101 research outputs found
Automatic Generation of High-Coverage Tests for RTL Designs using Software Techniques and Tools
Register Transfer Level (RTL) design validation is a crucial stage in the
hardware design process. We present a new approach to enhancing RTL design
validation using available software techniques and tools. Our approach converts
the source code of a RTL design into a C++ software program. Then a powerful
symbolic execution engine is employed to execute the converted C++ program
symbolically to generate test cases. To better generate efficient test cases,
we limit the number of cycles to guide symbolic execution. Moreover, we add
bit-level symbolic variable support into the symbolic execution engine.
Generated test cases are further evaluated by simulating the RTL design to get
accurate coverage. We have evaluated the approach on a floating point unit
(FPU) design. The preliminary results show that our approach can deliver
high-quality tests to achieve high coverage
Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication
In the multiple unmanned aerial vehicle (UAV)- assisted downlink
communication, it is challenging for UAV base stations (UAV BSs) to realize
trajectory design and resource assignment in unknown environments. The
cooperation and competition between UAV BSs in the communication network leads
to a Markov game problem. Multi-agent reinforcement learning is a significant
solution for the above decision-making. However, there are still many common
issues, such as the instability of the system and low utilization of historical
data, that limit its application. In this paper, a novel graph-attention
multi-agent trust region (GA-MATR) reinforcement learning framework is proposed
to solve the multi-UAV assisted communication problem. Graph recurrent network
is introduced to process and analyze complex topology of the communication
network, so as to extract useful information and patterns from observational
information. The attention mechanism provides additional weighting for conveyed
information, so that the critic network can accurately evaluate the value of
behavior for UAV BSs. This provides more reliable feedback signals and helps
the actor network update the strategy more effectively. Ablation simulations
indicate that the proposed approach attains improved convergence over the
baselines. UAV BSs learn the optimal communication strategies to achieve their
maximum cumulative rewards. Additionally, multi-agent trust region method with
monotonic convergence provides an estimated Nash equilibrium for the multi-UAV
assisted communication Markov game.Comment: 13 page
Performance Analysis of Discrete-Phase-Shifter IRS-aided Amplify-and-Forward Relay Network
As a new technology to reconfigure wireless communication environment by
signal reflection controlled by software, intelligent reflecting surface (IRS)
has attracted lots of attention in recent years. Compared with conventional
relay system, the relay system aided by IRS can effectively reduce the cost and
energy consumption, and significantly enhance the system performance. However,
the phase quantization error generated by IRS with discrete phase shifter may
degrade the receiving performance of the receiver. To analyze the performance
loss caused by IRS phase quantization error, based on the law of large numbers
and Rayleigh distribution, the closed-form expressions for the signal-to-noise
ratio (SNR) performance loss and achievable rate of the IRS-aided
amplify-and-forward (AF) relay network, which are related to the number of
phase shifter quantization bits, are derived under the line-of-sight (LoS)
channels and Rayleigh channels, respectively. Moreover, their approximate
performance loss closed-form expressions are also derived based on the Taylor
series expansion. Simulation results show that the performance losses of SNR
and achievable rate decrease with the number of quantization bits increases
gradually. When the number of quantization bits is larger than or equal to 3,
the SNR performance loss of the system is smaller than 0.23dB, and the
achievable rate loss is less than 0.04bits/s/Hz, regardless of the LoS channels
or Rayleigh channels
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images
Chest imaging plays an essential role in diagnosing and predicting patients
with COVID-19 with evidence of worsening respiratory status. Many deep
learning-based approaches for pneumonia recognition have been developed to
enable computer-aided diagnosis. However, the long training and inference time
makes them inflexible, and the lack of interpretability reduces their
credibility in clinical medical practice. This paper aims to develop a
pneumonia recognition framework with interpretability, which can understand the
complex relationship between lung features and related diseases in chest X-ray
(CXR) images to provide high-speed analytics support for medical practice. To
reduce the computational complexity to accelerate the recognition process, a
novel multi-level self-attention mechanism within Transformer has been proposed
to accelerate convergence and emphasize the task-related feature regions.
Moreover, a practical CXR image data augmentation has been adopted to address
the scarcity of medical image data problems to boost the model's performance.
The effectiveness of the proposed method has been demonstrated on the classic
COVID-19 recognition task using the widespread pneumonia CXR image dataset. In
addition, abundant ablation experiments validate the effectiveness and
necessity of all of the components of the proposed method.Comment: Accepted by the IEEE Journal of Biomedical and Health Informatic,
doi: 10.1109/JBHI.2023.324794
Three Efficient Beamforming Methods for Hybrid IRS plus AF Relay-aided Metaverse
In this paper, an optimization problem is formulated to maximize
signal-to-noise ratio (SNR) by jointly optimizing the beamforming matrix at AF
relay and the reflecting coefficient matrices at IRS subject to the constraints
of transmit power budgets at the base station (BS)/AF relay/hybrid IRS and that
of unit-modulus for passive IRS phase shifts. To achieve high rate performance
and extend the coverage range, a high-performance method based on semidefinite
relaxation and fractional programming (HP-SDR-FP) algorithm is presented. Due
to its extremely high complexity, a low-complexity method based on successive
convex approximation and FP (LC-SCA-FP) algorithm is put forward. To further
reduce the complexity, a lower-complexity method based on whitening filter,
general power iterative and generalized Rayleigh-Ritz (WF-GPI-GRR) is proposed,
where different from the above two methods, it is assumed that the amplifying
coefficient of each active IRS element is equal, and the corresponding
analytical solution of the amplifying coefficient can be obtained according to
the transmit powers at AF relay and hybrid IRS. Simulation results show that
the proposed three methods can greatly improve the rate performance compared to
the existing technology-aided metaverse, such as the passive IRS plus AF
relay-aided metaverse and only AF relay-aided metaverse. In particular, a 50.0%
rate gain over the existing technology-aided metaverse is approximately
achieved in the high power budget region of hybrid IRS. Moreover, it is
verified that the proposed three efficient beamforming methods have an
increasing order in rate performance: WF-GPI-GRR, LC-SCA-FP and HP-SDR-FP
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Robust watermarking algorithm for medical volume data in internet of medical things
The advancement of 5G technology, big data and cloud storage has promoted the rapid development of the Internet of Medical Things (IoMT). Based on the strict security requirements and high level of accuracy required for disease diagnosis and pathological analysis, 3D medical volume data have been created in large numbers. The IoMT facilitates the rapid transfer of medical information and also makes the protection of pathological information and privacy information of patients increasingly prominent. To solve the security problem, a robust zero-watermarking algorithm based on 3D hyperchaos and 3D dual-tree complex wavelet transform is proposed according to the selected feature of medical volume data. The feature combines human visual features with improved perceptual hashing techniques. It is a robust and efficient binary sequence. When implementing the proposed algorithm, the watermark is first scrambled with 3D hyperchaos to enhance security. Then, 3D DTCWT-DCT transformation is applied to medical volume data, and the low-frequency coefficients that can represent the features are selected and binarized to generate the secret key to complete the watermark embedding and extraction. Zero embedding and blind extraction ensure that the original medical volume data is not altered in any form, which meets the special requirements for diagnosis. Simulation results show that the algorithm is robust and can effectively resist common attacks and geometric attacks. It used fewer robust features to effectively bound medical volume data and watermark information, saved bandwidth, and satisfied the security of transmission and storage of medical volume data in the Internet of medical things. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value by 46.67% under geometric attacks
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Robust and secure zero-watermarking algorithm for medical images based on Harris-SURF-DCT and chaotic map
To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. *en, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better
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Robust zero watermarking algorithm for medical images based on Zernike-DCT
Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. *e algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. *e algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm
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