The latest remote sensing image saliency detectors primarily rely on RGB information alone. However, spatial and geometric information embedded in depth images is robust to variations in lighting and color. Integrating depth information with RGB images can enhance the spatial structure of objects. In light of this, we innovatively propose a remote sensing image saliency detection model that fuses RGB and depth information, named the multimodal guided transformer architecture (MGTA). Specifically, we first introduce the strong correlated complementary fusion (SCCF) module to explore cross-modal consistency and similarity, maintaining consistency across different modalities while uncovering multidimensional common information. Additionally, the global-local context information interaction (GLCII) module is designed to extract global semantic information and local detail information, effectively utilizing contextual information while reducing the number of parameters. Finally, a cascaded feature-guided decoder (CFGD) is employed to gradually fuse hierarchical decoding features, effectively integrating multi-level data and accurately locating target positions. Extensive experiments demonstrate that our proposed model outperforms 14 state-of-the-art methods. The code and results of our method are available at https://github.com/Zackisliuzao/MGTANet.

In recent years, salient object detection (SOD) has attracted more and more attention. However, the SOD in remote sensing images (RSI-SOD) faces various issues, including large scene span, cluttered background and changeable object scale. To address these challenges, an edge complementary multi-scale aggregation network (ECMANet) is proposed in this paper. Specifically, a multi-scale feature aggregation module (MFAM) is designed to extract hierarchical multi-scale information and reduce the noise interference of different scale information. In addition, foreground edge guidance module (FEGM) is designed to cross-refine foreground information and edge information. Finally, the foreground, edge, and background are generated by background-foreground fusion module (BFFM) to complement the overall network information. Extensive experiments are conducted on two popular datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

In recent years, numerous outstanding technologies have been proposed for salient object detection (SOD) in remote sensing images (RSIs), but most of them focus solely on improving performance while disregarding computational, thereby lacking portability and mobility. This article introduces a novel lightweight progressive multilevel feature collaborative network, termed LPMFCNet. This framework constructs progressive feature information through multilevel image content extraction and designs a multichannel interactive deep neural network with information fusion and filtering functions. First, a spatial detail enhancement module (SDEM) is devised to acquire distant feature information through intermediate branch expansion of receptive fields while preserving multiscale information extraction. Second, an advanced semantic interaction module (ASIM) is proposed to model distant dependency relationships between deep semantic features to better identify the positional information of salient objects. Finally, a multilevel feature collaboration module (MFCM) is designed to collaboratively utilize target features from a multilevel perspective, which fully mining deep-level semantic positional information while retaining target detail information. Extensive experimental comparisons are conducted on two remote sensing datasets with 17 advanced methods. Results demonstrate that the proposed method exhibits superior detection performance while maintaining lightweightness. The LPMFCNet only contains 3.26M parameters and runs 0.5G FLOPs for a$256\times 256$image.

BACKGROUND: Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate rapidly developing NMR applications with emerging technologies. Scripting systems offer open environments for NMR users to write custom programs. However, existing scripting systems have limited capabilities for both extending the functionality of NMR software's non-script main program and using advanced native script libraries to support specialized application domains (e.g., biomacromolecules and metabolomics). Therefore, it is essential to design a novel scripting system to address both of these needs. RESULT: Here, a novel NMR scripting system named SpinSPJ is proposed. It works as a plug-in in the Java based NMR spectrometer software SpinStudioJ. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module has been developed as a bridge to integrate the runtime environments of Java and CPython. The module works as an extension in the CPython environment and interacts with Java via the Java Native Interface. Leveraging this bridge, Java based instrument control and data processing methods of SpinStudioJ can be called with the CPython style. Compared with traditional scripting systems, SpinSPJ better supports both extending the non-script main program and implementing advanced NMR applications with a rich variety of script libraries. NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython native libraries. CONCLUSION: SpinSPJ offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/spinspj .

In this brief, a new distributed thermal management scheme using task migrations based on a new temperature metric called effective initial temperature is proposed to reduce the on-chip temperature variance and the occurrence of hot spots for many-core microprocessors. The new temperature metric derived from frequency domain moment matching technique incorporates both initial temperature and other transient effects to make optimized task migration decisions, which leads to more effective reduction of hot spots in the experiments on a 100-core microprocessor than the existing distributed thermal management methods.

In this brief, we propose an efficient parallel finite difference-based thermal simulation algorithm for 3-D-integrated circuits (ICs) using generalized minimum residual method (GMRES) solver on CPU-graphic processing unit (GPU) platforms. First, the new method starts from basic physics-based heat equations to model 3-D-ICs with intertier liquid cooling microchannels and directly solves the resulting partial differential equations. Second, we develop a new parallel GPU-GMRES solver to compute the resulting thermal systems on a CPU-GPU platform. We also explore different preconditioners (implicit and explicit) and study their performances on thermal circuits and other types of matrices. Experimental results show the proposed GPU-GMRES solver can deliver orders of magnitudes speedup over the parallel LU-based solver and up to 4× speedup over CPU-GMRES for both dc and transient thermal analyzes on a number of thermal circuits and other published problems.

Thermal issue is the leading design constraint for 3-D stacked integrated circuits (ICs) and through silicon vias (TSVs) are used to effectively reduce the temperature of 3-D ICs. Normally, TSV is considered as a good thermal conductor in its vertical direction, and its vertical thermal resistance has been well modeled. However, lateral heat transfer of TSVs, which is also important, was largely ignored in the past. In this paper, we propose an accurate physics-based model for lateral thermal resistance of TSVs in terms of physical and material parameters, and study the conditions for model accuracy. For TSV arrays or farm, we show that the space or pitch between TSVs has a significant impact on TSV thermal behavior and should be properly considered in the TSV models. The proposed lateral thermal resistance model is fully compatible with the existing modeling approaches, and thus we could build a more accurate complete TSV thermal model. The new TSV thermal model can be easily integrated into a finite difference (FD) based thermal analysis framework to improve analysis efficiency. The accuracy of the model is validated against a commercial finite element tool-COMSOL. Experimental results show that the improved TSV thermal model (with proposed lateral thermal model) could greatly improve the accuracy of FD method in thermal simulation comparing with the existing method.

This paper proposes a new thermal nonlinear modeling technique for packaged integrated systems. Thermal behavior of complicated systems like packaged electronic systems may exhibit nonlinear and temperature dependent properties. As a result, it is difficult to use a low order linear model to approximate the thermal behavior of the packaged integrated systems without accuracy loss. In this paper, we try to mitigate this problem by using piecewise linear (PWL) approach to characterizing the thermal behavior of those systems. The new method (called ThermSubPWL), which is the first proposed approach to nonlinear thermal modeling problem, identifies the linear local models for different temperature ranges using the subspace identification method. A linear transformation method is proposed to transform all the identified linear local models to the common state basis to build the continuous piecewise linear model. Experimental results validate the proposed method on a realistic packaged integrated system modeled via the multi-domain/physics commercial tool, COMSOL, under practical power signal inputs. The new piecewise models can lead to much smaller model order without accuracy loss, which translates to significant savings in both the simulation time and the time required to identify the reduced models compared to applying the high order models.

Dynamic thermal management method is a viable way to effectively mitigate the thermal emergences. In this paper, a new thermal management scheme is proposed to reduce the on-chip temperature variance and the occurrence of hot spots by considering more transient thermal effects. The new method performs the task migrations to reduce the temperature variations across the chip. Instead of intuitively assigning the heavy tasks to the low temperature cores to balance the thermal profile based on steady state thermal analysis, the proposed method applies moment matching based transient thermal analysis techniques for fast thermal estimation and prediction to guide the migration process. We show that by considering the dominant temperature moment component, the resulting algorithm can lead to significant reduction of hot spots without full transient thermal simulation. Our experimental results on a 16 core microprocessor demonstrate that the proposed method can reduce the number of the hot spots by 50% compared to the simple lowest temperature based task scheduling method, leading to more uniform on-chip temperature distribution across the microprocessor cores.

Thermal issues are among the major concerns for 3D stacked ICs, and Through silicon vias (TSVs) are used to effectively reduce the temperature of 3D ICs. Normally, TSV is considered as a good thermal conductor in its vertical direction, and its vertical thermal resistance has been studied extensively. However, lateral heat transfer of TSVs, which is also important, was largely ignored in the past. In this paper, we propose an accurate physics-based model for lateral resistance of TSVs in terms of physical and material parameters, and discuss the conditions valid for model accuracy. In addition to modeling the lateral thermal resistance of a single TSV, the proposed thermal model is also applicable to TSV arrays or TSV farms. We show that the TSV insulation linear and space between TSVs could impose a significant impact on TSV thermal behavior. The new TSV thermal model can be easily integrated into a finite difference based thermal analysis framework to improve analysis efficiency. The accuracy of the model is validated against a commercial finite element tool - COMSOL. Experimental results show that the proposed TSV lateral thermal resistance model is very accurate for both a single TSV and TSV arrays.