154 research outputs found
Mixed-source charger-supply CMOS IC
The proposed research objective is to develop, test, and evaluate a mixer and charger-supply CMOS IC that derives and mixes energy and power from mixed sources to accurately supply a miniaturized system. Since the energy-dense source stores more energy than the power-dense source while the latter supplies more power than the former, the proposed research aims to develop an IC that automatically selects how much and from which source to draw power to maximize lifetime per unit volume. Today, the state of the art lacks the intelligence and capability to select the most appropriate source from which to extract power to supply the time-varying needs of a small system. As such, the underlying objective and benefit of this research is to reduce the size of a complete electronic system so that wireless sensors and biomedical implants, for example, as a whole, perform well, operate for extended periods, and integrate into tiny spaces.Ph.D
Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning
Occluded person re-identification (Re-ID) in images captured by multiple
cameras is challenging because the target person is occluded by pedestrians or
objects, especially in crowded scenes. In addition to the processes performed
during holistic person Re-ID, occluded person Re-ID involves the removal of
obstacles and the detection of partially visible body parts. Most existing
methods utilize the off-the-shelf pose or parsing networks as pseudo labels,
which are prone to error. To address these issues, we propose a novel Occlusion
Correction Network (OCNet) that corrects features through relational-weight
learning and obtains diverse and representative features without using external
networks. In addition, we present a simple concept of a center feature in order
to provide an intuitive solution to pedestrian occlusion scenarios.
Furthermore, we suggest the idea of Separation Loss (SL) for focusing on
different parts between global features and part features. We conduct extensive
experiments on five challenging benchmark datasets for occluded and holistic
Re-ID tasks to demonstrate that our method achieves superior performance to
state-of-the-art methods especially on occluded scene.Comment: ICASSP 202
Mouse neuroblastoma cell-based model and the effect of epileptic events on calcium oscillations and neural spikes
Recently, mouse neuroblastoma cells have been considered as an attractive model for the study of human neurological and prion diseases, and they have been intensively used as a model system in different areas. For example, the differentiation of neuro2a (N2A) cells, receptor-mediated ion current, and glutamate-induced physiological responses have been actively investigated with these cells. These mouse neuroblastoma N2A cells are of interest because they grow faster than other cells of neural origin and have a number of other advantages. The calcium oscillations and neural spikes of mouse neuroblastoma N2A cells in epileptic conditions are evaluated. Based on our observations of neural spikes in these cells with our proposed imaging modality, we reported that they can be an important model in epileptic activity studies. We concluded that mouse neuroblastoma N2A cells produce epileptic spikes in vitro in the same way as those produced by neurons or astrocytes. This evidence suggests that increased levels of neurotransmitter release due to the enhancement of free calcium from 4-aminopyridine causes the mouse neuroblastoma N2A cells to produce epileptic spikes and calcium oscillations.open0
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
Referring Image Segmentation (RIS) aims to segment target objects expressed
in natural language within a scene at the pixel level. Various recent RIS
models have achieved state-of-the-art performance by generating contextual
tokens to model multimodal features from pretrained encoders and effectively
fusing them using transformer-based cross-modal attention. While these methods
match language features with image features to effectively identify likely
target objects, they often struggle to correctly understand contextual
information in complex and ambiguous sentences and scenes. To address this
issue, we propose a novel bidirectional token-masking autoencoder (BTMAE)
inspired by the masked autoencoder (MAE). The proposed model learns the context
of image-to-language and language-to-image by reconstructing missing features
in both image and language features at the token level. In other words, this
approach involves mutually complementing across the features of images and
language, with a focus on enabling the network to understand interconnected
deep contextual information between the two modalities. This learning method
enhances the robustness of RIS performance in complex sentences and scenes. Our
BTMAE achieves state-of-the-art performance on three popular datasets, and we
demonstrate the effectiveness of the proposed method through various ablation
studies
Meteorological and sea surface water measurement data from Icebreaker Research Vessel ARAON for 2010-2019 Arctic research expeditions
Despite of its economic and scientific significances with mineral resources, the Northern Sea Routes, and climate change, the Arctic Ocean has been a challenge for long-term continuous environmental observations. Since its inception in 2009, the ice-breaker research vessel ARAON has been conducting an annual expedition in the Arctic Ocean for the last 10 years from 2010. All the Arctic expeditions have been carried out mainly in August-September when the sea ice extent shrinks and the thickness becomes relatively thin around the Bering Sea, Chukchi Sea, Beaufort Sea, and high latitudes over the Russia, the US, and Canada. IBRV ARAON can conduct research activities through a variety of research equipment such as on-board meteorological data and surface temperature & salinity monitoring data of seawater. In this study, meteorological observation elements including solar radiation, air temperature, relative humidity, wind speed, and wind direction are presented. In addition, sea surface water temperature and salinity monitoring elements including water temperature, salinity, conductivity, and sound speed are presented
Pixel-Level Equalized Matching for Video Object Segmentation
Feature similarity matching, which transfers the information of the reference
frame to the query frame, is a key component in semi-supervised video object
segmentation. If surjective matching is adopted, background distractors can
easily occur and degrade the performance. Bijective matching mechanisms try to
prevent this by restricting the amount of information being transferred to the
query frame, but have two limitations: 1) surjective matching cannot be fully
leveraged as it is transformed to bijective matching at test time; and 2)
test-time manual tuning is required for searching the optimal hyper-parameters.
To overcome these limitations while ensuring reliable information transfer, we
introduce an equalized matching mechanism. To prevent the reference frame
information from being overly referenced, the potential contribution to the
query frame is equalized by simply applying a softmax operation along with the
query. On public benchmark datasets, our proposed approach achieves a
comparable performance to state-of-the-art methods
A 1.35GHz All-Digital Fractional-N PLL with Adaptive Loop Gain Controller and Fractional Divider
A 1.35GHz all-digital phase-locked loop (ADPLL)
with an adaptively controlled loop filter and a 1/3rd-resolution
fractional divider is presented. The adaptive loop gain controller
(ALGC) effectively reduces the nonlinear characteristics of the
bang-bang phase-frequency detector (BBPFD). The fractional
divider partially compensates for the input phase error which is
caused by the fractional-N frequency synthesis operation. A
prototype ADPLL using a BBPFD with a dead zone free retimer,
an ALGC, and a fractional divider is fabricated in 0.13m
CMOS. The core occupies 0.19mm2 and consumes 13.7mW from
a 1.2V supply. The measured RMS jitter was 4.17ps at a
1.35GHz clock output
A High-Speed Range-Matching TCAM for Storage-Efficient Packet Classification
AbstractโA critical issue in the use of TCAMs for packet
classification is how to efficiently represent rules with ranges,
known as range matching. A range-matching ternary content
addressable memory (RM-TCAM) including a highly functional
range-matching cell (RMC) is presented in this paper. By offering
various range operators, the RM-TCAM can reduce storage
expansion ratio from 4.21 to 1.01 compared with conventional
TCAMs, under real-world packet classification rule sets, which
results in reduced power consumption and die area. A new pre-discharging
match-line scheme is used to realize high-speed searching
in a dynamic match-line structure. An additional charge-recycling
driver further reduces the power consumption of search lines.
Simulation results of a 256 64-bit range-matching TCAM, when
implemented in the 0.13- m CMOS technology, achieves a 1.99-ns
search time with an energy efficiency of 1.26 fJ/bit/search. While a
TCAM including range encoding approach requires an additional
SRAM or DRAM, the RM-TCAM can improve storage efficiency
without any extra components as well as reduce the die area
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