no code implementations • 6 May 2024 • Mengchen Fan, Baocheng Geng, Keren Li, Xueqian Wang, Pramod K. Varshney
This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation.
no code implementations • 14 Sep 2023 • Nandan Sriranga, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney
The distributed system is such that the sensors and the FC sample observations periodically, where the sampling times are not necessarily synchronous, i. e., the sampling times at different sensors and the FC may be different from each other.
no code implementations • 27 Apr 2023 • Chen Quan, Yunghsiang S. Han, Baocheng Geng, Pramod K. Varshney
The proposed detectors can achieve the detection performance close to the benchmark likelihood ratio test (LRT) detector, which has perfect knowledge of the attack parameters and sparsity degree.
no code implementations • 25 Jan 2023 • Chen Quan, Baocheng Geng, Yunghsiang S. Han, Pramod K. Varshney
Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced.
no code implementations • 18 Jan 2023 • Baocheng Geng, Pramod K. Varshney
Recently, modeling of decision making and control systems that include heterogeneous smart sensing devices (machines) as well as human agents as participants is becoming an important research area due to the wide variety of applications including autonomous driving, smart manufacturing, internet of things, national security, and healthcare.
no code implementations • 18 Jan 2023 • Baocheng Geng, Chen Quan, Tianyun Zhang, Makan Fardad, Pramod K. Varshney
The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans.
no code implementations • 18 Jan 2023 • Nandan Sriranga, Baocheng Geng, Pramod K. Varshney
In this work, we consider a binary hypothesis testing problem involving a group of human decision-makers.
no code implementations • 18 Jul 2022 • Chen Quan, Nandan Sriranga, Haodong Yang, Yunghsiang S. Han, Baocheng Geng, Pramod K. Varshney
In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision.
no code implementations • 17 Mar 2022 • Shan Zhang, Pranay Sharma, Baocheng Geng, Pramod K. Varshney
To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration.
no code implementations • 21 Jan 2022 • Chen Quan, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney
The ordered transmission (OT) scheme reduces the number of transmissions needed in the network to make the final decision, while it maintains the same probability of error as the system without using OT scheme.
no code implementations • 17 Jul 2020 • Chen Quan, Animesh Yadav, Baocheng Geng, Pramod K. Varshney, H. Vincent Poor
This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i. e., sparse code multiple access (SCMA).
no code implementations • 3 Sep 2019 • Baocheng Geng, Qunwei Li, Pramod K. Varshney
We consider the $M$-ary classification problem via crowdsourcing, where crowd workers respond to simple binary questions and the answers are aggregated via decision fusion.
no code implementations • 1 May 2018 • Baocheng Geng, Qunwei Li, Pramod K. Varshney
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems.