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This Special Issue of the IEEE Journal of Solid-State Circuits is dedicated to a collection of the best papers selected from the 2020 IEEE International Solid-State Circuits Conference (ISSCC) that took place on February 16–20, 2020, in San Francisco, CA, USA. This issue covers papers from the Analog, Power Management, Data Converters, RF, and Wireless committees. [ABSTRACT FROM AUTHOR]

The 2013 IEEE Pulsed Power and Plasma Science Conference (PPPS 2013) was held at the Hyatt Regency in San Francisco, CA, USA in June 2013. The PPPS 2013 combined the 19th IEEE Pulsed Power Conference (PPC) and the 40th IEEE International Conference on Plasma Science (ICOPS). This conference marks the third combined IEEE PPPS meeting, following the very successful PPPS 2001 in Las Vegas, NV, USA, and the PPPS 2007 in Albuquerque, NM, USA. The most recent ICOPS was held in 2012 in Edinburg, Scotland, and the most recent PPC was held in 2011 in Chicago, IL, USA. [ABSTRACT FROM PUBLISHER]

In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations. It is challenging to make migration decisions optimally because of the uncertainty in such a dynamic cloud environment. In this paper, we formulate the service migration problem as a Markov decision process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for the uniform 1-D user mobility, while it provides a close approximation for uniform 2-D mobility with a constant additive error. We also propose a new algorithm and a numerical technique for computing the optimal solution, which is significantly faster than traditional methods based on the standard value or policy iteration. We illustrate the application of our solution in practical scenarios where many theoretical assumptions are relaxed. Our evaluations based on real-world mobility traces of San Francisco taxis show the superior performance of the proposed solution compared to baseline solutions. [ABSTRACT FROM AUTHOR]

The three papers in this special section were presented at the Symposium on Interactive 3D Graphics and Games (I3D) that was held in San Francisco, CA, 18-20 February 2011. [ABSTRACT FROM AUTHOR]

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders. [ABSTRACT FROM AUTHOR]

The use of SF6 gas decomposition components for the fault diagnosis of gas-insulated equipment (GIE) has received widespread attention and been extensively adopted by the power industry. However, the present focus is mainly on the use of a component type or absolute content for generating qualitative judgments on the existence of a fault, and the practical diagnosis method for field diagnosis is extremely lacking. In this paper, ten-year fault data were collected and analyzed. Decomposition components were extracted and combined, based on the decomposition characteristics of SF6, and enhancement factors were given to three component characteristic quantities using fuzzy C-means clustering algorithm. The component characteristic quantities that characterized the high-energy discharge fault, partial discharge, and partial over-thermal fault of the device were extracted: % (SOF2+SO2), % (9SO2F2), and % (5CO2). With adoption of these characteristic quantities as the coordinate axes of the triangle coordinate system, a triangle fault diagnosis method for field diagnosis of SF6 GIE was constructed. The diagnosis method was validated by a large number of experimental data and field failure data, and 128 of the 150 experimental samples were correctly recognized. Moreover, the test conditions did not considerably affect the diagnostic results of the method. This technique can provide a simple and effective field diagnostics technology for the power industry. [ABSTRACT FROM AUTHOR]

Surface charging of spacers is one of the main factors restricting the industrialization of DC gas insulated lines (GILs). The effects of ion mobility, gas pressure and temperature on surface charge behavior are important issues to be clarified. In this paper, a selection method of ion mobility is reviewed and reconsidered. A multiphysics-coupled model considering ion mobility, species of ions, temperature, and gas pressure is investigated. A mathematical model of ion mobility under low and high electric fields is presented and the critical electric field strengths are calculated, based on the current density vs electric field strength. The results show that the two critical electric field strength have different characteristics with gas pressure and temperature. The ion mobility in SF 6 gas has a completely different calculation model in high electric field and low electric field, and these two ion mobilities are equal only at a specific temperature and gas pressure. This paper can provide a reference for the selection of ion mobility in DC GILs under conditions of temperature and pressure. [ABSTRACT FROM AUTHOR]

To model the responses of electromagnetic surveys for geophysical subsurface sensing, a mixed total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method is proposed in this paper. The proposed TF/SF DGFD method is implemented at a subdomain level based on the domain decomposition technique. Different subdomains can employ either the TF DGFD framework or the SF DGFD framework, which are then coupled through the Riemann transmission condition. To balance the computation efficiency and accuracy for practical applications, the proposed method prefers to using the SF DGFD framework for subdomains with sources and using the TF DGFD framework for the remaining subdomains. At the interfaces between total field and scattered field subdomains, the Riemann transmission condition is slightly modified by incorporating the background fields due to the physically imposed sources in the background media. In this way, the proposed method only requires surface integrals of the background fields as extra overhead instead of elementwise integration of the scattering objects for the purely scattered field-based method, which can improve the computational efficiency. Also, it is more accurate than the purely TF DGFD method given the same mesh. Numerical examples are studied to examine the performance of the proposed method, which is proven to have better accuracy than the TF DGFD method. The TF/SF DGFD method will facilitate modeling of electromagnetic surveys under complicated geophysical environments for subsurface sensing. [ABSTRACT FROM AUTHOR]

The Symposium on Foundations in Software Engineering is an annual conference sponsored by Association for Computing Machinery, a computer portal and dedicated to the presentation of innovative research results contributing to an engineering discipline for software systems. This special section of the July 1998 issue of the journal IEEE Transactions on Software Engineering features three of the best papers from the fourth symposium, held in October 1996 in San Francisco, California. During its first half-decade, research results presented at this conference have covered engineering research topics. However, a number of distinguishing properties tend to characterize the best of the papers and have helped set the standards for high-quality software engineering research. The three papers in this special section illustrate these properties. One of the critical challenges for software engineering research is to find practical tools that help system builder restructure existing systems. Mark Moriconi was general chair for the symposium and deserves considerable credit for its success.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent. [ABSTRACT FROM AUTHOR]

The assumption of reflection symmetry is one of the major deficiencies for model-based decompositions. The introduction of helix scattering components can mitigate some of the impact of this assumption while simultaneously generating some new negative power problems. The helix mechanism has been developed in the Krogager coherent target decomposition where there is no negative power problem. Why do these problems happen in model-based decompositions? In this paper, we review the Krogager decomposition based on the coherency matrix form of the unified CTD model and come to the conclusion that the imaginary part of T23 is an overestimate of the helix contribution, which is the main cause of the problem. Furthermore, we review the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions and expand them to the multilook coherency matrix. Two new models of the nonlinear programming problem are proposed to get the transformed coherency matrix: 1) by subtracting the asymmetric component; and 2) by the procedure of con-diagonalization. A new parameter is established to measure the degree of reflection asymmetry of the coherency matrices. Experimental results in the L-band Airborne SAR (AIRSAR) San Francisco data show that the two new models can reduce the correlations between copolarized and cross-polarized channels significantly. In this case, the assumption of reflection symmetry is reasonable for the transformed coherency matrices, and negative power problems related to helix scattering no longer exist in the model-based decompositions; hence, the helix scattering component is not needed. Lastly, the Freeman and Durden decomposition has been applied to the transformed coherency matrices. Experimental results showed that the decomposition results can reflect the predominant characteristics of the ground objects. [ABSTRACT FROM PUBLISHER]

This Special Section of the IEEE Journal of Solid-State Circuits is dedicated to a collection of the best articles selected from the 2021 IEEE International Solid-State Circuits Conference (ISSCC) that took place on February 13–22, 2021, in San Francisco, CA, USA. This Special Section covers articles from the Wireline, Digital Circuits, Digital Architectures and Systems (DASs), Machine Learning and AI, and Memory Committees. [ABSTRACT FROM AUTHOR]

When wireless body area networks (WBANs) using the same channel transmit superframes (SFs) simultaneously, they interfere with each other. To resolve this problem, we propose a co-channel inter-WBAN coexistence method adopting bio-inspired models. The proposed method avoids the inter-WBAN interference by distributing the timing at which each WBAN sends its SF in a distributed manner. We also identify the position of a contention-free period in an SF whereby the time slots in the period do not overlap with any time slots in the SFs of the coexisting WBANs. In addition, we show that the proposed method can limit the number of WBANs interfering with each other during a contention access period. Through simulation studies, we show that the proposed method can adapt to changes in the density and topology of the coexisting WBANs. We also show that in contrast to other conventional methods, our method guarantees that the data frames sent during a contention-free period in an SF are free from collisions regardless of the number of coexisting WBANs. Thus, the proposed method enhances the throughput of a WBAN by increasing the successful data frame transmission rate. [ABSTRACT FROM AUTHOR]

In order to more efficiently mine the features of polarimetric synthetic aperture radar (PolSAR) and establish a more appropriate classification model, this article proposes an improved convolutional autoencoder (ICAE) based on texture feature fusion (TFF-ICAE) for PolSAR terrain classification. First, TFF-ICAE specifically designs a multi-indicator squeeze-and-excitation (MI-SE) block and incorporates it into the CAE network. MI-SE can enhance the essential feature information while suppressing the interference information as much as possible, and it can effectively increase the between-class distance while reducing the within-class distance. Then, TFF-ICAE uses gray level co-occurrence matrix (GLCM) to capture the texture features, and it optimally fuses these texture features and the deep features extracted by ICAE to complete the multilevel feature fusion, elevating the feature representation completeness of the terrain. That is, TFF-ICAE effectively enhances the feature separation capability of different categories while greatly elevating the feature representation completeness. Experiments on the datasets of San Francisco, Oberpfaffenhofen, and Flevoland show that the proposed TFF-ICAE, respectively, achieves overall accuracies of 93.44%, 97.61%, and 97.78%, which are at least 0.92%, 1.52%, and 0.97% higher than other algorithms. Undoubtedly, the superiority of TFF-ICAE is verified on these datasets. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%–4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%–3% compared with QP. These conclusions can provide guidance for future payloads’ design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems. [ABSTRACT FROM AUTHOR]

We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4 × 4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3 × 3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (αGD), helicity parameter (τGD), and purity parameter (PGD). A PGD/αGD unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) as a typical multi-channel sensor can obtain refined geometrical and geophysical information. In the PolSAR land cover classification task, feature extraction is regarded as a critical step for the final classification. It can employ multi-modal features from the original scattering data, polarimetric target decomposition, and other transformation space. Then, how to efficiently combine multi-modal polarimetric information and extract discriminant features is an important challenge for PolSAR image processing. Graph embedding methods have become a significant technique to deal with feature extraction and dimensionality reduction (DR) problems in recent years. It provides a unified linearization framework in machine learning and other pattern recognition tasks. In this article, an extended tensor embedding framework is introduced to extract the intrinsic features for PolSAR land cover classification. First, each pixel is represented by a feature cube that is constructed by groups of polarimetric scattering signals and target decomposition features in a fixed size patch. Second, an intrinsic matrix is constructed to describe the original geometrical and statistical properties of the samples, and a penalty matrix is designed to represent some constraints. Third, the vector-based algorithms are transformed into tensor space in an unified framework and based on the pair of matrices to obtain the projection matrices in each mode by an iterative optimization process. The effectiveness of the proposed methods is demonstrated on three RADARSAT2 data sets covering the regions of Xi’an, San Francisco, and Flevoland, respectively. The visualization and quantification results show that the proposed method has superiority in land cover classification. [ABSTRACT FROM AUTHOR]