Test networks for reliability-based distribution studies.

This dataset includes 5 test netwroks with 37, 85, 137, 145, and 230 nodes.


Vibration measurement on SAG mill drive motor for Energy harvesting or predictive maintenance


We introduce a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits.


The corpus consists of three sets: the core, evaluation, and complete set. The complete set contains all the data (i.e., complete set = core set + evaluation set) and allows the user to freely split the training/test set. Core/evaluation sets suggest a default training/test split. For each set, all *.wav files are in the /data directory and the meta information is in meta.csv file. The protocol is described in the readme.txt. A PyTorch data loader script is provided as an example of how to use the data. A python resample script is provided for resampling the dataset into the desired sample rate.


This dataset accompanies the article "Palisade: A Framework for Anomaly Detection in Embedded Systems."  It contains traces, programs, and specifications used in the case studies from the paper.


Case Study 1: Autonomous Vehicle - Comparison between Siddhi and Palisade nfer processor

  • cs1_gear_flip_flop_data.csv - the data used in the Gear Flip-Flop anomaly study and the comparison with Siddhi
  • cs1_comparison.nfer - the nfer specification used in the comparison with Siddhi
  • cs1_comparison.siddhi - the siddhi specification used in the comparison with Siddhi


Case Study 2: ADAS-on-a-treadmill - Comparison between Beep Beep 3 and Palisade rangeCheck and lossDetect processors

  • cs2_platoon_dead_spot_data.csv - the data used in the Platoon Dead-Spot anomaly study and the comparison with Beep Beep 3
  • cs2_platoon_no_anomaly_data.csv - data used for training in the Platoon Dead-Spot anomaly study
  • cs2_platoon_range_model.json - trained model used by the rangeCheck processor
  • RangeCheck.java - Beep Beep 3 program to check both range and loss
  • BenchSink.java - Beep Beep 3 program to print events
  • BenchPublisher.java - Beep Beep 3 program to read from a file and publish events to the RangeCheck program
  • BenchEvent.java - Custom Beep Beep 3 event class used in the comparison



Description of the proposed method is presented with the support of experimental videos.


The aircraft fuel distribution system has two primary functions: storing fuel and distributing fuel to the engines. These functions are provided in refuelling and consumption phases, respectively. During refuelling, the fuel is first loaded in the Central Reservation Tank and then distributed to the Front and Rear Tanks. In the consumption phase, the two engines receive an adequate level of fuel from the appropriate tanks. For instance, the Port Engine (PE) will receive fuel from Front Tank and the Starboard Engine (SE) will receive fuel from Rear Tank.


You can easily read the CSV files and apply your method.The dataset has five parts, one normal and four abnormal scenarios.



Data for 24-node, 54-node, 86-node, and 138-node test networks for reliability-oriented distribution expansion planning applications; and the results for 24-node and 54-node systems.


This file contains Python code to extract the single-diode model parameters from the Photovoltaic IV-curve. 


Collision detection (CD) is a key capability of carrier sense multiple access (CSMA) based medium access control (MAC) protocol. Applying CD, the transmitter can abort transmission immediately so that the power can be saved. This technique does not need the peer receiver to give feedback on whether there is a packet collision, and hence, the overall overhead is significantly low. The challenge, however, is to operate in transmit time and instantly detect the week colliding signal in the presence of strong self-interference (SI).


Instant collision detection (CD) can be achieved at the transmitter side more efficiently. To detect the collision, though, the device has to overcome the strong self-interference (SI) in such a way that it can listen to the channel in transmit time. This capability is feasible by in-band full-duplex (IBFD) technology, which allows two nodes to communicate concurrently over the same frequency channel. Recent works have shown the network-level benefits of using IBFD for collision detection, in the sense of power efficiency, throughput, and delay performance. By any means, the performance of these MAC protocols highly depends on the rapidity and precision of the CD method, although the collision detection in this context has still not been investigated thoroughly. By leveraging multiple hidden convolutional layers, modern machine learning techniques have confirmed their effectiveness in a wide range of applications, such as automatic image recognition, and network optimization. Motivated by its remarkable success in various fields as well as its real-time functionality, in this work we investigate whether a convolutional neural network (CNN) can be exploited to accelerate CD without sacrificing the detection accuracy. Meanwhile, we realize that the CD problem can be mapped to traditional SNR estimation problem. When there is a collision, the signal SNR will drop. Lots of domain knowledge are there with regard to signal demodulation and SNR estimation. On the contrary, CNN could be regarded as a kind of domain-specific knowledge less method. It will be interesting to see the performance comparison between the two methodologies. This kind of comparison will inspire the research community to study further about how should we combine the domain-specific knowledge (DSK) with CNN. Besides, to encourage future studies, we offer free access to the dataset and programs in IEEE DataPort, which allows researchers to reproduce our results out of the box or investigate different approaches.