- Nonlinear signal processing
- Analog signal processing
- Discrete-time signal processing
- Continuous-time signal processing
- Digital signal processing
E-nose can be used for food authentication and adulteration assessment. Recently, halal authentication has gained attention because of cases of pork adulteration in beef. In this study, The electronic nose was built using nine MQ series gas sensors from Zhengzhou Winsen Electronics Technology Co., Ltd for detection pork adulteration in beef. The list of gas sensors are MQ2, MQ4, MQ6, MQ9, MQ135, MQ136, MQ137, and MQ138. These gas sensors were assembled with an Arduino microcontroller.
This paper presents a novel implementation scheme
of the essential circuit blocks for high performance, full-precision
Booth multipliers leveraging a hybrid logic style. By exploiting
the behavior of parasitic capacitance of MOSFETs, a carefully
engineered design style is employed to reduce dynamic power dissipation
while improving the glitch immunity of the circuit blocks.
The circuit-level techniques along with the proposed signal-flow
optimization scheme prevent the generation and propagation
This dataset provides the magneto-inertial signals from six MIMU (2 Xsens, 2 APDM, 2 Shimmer) and orientation from 8 reflective markers (VICON) at 3 different speeds (slow, medium, fast). Proprietary orientations from MIMU vendors are also included. All data are synchronized at 100 Hz.
Each voice sample is stored as a .WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.
The output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female).
This RSSI Dataset is a comprehensive set of Received Signal Strength Indicator (RSSI) readings gathered from three different types of scenarios. Three wireless technologies were used which consisted of:
- Zigbee (IEEE 802.15.4),
- Bluetooth Low Energy (BLE), and
- WiFi (IEEE 802.11n 2.4GHz band).
The scenarios took place in three rooms with different sizes and inteference levels. For the experimentation, the equipment utilized consisted of Raspberry Pi 3 Model Bs, Gimbal Series 10 Beacons, and Series 2 Xbees with Arduino Uno microcontrollers.
A set of tests was conducted to determine the accuracy between multiple types of system designs including: Trilateration, Fingerprinting with K-Nearest Neighbor (KNN) processing, and Naive Bayes processing while using a running average filter. For the experiments, all tests were done on tables which allowed tests to be simulated at a height where a user would be carrying a device in their pocket. Devices were also kept in the same orientation throughout all the tests in order to reduce the amount of error that would occur in the measuring of RSSI values.
Three different experimental scenarios were utilized with varying conditions in order to determine how the proposed system will function according to the environmental parameters.
Scenario 1 was a 6.0 x 5.5 m wide meeting room. The environmental area was cleared of all transmitting devices to create a clear testing medium where all the devices can transmit without interference. Transmitters were placed 4 m apart from one another in the shape of a triangle. Fingerprint points were taken with a 0.5 m spacing in the center between the transmitters. This created 49 fingerprints that would comprise the database. For testing, 10 points were randomly selected.
Scenario 2 was a 5.8 x 5.3 m meeting room. This area was a high noise environment as additional transmitting devices were placed around the environment in order to create interference in the signals. There were 16 fingerprints gathered with a larger distance selected between the points. In this Scenario, 6 testing points were randomly selected to be used for comparing the algorithms.
Scenario 3 was a 10.8 x 7.3 m computer lab. This lab was a large area with a typical amount of noise occurring due to the WiFi and BLE transmitting that were in the area. The large space also allowed for signals to experience obstructions, reflections, and interference. Transmitters were placed so Line-of-Sight (LoS) was available between the transmitters to the receiver. In total, 40 fingerprints were gathered with an alternating pattern occurring between the points. Points were taken to be 1.2 m apart in one direction, and 0.6 m apart in the other. For testing 16 randomly selected points were taken.
In the testing environment, fingerprints were gathered to be used in the creation of a database, while test points were selected to be used against the database for the comparison. The figures of each topology can be found inside the dataset folder. In the figures, the black dots represent the location of the transmitters and the red dots represent the locations where fingerprints and test points were gathered where appropriate.
S. Sadowski, P. Spachos, K. Plataniotis, "Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things", IEEE Internet of Things Journal.
The RSSI dataset contains a folder for each experimental scenario and furthermore on wireless technology (i.e. Zigbee, BLE, and WiFi). Each folder contains three additional folders where the data was gathered (Pathloss, Database, and Tests). Pathloss contains 18 files measuring the RSSI at varying distances from the devices. The number of files located in Database and Tests varies based on the scenario.
For each technology, the file name corresponds to the point as to where the data was gathered. For specific locations, the (x,y) coordinates can be seen in the appropriate .xlsx file.
For the files in the Database and Tests folders, there are approximately 300 reading. In the Pathloss folder, there are approximately 50 only occurring from a single node. Readings appear in the format "Node Letter: Value" where:
Letter corresponds to the transmitter that signal was sent from, represented by 'A', 'B', or 'C'.
Value is the RSSI reading.
Dataset of V2V (vehicle to vehicle communication), GPS, inertial and WiFi data collected during a road vehicle trip in the city of Porto, Portugal. Four cars were driven along the same route (approx. 12 km), facing everyday traffic conditions with regular driving behavior. No special environments or settings were chosen, other than keeping the vehicles in communication reach of each other for as long as possible while being safe and compliant with the road rules.
There is a folder with data collected by each of the vehicles.
ID1 - Seat Leon
ID2 - Audi A4
ID3 - Nissan Micra
ID4 - Fiat Punto
Equipment collecting data:
- NEC LinkBird MX
- GPS receiver (rooftop, connected to the LinkBird)
- Smartphone Nexus 4 running SensorReader (SR) fixed on the windshield
- Smartphone Nexus 4 or Nexus 5 running SenseMyCity (SMC) fixed on the dashboard (different positions in each vehicle)
Speech Processing in noisy condition allows researcher to build solutions that work in real world conditions. Environmental noise in Indian conditions are very different from typical noise seen in most western countries. This dataset is a collection of various noises, both indoor and outdoor ollected over a period of several months. The audio files are of the format RIFF (little-endian) data, WAVE audio, Microsoft PCM, 8 bit, mono 11025 Hz and have been recorded using the Dialogic CTI card.
Tactile perception of the material properties in real-time using tiny embedded systems is a challenging task and of grave importance for dexterous object manipulation such as robotics, prosthetics and augmented reality [1-4] . As the psychophysical dimensions of the material properties cover a wide range of percepts, embedded tactile perception systems require efficient signal feature extraction and classification techniques to process signals collected by tactile sensors in real-time.
There are four CSV files (X, Y, Z, and S) in the dataset corresponding to the sensor recordings. The 3-dimensional accelerometer sensor recordings are denoted by X, Y, and Z, respectively. The sound recordings from the electret condenser microphone are denoted by S. As there are 12 classes in the dataset, there is one line for each class in the CSV files. For each texture, 20 seconds of recordings are collected. Therefore, each line in the X, Y, Z files has 4,000 samples (20 sec x 200Hz sampling rate) and each line in the S file has 160,000 samples (20 sec x 8 kHz). The training and test sets for the machine learning classifiers can be created by snipping short frames out of these recordings and applying signal feature extraction. For example, the first 400 columns of the 12th row of X.csv and the first 16,000 columns of the 12th row of S.csv both correspond to the first 2 seconds of the recordings for texture class 12. The Python programs we have developed will be made available upon request.
Please cite the dataset and accompanying paper if you use this dataset:
- Kursun, O. and Patooghy, A. (2020) "An Embedded System for Collection and Real-time Classification of a Tactile Dataset", IEEE Access (accepted for publication).
- Kursun, O. and Patooghy, A. (2020) "Texture Dataset Collected by Tactile Sensors", IEEE Dataport, 2020.