79 research outputs found
Characterization of Via Structures in Multilayer Printed Circuit Boards with an Equivalent Transmission-Line Model
Vias are typical discontinuities for high-speed signal transmission in printed circuit boards (PCBs). This paper proposes an equivalent multiconductor transmission-line model for via structures in multilayer PCBs. the proposed model is based on the physic-Based circuit model, taking into account distributed effects. Various via structures with arbitrary via number, trace connections, and via stubs can be effectively handled in the equivalent transmission-line model, which is sufficiently accurate and fast to be integrated into circuit simulators for via and link-path analysis. with the proposed model, well-known transmission-line theories can be borrowed for via modeling and characterization for signal integrity in high-speed digital circuits. the proposed model is validated with both full-wave simulations and measurements for multilayer PCBs with different via structures. © 1964-2012 IEEE
A CMOS Analog Front-End for Tunnelling Magnetoresistive Spintronic Sensing Systems
This paper presents a CMOS readout circuit for
an integrated and highly-sensitive tunnel-magnetoresistive
(TMR) sensor. Based on the characterization of the TMR sensor
in the finite-element simulation, using COMSOL Multiphysics,
the circuit including a Wheatstone bridge and an analogue
front-end (AFE) circuit, were designed to achieve low-noise and
low-power sensing. We present a transimpedance amplifier
(TIA) that biases and amplifies a TMR sensor array using
switched-capacitors external noise filtering and allows the
integration of TMR sensors on CMOS without decreasing the
measurement resolution. Designed using TSMC 0.18 μm 1V
technology, the amplifier consumes 160 nA at 1.8 V supply to
achieve a dc gain of 118 dB and a bandwidth of 3.8 MHz. The
results confirm that the full system is able to detect the magnetic
field in the pico-Tesla range with low circuit noise
(2.297 pA/√Hz) and low power consumption (86 μW). A
concurrent reduction in the power consumption and attenuation
of noise in TMR sensors makes them suitable for long-term
deployment in spintronic sensing systems
Stochastic Electron Acceleration in Shell-Type Supernova Remnants II
We discuss the generic characteristics of stochastic particle acceleration by
a fully developed turbulence spectrum and show that resonant interactions of
particles with high speed waves dominate the acceleration process. To produce
the relativistic electrons inferred from the broadband spectrum of a few
well-observed shell-type supernova remnants in the leptonic scenario for the
TeV emission, fast mode waves must be excited effectively in the downstream and
dominate the turbulence in the subsonic phase. Strong collisionless
non-relativistic astrophysical shocks are studied with the assumption of a
constant Aflven speed. The energy density of non-thermal electrons is found to
be comparable to that of the magnetic field. With reasonable parameters, the
model explains observations of shell-type supernova remnants. More detailed
studies are warranted to better understand the nature of supernova shocks.Comment: 5 pages, 7 figures, submitted to Proceedings of the Conference on
"2008 Heidelberg International Symposium on High Energy Gamma-Ray Astronomy
Experimental investigation of a novel vertical loop-heat-pipe PV/T heat and power system under different height differences
For a novel vertical solar loop-heat-pipe photovoltaic/thermal system, the height difference between evaporator and condenser plays an important role in the heat transport capacity, which has significant impact on the solar thermal efficiency and parametrical optimization of this system. Therefore, based on the results derived from the authors’ previous analytical investigation and computer modelling studies, a prototype of this novel system was designed, constructed, and an experimental investigation under different height difference was undertaken to study the impact of height difference on the system performance. It was found that the relationship between the solar thermal efficiency of this vertical system and the height difference is nonlinear. In present study, the optimal height difference is around 1.1 m, which was selected as an optimal value for the following experimental investigations, and below 1.1 m, the PV module surface temperature decreased with the increase of the height difference. Furthermore, the transient solar thermal and electrical performance of this system with the selected optimal height difference were investigated under outdoor real weather condition. These results of this experimentation can help optimize the system construction and thus help to develop the high thermal performance and low-cost solar PV/T system for space heating and power generation
A CMOS hall sensor modelling with readout circuitry and microcontroller processing for magnetic detection
A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 μm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array. The system puts forward a complete solution from Hall sensors, analog front-end circuit, analog-to-digital converter (ADC) to microcontroller unit. Using Ansoft Maxwell and COMSOL Multiphysics software for simulation verification, the minimum diameter of magnetic particles that can be detected in the system is 2 μm. The measured signal to noise and distortion ratio, spurious free dynamic range, and effective number of bits of the proposed ADC are 70.61 dB, 90.08 dB, and 11.44-bit, respectively. The microsystem based on STM32 combines hardware and software design, which can effectively adjust the motion parameters and realize the real-time display in the LCD screen of the magnetic field and voltage information. Compared to the prior system, the portability, cost, and efficiency have been considerably improved, which is aimed at the rapid measurement of heavy metal particles such as Fe, Co, and Ni in ambient air and blood
Detection techniques of biological and chemical Hall sensors
Integrated magnetic Hall effect sensors have been widely used in people's daily life over the past decades, and still are gaining enormous attention from researchers to establish novel applications, especially in biochemistry and biomedical healthcare. This paper reviews, classifies, compares and concludes state-of-the-art integrated Hall magnetic sensors in terms of cost, power, area, performance and application. Current applications of the Hall sensors such as detecting magnetic nanoparticles (MNPs) labeled on biomolecule, monitoring blood pulse wave velocity, characterizing soft biological materials, controlling syringe injection rate and eye surgery by training systems, and assisting magnetic resonance imaging (MRI) will be discussed comprehensively and future applications and trends will be highlighted. This review paper will introduce Hall sensor's advantages such as simple design and technology of manufacturing, low cost, low power consumption, possibility of the miniaturizing, noninvasive and room temperature measurement, with respect to the other magnetic sensing systems and methods
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
In the field of multi-task reinforcement learning, the modular principle,
which involves specializing functionalities into different modules and
combining them appropriately, has been widely adopted as a promising approach
to prevent the negative transfer problem that performance degradation due to
conflicts between tasks. However, most of the existing multi-task RL methods
only combine shared modules at the task level, ignoring that there may be
conflicts within the task. In addition, these methods do not take into account
that without constraints, some modules may learn similar functions, resulting
in restricting the model's expressiveness and generalization capability of
modular methods. In this paper, we propose the Contrastive Modules with
Temporal Attention(CMTA) method to address these limitations. CMTA constrains
the modules to be different from each other by contrastive learning and
combining shared modules at a finer granularity than the task level with
temporal attention, alleviating the negative transfer within the task and
improving the generalization ability and the performance for multi-task RL. We
conducted the experiment on Meta-World, a multi-task RL benchmark containing
various robotics manipulation tasks. Experimental results show that CMTA
outperforms learning each task individually for the first time and achieves
substantial performance improvements over the baselines.Comment: This paper has been accepted at NeurIPS 2023 as a poste
Stochastic Acceleration in the Western Hotspot of Pictor A
Chandra's high resolution observations of radio galaxies require a revisit of
the relevant electron acceleration processes. Although the diffusive shock
particle acceleration model may explain spectra of spatially unresolved
sources, it encounters difficulties in explaining the structure and spectral
properties of recently discovered Chandra X-ray features in several low-power
radio sources. We argue that these observations strongly suggest stochastic
electron acceleration by magnetized turbulence, and show that the simplest
stochastic particle acceleration model with energy independent acceleration and
escape timescales can overcome most of these difficulties. We use the bright
core of the western hotspot of Pictor A as an example to demonstrate the model
characteristics, which may be tested with high energy observations.Comment: 12 pages, 2 figures. Accepted by ApJ Letter
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
Synthesizing high-fidelity head avatars is a central problem for computer
vision and graphics. While head avatar synthesis algorithms have advanced
rapidly, the best ones still face great obstacles in real-world scenarios. One
of the vital causes is inadequate datasets -- 1) current public datasets can
only support researchers to explore high-fidelity head avatars in one or two
task directions; 2) these datasets usually contain digital head assets with
limited data volume, and narrow distribution over different attributes. In this
paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive
advance in head avatar research. It contains massive data assets, with 243+
million complete head frames, and over 800k video sequences from 500 different
identities captured by synchronized multi-view cameras at 30 FPS. It is a
large-scale digital library for head avatars with three key attributes: 1) High
Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K
cameras in 360 degrees. 2) High Diversity: The collected subjects vary from
different ages, eras, ethnicities, and cultures, providing abundant materials
with distinctive styles in appearance and geometry. Moreover, each subject is
asked to perform various motions, such as expressions and head rotations, which
further extend the richness of assets. 3) Rich Annotations: we provide
annotations with different granularities: cameras' parameters, matting, scan,
2D/3D facial landmarks, FLAME fitting, and text description.
Based on the dataset, we build a comprehensive benchmark for head avatar
research, with 16 state-of-the-art methods performed on five main tasks: novel
view synthesis, novel expression synthesis, hair rendering, hair editing, and
talking head generation. Our experiments uncover the strengths and weaknesses
of current methods. RenderMe-360 opens the door for future exploration in head
avatars.Comment: Technical Report; Project Page: 36; Github Link:
https://github.com/RenderMe-360/RenderMe-36
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