1,743 research outputs found
On the Convergence of Decentralized Gradient Descent
Consider the consensus problem of minimizing where
each is only known to one individual agent out of a connected network
of agents. All the agents shall collaboratively solve this problem and
obtain the solution subject to data exchanges restricted to between neighboring
agents. Such algorithms avoid the need of a fusion center, offer better network
load balance, and improve data privacy. We study the decentralized gradient
descent method in which each agent updates its variable , which is
a local approximate to the unknown variable , by combining the average of
its neighbors' with the negative gradient step .
The iteration is where the averaging coefficients form a symmetric doubly stochastic matrix
. We analyze the convergence of this
iteration and derive its converge rate, assuming that each is proper
closed convex and lower bounded, is Lipschitz continuous with
constant , and stepsize is fixed. Provided that where , the objective error at the averaged
solution, , reduces at a speed of
until it reaches . If are further (restricted) strongly
convex, then both and each converge
to the global minimizer at a linear rate until reaching an
-neighborhood of . We also develop an iteration for
decentralized basis pursuit and establish its linear convergence to an
-neighborhood of the true unknown sparse signal
Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics
Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe
Compressive Spectrum Sensing in Cognitive IoT
PhDWith the rising of new paradigms in wireless communications such as Internet of things
(IoT), current static frequency allocation policy faces a primary challenge of spectrum
scarcity, and thus encourages the IoT devices to have cognitive capabilities to access
the underutilised spectrum in the temporal and spatial dimensions. Wideband spectrum
sensing is one of the key functions to enable dynamic spectrum access, but entails a
major implementation challenge in terms of sampling rate and computation cost since
the sampling rate of analog-to-digital converters (ADCs) should be higher than twice of
the spectrum bandwidth based on the Nyquist-Shannon sampling theorem. By exploiting
the sparse nature of wideband spectrum, sub-Nyquist sampling and sparse signal recovery
have shown potential capabilities in handling these problems, which are directly related
to compressive sensing (CS) from the viewpoint of its origin.
To invoke sub-Nyquist wideband spectrum sensing in IoT, blind signal acquisition with
low-complexity sparse recovery is desirable on compact IoT devices. Moreover, with
cooperation among distributed IoT devices, the complexity of sampling and reconstruc-
tion can be further reduced with performance guarantee. Specifically, an adaptively-
regularized iterative reweighted least squares (AR-IRLS) reconstruction algorithm is
proposed to speed up the convergence of reconstruction with less number of iterations.
Furthermore, a low-complexity compressive spectrum sensing algorithm is proposed to
reduce computation complexity in each iteration of IRLS-based reconstruction algorithm,
from cubic time to linear time. Besides, to transfer computation burden from the IoT
devices to the core network, a joint iterative reweighted sparse recovery scheme with
geo-location database is proposed to adopt the occupied channel information from geo-
location database to reduce the complexity in the signal reconstruction. Since numerous
IoT devices access or release the spectrum randomly, the sparsity levels of wideband spec-trum signals are varying and unknown. A blind CS-based sensing algorithm is proposed
to enable the local secondary users (SUs) to adaptively adjust the sensing time or sam-
pling rate without knowledge of spectral sparsity. Apart from the signal reconstruction
at the back-end, a distributed sub-Nyquist sensing scheme is proposed by utilizing the
surrounding IoT devices to jointly sample the spectrum based on the multi-coset sam-
pling theory, in which only the minimum number of low-rate ADCs on the IoT devices
are required to form coset samplers without the prior knowledge of the number of occu-
pied channels and signal-to-noise ratios. The models of the proposed algorithms are
derived and verified by numerical analyses and tested on both real-world and simulated
TV white space signals
Sense, Predict, Adapt, Repeat: A Blueprint for Design of New Adaptive AI-Centric Sensing Systems
As Moore's Law loses momentum, improving size, performance, and efficiency of
processors has become increasingly challenging, ending the era of predictable
improvements in hardware performance. Meanwhile, the widespread incorporation
of high-definition sensors in consumer devices and autonomous technologies has
fueled a significant upsurge in sensory data. Current global trends reveal that
the volume of generated data already exceeds human consumption capacity, making
AI algorithms the primary consumers of data worldwide. To address this, a novel
approach to designing AI-centric sensing systems is needed that can bridge the
gap between the increasing capabilities of high-definition sensors and the
limitations of AI processors. This paper provides an overview of efficient
sensing and perception methods in both AI and sensing domains, emphasizing the
necessity of co-designing AI algorithms and sensing systems for dynamic
perception. The proposed approach involves a framework for designing and
analyzing dynamic AI-in-the-loop sensing systems, suggesting a fundamentally
new method for designing adaptive sensing systems through inference-time
AI-to-sensor feedback and end-to-end efficiency and performance optimization
- …