This work aims to implement in Matlab and Simulink the perturb-and-observe (P&O) and incremental conductance Maximum Power Point Tracking (MPPT) algorithms that are published in the scientific literature.

Instructions: 

1. Open the .slx file (PVArray_DC_DC_Buck_MPPT.slx) in Matlab 2012b or a newer version. 2. Default settings of "PVArray_DC_DC_Buck_MPPT.slx" Simulink model are given in Model Proprieties: File -> Model Proprieties -> Model Proprieties -> Callbacks -> PreLoadFcn* as follow:           load('25PVArrayExperimentalData.mat');           MPPT_IncCond=Simulink.Variant('MPPT_MODE==1')           MPPT_PandO=Simulink.Variant('MPPT_MODE==2')           MPPT_MODE=1           Constant_800=Simulink.Variant('Irradiance_Mode==1')           Constant_1000=Simulink.Variant('Irradiance_Mode==2')           Step=Simulink.Variant('Irradiance_Mode==3')           Irradiance_Mode=2 3. To run the "PVArray_DC_DC_Buck_MPPT.slx" Simulink model with P&O algorithm activate "MPPT_PandO=Simulink.Variant" at the Matlab command prompt by setting the "MPPT_MODE" with "2": "MPPT_MODE=2". Use the same procedure to change "Irradiance_Mode". To simulate the PV Array at 70°C use the command: "load('70PVArrayExperimentalData.mat');" *For more details about Variant Subsystems see the Matlab Documentation: https://www.mathworks.com/help/simulink/examples/variant-subsystems.html

Categories:
1719 Views

This work presents the performance evaluation of incremental conductance maximum power point tracking (MPPT) algorithm for solar photovoltaic (PV) systems under rapidly changing irradiation condition. The simulation model, carried out in Matlab and Simulink, includes the PV solar panel, the dc/dc buck converter and the MPPT controller. This model provides a good evaluation of performance of MPPT control for PV systems.

Instructions: 

Open the MDL files in Matlab 2014a or a newer version.

 

Categories:
1998 Views

Costas arrays are permutation matrices that meet the added Costas condition that, when used as a frequency-hop scheme, allow at most one time-and-frequency-offset signal bin to overlap another.  Databases to various orders have been available for many years.  Here we have a database that is far more extensive than any available before it.  A very powerful and easy-to-use Windows utility with a GUI accompanies the database.

Instructions: 

Download the file GetStarted.zip.  This file contains the Instructions as a PDF file, the extraction and analysis utility in its own ZIP file, and several information files includign an enumeration database in an Excel file.

 

Unpack this file in a folder that you want to be the location of your Costas array database.  Be sure and unpack subfolders, so that you dee subfolders /Searches and /Generated when you are done.  Folder /Searches contains all Costas arrays to order 29, and folder /Generated contains all generated Costas arrays to order 100.  The file Read_CA_Database_00.zip contains the extraction and analysis utility.  It may be extracted in-place or, if the database is on a network drive or other location inconvenient for DLLs, in its own folder anywhere on a local drive such as your C:\ drive.  See the Instructions PDF for details.

 

Then, as you need them, add these files: CA_Database_101-200.zip        More data for /Generated folder CA_Database_201-300.zip        More data for /Generated folder CA_Database_301-400.zip        More data for /Generated folder CA_Database_401-500.zip        More data for /Generated folder CA_Database_501-600.zip        More data for /Generated folder CA_Database_601-700.zip        More data for /Generated folder CA_Database_701-800.zip        More data for /Generated folder CA_Database_801-900.zip        More data for /Generated folder CA_Database_901-950.zip       More data for /Generated folder

CA_Database_951-1000.zip    More data for /Generated folder CA_Database_1001-1030.zip    More data for /Generated folder

 

This is a file that was produced by the extraction/analysis utility FrHop_LUB_Database.zip        Frequency hop LUB list; useful with PLL-based waveform generators

 

For further information, see the file Costas Arrays to Order 1030 INSTRUCTIONS.pdf

Categories:
902 Views

The dataset stores a random sampling distribution with cardinality of support of 4,294,967,296 (i.e., two raised to the power of thirty-two). Specifically, the source generator is fixed as a symmetric-key cryptographic function with 64-bit input and 32-bit output. A total of 17,179,869,184 (i.e., two raised to the power of thirty-four) randomly chosen inputs are used to produce the sampling distribution as the dataset. The integer-valued sampling distribution is formatted as 4,294,967,296 (i.e., two raised to the power of thirty-two) entries, and each entry occupies one byte in storage.

Instructions: 

The big dataset file is 4GB in size. The dataset contains 4,294,967,296 entries and each entry occupies one byte in storage. The MD5 checksum is 4ee9 a09a a509 fd70 4152 2fd2 f263 ae25. The SHA256 checksum is d9a4 fb8d d9f0 de29 b1e2 3316 c78d 8e65 4ec7 d60f 7ebc ec9e ee57 6fa2 e392 3b57. Note that the above hash checksum results are displayed in groups of four digits.

Categories:
420 Views

HazeRD is an outdoor scene dataset for benchmarking dehazing algorithms. HazeRD contains 10 different scenes based on the architectural biometrics project. For each scene, the ground RGB images, depth maps, and synthesized hazy images following the atmospheric optics are provided; the hazy images come with five different haze level using real life physical parameters. The main features of HazeRD to other dehazing datasets are: HazeRD focuses on outdoor scenes whereas other datasets provide indoor scenes; and, the synthesis is based on real life parameters. 

Categories:
1110 Views

The dataset contains depth frames collected using Microsoft Kinect v1 in top-view configuration and can be used for fall detection.

Categories:
1507 Views

The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are:

  • To encourage research on algorithms that scale to commercial sizes
  • To provide a reference dataset for evaluating research
  • As a shortcut alternative to creating a large dataset with APIs (e.g. The Echo Nest's)
  • To help new researchers get started in the MIR field

 

Categories:
428 Views

Pages