454 research outputs found
CORRELATION ANALYSIS OF LEARNING BEHAVIOR TRAINING IN IDEOLOGICAL AND POLITICAL COURSES AND COLLEGE STUDENTS’ ANXIETY
CORRELATION ANALYSIS OF LEARNING BEHAVIOR TRAINING IN IDEOLOGICAL AND POLITICAL COURSES AND COLLEGE STUDENTS’ ANXIETY
Smartphone-enabled Biotelemetric System For a Smart Contact Lens
Diabetes describes a disordered metabolic state with an overabundance of glucose in
the bloodstream, due to insu cient production or utilization of insulin to allow tissue cells
from consuming glucose. People with unmanaged diabetes could lead to many serious
complications such as heart disease, stroke, coma, kidney failure, blindness, amputation,
and premature death. Diabetes can be managed by monitoring the blood glucose level, and
control the glucose level by taking insulin, and exercising a carefully planned lifestyle with
appropriate diet and physical activities. An elegant solution for glucose monitoring is the
integration of electrochemical-based glucose sensor and microelectronics within a contact
lens, namely a smart contact lens, which can measure the tear glucose in the eye, and
correlate it to blood glucose. Currently, there is no functional smart contact lens devices
for glucose detection in the market.
This thesis focuses on providing proof of concept prototypes for implementing energy
harvesting and wireless data transmission on a smart contact lens. An all-in-one solution is
proposed to harvest energy from a smartphone, and use the same smartphone to support
glucose data extraction by backscattering. The appropriate prototype architectures are
justi ed based on a system speci cation estimated from related works. The prototypes are
designed in simulation, and then fabricated on PCBs using o -the-shelf components and
equipment. Measurements are conducted on the prototypes to evaluate their performance
against the initial assessment of requirements from related works
Pushing the limits of self-supervised speaker verification using regularized distillation framework
Training robust speaker verification systems without speaker labels has long
been a challenging task. Previous studies observed a large performance gap
between self-supervised and fully supervised methods. In this paper, we apply a
non-contrastive self-supervised learning framework called DIstillation with NO
labels (DINO) and propose two regularization terms applied to embeddings in
DINO. One regularization term guarantees the diversity of the embeddings, while
the other regularization term decorrelates the variables of each embedding. The
effectiveness of various data augmentation techniques are explored, on both
time and frequency domain. A range of experiments conducted on the VoxCeleb
datasets demonstrate the superiority of the regularized DINO framework in
speaker verification. Our method achieves the state-of-the-art speaker
verification performance under a single-stage self-supervised setting on
VoxCeleb. The codes will be made publicly-available
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement
Disentangling uncorrelated information in speech utterances is a crucial
research topic within speech community. Different speech-related tasks focus on
extracting distinct speech representations while minimizing the affects of
other uncorrelated information. We present a large-scale speech corpus to
facilitate the research of speech representation disentanglement. 3D-Speaker
contains over 10,000 speakers, each of whom are simultaneously recorded by
multiple Devices, locating at different Distances, and some speakers are
speaking multiple Dialects. The controlled combinations of multi-dimensional
audio data yield a matrix of a diverse blend of speech representation
entanglement, thereby motivating intriguing methods to untangle them. The
multi-domain nature of 3D-Speaker also makes it a suitable resource to evaluate
large universal speech models and experiment methods of out-of-domain learning
and self-supervised learning. https://3dspeaker.github.io
SOA pattern effect mitigation by neural network based pre-equalizer for 50G PON
Semiconductor optical amplifier (SOA) is widely used for power amplification in O-band, particularly for passive optical networks (PONs) which can greatly benefit its advantages of simple structure, low power consumption and integrability with photonics circuits. However, the annoying nonlinear pattern effect degrades system performance when the SOA is needed as a pre-amplifier in PONs. Conventional solutions for pattern effect mitigation are either based on optical filtering or gain clamping. They are not simple or sufficiently flexible for practical deployment. Neural network (NN) has been demonstrated for impairment compensation in optical communications thanks to its powerful nonlinear fitting ability. In this paper, for the first time, NN-based equalizer is proposed to mitigate the SOA pattern effect for 50G PON with intensity modulation and direct detection. The experimental results confirm that the NN-based equalizer can effectively mitigate the SOA nonlinear pattern effect and significantly improve the dynamic range of receiver, achieving 29-dB power budget with the FEC limit at 1e-2. Moreover, the well-trained NN model in the receiver side can be directly placed at the transmitter in the optical line terminal to pre-equalize the signal for transmission so as to simplify digital signal processing in the optical network unit
Blockchain Network Analysis: A Comparative Study of Decentralized Banks
Decentralized finance (DeFi) is known for its unique mechanism design, which
applies smart contracts to facilitate peer-to-peer transactions. The
decentralized bank is a typical DeFi application. Ideally, a decentralized bank
should be decentralized in the transaction. However, many recent studies have
found that decentralized banks have not achieved a significant degree of
decentralization. This research conducts a comparative study among mainstream
decentralized banks. We apply core-periphery network features analysis using
the transaction data from four decentralized banks, Liquity, Aave, MakerDao,
and Compound. We extract six features and compare the banks' levels of
decentralization cross-sectionally. According to the analysis results, we find
that: 1) MakerDao and Compound are more decentralized in the transactions than
Aave and Liquity. 2) Although decentralized banking transactions are supposed
to be decentralized, the data show that four banks have primary external
transaction core addresses such as Huobi, Coinbase, Binance, etc. We also
discuss four design features that might affect network decentralization. Our
research contributes to the literature at the interface of decentralized
finance, financial technology (Fintech), and social network analysis and
inspires future protocol designs to live up to the promise of decentralized
finance for a truly peer-to-peer transaction network
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