916 research outputs found
Hop-Reservation Multiple Access with Variable Slots
AbstractHop-reservation multiple access control protocols in Ad Hoc networks are widely researched for its virtue in anti-jamming. Several typical such protocols are introduced and compared. Based on the analysis about their performance on anti-jamming and ability to serve upper protocols, a hop-reservation multiple access protocol with variable slot (HMAVS) is proposed. By the adaptation of variable length slots, the hop speed of control channel can be supported to the largest extent while diverse applications can be served without additional cost. Simulation results demonstrate the preference of HMAVS to other existing protocols
A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment
Three-dimensional (3D) kidney parsing on computed tomography angiography
(CTA) images is of great clinical significance. Automatic segmentation of
kidney, renal tumor, renal vein and renal artery benefits a lot on
surgery-based renal cancer treatment. In this paper, we propose a new
nnhra-unet network, and use a multi-stage framework which is based on it to
segment the multi-structure of kidney and participate in the KiPA2022
challenge
Intergrated Segmentation and Detection Models for Dentex Challenge 2023
Dental panoramic x-rays are commonly used in dental diagnosing. With the
development of deep learning, auto detection of diseases from dental panoramic
x-rays can help dentists to diagnose diseases more efficiently.The Dentex
Challenge 2023 is a competition for automatic detection of abnormal teeth along
with their enumeration ids from dental panoramic x-rays. In this paper, we
propose a method integrating segmentation and detection models to detect
abnormal teeth as well as obtain their enumeration ids.Our codes are available
at https://github.com/xyzlancehe/DentexSegAndDet
Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data
Creating synthetic voices with found data is challenging, as real-world
recordings often contain various types of audio degradation. One way to address
this problem is to pre-enhance the speech with an enhancement model and then
use the enhanced data for text-to-speech (TTS) model training. This paper
investigates the use of conditional diffusion models for generalized speech
enhancement, which aims at addressing multiple types of audio degradation
simultaneously. The enhancement is performed on the log Mel-spectrogram domain
to align with the TTS training objective. Text information is introduced as an
additional condition to improve the model robustness. Experiments on real-world
recordings demonstrate that the synthetic voice built on data enhanced by the
proposed model produces higher-quality synthetic speech, compared to those
trained on data enhanced by strong baselines. Code and pre-trained parameters
of the proposed enhancement model are available at
\url{https://github.com/dmse4tts/DMSE4TTS
Annual Report of the Commission of the Department of Public Utilities for the Year Ending November 30, 1937
Millimeter wave (mmWave) communications provide great potential for
next-generation cellular networks to meet the demands of fast-growing mobile
data traffic with plentiful spectrum available. However, in a mmWave cellular
system, the shadowing and blockage effects lead to the intermittent
connectivity, and the handovers are more frequent. This paper investigates an
``all-mmWave'' cloud radio access network (cloud-RAN), in which both the
fronthaul and the radio access links operate at mmWave. To address the
intermittent transmissions, we allow the mobile users (MUs) to establish
multiple connections to the central unit over the remote radio heads (RRHs).
Specifically, we propose a multipath transmission framework by leveraging the
``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH
association and the packet transmission scheduling according to the
time-varying network statistics, such that a MU experiences the minimum
queueing delay and packet drops. The joint RRH association and transmission
scheduling problem is formulated as a Markov decision process (MDP). Due to the
problem size, a low-complexity online learning scheme is put forward, which
requires no a priori statistic information of network dynamics. Simulations
show that our proposed scheme outperforms the state-of-art baselines, in terms
of average queue length and average packet dropping rate
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