Sensors Best Paper Award 2015

Since 2011, an annual award system was instituted to recognize outstanding Sensors papers that are related to sensing technologies and applications and meet the aims, scope and high standards of this journal [1–4]. This year, the winners were chosen by the Section Editor-in-Chiefs of Sensors from among all the papers published in 2011 to track citations. Reviews and full research articles were considered separately. We gladly announce that the following eight papers were awarded the Sensors Best Paper Award in 2015

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

Enhanced Sensitivity of CMOS Image Sensors by Stacked Diodes

We have investigated and compared the performance of photodiodes built with stacked p/n junctions operating in parallel versus conventional ones made with single p/n junctions. We propose a method to characterize and compare photodiodes sensitivity. For this purpose, a dedicated chip in the standard AMS 180-nm HV technology has been fabricated. Four different sensor structures were implemented and compared. Experimental results are provided. Measurements show sensitivity enhancement ranging from 55% to 70% within the 500-1100 nm spectral region. The larger increment is happening in the near infrared band (up to 62%). Such results make stacked photodiodes suitable candidates for the implementation of photosensors in vision chips designed for standard CMOS technologies.Ministerio de Economía y Competitividad TEC2012-33634, TEC2015-66878- C3-1-RJunta de Andalucía TIC 2012-2338Office of Naval Research (USA) N00014141035

Wearable flexible lightweight modular RFID tag with integrated energy harvester

A novel wearable radio frequency identification (RFID) tag with sensing, processing, and decision-taking capability is presented for operation in the 2.45-GHz RFID superhigh frequency (SHF) band. The tag is powered by an integrated light harvester, with a flexible battery serving as an energy buffer. The proposed active tag features excellent wearability, very high read range, enhanced functionality, flexible interfacing with diverse low-power sensors, and extended system autonomy through an innovative holistic microwave system design paradigm that takes antenna design into consideration from the very early stages. Specifically, a dedicated textile shorted circular patch antenna with monopolar radiation pattern is designed and optimized for highly efficient and stable operation within the frequency band of operation. In this process, the textile antenna's functionality is augmented by reusing its surface as an integration platform for light-energy-harvesting, sensing, processing, and transceiver hardware, without sacrificing antenna performance or the wearer's comfort. The RFID tag is validated by measuring its stand-alone and on-body characteristics in free-space conditions. Moreover, measurements in a real-world scenario demonstrate an indoor read range up to 23 m in nonline-of-sight indoor propagation conditions, enabling interrogation by a reader situated in another room. In addition, the RFID platform only consumes 168.3 mu W, when sensing and processing are performed every 60 s

CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends

CMOS Image Sensors (CIS) are key for imaging technol-ogies. These chips are conceived for capturing opticalscenes focused on their surface, and for delivering elec-trical images, commonly in digital format. CISs may incor-porate intelligence; however, their smartness basicallyconcerns calibration, error correction and other similartasks. The term CVISs (CMOS VIsion Sensors) definesother class of sensor front-ends which are aimed at per-forming vision tasks right at the focal plane. They havebeen running under names such as computational imagesensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical imple-mentation. However, while inputs of both CIS and CVISare images captured by photo-sensors placed at thefocal-plane, CVISs primary outputs may not be imagesbut either image features or even decisions based on thespatial-temporal analysis of the scenes. We may hencestate that CVISs are more “intelligent” than CISs as theyfocus on information instead of on raw data. Actually,CVIS architectures capable of extracting and interpretingthe information contained in images, and prompting reac-tion commands thereof, have been explored for years inacademia, and industrial applications are recently ramp-ing up.One of the challenges of CVISs architects is incorporat-ing computer vision concepts into the design flow. Theendeavor is ambitious because imaging and computervision communities are rather disjoint groups talking dif-ferent languages. The Cellular Nonlinear Network Univer-sal Machine (CNNUM) paradigm, proposed by Profs.Chua and Roska, defined an adequate framework forsuch conciliation as it is particularly well suited for hard-ware-software co-design [1]-[4]. This paper overviewsCVISs chips that were conceived and prototyped at IMSEVision Lab over the past twenty years. Some of them fitthe CNNUM paradigm while others are tangential to it. Allthem employ per-pixel mixed-signal processing circuitryto achieve sensor-processing concurrency in the quest offast operation with reduced energy budget.Junta de Andalucía TIC 2012-2338Ministerio de Economía y Competitividad TEC 2015-66878-C3-1-R y TEC 2015-66878-C3-3-

LQG Control and Sensing Co-Design

We investigate a Linear-Quadratic-Gaussian (LQG) control and sensing co-design problem, where one jointly designs sensing and control policies. We focus on the realistic case where the sensing design is selected among a finite set of available sensors, where each sensor is associated with a different cost (e.g., power consumption). We consider two dual problem instances: sensing-constrained LQG control, where one maximizes control performance subject to a sensor cost budget, and minimum-sensing LQG control, where one minimizes sensor cost subject to performance constraints. We prove no polynomial time algorithm guarantees across all problem instances a constant approximation factor from the optimal. Nonetheless, we present the first polynomial time algorithms with per-instance suboptimality guarantees. To this end, we leverage a separation principle, that partially decouples the design of sensing and control. Then, we frame LQG co-design as the optimization of approximately supermodular set functions; we develop novel algorithms to solve the problems; and we prove original results on the performance of the algorithms, and establish connections between their suboptimality and control-theoretic quantities. We conclude the paper by discussing two applications, namely, sensing-constrained formation control and resource-constrained robot navigation.Comment: Accepted to IEEE TAC. Includes contributions to submodular function optimization literature, and extends conference paper arXiv:1709.0882