Signal Processing
This paper is a novel digital signal processing software of the advanced conversion of text-to-speech synthesis technology, which has been available as a range of hardware products for more than ten years, to software. It was initially created as a replacement for character cell terminals and telephony applications, but it is now also used to give people who are visually impaired access to information. With a digital formant synthesizer used to mimic the human vocal tract, text-to-speech quality is very high in both understandability and naturalness.
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The task of algorithm implementation in several applications of signal processing is the most interesting task for the engineers of the Digital Signal Processing field. The pressure most engineers have and looking for is how to solve these algorithms with less time and pain. This paper implements a technique and tools created to use with the microprocessor TMS 320 from Texas Instrument as a part of the Digital Signal Processing family which is helpful and expedited for such development.
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We have created a new in-Air Signature dataset using Smart Phone that we called IASSP dataset. Forty participants voluntarily took part in each of the two databases’ construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.
The participants were seated in a comfortable chair, with their dominant hand placed approximately 7 cm away from the camera of a smartphone, which was directly in front of them.
The data recorded on two files:
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This is a dataset designed for the paper "Bearing Unbalance Fault Diagnosis Based on RFH-GAN and PSA-DRSN".
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Seismic data, obtained from sensors placed on the Earth's surface or subsurface, provides valuable insights into the composition and structure of the Earth's subsurface layers. This data is typically collected in the form of digital recordings, which represent the vibrations produced by seismic waves generated by controlled sources or natural events such as earthquakes. Converting seismic data into speech signals allows researchers and professionals to gain aural insights into the subsurface characteristics.
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A 3D WUCT system using a single ultrasound transducer is designed and automated. The dataset consist of the WUCT reconstruction results dataset used to train U-Net based semantic segmentation model. Also, dataset i) to study the effect of increase in the number of virtual transducer on reconstruction quality and, ii) effect of variation in the applied pulse width on the reconstruction are provided. The U-Net based semantic segmentation model is trained and used to evaluate dice coefficient corresponding to the phantom’s actual profile and reconstructed profile.
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This research introduces the Open Seizure Database and Toolkit as a novel, publicly accessible resource designed to advance non-electroencephalogram seizure detection research. This paper highlights the scarcity of resources in the non-electroencephalogram domain and establishes the Open Seizure Database as the first openly accessible database containing multimodal sensor data from 49 participants in real-world, in-home environments.
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Radio-Frequency (RF) based User identification enables many attractive applications such as smart homes, and security management. However, laborious data collection is required due to appearance changes, inconsistent walking paths, and environmental variations. Furthermore, multi-user identification persists as an imperative for real-world applications. To this end, we propose an RFID-based user identification system (RF-UI), a few-shot, cross-interference factor, and a continuous user identification system.
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Existing work on radar modulation recognition is
widely based on the assumption of a single in-pulse signal,
whereas overlapping two pulse signals is an actual situation. This
paper introduces a challenging problem: modulation recognition
of overlapping intra-pulse signals, where the difficulty lies in
the fact that the number of samples grows stepwise with the
permutation of the sub-signals. In this paper, for the first time,
a series of methods for target detection are used to solve this
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<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.
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