BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Sidney Fels

Sidney Fels contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.ASSoundComputation and LanguageComputer Visioncs.CYeess.IVHuman-Computer InteractionMachine Learning

Trust snapshot

Quick read

Trust 19 - UnverifiedVerification L1Unclaimed author
5works
0followers
8topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

5 published item(s)

preprint2026arXiv

Patient-Specific Optimization for Mandibular Reconstruction Planning with Enhanced Bone Union

Mandibular reconstruction with vascularized bone grafts is complicated by donor-host nonunion, and current virtual surgical planning produces a geometric plan rather than a configuration that explicitly promotes bone union. We present OsteoOpt++, an image-to-decision planning loop for patient-specific mandibular reconstruction. A pre-operative computed tomography (CT) is converted into a personalized digital twin through template-to-patient registration and CT-derived updates of the muscle and temporomandibular-joint parameters. Bayesian optimization with an expected-improvement-plus acquisition rule then searches six clinically controllable cut-plane and donor-positioning variables under an apposition-driven objective and a safety-factor-regularized variant. The workflow was evaluated on three generic defects (body, symphysis, and ramus-body) and a total of 3+1 patient-specific cases, with 3 used for optimization and 1 for validation. In the generic cases, against a common surgical approach, cycle-averaged donor-mandible apposition increased by up to 29 percentage points (329% relative); in the patient-specific cases, against the surgeon-implemented day-5 post-operative configuration, by up to 26 percentage points. A 10% sensitivity analysis over eleven modeling parameters capped the change in the apposition-driven objective at 3% for generic cases and 4% for patient-specific cases, and the longitudinal case showed Dice overlap of 0.70 and 0.76 between predicted apposition and year-1 bone formation. Clinically, this provides surgeons with a pre-operative, image-driven recommendation for cut-plane orientation and donor placement that is predicted to improve union conditions over the configurations currently delivered in the operating room. The optimization and patient-specific modeling code is open source at https://github.com/hamidreza-aftabi/OsteoOpt.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A comparative study of two-dimensional vocal tract acoustic modeling based on Finite-Difference Time-Domain methods

The two-dimensional (2D) numerical approaches for vocal tract (VT) modelling can afford a better balance between the low computational cost and accurate rendering of acoustic wave propagation. However, they require a high spatio-temporal resolution in the numerical scheme for a precise estimation of acoustic formants at the simulation run-time expense. We have recently proposed a new VT acoustic modelling technique, known as the 2.5D Finite-Difference Time-Domain (2.5D FDTD), which extends the existing 2D FDTD approach by adding tube depth to its acoustic wave solver. In this work, first, the simulated acoustic outputs of our new model are shown to be comparable with the 2D FDTD and a realistic 3D FEM VT model at a low spatio-temporal resolution. Next, a radiation model is developed by including a circular baffle around the VT as head geometry. The transfer functions of the radiation model are analyzed using five different vocal tract shapes for vowel sounds /a/, /e/, /i/, /o/ and /u/.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

SPEAK WITH YOUR HANDS Using Continuous Hand Gestures to control Articulatory Speech Synthesizer

This work presents our advancements in controlling an articulatory speech synthesis engine, \textit{viz.}, Pink Trombone, with hand gestures. Our interface translates continuous finger movements and wrist flexion into continuous speech using vocal tract area-function based articulatory speech synthesis. We use Cyberglove II with 18 sensors to capture the kinematic information of the wrist and the individual fingers, in order to control a virtual tongue. The coordinates and the bending values of the sensors are then utilized to fit a spline tongue model that smoothens out the noisy values and outliers. Considering the upper palate as fixed and the spline model as the dynamically moving lower surface (tongue) of the vocal tract, we compute 1D area functional values that are fed to the Pink Trombone, generating continuous speech sounds. Therefore, by learning to manipulate one's wrist and fingers, one can learn to produce speech sounds just through one's hands, without the need for using the vocal tract.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ultra2Speech -- A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images

Thousands of individuals need surgical removal of their larynx due to critical diseases every year and therefore, require an alternative form of communication to articulate speech sounds after the loss of their voice box. This work addresses the articulatory-to-acoustic mapping problem based on ultrasound (US) tongue images for the development of a silent-speech interface (SSI) that can provide them with an assistance in their daily interactions. Our approach targets automatically extracting tongue movement information by selecting an optimal feature set from US images and mapping these features to the acoustic space. We use a novel deep learning architecture to map US tongue images from the US probe placed beneath a subject's chin to formants that we call, Ultrasound2Formant (U2F) Net. It uses hybrid spatio-temporal 3D convolutions followed by feature shuffling, for the estimation and tracking of vowel formants from US images. The formant values are then utilized to synthesize continuous time-varying vowel trajectories, via Klatt Synthesizer. Our best model achieves R-squared (R^2) measure of 99.96% for the regression task. Our network lays the foundation for an SSI as it successfully tracks the tongue contour automatically as an internal representation without any explicit annotation.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Human Computer Interaction Design for Mobile Devices Based on a Smart Healthcare Architecture

Smart and IoT-enabled mobile devices have the potential to enhance healthcare services for both patients and healthcare providers. Human computer interaction design is key to realizing a useful and usable connection between the users and these smart healthcare technologies. Appropriate design of such devices enhances the usability, improves effective operation in an integrated healthcare system, and facilitates the collaboration and information sharing between patients, healthcare providers, and institutions. In this paper, the concept of smart healthcare is introduced, including its four-layer information architecture of sensing, communication, data integration, and application. Human Computer Interaction design principles for smart healthcare mobile devices are outlined, based on user-centered design. These include: ensuring safety, providing error-resistant displays and alarms, supporting the unique relationship between patients and healthcare providers, distinguishing end-user groups, accommodating legacy devices, guaranteeing low latency, allowing for personalization, and ensuring patient privacy. Results are synthesized in design suggestions ranging from personas, scenarios, workflow, and information architecture, to prototyping, testing and iterative development. Finally, future developments in smart healthcare and Human Computer Interaction design for mobile health devices are outlined.

0 citations0 reviewsNo code linkedOpen access