The following dataset consists of utterances, recorded using 24 volunteers raised in the Province of Manitoba, Canada. To provide a repeatable set of test words that would cover all of the phonemes, the Edinburg Machine Readable Phonetic Alphabet (MRPA) [KiGr08], consisting of 44 words is used. Each recording consists of one word uttered by the volunteer and recorded in one continuous session.


This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here:

The DataPort Repository contains the data used primarily for generating Figure 1.


** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **

All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called in that repository.

You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here:


The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.


In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation.


The data set (induced by different odor types) collected using a Cerebus neural signal acquisition equipment involved thirteen odor stimulating materials, five of which (smelling like rose (A), caramel (B), rotten (C), canned peach (D), and excrement (E)) were selected from the T&T olfactometer (from the Daiichi Yakuhin Sangyo Co., Ltd., Japan) and the remaining eight from essential oils (i.e., mint (F), tea tree (G), coffee (H), rosemary (I), jasmine (J), lemon (K), vanilla (L) and lavender (M)).


Conventionally, the texture of the object is used for material imaging. However, this method can mistake an image of an object, for the object itself. This dataset furthers a new and more relevant method to classify the material of an object. This data is richer, compared to RGB images, because the time of flight responses correlate with the material property of an object. This makes the features, thus extracted, more suitable to infer the material information.


Two in-air signature databases were created. Forty participants voluntarily took part in each of the two databases’ construction. Some of them participated in both databases construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.


MANET dataset of outdoor experments for comparing differnet routing algorithms.

This dataset contains outdoor runs of MANET (Mobile Ad-hoc network) routing algorithms to compare the performance of four different routing algorithms. Nov 2006


collection environment

Most comparisons of wireless ad hoc routing algorithms involve simulated or indoor trial runs, or outdoor runs with only a small number of nodes, potentially leading to an incorrect picture of algorithm performance. For outdoor comparison of four different routing algorithms, APRL, AODV, ODMRP, and STARA, we run on top of thirty-three 802.11-enabled laptops moving randomly through an athletic field. This comparison provides insight into the behavior of ad hoc routing algorithms at larger real-world scales than have been considered so far.

The outdoor routing experiment took place on a rectangular athletic field measuring approximately 225 (north-south) by 365 (eastwest) meters. This field can be roughly divided into four flat, equalsized sections, three of which are at the same altitude, and one of which (at the southeast corner) is approximately four to six meters lower. There was a short, steep slope between the upper and lower sections. We chose this particular athletic field because it was physically distant from campus and the campus wireless network, reducing potential interference.

network configuration

We configured the 802.11 cards to use wireless channel 9 for maximum separation from the standard channels of 1, 6 and 11, further reducing potential interference. We used 41 laptops, 40 as application laptops, and one as a control laptop.

The routing experiments ran on top of a set of 41 Gateway Solo 9300 laptops, each with a 10GB disk, 128MB of main memory, and a 500MHz Intel Pentium III CPU with 256KB of cache. We used one laptop to control each experiment, leaving 40 laptops to actually run the ad hoc routing algorithms. Each laptop ran Linux kernel version 2.2.19 with PCMCIA card manager version 3.2.4 and had a Lucent (Orinoco) Wavelan Turbo Gold 802.11b wireless card. Although these cards can transmit at different bit rates, can auto-adjust this bit rate depending on the observed signal-to-noise ratio, and can auto-adjust the channel to arrive at a consistent channel for all the nodes in the ad hoc network, we used an ad hoc mode in which the transmission rate was fixed at 2 Mb/s, and in which the channel could be chosen manually but was fixed thereafter. Specifically, we used Lucent (Orinoco) firmware version 4.32 and the proprietary ad hoc "demo" mode originally developed by Lucent.

Although the demo mode has been deprecated in favor of the IEEEdefined IBSS, we used the demo mode to ensure consistency with a series of ad hoc routing experiments of which this outdoor experiment was a culminating event. 6 The fixed rate also made it much easier to analyze the routing results, since we did not need to account for automatic changes in each card's transmission rate. On the other hand, we would expect to see variation in the routing results if we had used IBSS instead, both due to its multi-rate capabilities and its general improvements over the demo mode. The routing results remain representative, however, since demo mode provides sufficient functionality to serve as a reasonable data-link layer. Finally, each laptop had a Garmin eTrex GPS unit attached via the serial port. These GPS units did not have differential GPS capabilities, but were accurate to within thirty feet during the experiment.

data collection methodology

We log the events of routing algorithms in each laptop. A GPS service runs on each laptop, reading and recording the current laptop position from the attached GPS unit.

disruptions to data collection

During the experiment, seven laptops generated no network traffic due to hardware and configuration issues, and an eighth laptop generated the position beacons only for the first half of the experiment. The seven complete failures left thirty-three laptops actually participating in the ad hoc routing.

This dataset contains the following traceset:


Traceset of outdoor MANET experments for comparing differnet routing algorithms.

last modified


reason for most recent change

the initial version

release date


date/time of measurement start


date/time of measurement end


network type

802.11 ad-hoc



Measurement trace from wireless network at Dartmouth College.

This dataset includes measurement trace for over 450 access points and several thousand users at dartmouth college.



last modified: 2006-11-14

reason for most recent change: Infocom 2004 trace is added.

short description: Two-year records showing the location (AP association) of each wireless card seen on campus.

description: Over three years of nearly continuous records showing the location (access-point association) of each wireless card seen on campus. We used this data for our study of location predictors, published in [INFOCOM'04 paper] and a subsequent, expanded [technical report]. This data is derived from the syslog data.

The trace used for this paper is gzipped tar file [51MB].


release date: 2004-08-05

methodology: We extracted user traces from dartmouth/campus/syslog. Each user's trace is a series of locations, that is, access-point names. We introduced the special location 'OFF' to represent the user's departure from the network (which occurs when the user turns off their computer or their wireless card, or moves out of range of all access points). The traces varied widely in length (the number of locations in the sequence). Users with longer traces were either more active (using their card more), more mobile (thus changing access points more often), or used the network for a longer period (some users have been on the network since April 2001, and some others have only recently arrived on campus).


sanitization: same as dartmouth/campus/syslog

disruptions to data collection: same as dartmouth/campus/syslog

limitation: same as dartmouth/campus/syslog


This dataset has been taken using the Photonic Mixer Device (PMD) Selene Module. To capture the image, we have constructed a demonstrator setup consisting of five materials (i.e., foam board (location: center), crepe paper (location: top), polystyrene (location: right), bubble wrap (location: left), wax (location: bottom)). Each image has been taken at 5 different distances (uniformly distributed between 82 cm to 47 cm) and at 3 different orientations (uniformly distributed between -10 degree to 10 degree) for each material. To avoid noise, each image has been taken in dark environment.