Communications

Cars, mobile phones, and smart home devices already provide automatic speech recognition (ASR) by default. However, human machine interfaces (HMI) in industrial settings, as opposed to consumer settings, operate under different conditions and thus, present different design challenges. Voice control, arguably the most natural form of communication, has the potential to shorten complex command sequences and menu structures in order to directly execute a final command.

We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson-Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions.

Update (07/30/2023): The dataset has been updated to be more realistic with specific characteristics described in [*]. 

We collect encrypted traffic from six widely-used Instant Messaging Applications (IMAs) installed on an Android device for descriptive and statistical analysis, as presented in our papers [*][**]. In particular, we collect traffic from:

1. Microsoft Teams,

2. Discord,

3. Facebook Messenger,

4. Signal,

5. Telegram, and

6. WhatsApp.

We present Hermes – a novel, low-cost, wireless, batteryless, energy harvesting system for aerial vehicles for sensing wind speed and Angle of Attack (AoA) concurrently. Hermes comprises a set of piezoelectric films which flutter due to incoming wind and the characteristics of this aeroelastic flutter are utilized for determining the wind speed and AoA of the head-wind. Hermes consists of five piezoelectric flags that are mounted on rigid clamps specifically placed at different angles.