Zobrazeno 1 - 10
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pro vyhledávání: '"Prabhakar, T"'
Vertical Take-Off and Landing (VTOL) vehicles have gained immense popularity in the delivery drone market and are now being developed for passenger transportation in urban areas to efficiently enable Urban Air Mobility (UAM). UAM aims to utilize the
Externí odkaz:
http://arxiv.org/abs/2408.01152
In this study, we present SeMaScore, generated using a segment-wise mapping and scoring algorithm that serves as an evaluation metric for automatic speech recognition tasks. SeMaScore leverages both the error rate and a more robust similarity score.
Externí odkaz:
http://arxiv.org/abs/2401.07506
Autor:
Pal, Joydeep, Choudhary, Deepak, Gnani, Nithish Krishnabharathi, Singh, Chandramani, Prabhakar, T. V.
Time-Aware Shaper (TAS) is a time-triggered scheduling mechanism that ensures bounded latency for time-critical Scheduled Traffic (ST) flows. The Linux kernel implementation (a.k.a TAPRIO) has limited capabilities due to varying CPU workloads and thu
Externí odkaz:
http://arxiv.org/abs/2310.07480
Autor:
Gnani, Nithish Krishnabharathi, Pal, Joydeep, Choudhary, Deepak, Verma, Himanshu, Rana, Soumya Kanta, Mhapsekar, Kaushal, Prabhakar, T. V., Singh, Chandramani
Tactile Internet based operations, e.g., telesurgery, rely on end-to-end closed loop control for accuracy and corrections. The feedback and control are subject to network latency and loss. We design two edge intelligence algorithms hosted at P4 progr
Externí odkaz:
http://arxiv.org/abs/2309.10383
Intelligent Electronic Devices (IEDs) are vital components in modern electrical substations, collectively responsible for monitoring electrical parameters and performing protective functions. As a result, ensuring the integrity of IEDs is an essentia
Externí odkaz:
http://arxiv.org/abs/2307.15338
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices
Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for
Externí odkaz:
http://arxiv.org/abs/2307.07199
We describe a comprehensive methodology for developing user-voice personalized automatic speech recognition (ASR) models by effectively training models on mobile phones, allowing user data and models to be stored and used locally. To achieve this, we
Externí odkaz:
http://arxiv.org/abs/2306.09384
Autor:
Vijay, Rathinamala, V., Prabhakar. T.
This paper presents a hybrid Mesh of Things (MoT) network performance model to evaluate the end-to-end Packet Delivery Ratio (PDR) and latency. These PDR and latency measures are used to identify both a de-tangled mesh as well as to track the mesh su
Externí odkaz:
http://arxiv.org/abs/2212.12221
In industrial process automation, sensors (pressure, temperature, etc.), controllers, and actuators (solenoid valves, electro-mechanical relays, circuit breakers, motors, etc.) make sure that production lines are working under the pre-defined conditi
Externí odkaz:
http://arxiv.org/abs/2211.12326
Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems. Since WER considers only literal word-level correctness, new evaluation metrics based on semantic similarity
Externí odkaz:
http://arxiv.org/abs/2211.01722