143 research outputs found
Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification
In this letter, we propose a multitask deep learning method for
classification of multiple hyperspectral data in a single training. Deep
learning models have achieved promising results on hyperspectral image
classification, but their performance highly rely on sufficient labeled
samples, which are scarce on hyperspectral images. However, samples from
multiple data sets might be sufficient to train one deep learning model,
thereby improving its performance. To do so, we trained an identical feature
extractor for all data, and the extracted features were fed into corresponding
Softmax classifiers. Spectral knowledge was introduced to ensure that the
shared features were similar across domains. Four hyperspectral data sets were
used in the experiments. We achieved higher classification accuracies on three
data sets (Pavia University, Pavia Center, and Indian Pines) and competitive
results on the Salinas Valley data compared with the baseline. Spectral
knowledge was useful to prevent the deep network from overfitting when the data
shared similar spectral response. The proposed method tested on two deep CNNs
successfully shows its ability to utilize samples from multiple data sets and
enhance networks' performance.Comment: Accepted by IEEE GRS
Greedy Signal Space Recovery Algorithm with Overcomplete Dictionaries in Compressive Sensing
Compressive Sensing (CS) is a new paradigm for the efficient acquisition of
signals that have sparse representation in a certain domain. Traditionally, CS
has provided numerous methods for signal recovery over an orthonormal basis.
However, modern applications have sparked the emergence of related methods for
signals not sparse in an orthonormal basis but in some arbitrary, perhaps
highly overcomplete, dictionary, particularly due to their potential to
generate different kinds of sparse representation of signals. To this end, we
apply a signal space greedy method, which relies on the ability to optimally
project a signal onto a small number of dictionary atoms, to address signal
recovery in this setting. We describe a generalized variant of the iterative
recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more
challenging setting. Here, using the Dictionary-Restricted Isometry Property
(D-RIP) rather than classical RIP, we derive a low bound on the number of
measurements required and then provide the proof of convergence for the
algorithm. The algorithm in noisy and noise-free measurements has low
computational complexity and provides high recovery accuracy. Simulation
results show that the algorithm outperforms best compared with the existing
recovery algorithms
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning
Current hyperspectral image classification assumes that a predefined
classification system is closed and complete, and there are no unknown or novel
classes in the unseen data. However, this assumption may be too strict for the
real world. Often, novel classes are overlooked when the classification system
is constructed. The closed nature forces a model to assign a label given a new
sample and may lead to overestimation of known land covers (e.g., crop area).
To tackle this issue, we propose a multitask deep learning method that
simultaneously conducts classification and reconstruction in the open world
(named MDL4OW) where unknown classes may exist. The reconstructed data are
compared with the original data; those failing to be reconstructed are
considered unknown, based on the assumption that they are not well represented
in the latent features due to the lack of labels. A threshold needs to be
defined to separate the unknown and known classes; we propose two strategies
based on the extreme value theory for few-shot and many-shot scenarios. The
proposed method was tested on real-world hyperspectral images; state-of-the-art
results were achieved, e.g., improving the overall accuracy by 4.94% for the
Salinas data. By considering the existence of unknown classes in the open
world, our method achieved more accurate hyperspectral image classification,
especially under the few-shot context.Comment: Accepted by IEEE TGR
Learning-Based Massive Beamforming
Developing resource allocation algorithms with strong real-time and high
efficiency has been an imperative topic in wireless networks. Conventional
optimization-based iterative resource allocation algorithms often suffer from
slow convergence, especially for massive multiple-input-multiple-output (MIMO)
beamforming problems. This paper studies learning-based efficient massive
beamforming methods for multi-user MIMO networks. The considered massive
beamforming problem is challenging in two aspects. First, the beamforming
matrix to be learned is quite high-dimensional in case with a massive number of
antennas. Second, the objective is often time-varying and the solution space is
not fixed due to some communication requirements. All these challenges make
learning representation for massive beamforming an extremely difficult task. In
this paper, by exploiting the structure of the most popular WMMSE beamforming
solution, we propose convolutional massive beamforming neural networks (CMBNN)
using both supervised and unsupervised learning schemes with particular design
of network structure and input/output. Numerical results demonstrate the
efficacy of the proposed CMBNN in terms of running time and system throughput
Enhancing spin-orbit torque by strong interfacial scattering from ultra-thin insertion layers
Increasing dampinglike spin-orbit torque (SOT) is both of fundamental
importance for enabling new research into spintronics phenomena and also
technologically urgent for advancing low-power spin-torque memory, logic, and
oscillator devices. Here, we demonstrate that enhancing interfacial scattering
by inserting ultra-thin layers within a spin Hall metals with intrinsic or
side-jump mechanisms can significantly enhance the spin Hall ratio. The
dampinglike SOT was enhanced by a factor of 2 via sub-monolayer Hf insertion,
as evidenced by both harmonic response measurements and current-induced
switching of in-plane magnetized magnetic memory devices with the record low
critical switching current of ~73 uA (switching current density of 3.6x10^6
A/cm^2). This work demonstrates a very effective strategy for maximizing
dampinglike SOT for low-power spin-torque devices
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Face photo-sketch synthesis aims at generating a facial sketch/photo
conditioned on a given photo/sketch. It is of wide applications including
digital entertainment and law enforcement. Precisely depicting face
photos/sketches remains challenging due to the restrictions on structural
realism and textural consistency. While existing methods achieve compelling
results, they mostly yield blurred effects and great deformation over various
facial components, leading to the unrealistic feeling of synthesized images. To
tackle this challenge, in this work, we propose to use the facial composition
information to help the synthesis of face sketch/photo. Specially, we propose a
novel composition-aided generative adversarial network (CA-GAN) for face
photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face
photo/sketch and the corresponding pixel-wise face labels for generating a
sketch/photo. In addition, to focus training on hard-generated components and
delicate facial structures, we propose a compositional reconstruction loss.
Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add
compelling details. Experimental results show that our method is capable of
generating both visually comfortable and identity-preserving face
sketches/photos over a wide range of challenging data. Our method achieves the
state-of-the-art quality, reducing best previous Frechet Inception distance
(FID) by a large margin. Besides, we demonstrate that the proposed method is of
considerable generalization ability. We have made our code and results publicly
available: https://fei-hdu.github.io/ca-gan/.Comment: 10 pages, 8 figures, journa
Anisotropic Charge Carrier and Coherent Acoustic Phonon Dynamics of Black Phosphorus Studied by Transient Absorption Microscopy
Due to its corrugated hexagonal lattice structure, Black phosphorus (BP) has
unique anisotropic physical properties, which provides an additional freedom
for designing devices. Many body interactions, including interactions with
phonon, is crucial for heat dissipation and charge carrier mobility in device.
However, the rich properties of the coherent acoustic phonon, including
anisotropy, propagation and generation were not fully interrogated. In this
paper, the polarization-resolved transient absorption microscopy was conducted
on BP flakes to study the dynamics of photoexcited charge carriers and coherent
acoustic phonon. Polarization-resolved transient absorption images and traces
were recorded and showed anisotropic and thickness-dependent charge carriers
decay dynamics. The damping of the coherent acoustic phonon oscillation was
found to be anisotropic, which was attributed to the polarization-dependent
absorption length of the probe pulse. From the analysis of initial oscillation
amplitude and phase of coherent acoustic phonon oscillation, we proposed that
the direct deformation potential mechanism dominated the generation of coherent
acoustic phonons in our experiment. Besides, we obtained the sound velocity of
the coherent acoustic phonon from the oscillation frequency and the acoustic
echo, respectively, which agreed well with each other. These findings provide
significant insights into the rich acoustic phonon properties of BP, and
promise important application for BP in polarization-sensitive optical and
optoelectronic devices.Comment: just accepte
Energy-efficient ultrafast SOT-MRAMs based on low-resistivity spin Hall metal Au0.25Pt0.75
Many key electronic technologies (e.g., large-scale computing, machine
learning, and superconducting electronics) require new memories that are fast,
reliable, energy-efficient, and of low-impedance at the same time, which has
remained a challenge. Non-volatile magnetoresistive random access memories
(MRAMs) driven by spin-orbit torques (SOTs) have promise to be faster and more
energy-efficient than conventional semiconductor and spin-transfer-torque
magnetic memories. This work reports that the spin Hall effect of
low-resistivity Au0.25Pt0.75 thin films enables ultrafast antidamping-torque
switching of SOT-MRAM devices for current pulse widths as short as 200 ps. If
combined with industrial-quality lithography and already-demonstrated
interfacial engineering, our results show that an optimized MRAM cell based on
Au0.25Pt0.75 can have energy-efficient, ultrafast, and reliable switching, e.g.
a write energy of < 1 fJ (< 50 fJ) for write error rate of 50% (<1e-5) for 1 ns
pulses. The antidamping torque switching of the Au0.25Pt0.75 devices is 10
times faster than expected from a rigid macrospin model, most likely because of
the fast micromagnetics due to the enhanced non-uniformity within the free
layer. These results demonstrate the feasibility of Au0.25Pt0.75-based
SOT-MRAMs as a candidate for ultrafast, reliable, energy-efficient,
low-impedance, and unlimited-endurance memory
Fast, low-current spin-orbit torque switching of magnetic tunnel junctions through atomic modifications of the free layer interfaces
Future applications of spin-orbit torque will require new mechanisms to
improve the efficiency for switching nanoscale magnetic tunnel junctions
(MTJs), while also controlling the magnetic dynamics to achieve fast,
nanosecond scale performance with low write error rates. Here we demonstrate a
strategy to simultaneously enhance the interfacial magnetic anisotropy energy
and suppress interfacial spin memory loss by introducing sub-atomic and
monatomic layers of Hf at the top and bottom interfaces of the ferromagnetic
free layer of an in-plane magnetized three-terminal MTJ device. When combined
with a beta-W spin Hall channel that generates spin-orbit torque, the
cumulative effect is a switching current density of 5.4 x 106 A/cm2, more than
a factor of 3 lower than demonstrated in any other spin-orbit-torque magnetic
memory device at room temperature, and highly reliable switching with current
pulses only 2 ns long
Nanosecond Reversal of Three-Terminal Spin Hall Effect Memories Sustained at Cryogenic Temperatures
We characterize the nanosecond pulse switching performance of the
three-terminal magnetic tunnel junctions (MTJs), driven by the spin Hall effect
(SHE) in the channel, at a cryogenic temperature of 3 K. The SHE-MTJ devices
exhibit reasonable magnetic switching and reliable current switching by as
short pulses as 1 ns of A/m magnitude, exceeding the
expectation from conventional macrospin model. The pulse switching bit error
rates reach below for < 10 ns pulses. Similar performance is achieved
with exponentially decaying pulses expected to be delivered to the SHE-MTJ
device by a nanocryotron device in parallel configuration of a realistic memory
cell structure. These results suggest the viability of the SHE-MTJ structure as
a cryogenic memory element for exascale superconducting computing systems.Comment: 12 pages, 5 figure
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