Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propo...
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We consider a community detection problem for gossip dynamics with stubborn agents in this paper. It is assumed that the communication probability matrix for agent pairs has a block structure. More specifically, we as...
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Flash translation layer (FTL) is a part of software running on the NAND flash memory, which is ubiquitous in various devices. FTL algorithm hides the complexity of NAND flash characteristics and provides a simple and ...
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Magnetic particle imaging (MPI) is a novel molecular imaging technique that enables highly sensitive visualization of the distribution of magnetic nanoparticles (MNPs) within biological tissues. However, dynamic rando...
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Magnetic particle imaging (MPI) is a novel molecular imaging technique that enables highly sensitive visualization of the distribution of magnetic nanoparticles (MNPs) within biological tissues. However, dynamic random noises contained in the MPI signal lead to artifacts. A common approach to improving signal-to-noise ratio (SNR) is averaging signals from multiple repeated measurements, significantly reducing real-time performance. To address this, we propose a deep learning (DL) method to remove random noise from short-time measurement signals, making them close to long-time measurement signals, thereby shortening the measurement time. We integrate the frequency indexes and coil channels of frequency components using a Transformer architecture, embedding it into the frequency domain signal to form multidimensional information. Subsequently, we introduce the row energy normalization (REN) method for signal preprocessing in model training. This approach makes the energy of the frequency components consistent, enhancing image reconstruction quality. Experiments on real-world datasets, including the OpenMPI dataset, the phantom dataset, and the in vivo dataset, demonstrate our method can significantly eliminate random noise, improve the SNR of limited measurement signals, and obtain high-quality reconstructed images. This method can significantly improve the SNR on the phantom dataset from −24.1417 to 1.0184 dB while shortening the acquisition time by 92.92%. Our method shortens acquisition time while ensuring imaging quality and robustness, enhancing the real-time imaging performance of MPI and providing a new approach for its biomedical applications.
The aim of this manuscript is to present a non-invasive method to recover the network structure of a dynamical system. We propose to use a controlled probing input and to measure the response of the network, in the sp...
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The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melan...
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ISBN:
(数字)9781665466646
ISBN:
(纸本)9781665466653
The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.
Photonic spin Hall effect is a manifestation of spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many...
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In this paper, multiple reconfigurable intelligent surfaces (RIS) aided secure precise wireless transmission (SPWT) schemes are proposed in the three-dimensional (3D) wireless communication scenario. Unavailable direc...
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The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely...
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ISBN:
(数字)9781728176871
ISBN:
(纸本)9781728176888
The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).
Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles d...
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Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.
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