Continuous-time signal processing

ARImulti-mic: real-world speech recordings on a humanoid robot (ARI)

This dataset includes “real-world” experiments. A recording campaign was held in the acoustic laboratory at Bar-Ilan University. This lab is a [6×6×2.4]m room with a reverberation time controlled by 60 interchangeable panels covering the room facets.


The "Queue Waiting Time Dataset" is a detailed collection of information that records the movement of waiting times in queues. This dataset contains important details such as the time of arrival, the start and finish times, the waiting time, and the length of the queue. The arrival time denotes the moment when customers enter the queue, while the start and finish times track the duration of the service process. The waiting time measures the time spent waiting in the queue, and the queue length shows the number of customers in the queue when a new customer arrives.


<p>Ten individuals in good health were enlisted to execute 16 distinct movements involving the wrist and fingers in real-time. Before commencing the experimental procedure, explicit consent was obtained from each participant. Participants were informed that they had the option to withdraw from the study at any point during the experimental session. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki and received approval from the local ethics committee at the National University of Sciences and Technology, Islamabad, Pakistan.


This dataset was initially collected by Mrs Athira P K  with the help of  teachers and students of Rahmania school for handicapped, Kozhikode, Kerala, India. Later the dataset was extended by many other BTech and MTech students with the help of their friends.

MUDRA NITC dataset consists of videos of static and dynamic gestures of Indian sign language. In static gestures mainly static alphabets videos and  preprocessed image frames are included.


Kinetic Step Box was developed with the purpose for analyzing body movements by detecting vertical Ground Reaction ForcesThe objective is to test on validity and reliability of vertical Ground Reaction Forces measurement from Kinetic Step Box compared with Standard Force Plate13 females and 7 males performed sitting to standing posture on Kinetic Step Box and Standard Force Plate for 3 sets of


This dataset presents the acceleration values of the spreader with the attached containers that are being unloaded from a container ship, as well as detected impacts to the vertical cell guides and other containers during hooking procedures inside the ship for 102 cycles. This dataset was partially used in a recent publication:


The code synthesizes a transfer function with real poles from tabulated frequency response data. The reproducible run of this capsule will synthesize the impedance matrix of a 500-kV double-circuit overhead power transmission line and also that of a 250-kV dc submarine power cable.


For the interaction of humans with machines but also for the interaction of machines with the environment, e.g. in robotic manipulation tasks, large area sensors like sensor skins are of high interest. Capacitive sensor have become widely used for touch sensor and proximity sensors and are well suited for such large area sensing.


 It is the key problem of machine condition monitoring to judge whether the rolling bearing has a fault or not and judge the fault location according to the noise signal. Aiming at this problem, a rolling bearing fault identification method is proposed based on Wavelet Frequency Band Subdivision (WFBS), Principal Component Analysis (PCA) and Multi-Level Clustering (MLC).


<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.