Signal Processing
As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.
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The Costas condition on a permutation matrix, expressed as row indices as elements of a vector c, can be expressed as A*c=b, where b is a vector of integers in which no element is zero. A particular formulation of the matrix A allows a singular value decomposition in which the eigenvalues are squared integers and the eigenvalues may be scaled to vectors with all integer elements. This is a database of the Costas constraint matrices A, the scaled eigenvectors, and the squared eigenvalues for orders 3 through 100.
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This MATLAB dataset (.mat) contains the collected real measurement data from a total of 470 access points (APs) deployed in the Linnanmaa campus of the University of Oulu, Finland. The measurements include IDs, dates of data collection, number of users, received traffic data, transmitted traffic data and location names of each AP. Each observation of traffic data and number of users provide the data value at every 10-minute interval between December 18, 2018 and February 12, 2019. Please cite this as: S. P. Sone & Janne Lehtomäki & Zaheer Khan.
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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.
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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
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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). Marker trajectories are provided. Proprietary orientations from MIMU vendors are also included. All data are synchronized at 100 Hz.
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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).
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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).
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