Due to popular requests from the designers of clock and data recovery (CDR) regarding the inefficiency of generating high accuracy phase interpolator (PI), in this work, we have developed a layout generator for such circuit, different from conventional constraint-driven works. In the first stage, we propose a customized template floorplanning plus pin generation demanded by the users. In the second stage, in order to generate high accuracy layout, we implement a gridless router for signal, power supply and clock. Experiments with several configurations indicate that our approach can generate high-quality corresponding layouts that align with user expectations, and even surpass the quality of manual designs on structurally regular high-performance PIs, which are not easy and efficient to be generated by prior primitive/grid-based methods.

This article presents an innovative multichannel hybrid 2D–3D-convolutional neural network (MH-2D-3D-CNN) model specifically designed for the challenging task of hyperspectral image classification (HSIC) with small labeled training sample sizes (SLTSSs). By integrating three-channel network architectures and incorporating target detection and attention mechanisms with 3D- and 2D-CNN techniques, our model emphasizes the extraction and fusion of spectral and spatial features, essential for accurate classification. In the first channel, we employ the normalized adaptive matched detector (NAMD) to enhance spectral features, enabling the extraction of intricate spectral information. Concurrently, 2D-CNN is applied to capture spatial features, enhancing the understanding of spatial structural information within the images. In the second channel, the convolutional block attention module (CBAM) is employed alongside 2D-CNN to further enhance the saliency of spatial features. The third channel adopts a concatenated approach, combining 3D-CNN and 2D-CNN to extract spectral and spatial features simultaneously, reinforcing spatial feature extraction capabilities. The merged feature maps from the three channels undergo final classification through fully connected layers, enhancing the model’s stability in feature extraction and learning performance. Experimental validation was conducted on three widely used public datasets (Indian Pines [IP], University of Pavia [PU], and Salinas [SA]) with a fixed 1% labeled training sample size. The results demonstrate remarkable overall accuracy (OA) scores of 87.49%, 98.13%, and 99.22%. These results outperform state-of-the-art methods and showcase significant advancements, particularly in SLTSS scenarios.

The broad applications of lithium-ion batteries in cyber-physical systems attract intensive research on building energy-efficient battery systems. Reconfigurable battery packs have been proposed to improve reliability and energy efficiency. Despite recent efforts, how to simultaneously maximize battery usage time and minimize switching count during reconfiguration is rarely addressed. In this work, we devise a control algorithm that, under a simplified battery model, achieves the longest usage time under a given constant power-load while the switching count is at most twice above the minimum. It is further generalized for arbitrary power-loads and adjusted for refined battery models. Simulation experiments show promising benefits of the proposed algorithm.

With recent advances in embedded processing capability, vision-based real-time fire detection has been enabled in surveillance devices. This paper presents an image-based fire detection framework based on deep learning. The key is to learn a fire detector relying on tiny-YOLO (You Only Look Once) v3 deep model. With the advantage of lightweight architecture of tiny-YOLOv3 and training data augmentation by some parameter adjusting, our fire detection model can achieve better detection accuracy in real-time with lower complexity in the training stage. Experimental results have verified the effectiveness of the proposed framework.

In this paper, we introduce a large-scale food images dataset namely AIFood, which is constructed to aim ingredient recognition in food image research. AIFood dataset includes 24 categories and totally 372,095 food images around the world. We collect food images from eight existing food image datasets and a food website. The food images are relabeled using 24 categories. We preliminarily label each image using existing food information, e.g. dish name or ingredient information. Next, we manually check food images to find out undiscovered ingredients and relabel them. Every image can be labeled more than one category. In addition, food images may have color cast or uneven contrast problems, which may disturb performance of image recognition system. So, we applied preprocessing method which contains automatic white balancing and contrast limited adaptive histogram equalization methods to improve visual quality of food images. We set constraints which are defined by luminance and chrominance of image to determine if the image is to be preprocessed.

Strings with length constraints are prominent in software security analysis. Recent endeavors have made significant progress in developing constraint solvers for strings and integers. Most prior methods are based on deduction with inference rules or analysis using automata. The former may be inefficient when the constraints involve complex string manipulations such as language replacement; the latter may not be easily extended to handle length constraints and may be inadequate for counterexample generation due to approximation. Inspired by recent work on string analysis with logic circuit representation, we propose a new method for solving string with length constraints by an implicit representation of automata with length encoding. The length-encoded automata are of infinite states and can represent languages beyond regular expressions. By converting string and length constraints into a dependency graph of manipulations over length-encoded automata, a symbolic model checker for infinite state systems can be leveraged as an engine for the analysis of string and length constraints. Experiments show that our method has its unique capability of handling complex string and length constraints not solvable by existing methods.

Automatic target generation process (ATGP) has been widely used for unsupervised hyperspectral target detection. It implements a succession of orthogonal subspace projections (OSPs) to extract targets of interest without prior knowledge. This paper extends ATGP to a kernel version of ATGP, called kernel ATGP (KATGP) to further deal with linear non-separation problem. It introduces nonlinear kernels to map original data space into a higher dimensional feature space so that ATGP can effectively find.

This paper extends target-constrained interference minimized filter (TCIMF) to multiclass-constrained background suppression classifier (MCBSC) for hyperspectral image classification. In order to capture spatial contextual information MCBSC makes use of a Gaussian filter to feed back a Gaussian-filtered MCBSC map to create a new set of hyperspectral images for MCBSC to be re-implemented again in an iterative manner, referred to as iterative MCBSC (IMCBSC). Finally, it uses Otsu's method to perform hyperspectral image classification. As shown by experiments, MCBSC generally performs better than existing spectral-spatial hyperspectral image classification techniques in terms of several quantitative measures, such as classification rate, false classification rate, precision rate, accuracy rate in addition to overall accuracy (OA) rate.

An information theoretical approach to multiple-band selection (MBS) is presented in this paper. It formulates a MBS problem as a channel capacity problem by considering the original band set as a channel input space and the selected multiple band set as a channel output space with the channel transition probabilities specified by band discrimination between original bands and selected bands. Then bands are selected by iteratively finding a best possible input space that yields the maximal channel capacity. As a result, there is no need of band prioritization and de-correlation generally required by traditional band selection (BS). Two iterative algorithms are developed for MBS, sequential channel capacity MBS (SQ-CCMBS) and successive channel band selection (SC-CCMBS).

Constrained energy minimization (CEM) has been widely used for subpixel detection. It takes advantage of inverting the global sample correlation matrix R to suppress background so as to enhance detection of targets of interest. This paper presents a progressive band processing of CEM (PBP-CEM) which can perform CEM for target detection progressively band by band according to band sequential format. In doing so, a new concept, called causal band correlation matrix (CBCM), is introduced to replace the global sample correlation matrix R. It is a global correlation matrix formed by only those bands that were already visited up to the band currently being processed while excluding bands yet to be visited in the future. The proposed PBP-CEM allows CEM to be processed whenever bands are available, without waiting for completing band collection. With such an advantage, CEM has potential in data transmission and communication, specifically in satellite data processing.

Linear spectral mixture analysis (LSMA) generally performs with signatures assumed to be known to form a linear mixing model to be known. Unfortunately, this is generally not the case in real world applications. An unsupervised fully constrained least squares (UFCLS) method has been proposed to find these desired signatures. Unfortunately, it requires prior knowledge about the number of signatures, p needed to be generated. The recently proposed virtual dimensionality (VD) can be used for this purpose. This paper develops a recursive UFCLS (RUFCLS) method to accomplish these two tasks in one-shot operation, viz., determine the value of p as well as find these p signatures simultaneously. Such RUFCLS can perform data unmixing progressively signature-by-signature via a recursive update equation with signatures used to form a linear mixing model for linear spectral unmixing generated by UFCLS. Most importantly, RUFCLS does not require any matrix inverse operation but only matrix multiplications and outer products of vectors. This significant advantage provides an effective computational means of determining the VD.

One key issue encountered in endmember extraction is to determine the number of endmembers, p, required to be extracted. Virtual dimensionality (VD) has been widely used for this purpose. However, VD was originally developed and defined as the number of spectrally distinct signatures which are not necessarily pure signatures. So, on some occasions the VD estimated value for p may not be accurate to be used for the number of endmembers. This paper develops an endmember-specified VD (ES-VD) which makes use of data sample vectors generated by a specific endemember finding algorithm (EFA) as target signal sources and then determine if these signal sources are indeed true endmember by a binary composite hypothesis testing to determine endmembers. As a result, ES-VD varies with different target signal sources produced by EFAs. Most importantly, ES-VD not only determines the value of VD and in the mean time it also finds desired endmembers.

Radial basis function (RBF) has been widely used in kernel-based approaches. This paper extended RBF kernels to weighted RBF (WRBF) kernels by introducing a weighting matrix A into RBF kernels. A key to success in implementing WRBF kernels is to design different appropriate weighting matrices to implement WRBF kernels. Three weighting matrices are of particular interest, covariance matrix, correlation matrix and within-class scatter matrix. Experimental results via various applications show that classifiers using WRBF kernels provide better performance than that using un-weigheted RBF kernels.

Support vector machine (SVM) has received considerable interest in hyperspectral image classification. In order to make SVM work effectively one challenge is selection of training samples. In supervised classification it is generally done by random sampling for cross validation where two issues must be addressed. One is how many training samples required to allow SVM to produce good performance and the other is how to deal with random selections of training samples which produce inconsistent results. This paper presents a new type of SVM, called iterative SVM (ISVM) to address these two issues. The idea is to implement an SVM iteratively in such a way that the sample size is not necessarily to be large while the random sampling issue can be also resolved. To substantiate the utility of ISVM Purdue data is further used for experiments.