A LowComplex Compressive Sensing Based Thermal Sensor Placement for Multicore Systems As the complexity of the multicore system grows, the large workload diversity results in serious thermal problems. In practical way, the number of placed thermal sensors is usually limited due to the manufacturing cost. In recent years, the compressive sensing (CS) theory is proven as an efficient way to reconstruct the original signals by using fewer sampling data. However, due to the high computational complexity during signal reconstruction, the compressive sensing theory is not appropriate to apply to the realtime temperature monitoring in the current multicore systems. In this paper, we first propose a gridbased sensor placement approach to allocate the numberlimited thermal sensors on the multicore systems. On the other hand, we adopt an algorithmic transformation of Matrix Inversion Bypass (MIB) to reduce the computational complexity of the Orthogonal Matching Pursuit (OMP)based signal reconstruction approach, which is a wide use signal reconstruction approach in the compressive sensing theory. The experimental results show that the proposed approach can reduce 52% average fullsystem temperature reconstruction error compared with the previous nonCSbased approaches. Besides, we can also reduce 22%27% execution time compared with the current CSbased temperature reconstruction algorithm.

Learning Based Regularization for Spatial Multiplexing Cameras In this paper, we consider learning based regularization for compressive sensing reconstruction using focal plane array sensors. While many optimization algorithms employ proximal operators for regularization, they are often inadequate in fully capturing the characteristics of complex natural images. Recently, deep learning based approaches obtained promising results in different imaging problems, creating the possibility to use them for regularization in an optimization framework. Here, we utilize this approach in compressive sensing based spatial multiplexing cameras. This technique is motivated by the high cost of producing large focal plane arrays in infrared sensors. Reconstruction from undersampled measurements can be done using a spatial multiplexing camera which relies on multiple snapshots for superresolving a scene. It acquires coded projections of a scene using a spatial light modulator and a lowresolution focal plane array. We first formulate the problem of finding a high resolution image from its undersampled measurements. Then, we develop a reconstruction method with learning based regularization to solve this problem using alternating direction method of multipliers framework. For this, we replace the proximal operator corresponding to the regularization function with a deep convolutional denoising network. We also enhance a previously proposed denoising networkxe2x80x99s training phase by introducing multiple noise realizations for each training patch, which results in better reconstruction performance. Numerical results for different imaging scenarios show successful recovery of high resolution images in terms of PSNR, SSIM and visual quality at significant noise levels.