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.


This is the First Arabic voice Commands Dataset to provide personalized control of devices at smart homes for elder persons and persons with disabilities. The dataset contains 12 speakers, each saying 36 different phrases or words in Arabic language. The goal of this dataset is to use it in an Arabic smart home system to control home devices through voice. Participants were asked to say each phrase multiple times. The phrases to record were presented in a random order.


<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.


Demo script and data for infocom 2022 paper titled "Don’t Miss Weak Packets: Boosting LoRa Reception with Antenna Diversities"


A Frequency-Dependent & Per-Port (FDPP) channel termination & renormalization method is presented asa more accurate substitute for the traditional method whichuses frequency-independent, uniform impedance as the serialization/deserialization (SerDes) transmit (TX)/receive (RX)termination impedances.


The performances of ENRZ (Ensemble NRZ) underthe interferences of crosstalk, in conjunction with NRZ (NonReturn-to-Zero), PAM4 (Pulse Amplitude Modulation of 4-level),and PAM3 are investigated. One-side and two-side crosstalk scenarios with varying levels of crosstalk are studied. The simulatedeye-diagram obtained with the four signaling techniques undera range of crosstalk levels are compared and the reasons thatlead to the prominent advantages of ENRZ are analyzed.