ALBERT

Description: Provides an overview of the technical articles and features presented in this issue.

Description: Presents the table of contents for this issue of the publication.

Description: Smart energy and electricity networks are a crucial component in building smart city architectures; their consistent and harmonized inclusion in the smart city design should be carefully considered through a detailed analysis of the impacts (environmental, energy, economic, societal) and the implementation of cost benefit analysis (CBA), not only in terms of managing the grid itself but also in a wider perspective that includes environmental, security, and social aspects. This paper first discusses the main impact that smart grid deployment has, in different respects, in smart cities and then presents a methodology for an extended CBA, able to go beyond the strictly financial aspects. It is based on previous developments at the European level. The methodology conceptually illustrated can naturally be extended to the assessment of proposals for the development of smart cities.

Description: This special issue brings together recent international research on one of the most challenging and multidisciplinary subjects of present and future engineering, architectural, medical, economic, information, and social sciences: the smart city paradigm.

Description: The development of sustainable transportation infrastructure for people and goods, using new technology and business models, can prove beneficial or detrimental for mobility, depending on its design and use. The focus of this paper is on the increasing impact new mobility services have on traffic patterns and transportation efficiency in general. Over the last decade, the rise of the mobile internet and the usage of mobile devices have enabled ubiquitous traffic information. With the increased adoption of specific smartphone applications, the number of users of routing applications has become large enough to disrupt traffic flow patterns in a significant manner. Similarly, but at a slightly slower pace, novel services for freight transportation and city logistics improve the efficiency of goods transportation and change the use of road infrastructure. This paper provides a general four-layer framework for modeling these new trends. The main motivation behind the development is to provide a unifying formal system description that can at the same time encompass system physics (flow and motion of vehicles) as well as coordination strategies under various information and cooperation structures. To showcase the framework, we apply it to the specific challenge of modeling and analyzing the integration of routing applications in today’s transportation systems. In this framework, at the lowest layer (flow dynamics), we distinguish routed users from nonrouted users. A distributed parameter model based on a nonlocal partial differential equation is introduced and analyzed. The second layer incorporates connected services (e.g., routing) and other applications used to optimize the local performance of the system. As inputs to those applications, we propose a third layer introducing the incentive design and global objectives, which are typically varying over the day depending on road and weather conditions, external events, etc. The high-level planning is ha- dled on the fourth layer taking social longterm objectives into account. We illustrate the framework by considering its ability to model at two different levels. Specific to vehicular traffic, numerical examples enable us to demonstrate the links between the traffic network layer and the routing decision layer. With a second example on optimized freight transport, we then discuss the links between the cooperative control layer and the lower layers. The congestion pricing in Stockholm is used to illustrate how also the social planning layer can be incorporated in future mobility services.

Description: The IEEE Smart City Initiative was created in 2013 to develop a worldwide network of cities, sharing their experience and organizing knowledge dissemination in their respective ecosystems. After more than three years, almost 15 cities joined the five core cities, creating a vibrant network of people, keen on producing and sharing knowledge, organizing events, and spreading the smart cities' culture around the world.

Description: With growing attention to sustainability and recognition of the impact of global warming problems, energy supply and consumption have become critically important. This paper presents the construction of a modeling platform accommodating cooperative energy management systems (EMSs), which virtually produces the model of a smart city with a distribution network (DN) by using a wide range of data obtained from the real world. The platform involves models of various EMSs, governing the operation of a power system or controlling consumer-installed devices, and simulating the power flow, electrical losses, and voltage in the DN. In addition, indices measuring the sustainability of the model city, such as CO 2 emission, are estimated from scenarios, for example, photovoltaic system installation, electric vehicle penetration, etc. The results can be visually displayed and the platform is highly versatile and applicable to various types of issues associated with smart cities. Two case studies are presented in detail.

Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Description: Efficient, resilient, and sustainable electricity delivery is a key cornerstone in increasingly large and complex urban environments, where citizens expect to keep or rise their living standards. In this context, cost-effective and ubiquitous digital technologies are driving the transformation of existing electrical infrastructures into truly smart systems capable of better providing the services a low-carbon society is demanding. The goal of this paper is twofold: 1) to review the dramatically evolving landscape of power systems, from the old framework based on centralized generation and control, aimed at serving inelastic customers through alternating current (ac) transmission networks and one-way distribution feeders, to a new paradigm centered mainly around two main axes: renewable generation, both centralized and distributed, and active customers (prosumers), interacting with each other through hybrid ac/dc smart grids; 2) to illustrate, through featured success stories, how several smart grid concepts and technologies have been put into practice in Spain over the last few years to optimize the performance of urban electrical assets.

Description: Smart cities use information and communication technologies (ICTs) to scale services include utilities and transportation to a growing population. In this paper, we discuss how smart city ICTs can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.

Description: This paper argues that the mainstream urban governance approaches are built upon the legacy of reductionist doctrine and public administration tools which are not fully compatible with the complex nature of urban infrastructure systems. However, recent technological innovations associated with the notion of smart cities and the emerging sociopolitical trends are opening up new opportunities to develop governance approaches that can overcome such incompatibilities in urban systems. On the other hand, successful introduction of innovations in urban infrastructures, which we understand as complex sociotechnical systems, requires smarter governance approaches that are compatible with systems paradigm. The pace of change in social and technological landscapes of cities is fast. This conceptual paper brings together insights from sociotechnical systems, systems theory, and governance literature to shed light on why city administrations should closely follow these changes and adapt the governance approaches accordingly; or governance may become a main hindrance for utilizing the benefits of technology to deal with increasingly complex urban challenges.

Description: Smart cities face the challenge of combining sustainable national welfare with high living standards. In the last decades, life expectancy increased globally, leading to various age-related issues in almost all developed countries. Frailty affects elderly who are experiencing daily life limitations due to cognitive and functional impairments and represents a remarkable burden for national health systems. In this paper, we proposed two different predictive models for frailty by exploiting 12 socioclinical databases. Emergency hospitalization or all-cause mortality within a year were used as surrogates of frailty. The first model was able to assign a frailty risk score to each subject older than 65 years old, identifying five different classes for tailor made interventions. The second prediction model assigned a worsening risk score to each subject in the first nonfrail class, namely the probability to move in a higher frailty class within the year. We conducted a retrospective cohort study based on the whole elderly population of the Municipality of Bologna, Italy. We created a baseline cohort of 95 368 subjects for the frailty risk model and a baseline cohort of 58 789 subjects for the worsening risk model, respectively. To evaluate the predictive ability of our models through calibration and discrimination estimates, we used, respectively, a six-year and a four-year observation period. Good discriminatory power and calibration were obtained, demonstrating a good predictive ability of the models.

Description: This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.

Description: In some sense, the ENIAC needs no introduction. Essentially everyone in the computing field has heard of the machine and knows that it was a very early electronic computing system. Many have seen the small subsets of the ENIAC on display in various museums. A number of books have been written about it, e.g., [1] and [2] , and it is discussed in many others, e.g., [3] and [4] . However, some background is in order before discussing the details of the ENIAC’s operation and simulation.

Description: Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building’s physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. This paper illustrates that creating an affinity between a building resident’s thermal preferences and a building apartment’s unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents’ thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building’s physical characteristics as well as the variation in residents’ thermal preferences. The work further demonstrates that both the unregulated temperature and the resident’s preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by- the apartment’s geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

Description: Presents the front cover for this issue of the publication.

Description: Among the many functions a smart city must support, transportation dominates in terms of resource consumption, strain on the environment, and frustration of its citizens. We study transportation networks under two different routing policies, the commonly assumed selfish user-centric routing policy and a socially optimal system-centric one. We consider a performance metric of efficiency–the Price of Anarchy (PoA)–defined as the ratio of the total travel latency cost under selfish routing over the corresponding quantity under socially optimal routing. We develop a data-driven approach to estimate the PoA, which we subsequently use to conduct a case study using extensive actual traffic data from the Eastern Massachusetts road network. To estimate the PoA, our approach learns from data a complete model of the transportation network, including origin-destination demand and user preferences. We leverage this model to propose possible strategies to reduce the PoA and increase efficiency.

Description: Presents information on future special issues/sections for this issue of the publication.

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Description: Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are based. The operational behavior of a bridge must be observed more regularly and over an extended period in order to sufficiently track its condition and avoid unexpected rehabilitation. Mobile sensor networks are conducive to monitoring bridges vibrations routinely, with benefits that have been demonstrated in recent structural health monitoring (SHM) research. Though smartphone accelerometers are imperfect sensors, they can contribute valuable information to SHM, especially when aggregated, e.g., via crowdsourcing. In an application on the Harvard Bridge (Boston, MA), it is shown that acceleration data collected using smartphones in moving vehicles contained consistent and significant indicators of the first three modal frequencies of the bridge. In particular, the results became more precise when informatics from several smartphone datasets were combined. This evidence is the first to support the hypothesis that smartphone data, collected within vehicles passing over a bridge, can be used to detect several modal frequencies of the bridge. The result defines an opportunity for local governments to make partnerships that encourage the collection of low-cost bridge vibration data, which can contribute to more effective management and informed decision-making.

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Description: One of the important goals of a smart city is to increase the quality of urban mobility. This paper will explore the use of dynamic tariffs for this purpose. Transactive control, the concept of feedback through economic transactions, is a promising concept for accomplishing such dynamic tariffs, and is the focus of this paper. Two specific examples of transactive control are considered in this paper, the first of which is the synthesis of dynamic toll prices with the goal of reducing traffic congestion in highways. We examine how a model-based approach can result in optimal toll pricing schemes. Sociotechnical models that combine behavioral models of drivers and traffic flow models, together with real-time traffic information obtained from on-road sensors are used to determine the transactive control strategies. The overall pricing strategy is evaluated using real traffic data from an existing dynamic toll-pricing framework. The second example of transactive control is in the context of Mobility on Demand (MoD), where new modes of transportation other than private and public are being proposed, providing a smorgasbord of options for passengers. We investigate a dynamic routing concept for multipassenger transport, and propose a transactive control strategy to regulate the achievable performance around desired values. Numerical simulations using actual passenger data are carried out to demonstrate the advantages of the proposed concept.

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Description: Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic diseases, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients’ medical history, recent and more distant, as described in their Electronic Health Records (EHRs). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVMs), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: $K$ -LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large data sets from the Boston Medical Center, the largest safety-net hospital system in New England.

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Description: The Edison Medal is one of the most prestigious awards given in the United States and Canada recognizing meritorious accomplishments in the fields of electronics and electrical engineering. The year 2009 marked the 100th anniversary of this medal named in honor of America’s most famous inventor, Thomas Alva Edison. Edison’s work exemplifies the development of large scale industrial research laboratories, the creation of new technology, and the installation of the first complete electrical systems in the 19th century. For more than a century, many important contributors to the development of electronics have received the Edison Medal over its storied history. This article outlines the origins of the Edison Medal and its legacy of honor. 1

Description: Two-dimensional magnetic recording (TDMR) is an emerging storage technology that aims to achieve areal densities on the order of 10 Tb/in 2 , mainly driven by innovative channels engineering with minimal changes to existing head/media designs within a systems framework. Significant additive areal density gains can be achieved by using TDMR over bit patterned media (BPM) and energy-assisted magnetic recording (EAMR). In TDMR, the sectors are inherently 2-D with reduced track pitch and bit widths, leading to severe 2-D intersymbol interference (ISI). This necessitates the development of powerful 2-D signal processing and coding algorithms for mitigating 2-D ISI, timing artifacts, jitter, and electronics noise resulting from irregular media grain positions and read-head electronics. The algorithms have to be eventually realized within a read/write channel architecture as a part of a system-on-chip (SoC) within the disk controller system. In this work, we provide a wide overview of TDMR technology, channel models and capacity, signal processing algorithms (detection and timing recovery), and error-correcting codes attuned to 2-D channels. The innovations and advances described not only make TDMR a promising future technology, but may serve a broader engineering audience as well.

Description: Situation-aware technologies enabled by multitarget tracking will lead to new services and applications in fields such as autonomous driving, indoor localization, robotic networks, and crowd counting. In this tutorial paper, we advocate a recently proposed paradigm for scalable multitarget tracking that is based on message passing or, more concretely, the loopy sum–product algorithm. This approach has advantages regarding estimation accuracy, computational complexity, and implementation flexibility. Most importantly, it provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitarget tracking. This fact makes it attractive for emerging applications requiring real-time operation on resource-limited devices. In addition, the message passing approach is intuitively appealing and suited to nonlinear and non-Gaussian models. We present message-passing-based multitarget tracking methods for single-sensor and multiple-sensor scenarios, and for a known and unknown number of targets. The presented methods can cope with clutter, missed detections, and an unknown association between targets and measurements. We also discuss the integration of message-passing-based probabilistic data association into existing multitarget tracking methods. The superior performance, low complexity, and attractive scaling properties of the presented methods are verified numerically. In addition to simulated data, we use measured data captured by two radar stations with overlapping fields-of-view observing a large number of targets simultaneously.

Description: This comprehensive review summarizes state of the art, challenges, and prospects of the neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the demand for developing neuro-inspired architecture beyond today’s von-Neumann architecture. Second, we summarize the various approaches to designing the neuromorphic hardware (digital versus analog, spiking versus nonspiking, online training versus offline training) and discuss why emerging nonvolatile memory is attractive for implementing the synapses in the neural network. Then, we discuss the desired device characteristics of the synaptic devices (e.g., multilevel states, weight update nonlinearity/asymmetry, variation/noise), and survey a few representative material systems and device prototypes reported in the literature that show the analog conductance tuning. These candidates include phase change memory, resistive memory, ferroelectric memory, floating-gate transistors, etc. Next, we introduce the crossbar array architecture to accelerate the weighted sum and weight update operations that are commonly used in the neuro-inspired machine learning algorithms, and review the recent progresses of array-level experimental demonstrations for pattern recognition tasks. In addition, we discuss the peripheral neuron circuit design issues and present a device-circuit-algorithm codesign methodology to evaluate the impact of nonideal device effects on the system-level performance (e.g., learning accuracy). Finally, we give an outlook on the customization of the learning algorithms for efficient hardware implementation.

Description: As women faculty in electrical and computer engineering (ECE), we have been involved in several efforts targeted at increasing the participation of women in ECE departments. For example, at Rutgers University, we have organized presentations and workshops for first year students that highlighted the societal aspects of ECE, such as bioelectrical engineering applications. Such focus did bear fruit; between 2010 and 2016, the sophomore female enrollment in ECE at Rutgers rose from 11% to 19%. At the University of San Diego, we have conducted research on the demographics and outcomes of undergraduate students in ECE, facilitated workshops to help faculty teach in more inclusive ways, and are currently working on a National Science Foundation (NSF)-funded effort to revolutionize engineering education [1] . Also, as chairs, we have been considering ways to diversify our faculty carefully considering the overall hiring process including innovative approaches such as cluster hires [2] , paying close attention to the language used when advertising faculty positions, the discussions during the review of the candidates, the candidate selection criteria, and the interview process [3] . However, the number of diverse graduate students is small to begin with, and in an era in which companies have realized the value of diversity, academia has to compete with companies such as Google and Microsoft for the best Ph.D. graduates. Without a diverse faculty, we cannot sustain a diverse student body.

Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Description: Presents the front cover for this issue of the publication.

Description: This paper describes the development and testing of a cold gas attitude control thruster produced for the BioSentinel spacecraft, a CubeSat that will operate beyond Earth orbit. The thruster will reduce the spacecraft rotational velocity after deployment, and for the remainder of the mission it will periodically unload momentum from the reaction wheels. The majority of the thruster is a single piece of 3-D-printed additive material which incorporates the propellant tanks, feed pipes, and nozzles. Combining these elements allows for more efficient use of the available volume and reduces the potential for leaks. The system uses a high-density commercial refrigerant as the propellant, due to its high volumetric impulse efficiency, as well as low toxicity and low storage pressure. Two engineering development units and one flight unit have been produced for the BioSentinel mission. The design, development, and test campaign for the thruster system is presented.

Description: Antenna is one of the key components onboard small satellites as its design determines the performance of all the wireless systems including telemetry, tracking and control, high-speed data downlink, navigation, intersatellite communications, intrasatellite communications, wireless power transfer, radars and sensors, etc. This paper presents a review of recent development in advanced antennas for small satellites (MiniSat, MicroSat, NanoSat, CubeSat, etc.). A number of recent examples of antennas for small satellite applications are shown and discussed. A conclusion and future development in antennas for small satellites are given in the end.

Description: Earth orbiting satellites come in a wide range of shapes and sizes to meet a diverse variety of uses and applications. Large satellites with masses over 1000 kg support high-resolution remote sensing of the Earth, high bandwidth communications services, and world-class scientific studies but take lengthy developments and are costly to build and launch. The advent of commercially available, high-volume, and hence low-cost microelectronics has enabled a different approach through miniaturization. This results in physically far smaller satellites that dramatically reduce timescales and costs and that are able to provide operational and commercially viable services. This paper charts the evolution and rise of small satellites from being an early curiosity with limited utility through to the present where small satellites are a key element of modern space capabilities.

Description: Small satellite is a disruptive technology in space industries. Traditionally, space industries were dominated by satellites which have thousands of kilograms and are bulky and expensive. Small satellites denote a new generation of miniaturized satellites which, by taking advantages of modern technologies (e.g., integrated circuits, digital signal processing, MEMS, and additive manufacturing), can achieve a significant reduction in volume, mass, development time, and cost of satellites. During recent decades, small satellites, including CubeSats, NanoSats, MiniSats, and MicroSats, have undergone rapid developments, and are playing an increasingly larger role in exploration, technology demonstration, scientific research, and education. These miniature satellites provide a low-cost platform for missions, including planetary space exploration, Earth observations, fundamental Earth and space science, and developing precursor science instruments like laser communications and millimeter-wave communications for intersatellite and intrasatellite links, and autonomous movement capabilities. They also allow educators an inexpensive means to engage students in all phases of satellite development, operation, and exploitation through real-world, hands-on research and development experience on rideshare launch opportunities. A number of miniaturized satellites can form spaceborne wireless sensor networks in the space, which are also going to play an important role in Internet of Space (IoS) of the future.

Description: Presents the table of contents for this issue of the publication.

Description: As small satellites become more popular and capable, strategies to provide in-space propulsion increase in importance. Applications range from orbital changes and maintenance, attitude control and desaturation of reaction wheels to drag compensation and de-orbit at spacecraft end-of-life. Space propulsion can be enabled by chemical or electric means, each having different performance and scalability properties. The purpose of this review is to describe the working principles of space propulsion technologies proposed so far for small spacecraft. Given the size, mass, power, and operational constraints of small satellites, not all types of propulsion can be used and very few have seen actual implementation in space. Emphasis is given in those strategies that have the potential of miniaturization to be used in all classes of vehicles, down to the popular 1-L, 1-kg CubeSats and smaller.

Description: Space-based radar observations have transformed our understanding of Earth over the last several decades. Driven by increasingly complex science questions, space radar missions have grown ever more sophisticated with costs rising often to hundreds of millions of dollars. At the other end of the cost and complexity spectrum, CubeSats have emerged in recent years as a disruptive innovation in the satellite sector and are now considered a means to address targeted science questions in a rapid and affordable manner. CubeSats enable new kinds of constellation-based Earth science observations not previously affordable with traditional spacecraft. Constellations of low-cost sensors provide both global spatial and high temporal coverage. As such, CubeSats are not only viable platforms to address current Earth science goals, but they also open a new realm of possibilities for science advancement and unique applications. Radar instruments have often been regarded as unsuitable for small satellite platforms due to their traditionally large size, weight, and power (SWaP). Burgeoning missions such as Radar in a CubeSat (RainCube) and CubeSat Imaging Radar for Earth Science (CIRES), being developed by Jet Propulsion Laboratory and SRI International, respectively, and funded by NASA’s Earth Science Technology Office (ESTO), are slated to dispel this notion. The key to the simplification and miniaturization of the radar subsystems in a manner that still offers compelling science and applications is 1) component technological advancement; and 2) an integrated instrument architecture and mission design that exploits the capabilities offered by CubeSat platforms. This paper reviews the state-of-the-art and future developments of CubeSat radar missions for Earth remote sensing and the implications for NASA’s current and future Earth Science program. The key enabling technologies for radio frequency (RF), digital, and antennas are surve- ed, as well as the evolution of the CubeSat avionics, in the aspects that mostly impact radar development, namely power, volume, and attitude control and knowledge and precision orbit determination (POD). We investigate various radar applications that could benefit from low-cost CubeSat platforms, such as altimetry, sounding, precipitation profiling, scatterometry, synthetic aperture radar (SAR), and interferometric SAR (InSAR). We also explore the science motivation and impact of future missions that are based on these technological advancements.

Description: Approximately half the world’s population is online with the rest is yearning to be connected. Two out of 3 people on Earth communicate with a mobile phone, while two-fifths use social media to keep contacts up-to-date. [1] . Many things in our lives, phones, cameras, sensors in our homes and offices, and cars are being connected to the Internet. Our world is becoming hyper-connected.

Description: The low-cost and short-lead time of small satellites has led to their use in science-based missions, earth observation, and interplanetary missions. Today, they are also key instruments in orchestrating technological demonstrations for On-Orbit Operations (O 3 ) such as inspection and spacecraft servicing with planned roles in active debris removal and on-orbit assembly. This paper provides an overview of the robotics and autonomous systems (RASs) technologies that enable robotic O 3 on smallsat platforms. Major RAS topics such as sensing & perception, guidance, navigation & control (GN&C) microgravity mobility and mobile manipulation, and autonomy are discussed from the perspective of relevant past and planned missions.

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Description: This paper comprehensively reviews the state-of-the-art development in formation control of small satellites. Satellite formation flying, distributed satellite systems, and fractionated satellite formation are discussed first. Various formation control architectures and methods of small satellites are then introduced, including the leader-following method, the behavior-based method, the virtual structure method, the cyclic pursuit method, the artificial potential function method, the algebraic graph method, and the noncontact force method. Coordinative control of multiple small satellites is also reviewed, covering coordinative control of satellite formation, coordinative attitude control of satellite formation, and coordinative coupled attitude and orbit control of satellite formation. The achievements and development trends of the formation control of small satellites are considered and analyzed.

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Description: Rapid advances in small satellite (often referred to as CubeSats) technology are providing opportunities for space exploration to a wide range of users (in particular universities) at substantially reduced costs. Many of the capabilities provided by larger satellites and spacecraft (>2000 kg) are now available through small satellite technologies. Ongoing improvements in attitude control, propulsion, advanced communications, and scientific instrumentation continue to enhance their benefits, even within such strict volume and mass constraints. With such advances in CubeSat technologies, the power and energy demands have also increased dramatically, necessitating the need for larger deployable solar arrays, lower power electronics, efficient energy storage systems, and even energy transfer/harvesting systems. In terms of energy storage, more advanced battery chemistries with higher energy densities and higher power capabilities over a wider operating temperature range are also a fundamental need. There already exist today numerous commercially available energy storage options suitable for CubeSat applications, although many missions rely on custom designs. Similar to standard satellite design, the selection of an appropriate energy storage system is driven by mission requirements related to power, energy, and lifetime. This paper will provide a general review of performance capabilities of state-of-the-art lithium-ion battery technologies, as well as other advanced energy storage systems for small satellite applications.

Description: Monitoring and understanding geophysical processes in the thermosphere is primordial for space physics, low Earth orbiters, and ground-based technologies. In the last half century, thermospheric variations, anomalies, and climatology have been investigated and reported, but were limited due to lack of observations and large uncertainty in the models. Today, Global Navigation Satellite Systems (GNSS) and accelerometers on small satellites can sense neutraldensity and wind variations with unprecedented accuracy, which contribute to understand thermospheric variations and improve the current empirical and physical models. In this paper, an overview of past and present developments and efforts in sensing and modeling thermospheric density and wind variations is presented, as well as the future challenges and perspectives for GNSS and accelerometers on small satellites.

Description: Due to the increasing demands of onboard sensor and autonomous processing, one of the principal needs and challenges for future spacecraft is onboard computing. Space computers must provide high performance and reliability (which are often at odds), using limited resources (power, size, weight, and cost), in an extremely harsh environment (due to radiation, temperature, vacuum, and vibration). As spacecraft shrink in size, while assuming a growing role for science and defense missions, the challenges for space computing become particularly acute. For example, processing capabilities on CubeSats (smaller class of SmallSats) have been extremely limited to date, often featuring microcontrollers with performance and reliability barely sufficient to operate the vehicle let alone support various sensor and autonomous applications. This article surveys the challenges and opportunities of onboard computers for small satellites (SmallSats) and focuses upon new concepts, methods, and technologies that are revolutionizing their capabilities, in terms of two guiding themes: hybrid computing and reconfigurable computing. These innovations are of particular need and value to CubeSats and other Smallsats. With new technologies, such as CHREC Space Processor (CSP), we demonstrate how system designers can exploit hybrid and reconfigurable computing on SmallSats to harness these advantages for a variety of purposes, and we highlight several recent missions by NASA and industry that feature these principles and technologies.

Description: A deployable structure serves an important function in small satellites as their performance is improved steadily. Larger deployable solar array paddles are expected as the satellite requires a larger amount of electric power. The synthetic aperture radar (SAR) system for small satellites requires a deployable antenna. Various deployable membrane structures have been proposed for the deorbit of micro/nanosatellites in low Earth orbit. The actuators and devices for a deployable structure continue to progress along with the development of a deployable structure. The hold-release mechanism has been becoming smaller and simpler. The shape control devices are actively researched for a high-precision deployable structure. There are several requirements imposed on the deployable structures, e.g., low cost, lightweight, small volume in stored configuration, reliable deployment, large aperture in deployed configuration, and accuracy/repeatability of a deployed shape. Dedicated efforts are made to satisfy these requirements in research and development of a deployable structure/device. This paper provides an overview of the past and current research and development of a deployable structure for small satellites.

Description: The convergence of remote sensing technologies with social media, coupled with advances in other location-aware technologies such as WiFi and smartphones, is moving us on a fast track to a situation in which we can readily know, at any time, where everybody and everything are located on the surface of the Earth, and to exploit the power of social media in different contexts. Remote sensing technology involves the use of systems and algorithms to record information about the surface of the Earth from a remote location [1] . Although reliable as a data source, remote sensing data may not always be available. However, these data can be complemented by other sources of data, such as geographic information systems (GIS) and social media [2] , in order to address time-critical applications. For instance, relating publicly available social media information with remote sensing or GIS data can lead to a more efficient management of emergency response (which refers to applications in which real/near-real-time response is needed, such as natural disasters). Social media are now playing a more relevant role in our daily lives and provide a unique opportunity to gain valuable insight on information flow and social networking within the society. As a result, the integration of social media data with other consolidated technologies such as remote sensing or GIS is of great importance.

Description: Presents the table of contents for this issue of the publication.

Description: In the past decade, flash memory has been in the spotlight across a variety of research communities from circuits to computer systems, and significant progress has been accomplished. This has enabled flash memory to become increasingly pervasive across the entire information technology infrastructure, from consumer electronics to cloud and supercomputing. This paper aims to provide a comprehensive survey on the important advancements and milestones in the domains across flash translation layer (FTL), operating systems, and applications. As the storage device hardware has been quickly commoditized, software becomes increasingly important to tap the potential of flash memory to its full extent. Therefore, a comprehensive survey with a focus on software aspects will be very valuable to the research community and industry. It is our hope that this survey paper will serve as a good reference for system practitioners and researchers.

Description: NAND flash memory-based solid-state drives (SSDs) are increasingly being used in both consumer and enterprise storage markets, due to their superior performance over hard disk drives (HDDs) and continuous bit cost reductions. With multiple-level cell technology memory device is capable of trading off the performance and endurance with bit density. The more bits per cell there are, the longer latency and shorter lifetime. On the other hand, the performance of such SSDs is limited due to NAND flash access speed as well as the need of garbage collection. Recently, storage class memories (SCMs) like resistive RAM (ReRAM) and phase change RAM (PRAM) have been developed to fill the bandwidth gap between DRAM and NAND flash memory. SCMs are nonvolatile and byte addressable, which are much faster and durable than NAND flash. Therefore, with SCMs, the storage performance would be significantly improved. Hybrid SSDs are promising cost-efficient storage solutions. Various types of memories like single-level cell (SLC), multiple-level cell (MLC), triple-level cell (TLC) NAND flash memories, and SCMs create lots of opportunities for new system architectures and algorithms. In this paper, the architecture and algorithm design overview of three types of hybrid drives including MLC/TLC NAND flash hybrid, SCM/MLC NAND flash hybrid, and SCM/MLC/TLC NAND flash tri-hybrid are presented. From the evaluation results, hybrid drives demonstrate better performance, endurance, and power consumption, compared to the MLC NAND flash only SSD. Furthermore, the relationship between device reliability and performance of the SCM/NAND flash hybrid SSD has been understood at a system level. There is a tradeoff between acceptable bit error rate of SCM and NAND flash. In addition, the decoding latency of SCM affects the performance of hybrid SSD more than that of NAND flash.

Description: The state-of-the-art solid-state drives (SSDs) now heterogeneously integrate NAND Flash and dynamic random access memories (DRAMs) to partially hide the limitation of the nonvolatile memory technology. However, due to the increased request for storage density coupled with performance that positions the storage tier closer to the latency of the processing elements, NAND Flash are becoming a serious bottleneck. DRAM as well are a limitation in the SSD reliability due to their vulnerability to the power loss events. Several emerging memory technologies are candidate to replace them, namely the storage class memories. Phase change memories and magnetic memories fall into this category. In this work, we review both technologies from the perspective of their possible application in future disk drives, opening up new computation paradigms as well as improving the storage characteristics in terms of latency and reliability.

Description: This paper reviews the reliability of solid-state drives (SSDs) based on NAND Flash memory from the perspectives of failure mechanisms, design mitigations, qualification methods, and field failure rates. NAND reliability is dominated by gradual memory-cell degradation in late life and defects such as interconnect shorts earlier in life. Design mitigations exist for these mechanisms. Qualification methods standardized in JEDEC JESD218 are designed to evaluate the mechanisms and mitigations, over a full drive lifetime, in the laboratory. Full-lifetime qualification provides confidence in the long-term reliability of SSDs that cannot be achieved by the early-life qualifications performed on hard disk drives. If NAND mechanisms are sufficiently suppressed, field reliability will be dominated by non-NAND mechanisms such as firmware bugs, power-loss events, radiation-induced soft errors, and failures in non-NAND components. A wide range of design validation and qualification tests are necessary to evaluate these non-NAND mechanisms. Published field reliability statistics indicate that SSDs are more reliable on average than HDDs, but they are not immune to failure, and there is wide variation among models. The NAND and non-NAND mechanisms are illustrated through new case studies of SSD internal qualification and field reliability data.

Description: Solid-state drives (SSDs) based on NAND flash are making deep inroads into data centers as well as the consumer market. In 2016, manufacturers shipped more than 130 million units totaling around 50 Exabytes of storage capacity. As the amount of data stored on solid state drives keeps increasing, it is important to understand the reliability characteristics of these devices. For a long time, our knowledge about flash reliability was derived from controlled experiments in lab environments under synthetic workloads, often using methods for accelerated testing. However, within the last two years, three large-scale field studies have been published that report on the failure behavior of flash devices in production environments subjected to real workloads and operating conditions. The goal of this paper is to provide an overview of what we have learned about flash reliability in production, and where appropriate contrasting it with prior studies performing controlled experiments.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Description: With a combination of high performance and nonvolatility, the arrival of 3D XPoint memory promises to fundamentally change the memory-storage hierarchy at the hardware, system software, and application levels. This memory will be deployed first as a block addressable storage device, known as the Intel Optane SSD, and even in this familiar form it will drive basic system change. Access times consistently as fast, or faster, than the rest of the system will blur the line between storage and memory. The low latencies from these solid-state drives (SSDs) allow rethinking even basic storage methodologies to be more memory-like. For example, the manner in which storage performance is measured shifts from input–output operations (IOs) at a given queue depth to response time for a given load, like memory is typically measured. System changes to match the low latency of these SSDs are already advanced, and in many cases they enable the application to utilize the SSD’s performance. In other cases, additional work is required, particularly on policies set originally with slow storage in mind. On top of these already-capable systems are real applications. System-level tests show that applications such as key–value stores and real-time analytics can benefit immediately. These application benefits include significantly faster runtime (up to $3\times $ ) and access to larger data sets than supported in DRAM. Newly viable mechanisms for expanding application memory footprint include native application support or native operating system paging, a significant change in the use of SSDs. The next step in this convergence is 3D XPoint memory accessed through processor load/store operations. Significant operating system support is already in place. The implications of consistently low latency storage and fast persistent memory on computing are great, with app- ications and systems taking advantage of this new technology as storage as the first to benefit.

Description: Every bit of information in a storage or memory device is bound by a multitude of performance specifications, and is subject to a variety of reliability impediments. At the other end, the physical processes tamed to remember our bits offer a constant source of risk to their reliability. These include a variety of noise sources, access restrictions, intercell interferences, cell variabilities, and many more issues. Tying together this vector of performance figures with that vector of reliability issues is a rich matrix of emerging coding tools and techniques. Channel coding schemes ensure target reliability and performance and have been at the core of memory systems since their nascent age. In this survey, we first overview the fundamentals of channel coding and summarize well-known codes that have been used in nonvolatile memories (NVMs). Next, we demonstrate why the conventional coding approaches ubiquitously based on symmetric channel models and optimization for the Hamming metric fail to address the needs of modern memories. We then discuss several recently proposed innovative coding schemes. Behind each coding scheme lies an interesting theoretical framework, building on deep ideas from mathematics and the information sciences. We also survey some of the most fascinating bridges between deep theory and storage performance. While the focus of this survey is primarily on the pervasive multilevel NAND Flash, we envision that other benefiting memory technologies will include phase change memory, resistive memories, and others.

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Description: Resistive random access memory (RRAM) is regarded as one of the most promising emerging memory technologies for next-generation embedded, standalone nonvolatile memory (NVM), and storage class memory (SCM) due to its speed, density, cost, and scalability. Considerable progress has been made in recent years on the manufacturability of RRAM, with low-density RRAM products now in production and the path to higher density parts becoming clearer. This review updates the learning on the fundamental materials and process integration needed for high-volume manufacturing and summarizes very recent progress on array level performance improvement methodology using novel techniques, and circuit level contributions for different applications. The device performance, array integration, and device/circuit codesign for memory systems are discussed. Novel applications besides embedded memory and standalone memory are addressed, including hardware security, neuromorphic computing, and nonvolatile logic systems.

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Description: The large-scale availability of user-generated content in social media platforms has recently opened up new possibilities for studying and understanding the geospatial aspects of real-world phenomena and events. Yet, the large majority of user-generated content lacks proper geographic information (in the form of latitude and longitude coordinates). As a result, the problem of multimedia geotagging, i.e., extracting location information from user-generated text items when this is not explicitly available, has attracted increasing research interest. Here, we present a highly accurate geotagging approach for estimating the locations alluded by text annotations based on refined language models that are learned from massive corpora of social media annotations. We further explore the impact of different feature selection and weighting techniques on the performance of the approach. In terms of evaluation, we employ a large benchmark collection from the MediaEval Placing Task over several years. We demonstrate the consistently superior geotagging accuracy and low median distance error of the proposed approach using various data sets and comparing it against a number of state-of-the-art systems.

Description: In this paper, we discuss and review how combined multiview imagery from satellite to street level can benefit scene analysis. Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks such as object detection, robots guidance, or scene understanding. What makes the combination of overhead and street-level images challenging are the strongly varying viewpoints, the different scales of the images, their illuminations and sensor modality, and time of acquisition. Direct (dense) matching of images on a per-pixel basis is thus often impossible, and one has to resort to alternative strategies that will be discussed in this paper. For such purpose, we review recent works that attempt to combine images taken from the ground and overhead views for purposes like scene registration, reconstruction, or classification. After the theoretical review, we present three recent methods to showcase the interest and potential impact of such fusion on real applications (change detection, image orientation, and tree cataloging), whose logic can then be reused to extend the use of ground-based images in remote sensing and vice versa . Through this review, we advocate that cross fertilization between remote sensing, computer vision, and machine learning is very valuable to make the best of geographic data available from Earth observation sensors and ground imagery. Despite its challenges, we believe that integrating these complementary data sources will lead to major breakthroughs in Big GeoData. It will open new perspectives for this exciting and emerging field.

Description: The recent trend toward open Earth observation (EO) data has revived a general interest in satellite-based monitoring and mapping of the Earth surface. The open policy now applied to LANDSAT data, and the starting of Sentinel operations, whose data are freely distributed even for commercial purposes, tore down a financial barrier to wider use of EO data in business activities, especially those with a narrow financial margin. Notwithstanding the flood of open data, some aspects of the Earth surface still escape satisfactory monitoring from space, especially in complex, anthropic areas. Due to insufficient spatial resolution, lack of visibility, or unsuitable revisit times, important pieces of information may not emerge from spaceborne data. In situ sensing can represent a vital source of integrative information to fill the aforementioned gaps and build a more complete and accurate picture of the situation and trends in the observed area. The contribution of in situ sensing was envisaged quite early in the Earth observation history, but for a long time it remained limited to tailored sensors displaced in strategic locations. With the increasing circulation of smartphones, a new opportunity has recently opened for a different paradigm of in situ sensing, offering a huge mass of additional data by tapping on data generated by mobile devices. Even if such data may be less specialized and less usable for various reasons, the sheer size of the data flow ensures that statistical analysis will pick possible useful clues. The increasing availability of mobile connections has indeed revived the concept of “crowdsourcing,” i.e., entrusting a pool of actors with problem solution or information collection tasks. In our scenario, individuals carrying mobile devices can become “citizen sensors” on a voluntary basis by contributing data through their connected terminals. Ev- n considering the typical issues of crowdsourced data, like quality and reliability, the balance remains definitely positive. This paper provides an overview of the theme and discusses how it relates to an important, coordinated EO initiative like Copernicus. It finally presents a specific example realized in the framework of a recent research project under the Copernicus aegis.

Description: Social media has been utilized as a significant surrogate for spatial societal event forecasting. The accuracy and discernibility of a spatial event forecasting model are two key concerns, as they determine how accurate and how detailed the model’s predictions will be. Existing research focuses almost exclusively on the accuracy alone, seldom considering the accuracy and discernibility simultaneously because this would require a considerably more sophisticated model while suffering from several challenges, namely: 1) the precise formulation of the tradeoff between accuracy and discernibility; 2) the scarcity of social media data with a high spatial resolution; and 3) the characterization of spatial correlation and heterogeneity. This paper proposes a novel feature learning framework that concurrently addresses all the above challenges by formulating prediction tasks for different locations with different spatial resolutions, allowing the heterogeneous relationships among the tasks to be characterized. This characterization is then integrated into our new models based on multitask learning, with parameters optimized by our proposed algorithm based on the alternative direction method of multipliers (ADMM) and dynamic programming. Extensive experimental evaluations performed on several data sets from different domains demonstrated the effectiveness of our proposed approach.

Description: Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various data sets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning data sets and methods for scene classification is still lacking. In addition, almost all existing data sets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale data set, termed “NWPU-RESISC45,” which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This data set contains 31 500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 1) is large-scale on the scene classes and the total image number; 2) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion; and 3) has high within-class diversity and between-class similarity. The creation of this data set will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed data set, and the results are reported as a useful baseline for future research.

Description: The significant development of social media over the past decade has been complemented by the rise of spatial technologies to provide new mapping mechanisms that allow users engage with online information services and also with each other in an unprecedented way. Users of these technologies now provide a comprehensive geosocial overlay of the physical environment of the planet.

Description: With fast development of information engineering and social network, people’s locations can be conveniently sensed by spatial technology, such as global positioning systems (GPS), base stations, Wi-Fi access points and even from the appearances of the photos they have taken. The social networks and the online shopping platforms have been gathering billions of users, who share a large amount of images taken in places they live in and visit. We can leverage the social networks to express our opinions about the services and places of interest (POIs). The interactions among users, and user and POIs or services generate big social media data, which have rich information for user, location, and service cognition. Many real-time network applications rely heavily on the accurate social users’ locations. How to sense the locations from multisource social media data is very important and challenging. Thus, in this paper, we give a systematic review of the works that combine social media and spatial technology for POI cognition and image localization.

Description: The articles in this special issue cover all the most recent advances in the solid-state drive field from both hardware and software/firmware perspectives. If we look at the DRAM history, DRAM data access speeds have increased at a faster rate than HDDs. The gap in read and write performances between DRAM and HDD has widened in the last years, leaving an opportunity for a new intermediate memory/storage technology between HDDs and DRAM: nand Flash-based SSDs can fill this performance gap, thus profoundly changing the traditional memory hierarchy below the microprocessor.

Description: Presents the front cover for this issue of the publication.

Description: This paper reviews the recent historical trends of the NAND Flash technology, highlighting the evolution of its main parameters and explaining what allowed it to become not only the most important integrated solution for nonvolatile storage of high volumes of data but also a strong rival eroding the market share of hard-disk drives. The scaling trend followed by planar arrays will be discussed with close attention, along with the major physical constraints impacting the performance and the reliability of modern deca-nanometer technologies. This will make clear why the development of further planar nodes with feature size below ~15 nm, representing today’s state of the art, can be considered less favorable than turning all the efforts toward the integration of 3-D arrays. The most promising 3-D architectures will then be reviewed, discussing their benefits and issues and addressing the impact of the change of the integration paradigm from the standpoint of the major NAND applications.

Description: NAND flash memory is ubiquitous in everyday life today because its capacity has continuously increased and cost has continuously decreased over decades. This positive growth is a result of two key trends: 1) effective process technology scaling; and 2) multi-level (e.g., MLC, TLC) cell data coding. Unfortunately, the reliability of raw data stored in flash memory has also continued to become more difficult to ensure, because these two trends lead to 1) fewer electrons in the flash memory cell floating gate to represent the data; and 2) larger cell-to-cell interference and disturbance effects. Without mitigation, worsening reliability can reduce the lifetime of NAND flash memory. As a result, flash memory controllers in solid-state drives (SSDs) have become much more sophisticated: they incorporate many effective techniques to ensure the correct interpretation of noisy data stored in flash memory cells. In this article, we review recent advances in SSD error characterization, mitigation, and data recovery techniques for reliability and lifetime improvement. We provide rigorous experimental data from state-of-the-art MLC and TLC NAND flash devices on various types of flash memory errors, to motivate the need for such techniques. Based on the understanding developed by the experimental characterization, we describe several mitigation and recovery techniques, including 1) cell-to-cell interference mitigation; 2) optimal multi-level cell sensing; 3) error correction using state-of-the-art algorithms and methods; and 4) data recovery when error correction fails. We quantify the reliability improvement provided by each of these techniques. Looking forward, we briefly discuss how flash memory and these techniques could evolve into the future.

Description: Who controls engineering education? And how has this control evolved over time? While some folks may presume that ABET has undisputed control over engineering degree programs in the United States, those familiar with ABET Engineering Criterion 2000’s origins know otherwise [1] . Moreover, history shows that the development of new standards in engineering education has always been a shared responsibility, with this responsibility being distributed in ways that reflect the broader fragmentation of the engineering profession. Still, the flurry of concern generated by ABET’s proposed new accreditation standard suggests that issues of control, or governance, will remain a common feature within U.S. engineering education [2] , [3] . As we converge around ABET’s latest standard, we should use the broader lessons of history to understand how our recent conversations fit within a broader historical pattern, and use this to guide our future actions.

Description: NAND Flash memories have changed and keep changing our lives. In the past two decades, NAND-based systems, in the form of Flash cards and USB keys, have replaced films and floppy disks. But disruption did not stop there. Today, NAND is really ubiquitous, as it plays the role of storage element inside smartphones and tablets; even further, it is now expanding its reach because solid-state drives (SSDs), i.e., drives built with several NAND devices, are replacing hard disk drives (HDDs) in more and more applications. To fuel this continuous evolution, NAND has to remain very aggressive in terms of cost per bit. When approaching 10-nm technologies, planar NAND is running out of steam: industry and academia have worked hard on finding a solution to this problem for more than a decade. Three-dimensional integration turned out to be the most promising alternative, and it is now eventually reaching the market. This paper is about 3-D NAND Flash memories and the related integration challenges. Charge trap and floating gate 3-D technologies will be discussed with the aid of several bird’s-eye views. Advanced layout techniques will thoroughly be analyzed. Finally, future scaling trends will be presented.

Description: Solid-state drives (SSDs) faced an astonishing development in the last few years, becoming the cornerstone to new paradigms and markets of the information technology, such as cloud computing and big data centers. So far, the SSD design approach has focused on the optimization of the Flash translation layer, the firmware devoted to fulfill the compatibility with traditional hard-disk drives. For hyperscaled SSDs this strategy is no longer valid since their performance and reliability are strictly linked to that of the NAND Flash memories that constitute the storage medium, in particular when the multilevel cell paradigm is considered. For this reason, the design flow must follow a bottom-up approach that, starting from an accurate knowledge of the time and use dependent reliability of the NAND Flash memories, selects the most appropriate error correction strategy to extend the SSD lifetime while reducing its performance degradation. Then, the design flow moves to that of the SSD controller and of the interface toward the host where the application is running. This paper will thoroughly discuss this bottom-up approach, and finally, it will show how it is possible to leverage new approaches, such as the software-defined storage system that, by exploiting a hardware/software codesign of the SSD controller architecture and of the host application, will be able to revolutionize the traditional computer/memory interaction.

Description: Why are terms like “dread,” “danger,” and “intractable” applied to describe the management of the spent nuclear fuel produced by today’s commercial nuclear reactors? The small mass and compact form of this spent fuel are the most positive attributes of nuclear power, when compared to the massive volumes of waste we discharge when fossil fuels are burned.

Description: Decades after the idea was first proposed, it appears that underground repositories to manage radioactive spent fuel from commercial reactors are finally going to be constructed. In November 2015, the ruling center-right party in Finland became the first government to grant a construction license for such a repository [1] . The U.S. Department of Energy is pursuing a consent-based process to set up a similar repository. Do these developments mean that a long-sought solution to the problem of nuclear waste is imminent?

Description: A genome sequence assembly provides the foundation for studies of genotypic and phenotypic variation, genome structure, and evolution of the target organism. In the past four decades, there has been a surge of new sequencing technologies, and with these developments, computational scientists have developed new algorithms to improve genome assembly. Here we discuss the relationship between sequencing technology improvements and assembly algorithm development and how these are applied to extend and improve human and nonhuman genome assemblies.

Description: Microorganisms are everywhere. Recent studies showed that the mixture of microbes or the microbiome on the human body plays important roles in human physiology and diseases. Metagenomic sequencing is a key technology for studying microbiomes. It produces massive amounts of data in the form of short sequencing reads. A single metagenomic sample can contain 10 7 to 10 8 reads of about 100-nucleotide (nt) length each in a typical shotgun metagenomic sequencing study. They contain rich information about microbiomes and their functions, but reading out those information from the huge highly fragmented data has multiple challenges for mathematical models, bioinformatics methods, and computer algorithms. In this paper, we review the basic bioinformatics tasks and existing methods in processing and analyzing metagenomic data, and discuss remaining open challenges and practical observations. The aim of the paper is to provide readers a whole picture of metagenomic data processing and analysis, and a reference and perspective to start with for computational scientists who are interested in this exciting field.

Description: A crucial step in the understanding of any phenotype is the correct identification of the signaling pathways that are significantly impacted in that phenotype. However, most current pathway analysis methods produce both false positives as well as false negatives in certain circumstances. We hypothesized that such incorrect results are due to the fact that the existing methods fail to distinguish between the primary dis-regulation of a given gene itself and the effects of signaling coming from upstream. Furthermore, a modern whole-genome experiment performed with a next-generation technology spends a great deal of effort to measure the entire set of 30000–100000 transcripts in the genome. This is followed by the selection of a few hundreds differentially expressed genes, step that literally discards more than 99% of the collected data. We also hypothesized that such a drastic filtering could discard many genes that play crucial roles in the phenotype. We propose a novel topology-based pathway analysis method that identifies significantly impacted pathways using the entire set of measurements, thus allowing the full use of the data provided by NGS techniques. The results obtained on 24 real data sets involving 12 different human diseases, as well as on 8 yeast knock-out data sets show that the proposed method yields significant improvements with respect to the state-of-the-art methods: SPIA, GSEA, and GSA. Availability: Primary dis-regulation analysis is implemented in R and included in ROntoTools Bioconductor package (versions $geq$ 2.0.0). https://www.bioconductor.org/packages/release/bioc/html/ROntoTools.html .