62 research outputs found
Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network
In this paper, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cram\'{e}r-Rao lower bound (BCRLB) and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a non-linear and non-convex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization (PSO) algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks
Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation
The growing need for instant spoken language transcription and translation is
driven by increased global communication and cross-lingual interactions. This
has made offering translations in multiple languages essential for user
applications. Traditional approaches to automatic speech recognition (ASR) and
speech translation (ST) have often relied on separate systems, leading to
inefficiencies in computational resources, and increased synchronization
complexity in real time. In this paper, we propose a streaming
Transformer-Transducer (T-T) model able to jointly produce many-to-one and
one-to-many transcription and translation using a single decoder. We introduce
a novel method for joint token-level serialized output training based on
timestamp information to effectively produce ASR and ST outputs in the
streaming setting. Experiments on {it,es,de}->en prove the effectiveness of our
approach, enabling the generation of one-to-many joint outputs with a single
decoder for the first time.Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Joint Collaborative Radar Selection and Transmit Resource Allocation in Multiple Distributed Radar Networks with Imperfect Detection Performance
In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms
Growth of millimeter-sized high-quality CuFeSe single crystals by the molten salt method and study of their semiconducting behavior
An eutectic AlCl/KCl molten salt method in a horizontal configuration was
employed to grow millimeter-sized and composition homogeneous CuFeSe single
crystals due to the continuous growth process in a temperature gradient induced
solution convection. The typical as-grown CuFeSe single crystals in cubic
forms are nearly 1.61.21.0 mm3 in size. The chemical
composition and homogeneity of the crystals was examined by both inductively
coupled plasma atomic emission spectroscopy and energy dispersive spectrometer
with Cu:Fe:Se = 0.96:1.00:1.99 consistent with the stoichiometric composition
of CuFeSe. The magnetic measurements suggest a ferrimagnetic or weak
ferromagnetic transition below T = 146 K and the resistivity reveals a
semiconducting behavior and an abrupt increase below T
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Model pre-training is essential in human-centric perception. In this paper,
we first introduce masked image modeling (MIM) as a pre-training approach for
this task. Upon revisiting the MIM training strategy, we reveal that human
structure priors offer significant potential. Motivated by this insight, we
further incorporate an intuitive human structure prior - human parts - into
pre-training. Specifically, we employ this prior to guide the mask sampling
process. Image patches, corresponding to human part regions, have high priority
to be masked out. This encourages the model to concentrate more on body
structure information during pre-training, yielding substantial benefits across
a range of human-centric perception tasks. To further capture human
characteristics, we propose a structure-invariant alignment loss that enforces
different masked views, guided by the human part prior, to be closely aligned
for the same image. We term the entire method as HAP. HAP simply uses a plain
ViT as the encoder yet establishes new state-of-the-art performance on 11
human-centric benchmarks, and on-par result on one dataset. For example, HAP
achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K
for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose
estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.Comment: Accepted by NeurIPS 202
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