mission.Duration = hours(2); %should remove, I’ve already defined above
sampleTime = 10;
mission.scenario = satelliteScenario(mission.StartDate, mission.StopTime,sampleTime);
mission.viewer = satelliteScenarioViewer(mission.scenario);
%Add Vehicles to a Satellite Scenario
car = platform(mission.scenario,carPath, Name = "Car");
%Add Aircrafts to a Satellite Scenario
aircraft = platform(mission.scenario,aircraft.aircraftTT,Name="Aircraft1", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft);
aircraft1 = platform(mission.scenario,aircraft1.aircraftTT1,Name="Aircraft2", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft1);
aircraft2 = platform(mission.scenario,aircraft2.aircraftTT2,Name="Aircraft3", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft2);
aircraft3 = platform(mission.scenario,aircraft3.aircraftTT3,Name="Aircraft4", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft3);
aircraft4 = platform(mission.scenario,aircraft4.aircraftTT4,Name="Aircraft5", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft4);
%Add a Starlink Constellation to the Satellite Scenario-TLE files
sat = satellite(mission.scenario,"2024-001B.tle", Visual3DModel="SmallSat.glb");
show(sat)
groundTrack(sat,"LeadTime",1200);
%Aircraft-to-Vehicles Access Analysis
Preq = -35.5; % Required signal power in dBm
Ptx = 17.5; % Transmitted power in dBm
% Configure the ground station, satellites, and link characteristics
% Set the ground station characteristics with parabolic telescope
%gs = struct;
car.Height = 1; % Height above the mean sea level in km
car.OpticsEfficiency = 0.8; % Optical antenna efficiency
car.ApertureDiameter = 1; % Antenna aperture diameter in m
car.PointingError = 1e-6; % Pointing error in rad
% Set the satellite A characteristics with parabolic telescope
aircraft = struct;
aircraft.Height = 550; % Height above the mean sea level in km
aircraft.OpticsEfficiency = 0.8; % Optical antenna efficiency
aircraft.ApertureDiameter = 0.07; % Antenna aperture diameter in m
aircraft.PointingError = 1e-6; % Pointing error in rad
% Set the link characteristics
link = struct;
link.Wavelength = 1550e-9; % m
link.TroposphereHeight = 20; % km (Typically ranges from 6-20 km)
link.ElevationAngle = 50; % degrees
%link.Type = "downlink"; % "downlink"|"inter-satellite"|"uplink"
% When the Type field is set to "uplink" or "downlink", you must specify
% the CloudType field, as defined in [5] table 1
link.CloudType = "Thin cirrus";
tx = transmitter(car);
rx = receiver(aircraft,MountingAngles=[0; 180; 0]);
% Calculate transmitter and receiver gain
txGain = (pi*tx.ApertureDiameter/link.Wavelength)^2;
Gtx = 10*log10(txGain); % in dB
rxGain = (pi*rx.ApertureDiameter/link.Wavelength)^2;
Grx = 10*log10(rxGain); % in dB
% Calculate transmitter and receiver pointing loss in dB
txPointingLoss = 4.3429*(txGain*(tx.PointingError)^2);
rxPointingLoss = 4.3429*(rxGain*(rx.PointingError)^2);
absorptionLoss = 0.01; % Absorption loss in dB
% Calculate the distance of the optical beam that propagates through
% the troposphere layer of the atmosphere in km
dT = (link.TroposphereHeight – gs.Height).*cscd(link.ElevationAngle);
% Calculate the slant distance for uplink and downlink between
% satellite A and the ground station for circular orbit in m
dGS = slantRangeCircularOrbit(link.ElevationAngle,satA.Height*1e3,gs.Height*1e3);
% Calculate free-space path loss between the ground station and
% satellite in dB
pathLoss = fspl(dGS,link.Wavelength);
% Calculate loss due to geometrical scattering
% cnc – cloud number concentration in cm-3
% lwc – Liquid water content in g/m-3
[cnc,lwc] = getCloudParameters(link.CloudType);
visibility = 1.002/((lwc*cnc)^0.6473); % Calculate visibility in km
% Get particle size related coefficient
if visibility<=0.5
delta = 0;
elseif visibility>0.5 && visibility<=1
delta = visibility – 0.5;
elseif visibility>1 && visibility<=6
delta = 0.16*visibility + 0.34;
elseif visibility>=6 && visibility<=50
delta = 1.3;
else % visibility>50
delta = 1.6;
end
geoCoeff = (3.91/visibility)* …
((link.Wavelength*1e9/550)^-delta); % Extinction coefficient
geoScaLoss = 4.3429*geoCoeff*dT; % Geometrical scattering loss in dB
% Calculate loss due to Mie scattering
lambda_mu = link.Wavelength*1e6; % Wavelength in microns
% Calculate empirical coefficients
a = (0.000487*(lambda_mu^3)) – (0.002237*(lambda_mu^2)) + …
(0.003864*lambda_mu) – 0.004442;
b = (-0.00573*(lambda_mu^3)) + (0.02639*(lambda_mu^2)) – …
(0.04552*lambda_mu) + 0.05164;
c = (0.02565*(lambda_mu^3)) – (0.1191*(lambda_mu^2)) + …
(0.20385*lambda_mu) – 0.216;
d = (-0.0638*(lambda_mu^3)) + (0.3034*(lambda_mu^2)) – …
(0.5083*lambda_mu) + 0.425;
mieER = a*(gs.Height^3) + b*(gs.Height^2) + …
c*(gs.Height) + d; % Extinction ratio
mieScaLoss = (4.3429*mieER)./sind(link.ElevationAngle); % Mie scattering loss in dB
% Calculate link margin for uplink or downlink in dB
linkMargin = Ptx + 10*log10(tx.OpticsEfficiency) + …
10*log10(rx.OpticsEfficiency) + Gtx + Grx – …
txPointingLoss – rxPointingLoss – pathLoss – …
absorptionLoss – geoScaLoss – mieScaLoss – Preq;
disp("Link margin for "+num2str(link.Type)+" is "+num2str(linkMargin)+" dB")%Load the Aircraft Trajectory
aircraft = load("aircraftTT.mat");
aircraft1 = load("aircraftTT1.mat");
aircraft2 = load("aircraftTT2.mat");
aircraft3 = load("aircraftTT3.mat");
aircraft4 = load("aircraftTT4.mat");
geoplot(aircraft.aircraftTT.AircraftLLA(:,1), aircraft.aircraftTT.AircraftLLA(:,2), "b–",…
aircraft1.aircraftTT1.AircraftLLA1(:,1), aircraft1.aircraftTT1.AircraftLLA1(:,2), "r–",…
aircraft2.aircraftTT2.AircraftLLA2(:,1), aircraft2.aircraftTT2.AircraftLLA2(:,2), "k–",…
aircraft3.aircraftTT3.AircraftLLA3(:,1), aircraft3.aircraftTT3.AircraftLLA3(:,2), "g–",…
aircraft4.aircraftTT4.AircraftLLA4(:,1), aircraft4.aircraftTT4.AircraftLLA4(:,2), "c–"…
)
title("Aircrafts Trajectories")
%legend(‘show’);
geobasemap streets
geolimits([38.0887 38.1099],[140.6330 140.6660])
%Load the Vehicles path
gx = geoaxes;
geolimits(gx,[38.0887,38.1099],[140.6330,140.6660]);
title("Vehicles Path")
geobasemap(gx,"streets");
useManualInput = true;
if useManualInput
waypoints = zeros(15,3); %#ok<*UNRCH>
numWaypoints = size(waypoints,1);
[lat,lon] = ginput(numWaypoints);
waypoints = [lat lon ones(numWaypoints,1)];
else
waypoints = […
42.265387594440746, -71.566584651695194, 1; …
42.263702708358274, -71.569614091115028, 1; …
42.262575723421115, -71.573098554161504, 1; …
42.260479485544565, -71.575579488911472, 1; …
42.258318030927740, -71.578013745650068, 1; …
42.256140084405871, -71.580417734892066, 1; …
42.253864954613888, -71.582655636609843, 1; …
42.251556291741792, -71.584827877421873, 1; …
42.249201618438420, -71.586910025508118, 1; …
42.246834381495290, -71.588965321234468, 1; …
42.244433745609328, -71.590949233154006, 1; …
42.242264173597064, -71.593357390593781, 1; …
42.240253137371795, -71.596002172990822, 1; …
42.238444180204169, -71.598914079082988, 1; …
42.236661606027504, -71.601851940116248, 1; …
42.235163293921218, -71.605069451220544, 1; …
42.233664981814933, -71.608286962324840, 1; …
42.232221634012099, -71.611549664950445, 1; …
42.230785995268782, -71.614818705946718, 1; …
42.229491349524331, -71.618178093198480, 1; …
42.228523450114722, -71.621746854731867, 1; …
42.227591252458026, -71.625329339092573, 1; …
42.227021657836168, -71.629051198649620, 1; …
42.226488781649429, -71.632774933477108, 1; …
42.226848543969822, -71.636544256810438, 1; …
42.227208306290223, -71.640313580143768, 1; …
42.227475917842995, -71.644096605079170, 1; …
42.227730436282329, -71.647881576787640, 1; …
42.227949419806208, -71.651670397035346, 1; …
42.228142838835581, -71.655461985995458, 1; …
42.228336257864946, -71.659253574955557, 1];
lat = waypoints(:,1);
lon = waypoints(:,2);
end
geoplot(gx,lat,lon,’r-‘)
title("Vehicles Path")
% Find the distance in statute miles between each waypoint
distInDeg = distance(waypoints(1:end-1,1:2),waypoints(2:end,1:2)); % deg
distInSM = deg2sm(distInDeg); % statute miles
carSpeed = 70 / 3600; % miles per second
timeOfArrival = [0; cumsum(distInSM) / carSpeed]; % seconds
carPath = geoTrajectory(waypoints,timeOfArrival);
%Define Mission Start Date and Duration"Creating the scenario"
mission.StartDate = datetime(2024,7,24,02,42,30,TimeZone="UTC");
mission.StopTime = mission.StartDate + hours(2) + minutes(2);
mission.Duration = hours(2); %should remove, I’ve already defined above
sampleTime = 10;
mission.scenario = satelliteScenario(mission.StartDate, mission.StopTime,sampleTime);
mission.viewer = satelliteScenarioViewer(mission.scenario);
%Add Vehicles to a Satellite Scenario
car = platform(mission.scenario,carPath, Name = "Car");
%Add Aircrafts to a Satellite Scenario
aircraft = platform(mission.scenario,aircraft.aircraftTT,Name="Aircraft1", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft);
aircraft1 = platform(mission.scenario,aircraft1.aircraftTT1,Name="Aircraft2", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft1);
aircraft2 = platform(mission.scenario,aircraft2.aircraftTT2,Name="Aircraft3", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft2);
aircraft3 = platform(mission.scenario,aircraft3.aircraftTT3,Name="Aircraft4", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft3);
aircraft4 = platform(mission.scenario,aircraft4.aircraftTT4,Name="Aircraft5", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft4);
%Add a Starlink Constellation to the Satellite Scenario-TLE files
sat = satellite(mission.scenario,"2024-001B.tle", Visual3DModel="SmallSat.glb");
show(sat)
groundTrack(sat,"LeadTime",1200);
%Aircraft-to-Vehicles Access Analysis
Preq = -35.5; % Required signal power in dBm
Ptx = 17.5; % Transmitted power in dBm
% Configure the ground station, satellites, and link characteristics
% Set the ground station characteristics with parabolic telescope
%gs = struct;
car.Height = 1; % Height above the mean sea level in km
car.OpticsEfficiency = 0.8; % Optical antenna efficiency
car.ApertureDiameter = 1; % Antenna aperture diameter in m
car.PointingError = 1e-6; % Pointing error in rad
% Set the satellite A characteristics with parabolic telescope
aircraft = struct;
aircraft.Height = 550; % Height above the mean sea level in km
aircraft.OpticsEfficiency = 0.8; % Optical antenna efficiency
aircraft.ApertureDiameter = 0.07; % Antenna aperture diameter in m
aircraft.PointingError = 1e-6; % Pointing error in rad
% Set the link characteristics
link = struct;
link.Wavelength = 1550e-9; % m
link.TroposphereHeight = 20; % km (Typically ranges from 6-20 km)
link.ElevationAngle = 50; % degrees
%link.Type = "downlink"; % "downlink"|"inter-satellite"|"uplink"
% When the Type field is set to "uplink" or "downlink", you must specify
% the CloudType field, as defined in [5] table 1
link.CloudType = "Thin cirrus";
tx = transmitter(car);
rx = receiver(aircraft,MountingAngles=[0; 180; 0]);
% Calculate transmitter and receiver gain
txGain = (pi*tx.ApertureDiameter/link.Wavelength)^2;
Gtx = 10*log10(txGain); % in dB
rxGain = (pi*rx.ApertureDiameter/link.Wavelength)^2;
Grx = 10*log10(rxGain); % in dB
% Calculate transmitter and receiver pointing loss in dB
txPointingLoss = 4.3429*(txGain*(tx.PointingError)^2);
rxPointingLoss = 4.3429*(rxGain*(rx.PointingError)^2);
absorptionLoss = 0.01; % Absorption loss in dB
% Calculate the distance of the optical beam that propagates through
% the troposphere layer of the atmosphere in km
dT = (link.TroposphereHeight – gs.Height).*cscd(link.ElevationAngle);
% Calculate the slant distance for uplink and downlink between
% satellite A and the ground station for circular orbit in m
dGS = slantRangeCircularOrbit(link.ElevationAngle,satA.Height*1e3,gs.Height*1e3);
% Calculate free-space path loss between the ground station and
% satellite in dB
pathLoss = fspl(dGS,link.Wavelength);
% Calculate loss due to geometrical scattering
% cnc – cloud number concentration in cm-3
% lwc – Liquid water content in g/m-3
[cnc,lwc] = getCloudParameters(link.CloudType);
visibility = 1.002/((lwc*cnc)^0.6473); % Calculate visibility in km
% Get particle size related coefficient
if visibility<=0.5
delta = 0;
elseif visibility>0.5 && visibility<=1
delta = visibility – 0.5;
elseif visibility>1 && visibility<=6
delta = 0.16*visibility + 0.34;
elseif visibility>=6 && visibility<=50
delta = 1.3;
else % visibility>50
delta = 1.6;
end
geoCoeff = (3.91/visibility)* …
((link.Wavelength*1e9/550)^-delta); % Extinction coefficient
geoScaLoss = 4.3429*geoCoeff*dT; % Geometrical scattering loss in dB
% Calculate loss due to Mie scattering
lambda_mu = link.Wavelength*1e6; % Wavelength in microns
% Calculate empirical coefficients
a = (0.000487*(lambda_mu^3)) – (0.002237*(lambda_mu^2)) + …
(0.003864*lambda_mu) – 0.004442;
b = (-0.00573*(lambda_mu^3)) + (0.02639*(lambda_mu^2)) – …
(0.04552*lambda_mu) + 0.05164;
c = (0.02565*(lambda_mu^3)) – (0.1191*(lambda_mu^2)) + …
(0.20385*lambda_mu) – 0.216;
d = (-0.0638*(lambda_mu^3)) + (0.3034*(lambda_mu^2)) – …
(0.5083*lambda_mu) + 0.425;
mieER = a*(gs.Height^3) + b*(gs.Height^2) + …
c*(gs.Height) + d; % Extinction ratio
mieScaLoss = (4.3429*mieER)./sind(link.ElevationAngle); % Mie scattering loss in dB
% Calculate link margin for uplink or downlink in dB
linkMargin = Ptx + 10*log10(tx.OpticsEfficiency) + …
10*log10(rx.OpticsEfficiency) + Gtx + Grx – …
txPointingLoss – rxPointingLoss – pathLoss – …
absorptionLoss – geoScaLoss – mieScaLoss – Preq;
disp("Link margin for "+num2str(link.Type)+" is "+num2str(linkMargin)+" dB") %Load the Aircraft Trajectory
aircraft = load("aircraftTT.mat");
aircraft1 = load("aircraftTT1.mat");
aircraft2 = load("aircraftTT2.mat");
aircraft3 = load("aircraftTT3.mat");
aircraft4 = load("aircraftTT4.mat");
geoplot(aircraft.aircraftTT.AircraftLLA(:,1), aircraft.aircraftTT.AircraftLLA(:,2), "b–",…
aircraft1.aircraftTT1.AircraftLLA1(:,1), aircraft1.aircraftTT1.AircraftLLA1(:,2), "r–",…
aircraft2.aircraftTT2.AircraftLLA2(:,1), aircraft2.aircraftTT2.AircraftLLA2(:,2), "k–",…
aircraft3.aircraftTT3.AircraftLLA3(:,1), aircraft3.aircraftTT3.AircraftLLA3(:,2), "g–",…
aircraft4.aircraftTT4.AircraftLLA4(:,1), aircraft4.aircraftTT4.AircraftLLA4(:,2), "c–"…
)
title("Aircrafts Trajectories")
%legend(‘show’);
geobasemap streets
geolimits([38.0887 38.1099],[140.6330 140.6660])
%Load the Vehicles path
gx = geoaxes;
geolimits(gx,[38.0887,38.1099],[140.6330,140.6660]);
title("Vehicles Path")
geobasemap(gx,"streets");
useManualInput = true;
if useManualInput
waypoints = zeros(15,3); %#ok<*UNRCH>
numWaypoints = size(waypoints,1);
[lat,lon] = ginput(numWaypoints);
waypoints = [lat lon ones(numWaypoints,1)];
else
waypoints = […
42.265387594440746, -71.566584651695194, 1; …
42.263702708358274, -71.569614091115028, 1; …
42.262575723421115, -71.573098554161504, 1; …
42.260479485544565, -71.575579488911472, 1; …
42.258318030927740, -71.578013745650068, 1; …
42.256140084405871, -71.580417734892066, 1; …
42.253864954613888, -71.582655636609843, 1; …
42.251556291741792, -71.584827877421873, 1; …
42.249201618438420, -71.586910025508118, 1; …
42.246834381495290, -71.588965321234468, 1; …
42.244433745609328, -71.590949233154006, 1; …
42.242264173597064, -71.593357390593781, 1; …
42.240253137371795, -71.596002172990822, 1; …
42.238444180204169, -71.598914079082988, 1; …
42.236661606027504, -71.601851940116248, 1; …
42.235163293921218, -71.605069451220544, 1; …
42.233664981814933, -71.608286962324840, 1; …
42.232221634012099, -71.611549664950445, 1; …
42.230785995268782, -71.614818705946718, 1; …
42.229491349524331, -71.618178093198480, 1; …
42.228523450114722, -71.621746854731867, 1; …
42.227591252458026, -71.625329339092573, 1; …
42.227021657836168, -71.629051198649620, 1; …
42.226488781649429, -71.632774933477108, 1; …
42.226848543969822, -71.636544256810438, 1; …
42.227208306290223, -71.640313580143768, 1; …
42.227475917842995, -71.644096605079170, 1; …
42.227730436282329, -71.647881576787640, 1; …
42.227949419806208, -71.651670397035346, 1; …
42.228142838835581, -71.655461985995458, 1; …
42.228336257864946, -71.659253574955557, 1];
lat = waypoints(:,1);
lon = waypoints(:,2);
end
geoplot(gx,lat,lon,’r-‘)
title("Vehicles Path")
% Find the distance in statute miles between each waypoint
distInDeg = distance(waypoints(1:end-1,1:2),waypoints(2:end,1:2)); % deg
distInSM = deg2sm(distInDeg); % statute miles
carSpeed = 70 / 3600; % miles per second
timeOfArrival = [0; cumsum(distInSM) / carSpeed]; % seconds
carPath = geoTrajectory(waypoints,timeOfArrival);
%Define Mission Start Date and Duration"Creating the scenario"
mission.StartDate = datetime(2024,7,24,02,42,30,TimeZone="UTC");
mission.StopTime = mission.StartDate + hours(2) + minutes(2);
mission.Duration = hours(2); %should remove, I’ve already defined above
sampleTime = 10;
mission.scenario = satelliteScenario(mission.StartDate, mission.StopTime,sampleTime);
mission.viewer = satelliteScenarioViewer(mission.scenario);
%Add Vehicles to a Satellite Scenario
car = platform(mission.scenario,carPath, Name = "Car");
%Add Aircrafts to a Satellite Scenario
aircraft = platform(mission.scenario,aircraft.aircraftTT,Name="Aircraft1", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft);
aircraft1 = platform(mission.scenario,aircraft1.aircraftTT1,Name="Aircraft2", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft1);
aircraft2 = platform(mission.scenario,aircraft2.aircraftTT2,Name="Aircraft3", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft2);
aircraft3 = platform(mission.scenario,aircraft3.aircraftTT3,Name="Aircraft4", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft3);
aircraft4 = platform(mission.scenario,aircraft4.aircraftTT4,Name="Aircraft5", Visual3DModel="NarrowBodyAirliner.glb")
%camtarget(mission.viewer,aircraft4);
%Add a Starlink Constellation to the Satellite Scenario-TLE files
sat = satellite(mission.scenario,"2024-001B.tle", Visual3DModel="SmallSat.glb");
show(sat)
groundTrack(sat,"LeadTime",1200);
%Aircraft-to-Vehicles Access Analysis
Preq = -35.5; % Required signal power in dBm
Ptx = 17.5; % Transmitted power in dBm
% Configure the ground station, satellites, and link characteristics
% Set the ground station characteristics with parabolic telescope
%gs = struct;
car.Height = 1; % Height above the mean sea level in km
car.OpticsEfficiency = 0.8; % Optical antenna efficiency
car.ApertureDiameter = 1; % Antenna aperture diameter in m
car.PointingError = 1e-6; % Pointing error in rad
% Set the satellite A characteristics with parabolic telescope
aircraft = struct;
aircraft.Height = 550; % Height above the mean sea level in km
aircraft.OpticsEfficiency = 0.8; % Optical antenna efficiency
aircraft.ApertureDiameter = 0.07; % Antenna aperture diameter in m
aircraft.PointingError = 1e-6; % Pointing error in rad
% Set the link characteristics
link = struct;
link.Wavelength = 1550e-9; % m
link.TroposphereHeight = 20; % km (Typically ranges from 6-20 km)
link.ElevationAngle = 50; % degrees
%link.Type = "downlink"; % "downlink"|"inter-satellite"|"uplink"
% When the Type field is set to "uplink" or "downlink", you must specify
% the CloudType field, as defined in [5] table 1
link.CloudType = "Thin cirrus";
tx = transmitter(car);
rx = receiver(aircraft,MountingAngles=[0; 180; 0]);
% Calculate transmitter and receiver gain
txGain = (pi*tx.ApertureDiameter/link.Wavelength)^2;
Gtx = 10*log10(txGain); % in dB
rxGain = (pi*rx.ApertureDiameter/link.Wavelength)^2;
Grx = 10*log10(rxGain); % in dB
% Calculate transmitter and receiver pointing loss in dB
txPointingLoss = 4.3429*(txGain*(tx.PointingError)^2);
rxPointingLoss = 4.3429*(rxGain*(rx.PointingError)^2);
absorptionLoss = 0.01; % Absorption loss in dB
% Calculate the distance of the optical beam that propagates through
% the troposphere layer of the atmosphere in km
dT = (link.TroposphereHeight – gs.Height).*cscd(link.ElevationAngle);
% Calculate the slant distance for uplink and downlink between
% satellite A and the ground station for circular orbit in m
dGS = slantRangeCircularOrbit(link.ElevationAngle,satA.Height*1e3,gs.Height*1e3);
% Calculate free-space path loss between the ground station and
% satellite in dB
pathLoss = fspl(dGS,link.Wavelength);
% Calculate loss due to geometrical scattering
% cnc – cloud number concentration in cm-3
% lwc – Liquid water content in g/m-3
[cnc,lwc] = getCloudParameters(link.CloudType);
visibility = 1.002/((lwc*cnc)^0.6473); % Calculate visibility in km
% Get particle size related coefficient
if visibility<=0.5
delta = 0;
elseif visibility>0.5 && visibility<=1
delta = visibility – 0.5;
elseif visibility>1 && visibility<=6
delta = 0.16*visibility + 0.34;
elseif visibility>=6 && visibility<=50
delta = 1.3;
else % visibility>50
delta = 1.6;
end
geoCoeff = (3.91/visibility)* …
((link.Wavelength*1e9/550)^-delta); % Extinction coefficient
geoScaLoss = 4.3429*geoCoeff*dT; % Geometrical scattering loss in dB
% Calculate loss due to Mie scattering
lambda_mu = link.Wavelength*1e6; % Wavelength in microns
% Calculate empirical coefficients
a = (0.000487*(lambda_mu^3)) – (0.002237*(lambda_mu^2)) + …
(0.003864*lambda_mu) – 0.004442;
b = (-0.00573*(lambda_mu^3)) + (0.02639*(lambda_mu^2)) – …
(0.04552*lambda_mu) + 0.05164;
c = (0.02565*(lambda_mu^3)) – (0.1191*(lambda_mu^2)) + …
(0.20385*lambda_mu) – 0.216;
d = (-0.0638*(lambda_mu^3)) + (0.3034*(lambda_mu^2)) – …
(0.5083*lambda_mu) + 0.425;
mieER = a*(gs.Height^3) + b*(gs.Height^2) + …
c*(gs.Height) + d; % Extinction ratio
mieScaLoss = (4.3429*mieER)./sind(link.ElevationAngle); % Mie scattering loss in dB
% Calculate link margin for uplink or downlink in dB
linkMargin = Ptx + 10*log10(tx.OpticsEfficiency) + …
10*log10(rx.OpticsEfficiency) + Gtx + Grx – …
txPointingLoss – rxPointingLoss – pathLoss – …
absorptionLoss – geoScaLoss – mieScaLoss – Preq;
disp("Link margin for "+num2str(link.Type)+" is "+num2str(linkMargin)+" dB") satellite, car MATLAB Answers — New Questions
Vlookup and search partial value as condition
Hi everyone,
hopefully anyone of you can help me with the following problem.
I want the value of B10, B11, B12 etc. of Sheet1 be filled in in cell F10, F11, F12 etc. on Worksheet, when cell K10 of Worksheet matches (complete of partial) with cell A10, A11, A12 etc. on Sheet1.
Worksheet:
Sheet1:
I have tried several formulas, such as =ALS.FOUT(VERT.ZOEKEN($K10;Blad1!A:E;5;ONWAAR);”Niet gevonden”) and =VERT.ZOEKEN(E21;Blad1!A12:E357;Blad1!D12:D357), but both don’t work.
Hopefully you can tell me what i do wrong.
Thanks in advance.
With regards,
Diana
Hi everyone, hopefully anyone of you can help me with the following problem. I want the value of B10, B11, B12 etc. of Sheet1 be filled in in cell F10, F11, F12 etc. on Worksheet, when cell K10 of Worksheet matches (complete of partial) with cell A10, A11, A12 etc. on Sheet1. Worksheet:Sheet1:I have tried several formulas, such as =ALS.FOUT(VERT.ZOEKEN($K10;Blad1!A:E;5;ONWAAR);”Niet gevonden”) and =VERT.ZOEKEN(E21;Blad1!A12:E357;Blad1!D12:D357), but both don’t work. Hopefully you can tell me what i do wrong. Thanks in advance.With regards,Diana Read More
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I cannot find the SessionID in Sentinel anywhere else than in CloudAppEvents. Is this expected? How are we supposed to investigate stolen sessions without the sessionId information in Sentinel?
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I need to duplicate rows (without VBA), based on a cell value (column C in the example below). I found the formula below and it works in duplicating the rows, but I need the rows in the original order, not as per the actual results below:
Formula:
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Rows to duplicate:
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Expected results:
I need to duplicate rows (without VBA), based on a cell value (column C in the example below). I found the formula below and it works in duplicating the rows, but I need the rows in the original order, not as per the actual results below: Formula:=SORT(CHOOSEROWS(B3:C7, TOCOL(IFS(C3:C7>=SEQUENCE(, MAX(C3:C7),,1), SEQUENCE(ROWS(B3:B7))), 2))) Rows to duplicate: Actual Results: Expected results: Read More
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Both when restarting the app and when refreshing, only the same news articles that are several weeks old are displayed in the feed. When the app is reinstalled, however, new, up-to-date articles appear in the news feed on the start page. After closing and reopening the app a few times, the problem occurs again: Only outdated articles are displayed. Read More
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https://daimond-ern.com/15-best-refer-and-earn-apps-in-india-for-2024/
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Looking back on FY24: from Copilots empowering human achievement to leading AI Transformation
A year ago in July, Microsoft coined the term AI Transformation. It is almost hard to imagine at that time Copilots were not generally available and Azure OpenAI Service had only been available for six months. As Satya Nadella, our Chairman and CEO, stated in last quarter’s earnings: 60% of the Fortune 500 have adopted Copilots and 65% use Azure OpenAI Service. Today, Copilots define how AI is empowering human achievement.
Together with our customers and partners, we have established how AI Transformation enriches employee experiences, reinvents customer engagement, reshapes business processes and bends the curve on pragmatic innovation. Our approach to put a Copilot on every desk for every role, identify AI design patterns and build a strong cybersecurity foundation is helping organizations harness AI responsibly, securely and with purpose.
As I looked back on the year, I enjoyed reviewing hundreds of customer and partner examples from around the world that demonstrated how AI Transformation delivered tangible business outcomes and value for their organizations. I am pleased to highlight 20 of those stories that I found to be the most inspirational and illustrative of what we can achieve together.
Audi is taking in-car voice control to the next level by integrating ChatGPT into its MIB 3 infotainment system using Microsoft Azure OpenAI Service, making driving safer and more enjoyable. With this update, Audi’s voice assistant will become even more intuitive, handling complex questions and offering a truly conversational experience for drivers and passengers using natural language. Starting this month, nearly two million Audi models made since 2021 are being upgraded with this generative AI-powered experience, allowing drivers to more easily control infotainment, navigation and air-conditioning systems, as well as ask general knowledge questions.
To help address Taiwan’s chronic shortage of health care workers, Chi Mei Medical Center developed AI copilots built with Microsoft Azure OpenAI Service to help overworked staff lighten their workloads while ensuring patient safety. With copilots implemented throughout its operations and at multiple patient touchpoints, medical center staff are seeing impressive results. Doctors now spend 15 minutes instead of an hour writing medical reports, and nurses can document patient information in under 5 minutes per patient instead of 10 to 20 minutes. Pharmacists are now able to double the number of patients they see per day — from 15 to 30 — by leveraging a copilot for comprehensive clinical summaries across multiple databases. By bending the curve on innovation and deploying these copilots, staff have more time to focus on patient care and outcomes, while reducing burnout and stress.
Coles, a leading Australian supermarket chain, is transforming the retail experience by leveraging AI to deepen its relationships with shoppers and improve efficiency in its stores. Coles has deployed AI models that predict the flow of 20,000 stock keeping units to 850 stores with remarkable accuracy. They are now generating 1.6 billion predictions daily, ensuring every shopper finds exactly what they are looking for when they need it. Using an AI model, Coles is also reinventing customer engagement by identifying customer patterns to provide its more than 4 million loyalty club customers with bespoke product recommendations weekly. The company is also utilizing Microsoft Azure Stack HCI and NVIDIA’s A16 GPUs to streamline checkout processes, enhance queue monitoring and elevate customer satisfaction by significantly reducing wait times.
In its quest to transform aquaculture in Indonesia, eFishery harnessed the power of Microsoft Azure OpenAI Service to create Mas Ahya, a generative AI assistant available through a mobile application. Mas Ahya equips farmers with aquaculture expertise right at their fingertips, enabling them to monitor water quality, estimate market prices and manage feeding schedules effectively. It also offers real-time insights and recommendations to maintain ideal pond conditions for more precise feeding — shortening the time from fry to market size fish from four to three-and-a-half months. By utilizing this AI assistant for queries on a range of topics — from plankton levels to remedies for diseases or bacteria affecting their stock — farmers have seen shrimp survival rates soar from 60% to 90%, further boosting their shrimp export capacity.
EY is using Copilot for Microsoft 365 to help clients adopt an AI-powered approach to tackle their unique tax, finance and operational challenges; boost efficiency; and get more value from their data. Reconciling and combining large amounts of data faster and more accurately is a game changer for Finance and Tax, driving a differentiated experience and opportunity to deliver these capabilities through EY’s core platforms and solutions. The ability to apply Copilot to vast swaths of data to develop insights helps drive smarter decision-making across the business. For example, EY professionals are seeing productivity gains of up to 14 hours per week using an AI digital assistant to automate routine tasks, freeing up time for more strategic work. The initial rollout of Copilot was so positive that EY is now scaling it to 150,000 of its employees and helping clients drive their own transformations using EY’s Customer Zero copilot accelerators.
Global design, engineering and environmental services leader GHD is leveraging Copilot for Microsoft 365 to enrich the employee experience and transform its operations to help tackle some of the world’s most complex design and engineering challenges for its clients. GHD employees can now respond to client requests more quickly by using Copilot, reducing the time spent reviewing new requests for proposals from hours to just 15 minutes. More broadly, a recent survey of Copilot users at GHD revealed that 41% are saving time in their workday, with 29% saving more than 30 minutes and 12% saving more than an hour. Additionally, 75% of users feel more efficient and 45% find their work more rewarding when using Copilot.
Hanover Research faced the challenge of efficiently processing vast amounts of data to deliver timely insights to its clients. To tackle this, the custom market research and analytics provider partnered with Neudesic to create the Hanover Intelligent Virtual Engine (HIVE), a customized AI-powered research tool using Azure OpenAI Service that eliminates the need for analysts to manually comb through its vast repository of documents. HIVE helps analysts identify insights up to 10 times faster and creates more opportunities to provide clients with information that would otherwise be buried in data and unavailable. By leveraging Azure AI capabilities to reshape business processes, Hanover has significantly boosted its research efficiency, enabling it to provide more accurate and timely data-driven insights to its clients.
Lumen Technologies was among the first companies to invest broadly in Copilot, making Copilot for Microsoft 365 available last August. Since then, the company has expanded its use of Microsoft Copilot across its organization to empower its people by simplifying workflows and inspiring a forward-thinking mindset. Today, Copilot for Sales is saving its 3,000-plus sellers an average of four hours a week, equating to $50 million annually. Sellers can now spend more time with customers, improve their work-life balance and fundamentally change the way they work.
As an integrated risk assessment firm operating globally, Moody’s Corporation has harnessed the power of AI through Azure OpenAI Service, Microsoft Fabric and Microsoft Teams to significantly enhance productivity and insights. Through our co-innovation efforts, we built an internal tool — Moody’s Copilot — that enables employees to quickly synthesize vast amounts of research and data, and 94% of users reported increased productivity. Additionally, the launch of Moody’s Research Assistant allows customers to generate new insights from extensive credit research, data and analytics, potentially saving users over 25% of their time on typical financial analyst tasks.
OCBC bank is empowering its global workforce with generative AI-powered solutions to boost productivity and enhance customer engagement. Built upon Azure OpenAI, the bank developed a digital assistant for use within a secure and controlled Microsoft Teams environment to help employees in their daily work across customer service, research, product management and marketing roles. Since using the tool, employees have reduced time spent on tasks such as writing, research, translation and ideation by approximately 50%, with 72% of team members reporting significant improvements in their day-to-day productivity and more time to focus on customers.
As one of the largest combined natural gas and electric companies in the U.S., Pacific Gas & Electric Company (PG&E) had an ambitious goal to reshape its business processes by automating low-value tasks and rededicating employees to focus on high-value work. By implementing Microsoft Power Platform solutions, its Digital Creators and citizen developers created new business solutions, saving nearly 527,000 hours and generating approximately $75 million in savings annually. Using Microsoft Copilot Studio, the company also built a digital assistant that fulfills 25% to 40% of help desk demands, significantly optimizing agent workloads and providing sizeable labor savings and service-level improvements — saving teams 840 hours and the company more than $1.1 million per year.
As a leading insurance and reinsurance company in Peru — and one of the largest in Latin America — Pacífico Seguros partnered with TC1 Labs and leveraged Microsoft AI, Microsoft 365 and Azure AI solutions to reshape business processes, improve data management and enhance customer interactions. Pacífico Seguros reported a 40% increase in operational efficiency and a 30% reduction in response times to customer inquiries. It also implemented Microsoft Copilot for Security with a Zero Trust approach, drastically boosting responses to cyber threats and delivering ongoing protection of critical systems and data. This AI Transformation has improved service quality and enhanced security across its business.
Paysafe, a leader in specialized payments, supports 260 payment types in more than 40 currencies and enables seamless transactions for businesses and consumers worldwide. By leveraging Copilot for Microsoft 365 the company has transformed its operations to ensure secure and efficient processing across different payment methods. Paysafe uses Copilot to address the challenges of a diverse and multilingual global workforce, using it to translate everything into various languages — document policies, standard operating procedures and meetings — to save both time and money. Compared to preparing documentation from scratch or searching for information manually, Paysafe’s IT team saves between 10% and 50% of their time with Copilot.
Fintech company Saphyre creates real-time client account reconciliation and management solutions for institutional parties involved in financial trading. The company is using Microsoft Azure to provide intelligent, cloud-based solutions that automate and streamline complex financial trading workflows and modernize time-consuming processes efficiently and securely. Using Azure AI, Saphyre built a solution that helped clients reduce manual paperwork by 75%. It is also sharing data more securely using Microsoft security products so clients can be ready to trade three to five times faster compared to manual onboarding. This results in increased revenue opportunities for clients by completing trades more quickly and at better prices.
Softchoice has partnered with Microsoft to help companies drive efficiencies, reinvent customer engagement and address cybersecurity threats. By implementing Copilot for Microsoft 365, Softchoice has achieved significant productivity gains, reducing time spent summarizing technical meetings by 97%, creating internal training modules by 70% and developing customer-facing technical content by 62% to 67%. With Microsoft Copilot for Security built into daily dashboards, the company’s analysts are saving 20% to 30% of their time summarizing pertinent information needed to execute tasks. Analysts can more quickly determine if failed sign-ins are routine or accidental, saving 20-30 minutes each morning and freeing up time to focus on those that require more in-depth analysis. Security incidents can now be audited faster, reducing the time and resources needed to manually review by 50% and improving efficiency by 30% to 40%.
TomTom, a global leader in mapping and navigation technology, faced the challenge of a fragmented development process with too much time spent navigating multiple tools. By centralizing on the GitHub platform and integrating GitHub Copilot into its development processes, TomTom has streamlined workflows to deliver products to customers more quickly than ever. Additionally, 85% of developers report feeling more productive and 70% feel they can focus on more satisfying work by reducing cognitive workloads and enhancing collaboration among development teams.
Australia’s Torrens University set a goal to transform its digital learning environment to deliver a superior online student experience. It developed MyLearn — a modern online platform built with Azure OpenAI Service — to provide a consistent and convenient learning environment for students anytime, anywhere. By leveraging generative AI, the university has standardized and improved course curriculums, saving an impressive 20,000 hours and $2.4 million Australian dollars in time and resources. This AI-driven approach not only streamlined its digital learning environment but also set the stage for future savings and agile curriculum updates, creating a more engaging and efficient educational experience for students.
Unilever, a global leader in consumer products across over 190 countries, is working with Microsoft to revolutionize scientific discovery and positively impact the 3.4 billion people it serves daily. With Copilot and the advanced simulation capabilities of Azure Quantum Elements, Unilever can query scientific information using natural language, performing thousands of computational simulations in the time it would take to run tens of laboratory experiments. This technological leap, combined with its vast repository of proprietary data and a century of expertise in personal and household care, enables Unilever’s scientists to lead the industry in developing the next generation of eco-friendly household and personal products through sustainable product development.
As a leading provider of workplace benefits and services, Unum Group set out to enrich the employee experience by modernizing the manual and time-consuming process of retrieving policy information for inquiries from its client support center. With Azure OpenAI Service, the company developed an application that searches 1.3 terabytes of data with 95% accuracy, cutting response times to four to five seconds and significantly improving efficiency and customer satisfaction. The AI-generated results address 75% of contract-related questions, freeing up employee time for personalized solutions and interactions with clients. Employees trust the tool and feel it has improved their jobs, and now more than 90% of support center employees are using it.
Visma develops and tests software for over 1.8 million customers across Europe and Latin America through its 188 individual companies. Facing high inflation and tight labor markets, it turned to Microsoft AI technologies such as Azure OpenAI Service, Semantic Kernel and Azure AI Search to securely boost efficiency and customer satisfaction across its companies — each with their own customers and domain. Visma developers are using GitHub Copilot to streamline development processes, automate workflows and enhance collaboration. As a result, it has reported up to 50% reduction in development times. GitHub Copilot has also helped Visma developers unleash their creativity and bend the curve on innovation; the company has seen a marked increase in innovation since adopting the solution, even among those who have been working on the same code for 30 years.
Our mission has never been clearer: to empower every person and every organization on the planet to achieve more. We remain committed to democratizing intelligence and unlocking AI opportunities industry by industry in the year ahead by bringing Copilot to life on every device and across every role. We are focused on our co-innovation efforts to identify AI design patterns that will enable our customers and partners to build out their AI innovation environments. We are committed to helping you fortify your cybersecurity foundation by prioritizing security above all else — securing our products by design, by default and within our own operations as part of our Secure Future Initiative. As we continue leading in AI Transformation, we remain rooted in the fact we are at our best when we serve others, and I look forward to what we will accomplish together in the year ahead as your trusted cloud and AI partner.
The post Looking back on FY24: from Copilots empowering human achievement to leading AI Transformation appeared first on The Official Microsoft Blog.
A year ago in July, Microsoft coined the term AI Transformation. It is almost hard to imagine at that time Copilots were not generally available and Azure OpenAI Service had only been available for six months. As Satya Nadella, our Chairman and CEO, stated in last quarter’s earnings: 60% of the Fortune 500 have adopted…
The post Looking back on FY24: from Copilots empowering human achievement to leading AI Transformation appeared first on The Official Microsoft Blog.Read More
Why do I receive the error “Failed to open file” when I read an AVI file using AVIREAD command on MATLAB 7.8 (R2009a)?
I have an image processing program that I have written that has worked fine for the last 6-8 months, and suddenly last week it stopped working. I have tracked the problem down to AVI file handling. I can no longer open them. I have tried rebooting the machine and reinstalling MATLAB. But nothing seems to work. I have ensured that I have permissions to read and write on the file as well as the directory in which the file resides. Using the following commands, I get the following errors, it happens for any filename, whether the file has been used before or not:
>> clear all
>> clear mex
>> test = avifile(‘avitest.avi’)
ERROR: ??? Error using ==> avi
Failed to open file.
Error in ==> avifile.avifile at 173
aviobj.FileHandle = avi(‘open’,filename);
>> mov=aviread(‘Filename.avi’)
ERROR: ??? Error using ==> aviread at 76
Unable to open file.I have an image processing program that I have written that has worked fine for the last 6-8 months, and suddenly last week it stopped working. I have tracked the problem down to AVI file handling. I can no longer open them. I have tried rebooting the machine and reinstalling MATLAB. But nothing seems to work. I have ensured that I have permissions to read and write on the file as well as the directory in which the file resides. Using the following commands, I get the following errors, it happens for any filename, whether the file has been used before or not:
>> clear all
>> clear mex
>> test = avifile(‘avitest.avi’)
ERROR: ??? Error using ==> avi
Failed to open file.
Error in ==> avifile.avifile at 173
aviobj.FileHandle = avi(‘open’,filename);
>> mov=aviread(‘Filename.avi’)
ERROR: ??? Error using ==> aviread at 76
Unable to open file. I have an image processing program that I have written that has worked fine for the last 6-8 months, and suddenly last week it stopped working. I have tracked the problem down to AVI file handling. I can no longer open them. I have tried rebooting the machine and reinstalling MATLAB. But nothing seems to work. I have ensured that I have permissions to read and write on the file as well as the directory in which the file resides. Using the following commands, I get the following errors, it happens for any filename, whether the file has been used before or not:
>> clear all
>> clear mex
>> test = avifile(‘avitest.avi’)
ERROR: ??? Error using ==> avi
Failed to open file.
Error in ==> avifile.avifile at 173
aviobj.FileHandle = avi(‘open’,filename);
>> mov=aviread(‘Filename.avi’)
ERROR: ??? Error using ==> aviread at 76
Unable to open file. MATLAB Answers — New Questions
How do I set up MATLAB Web Server using Apache as an httpd server on Windows NT?
How do I set up MATLAB Web Server using Apache as an httpd server on Windows NT?How do I set up MATLAB Web Server using Apache as an httpd server on Windows NT? How do I set up MATLAB Web Server using Apache as an httpd server on Windows NT? apache, windows, nt, web, server MATLAB Answers — New Questions
When I log into MATLAB Grader, I am asked whether I am an instructor or student, with a message saying I do not yet have access. How do I gain access to MATLAB Grader?
When I log into MATLAB Grader, I am asked whether I am an instructor or student, with a message saying I do not yet have access. How do I gain access to MATLAB Grader?When I log into MATLAB Grader, I am asked whether I am an instructor or student, with a message saying I do not yet have access. How do I gain access to MATLAB Grader? When I log into MATLAB Grader, I am asked whether I am an instructor or student, with a message saying I do not yet have access. How do I gain access to MATLAB Grader? student, instructor, enroll, sms, license, login, grader, subscription MATLAB Answers — New Questions
Need some help generating White Noise Source
clear all
close all
clc
tic
L=10; %[m] Fiber length
PS=1;
n=1.45;
eps0=8.854e-12; % [F/m] Vacuum permittivity
mu0 = 4*pi*1e-7;%[H/m] Vacuum permeability
c=2.9979e8; % [m/sec] Speed of light
Z0=sqrt(mu0/eps0); %[Ohm] Vacuum impedance
dt = 6e-12; dz=dt*c/n; %Spacial and Temporal step sizes.
%dz=2.5e-4;
Fs=1/dt;
N=round(L/dz); % Fiber length discretization
T=10*2*L*n/c; %time taken for 10 round trips
Nt=round(T/dt);
%% material characteristics
A=80e-12; %[m^2] fiber’s effective area
I1_0=PS/A;
% figure;
FA=(-Nt/2:Nt/2-1)*Fs/Nt;
fc=3e9;
X = randn(1,Nt); %random noise generation
Y1 = 10*lowpass(X, fc, Fs, Steepness=0.8);
ypm1 = 1.2743e+06*exp(1i*Y1);
figure;
plot(FA,abs(fftshift(fft(Y1/Nt))));
xlim([-20e9 20
e9]);
figure;
semilogy(FA,(2*n*c*eps0*A*abs(fftshift(fft(ypm1/Nt).^2))))
I am trying to generate White noise source for my phase modulation technique.
The procedure is to pass the signal through a rectangular band filter to imprint the sinc envelope onto the waveform.
Likewise I have generated random noise and passed through low pass filter of 3GHz but the fft of the product is not upto the figure.
Can anyone suggest any changes.clear all
close all
clc
tic
L=10; %[m] Fiber length
PS=1;
n=1.45;
eps0=8.854e-12; % [F/m] Vacuum permittivity
mu0 = 4*pi*1e-7;%[H/m] Vacuum permeability
c=2.9979e8; % [m/sec] Speed of light
Z0=sqrt(mu0/eps0); %[Ohm] Vacuum impedance
dt = 6e-12; dz=dt*c/n; %Spacial and Temporal step sizes.
%dz=2.5e-4;
Fs=1/dt;
N=round(L/dz); % Fiber length discretization
T=10*2*L*n/c; %time taken for 10 round trips
Nt=round(T/dt);
%% material characteristics
A=80e-12; %[m^2] fiber’s effective area
I1_0=PS/A;
% figure;
FA=(-Nt/2:Nt/2-1)*Fs/Nt;
fc=3e9;
X = randn(1,Nt); %random noise generation
Y1 = 10*lowpass(X, fc, Fs, Steepness=0.8);
ypm1 = 1.2743e+06*exp(1i*Y1);
figure;
plot(FA,abs(fftshift(fft(Y1/Nt))));
xlim([-20e9 20
e9]);
figure;
semilogy(FA,(2*n*c*eps0*A*abs(fftshift(fft(ypm1/Nt).^2))))
I am trying to generate White noise source for my phase modulation technique.
The procedure is to pass the signal through a rectangular band filter to imprint the sinc envelope onto the waveform.
Likewise I have generated random noise and passed through low pass filter of 3GHz but the fft of the product is not upto the figure.
Can anyone suggest any changes. clear all
close all
clc
tic
L=10; %[m] Fiber length
PS=1;
n=1.45;
eps0=8.854e-12; % [F/m] Vacuum permittivity
mu0 = 4*pi*1e-7;%[H/m] Vacuum permeability
c=2.9979e8; % [m/sec] Speed of light
Z0=sqrt(mu0/eps0); %[Ohm] Vacuum impedance
dt = 6e-12; dz=dt*c/n; %Spacial and Temporal step sizes.
%dz=2.5e-4;
Fs=1/dt;
N=round(L/dz); % Fiber length discretization
T=10*2*L*n/c; %time taken for 10 round trips
Nt=round(T/dt);
%% material characteristics
A=80e-12; %[m^2] fiber’s effective area
I1_0=PS/A;
% figure;
FA=(-Nt/2:Nt/2-1)*Fs/Nt;
fc=3e9;
X = randn(1,Nt); %random noise generation
Y1 = 10*lowpass(X, fc, Fs, Steepness=0.8);
ypm1 = 1.2743e+06*exp(1i*Y1);
figure;
plot(FA,abs(fftshift(fft(Y1/Nt))));
xlim([-20e9 20
e9]);
figure;
semilogy(FA,(2*n*c*eps0*A*abs(fftshift(fft(ypm1/Nt).^2))))
I am trying to generate White noise source for my phase modulation technique.
The procedure is to pass the signal through a rectangular band filter to imprint the sinc envelope onto the waveform.
Likewise I have generated random noise and passed through low pass filter of 3GHz but the fft of the product is not upto the figure.
Can anyone suggest any changes. wns, fft, sinc MATLAB Answers — New Questions
Cannot enable this option in Teams on RDS Server
Hello
Please i need your help on this issue.
I have a Customer that cannot enable this option in teams on RDS server “Register the New Teams as the chat app for Microsoft 365”.
Hello Please i need your help on this issue. I have a Customer that cannot enable this option in teams on RDS server “Register the New Teams as the chat app for Microsoft 365”. Read More
