arXiv subjects

Payam Mousavi

Publications and source records attributed to Payam Mousavi.

4 recordsLinked to original sources

arXiv2024

A Novel Framework for Automated Warehouse Layout Generation

Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case.

cs.AI↗

arXiv2023

MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment

We present a novel end-to-end identity-agnostic face reenactment system, MaskRenderer, that can generate realistic, high fidelity frames in real-time. Although recent face reenactment works have shown promising results, there are still significant challenges such as identity leakage and imitating mouth movements, especially for large pose changes and occluded faces. MaskRenderer tackles these problems by using (i) a 3DMM to model 3D face structure to better handle pose changes, occlusion, and mouth movements compared to 2D representations; (ii) a triplet loss function to embed the cross-reenactment during training for better identity preservation; and (iii) multi-scale occlusion, improving inpainting and restoring missing areas. Comprehensive quantitative and qualitative experiments conducted on the VoxCeleb1 test set, demonstrate that MaskRenderer outperforms state-of-the-art models on unseen faces, especially when the Source and Driving identities are very different.

cs.CV↗

arXiv2021

Serverless Computing: A Survey of Opportunities, Challenges and Applications

The topic of serverless computing has proved to be a controversial subject both within academic and industrial communities. Many have praised the approach to be a platform for a new era of computing and some have argued that it is in fact a step backward. Though, both sides agree that there exist challenges that must be addressed in order to better utilize its potentials. This paper surveys existing challenges toward vast adoption of serverless services and also explores some of the challenges that have not been thoroughly discussed in the previous studies. Each challenge is discussed thoroughly and a number of possible directions for future studies is proposed. Moreover, the paper reviews some of the unique opportunities and potentials that the serverless computing presents.

cs.NI↗

arXiv2020

Maximum-likelihood parameter estimation in terahertz time-domain spectroscopy

We present a maximum-likelihood method for parameter estimation in terahertz time-domain spectroscopy. We derive the likelihood function for a parameterized frequency response function, given a pair of time-domain waveforms with known time-dependent noise amplitudes. The method provides parameter estimates that are superior to other commonly-used methods, and provides a reliable measure of the goodness of fit. We also develop a simple noise model that is parameterized by three dominant sources, and derive the likelihood function for their amplitudes in terms of a set of repeated waveform measurements. We demonstrate the method with applications to material characterization.

physics.data-an↗