August 2024 | Application Package Repository Telkom University

Month: August 2024

access teams meeting recording

/ 2024-08-01 / Microsoft

I have attached the screenshoot of meeting recording and empty get response

Is there any pre-requirements to get the recording and transcript through api? Thanks

Hi, I have question about access teams meeting recording through API. I accessed to azure app and got a usr token with permission onlineMeetings.ReadWrite and OnlineMeetingRecording.Read.All. I recorded the meeting and I saw available recording download on chat channel. But I cannot see any recording info when I get recording through api. I have attached the screenshoot of meeting recording and empty get responseIs there any pre-requirements to get the recording and transcript through api? Thanks Read More

Last time a specific data appear

/ 2024-08-01 / Microsoft

Hello everyone

I have a table where I keep track of which supplement my birds have got. In column A is the date the bird received the supplement. Column B is the type of supplement it got. Column C tells how many days there need to be inbetween two uses of this type of supplement. Column D gives the first date this supplement can be used again.

This is an simplified version of my table:

Date of use Type of supplement Time inbetween uses (in days) Date next use

01/04/2024 Supplement A 7 08/04/2024

03/04/2024 Supplement B 1 04/04/2024

04/04/2024 Supplement C 30 04/05/2024

06/04/2024 Supplement A 7 13/04/2024

07/04/2024 Supplement B 1 08/04/2024

09/04/2024 Supplement D 14 23/04/2024

12/04/2024 Supplement C 30 12/05/2024

As you see I used supplement A on the first of April 2024 and I need to wait 7 days before I can give it again, so I can give it again on the Eight of April 2024. But as you see, I have made a mistake and I give supplement A again on the Sixth of April 2024, which is two days too early. Now I want that the cell “06/04/2024” turns red because I use the supplement too early again. So I want to use conditional formatting in this case. I want to write a formula that excel searches the previous use of the supplement used in this line and than takes the value on the intersection of this row and the column D ‘Date next use’ and compare this with the ‘Date of use’ of the current row.

Now the problem I am having is that I can’t find out how to write the formula to find ‘the last use of a supplement’. Can anyone help me out please?

A big thank you in advance

Benjamin Herremans

Hello everyone I have a table where I keep track of which supplement my birds have got. In column A is the date the bird received the supplement. Column B is the type of supplement it got. Column C tells how many days there need to be inbetween two uses of this type of supplement. Column D gives the first date this supplement can be used again.This is an simplified version of my table:Date of use Type of supplement Time inbetween uses (in days) Date next use01/04/2024 Supplement A 7 08/04/202403/04/2024 Supplement B 1 04/04/202404/04/2024 Supplement C 30 04/05/202406/04/2024 Supplement A 7 13/04/202407/04/2024 Supplement B 1 08/04/202409/04/2024 Supplement D 14 23/04/202412/04/2024 Supplement C 30 12/05/2024As you see I used supplement A on the first of April 2024 and I need to wait 7 days before I can give it again, so I can give it again on the Eight of April 2024. But as you see, I have made a mistake and I give supplement A again on the Sixth of April 2024, which is two days too early. Now I want that the cell “06/04/2024” turns red because I use the supplement too early again. So I want to use conditional formatting in this case. I want to write a formula that excel searches the previous use of the supplement used in this line and than takes the value on the intersection of this row and the column D ‘Date next use’ and compare this with the ‘Date of use’ of the current row.Now the problem I am having is that I can’t find out how to write the formula to find ‘the last use of a supplement’. Can anyone help me out please? A big thank you in advanceBenjamin Herremans Read More

Send Email to Email in List on Specific Date

/ 2024-08-01 / Microsoft

Hey everyone! I am new to Power Automate and could use some assistance.

My goal is to automate an email sent to access card holders two weeks prior to the card expiration.

So far, I have a column setup for the correct date for the email to be sent and the recipient’s email, but I am not sure how to automate the email on that date.

Any help would be much appreciated.

Hey everyone! I am new to Power Automate and could use some assistance. My goal is to automate an email sent to access card holders two weeks prior to the card expiration. So far, I have a column setup for the correct date for the email to be sent and the recipient’s email, but I am not sure how to automate the email on that date. Any help would be much appreciated. Read More

cnn-lstm error

/ 2024-08-01 / Matlab

hello everyone
i have error whene i use cnn-lstm
this is the error
Error using trainNetwork (line 191)
Invalid training data. The output size (1024) of the last layer does not match the response size (1).

Error in Main_fn (line 266)
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);

Error in Fig12_generator (line 49)
[Rate_DL,Rate_OPT]=Main_fn(L,My_ar,Mz_ar,M_bar,K_DL,Pt,kbeams(rr),Training_Size);

but whene use cnn onle the code run without error and i make every possible to fix it but it not work and i change the shape of YTrain but still the error
function [Rate_DL,Rate_OPT]=Main_fn(L,My,Mz,M_bar,K_DL,Pt,kbeams,Training_Size)
%% Description:
%
% This is the function called by the main script for ploting Figure 10
% in the original article mentioned below.
%
% version 1.0 (Last edited: 2019-05-10)
%
% The definitions and equations used in this code refer (mostly) to the
% following publication:
%
% Abdelrahman Taha, Muhammad Alrabeiah, and Ahmed Alkhateeb, "Enabling
% Large Intelligent Surfaces with Compressive Sensing and Deep Learning,"
% arXiv e-prints, p. arXiv:1904.10136, Apr 2019.
% [Online]. Available: https://arxiv.org/abs/1904.10136
%
% The DeepMIMO dataset is adopted.
% [Online]. Available: http://deepmimo.net/
%
% License: This code is licensed under a Creative Commons
% Attribution-NonCommercial-ShareAlike 4.0 International License.
% [Online]. Available: https://creativecommons.org/licenses/by-nc-sa/4.0/
% If you in any way use this code for research that results in
% publications, please cite our original article mentioned above.

%% System Model Parameters

params.scenario=’O1_28′; % DeepMIMO Dataset scenario: http://deepmimo.net/
params.active_BS=3; % active basestation(/s) in the chosen scenario
D_Lambda = 0.5; % Antenna spacing relative to the wavelength
BW = 100e6; % Bandwidth

Ut_row = 850; % user Ut row number
Ut_element = 90; % user Ut position from the row chosen above
Ur_rows = [1000 1200]; % user Ur rows

Validation_Size = 6200; % Validation dataset Size
K = 512; % number of subcarriers
miniBatchSize = 500; % Size of the minibatch for the Deep Learning
% Note: The axes of the antennas match the axes of the ray-tracing scenario
Mx = 1; % number of LIS reflecting elements across the x axis
M = Mx.*My.*Mz; % Total number of LIS reflecting elements

% Preallocation of output variables
Rate_DL = zeros(1,length(Training_Size));
Rate_OPT = Rate_DL;
LastValidationRMSE = Rate_DL;

%— Accounting SNR in ach rate calculations
%— Definning Noisy channel measurements
Gt=3; % dBi
Gr=3; % dBi
NF=5; % Noise figure at the User equipment
Process_Gain=10; % Channel estimation processing gain
noise_power_dB=-204+10*log10(BW/K)+NF-Process_Gain; % Noise power in dB
SNR=10^(.1*(-noise_power_dB))*(10^(.1*(Gt+Gr+Pt)))^2; % Signal-to-noise ratio
% channel estimation noise
noise_power_bar=10^(.1*(noise_power_dB))/(10^(.1*(Gt+Gr+Pt)));

No_user_pairs = (Ur_rows(2)-Ur_rows(1))*181; % Number of (Ut,Ur) user pairs
RandP_all = randperm(No_user_pairs).’; % Random permutation of the available dataset

%% Starting the code
disp(‘======================================================================================================================’);
disp([‘ Calculating for M = ‘ num2str(M)]);
Rand_M_bar_all = randperm(M);

%% Beamforming Codebook
% BF codebook parameters
over_sampling_x=1; % The beamsteering oversampling factor in the x direction
over_sampling_y=1; % The beamsteering oversampling factor in the y direction
over_sampling_z=1; % The beamsteering oversampling factor in the z direction

% Generating the BF codebook
[BF_codebook]=sqrt(Mx*My*Mz)*UPA_codebook_generator(Mx,My,Mz,over_sampling_x,over_sampling_y,over_sampling_z,D_Lambda);
codebook_size=size(BF_codebook,2);

%% DeepMIMO Dataset Generation
disp(‘————————————————————-‘);
disp([‘ Calculating for K_DL = ‘ num2str(K_DL)]);
% —— Inputs to the DeepMIMO dataset generation code ———— %
% Note: The axes of the antennas match the axes of the ray-tracing scenario
params.num_ant_x= Mx; % Number of the UPA antenna array on the x-axis
params.num_ant_y= My; % Number of the UPA antenna array on the y-axis
params.num_ant_z= Mz; % Number of the UPA antenna array on the z-axis
params.ant_spacing=D_Lambda; % ratio of the wavelnegth; for half wavelength enter .5
params.bandwidth= BW*1e-9; % The bandiwdth in GHz
params.num_OFDM= K; % Number of OFDM subcarriers
params.OFDM_sampling_factor=1; % The constructed channels will be calculated only at the sampled subcarriers (to reduce the size of the dataset)
params.OFDM_limit=K_DL*1; % Only the first params.OFDM_limit subcarriers will be considered when constructing the channels
params.num_paths=L; % Maximum number of paths to be considered (a value between 1 and 25), e.g., choose 1 if you are only interested in the strongest path
params.saveDataset=0;
disp([‘ Calculating for L = ‘ num2str(params.num_paths)]);

% —————— DeepMIMO "Ut" Dataset Generation —————–%
params.active_user_first=Ut_row;
params.active_user_last=Ut_row;
DeepMIMO_dataset=DeepMIMO_generator(params);
Ht = single(DeepMIMO_dataset{1}.user{Ut_element}.channel);
clear DeepMIMO_dataset

% —————— DeepMIMO "Ur" Dataset Generation —————–%
%Validation part for the actual achievable rate perf eval
Validation_Ind = RandP_all(end-Validation_Size+1:end);
[~,VI_sortind] = sort(Validation_Ind);
[~,VI_rev_sortind] = sort(VI_sortind);
%initialization
Ur_rows_step = 100; % access the dataset 100 rows at a time
Ur_rows_grid=Ur_rows(1):Ur_rows_step:Ur_rows(2);
Delta_H_max = single(0);
for pp = 1:1:numel(Ur_rows_grid)-1 % loop for Normalizing H
clear DeepMIMO_dataset
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
for u=1:params.num_user
Hr = single(conj(DeepMIMO_dataset{1}.user{u}.channel));
Delta_H = max(max(abs(Ht.*Hr)));
if Delta_H >= Delta_H_max
Delta_H_max = single(Delta_H);
end
end
end
clear Delta_H
disp(‘=============================================================’);
disp([‘ Calculating for M_bar = ‘ num2str(M_bar)]);
Rand_M_bar =unique(Rand_M_bar_all(1:M_bar));
Ht_bar = reshape(Ht(Rand_M_bar,:),M_bar*K_DL,1);
DL_input = single(zeros(M_bar*K_DL*2,No_user_pairs));
DL_output = single(zeros(No_user_pairs,codebook_size));
DL_output_un= single(zeros(numel(Validation_Ind),codebook_size));
Delta_H_bar_max = single(0);
count=0;
for pp = 1:1:numel(Ur_rows_grid)-1
clear DeepMIMO_dataset
disp([‘Starting received user access ‘ num2str(pp)]);
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
%% Construct Deep Learning inputs
u_step=100;
Htx=repmat(Ht(:,1),1,u_step);
Hrx=zeros(M,u_step);
for u=1:u_step:params.num_user
for uu=1:1:u_step
Hr = single(conj(DeepMIMO_dataset{1}.user{u+uu-1}.channel));
Hr_bar = reshape(Hr(Rand_M_bar,:),M_bar*K_DL,1);
%— Constructing the sampled channel
n1=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
n2=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
H_bar = ((Ht_bar+n1).*(Hr_bar+n2));
DL_input(:,u+uu-1+((pp-1)*params.num_user))= reshape([real(H_bar) imag(H_bar)].’,[],1);
Delta_H_bar = max(max(abs(H_bar)));
if Delta_H_bar >= Delta_H_bar_max
Delta_H_bar_max = single(Delta_H_bar);
end
Hrx(:,uu)=Hr(:,1);
end
%— Actual achievable rate for performance evaluation
H = Htx.*Hrx;
H_BF=H.’*BF_codebook;
SNR_sqrt_var = abs(H_BF);
for uu=1:1:u_step
if sum((Validation_Ind == u+uu-1+((pp-1)*params.num_user)))
count=count+1;
DL_output_un(count,:) = single(sum(log2(1+(SNR*((SNR_sqrt_var(uu,:)).^2))),1));
end
end
%— Label for the sampled channel
R = single(log2(1+(SNR_sqrt_var/Delta_H_max).^2));
% — DL output normalization
Delta_Out_max = max(R,[],2);
if ~sum(Delta_Out_max == 0)
Rn=diag(1./Delta_Out_max)*R;
end
DL_output(u+((pp-1)*params.num_user):u+((pp-1)*params.num_user)+u_step-1,:) = 1*Rn; %%%%% Normalized %%%%%
end
end
clear u Delta_H_bar R Rn
%– Sorting back the DL_output_un
DL_output_un = DL_output_un(VI_rev_sortind,:);
%— DL input normalization
DL_input= 1*(DL_input/Delta_H_bar_max); %%%%% Normalized from -1->1 %%%%%

%% DL Beamforming
% —————— Training and Testing Datasets —————–%
% Reshape for CNN-LSTM
% Assuming each sample is a sequence of features where each feature vector should be treated as a 1D image (sequence length x 1 x 1)

DL_output_reshaped = reshape(DL_output.’, size(DL_output,2), 1, 1, size(DL_output,1));
DL_output_reshaped_un = reshape(DL_output_un.’, size(DL_output_un,2), 1, 1, size(DL_output_un,1));
DL_input_reshaped = reshape(DL_input, size(DL_input,1), 1, 1, size(DL_input,2));

for dd=1:numel(Training_Size)
disp([‘ Calculating for Dataset Size = ‘ num2str(Training_Size(dd))]);
Training_Ind = RandP_all(1:Training_Size(dd));

% Index the reshaped data for training and validation
XTrain = single(DL_input_reshaped(:,1,:,Training_Ind));
YTrain = single(DL_output_reshaped(:,:,1,Training_Ind));
XValidation = single(DL_input_reshaped(:,1,:,Validation_Ind));
YValidation = single(DL_output_reshaped(:,:,1,Validation_Ind));
YValidation_un = single(DL_output_reshaped_un(:,:,1,:));

%% DL Model definition with adjusted pooling and convolution layers
layers = [
imageInputLayer([size(XTrain,1), 1, 1],’Name’,’input’,’Normalization’,’none’)

convolution2dLayer(3, 64, ‘Padding’, ‘same’, ‘Name’, ‘conv1’)
batchNormalizationLayer(‘Name’, ‘bn1’)
reluLayer(‘Name’, ‘relu1’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool1’)

convolution2dLayer(3, 128, ‘Padding’, ‘same’, ‘Name’, ‘conv2’)
batchNormalizationLayer(‘Name’, ‘bn2’)
reluLayer(‘Name’, ‘relu2’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool2’)

convolution2dLayer(3, 256, ‘Padding’, ‘same’, ‘Name’, ‘conv3’)
batchNormalizationLayer(‘Name’, ‘bn3’)
reluLayer(‘Name’, ‘relu3’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool3’)

flattenLayer(‘Name’, ‘flatten’)
lstmLayer(128, ‘Name’, ‘lstm1’, ‘OutputMode’, ‘sequence’)
lstmLayer(128, ‘Name’, ‘lstm2’, ‘OutputMode’, ‘last’)

fullyConnectedLayer(512, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu4’)
dropoutLayer(0.5, ‘Name’, ‘dropout1’)

fullyConnectedLayer(1024, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu5’)
dropoutLayer(0.5, ‘Name’, ‘dropout2’)

fullyConnectedLayer(2048, ‘Name’, ‘fc3’)
reluLayer(‘Name’, ‘relu6’)
dropoutLayer(0.5, ‘Name’, ‘dropout3’)

fullyConnectedLayer(size(YTrain,3), ‘Name’, ‘fc4’)
regressionLayer(‘Name’, ‘output’)
];

options = trainingOptions(‘rmsprop’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 20, …
‘InitialLearnRate’, 1e-3, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.5, …
‘LearnRateDropPeriod’, 10, …
‘L2Regularization’, 1e-4, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {XValidation, YValidation}, …
‘ValidationFrequency’, 30, …
‘Verbose’, 1, …
‘Plots’, ‘none’, …
‘ExecutionEnvironment’, ‘cpu’);

[~,Indmax_OPT]= max(YValidation,[],3);
Indmax_OPT = squeeze(Indmax_OPT); %Upper bound on achievable rates
MaxR_OPT = single(zeros(numel(Indmax_OPT),1));
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);
YPredicted = predict(trainedNet,XValidation);
% ——————— Achievable Rate ————————–%
[~,Indmax_DL] = maxk(YPredicted,kbeams,2);
MaxR_DL = single(zeros(size(Indmax_DL,1),1)); %True achievable rates
for b=1:size(Indmax_DL,1)
MaxR_DL(b) = max(squeeze(YValidation_un(1,1,Indmax_DL(b,:),b)));
MaxR_OPT(b) = squeeze(YValidation_un(1,1,Indmax_OPT(b),b));
end
Rate_OPT(dd) = mean(MaxR_OPT);
Rate_DL(dd) = mean(MaxR_DL);
LastValidationRMSE(dd) = traininfo.ValidationRMSE(end);
clear trainedNet traininfo YPredicted
clear layers options Rate_DL_Temp MaxR_DL_Temp Highest_Rate
end
endhello everyone
i have error whene i use cnn-lstm
this is the error
Error using trainNetwork (line 191)
Invalid training data. The output size (1024) of the last layer does not match the response size (1).