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Solid-State Drives: Memory Driven Design Methodologies for Optimal Performance
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 9) |
| Halaman | : | 1589-1608 |
| Abstrak | : | 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 For hyperscaled SSDs this strategy is no longer valid since their performance and reliability are strictly linked to that of the NAND Flash memories that constitute the storage medium, in particular when the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. |
Solid-State Drives
| Pengarang | : | Rino Micheloni |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 9) |
| Halaman | : | 1586-1588 |
| Abstrak | : | 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. |
Spatial technology and social media in remote sensing: challenges and opportunities
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 9) |
| Halaman | : | 1583-1585 |
| Abstrak | : | 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. |
History of the Endoscope
| Pengarang | : | Piet C. De Groen |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1987-1995 |
| Abstrak | : | Endoscopy is a technique allowing inspection, manipulation, and treatment of internal organs using devices to enhance visualization from a distance of the target organs without the need of an incision large enough to allow the hand or fingers of the surgeon to enter the surgical field. As can be expected, endoscopy developed in areas where hollow organs were connected to the exterior via natural orifices: the urethra, the vagina, the rectum, the ear canal, and the throat and pharynx. Entry of natural orifices was safe, devoid of wounds, and thus with little risk of infection and death. As also can be expected in the early days without radio, TV, telephone, or internet, numerous physicians and nonphysician scientists invented smaller or larger contributions to the field, sometimes at the same time unaware of the inventions of others. Not all who contributed published their inventions or the application of their inventions, and for others records were lost or destroyed at times of war. As a result the history of the endoscope is not an exact science where each advancement can be placed on a reputable time line and assigned with confidence to a single inventor. The limited space assigned to this summary of the history of the endoscope will not allow any details or nuances; instead it will focus on a number of well-regarded and generally accepted important contributions and its inventors in the early phases of endoscopy and mostly on the technology of the more recent history of endoscopy (Table 1). In addition, rather than focus on the people behind the inventions, this history is aimed mostly at the engineering concepts and subsequent applications in the medical field. For those who desire a more detailed and nuanced history of endoscopy, the reader is referred to books, manuscripts, and websites that cover the entire field or specific subspecialty areas: there are many including cystoscopy, colposcopy, bronchoscopy, thoracoscopy, gastrointestinal endoscopy, laparoscopy, arthroscopy, laryngoscopy, and otoscopy |
Geotagging Text Content With Language Models and Feature Mining
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1971-1986 |
| Abstrak | : | 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. |
Spatial Event Forecasting in Social Media With Geographically Hierarchical Regularization
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1953-1970 |
| Abstrak | : | 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. |
On Combining Social Media and Spatial Technology for POI Cognition and Image Localization
| Pengarang | : | Wicaksono, Teguh Yudo |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1937-1952 |
| Abstrak | : | 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. |
A Novel Methodology to Label Urban Remote Sensing Images Based on Location-Based Social Media Photos
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1926-1936 |
| Abstrak | : | With the rapid development of the internet and popularization of intelligent mobile devices, social media is evolving fast and contains rich spatial information, such as geolocated posts, tweets, photos, video, and audio. Those location-based social media data have offered new opportunities for hazards and disaster identification or tracking, recommendations for locations, friends or tags, pay-per-click advertising, etc. Meanwhile, a massive amount of remote sensing (RS) data can be easily acquired in both high temporal and spatial resolution with a multiple satellite system, if RS maps can be provided, to possibly enable the monitoring of our location-based living environments with some devices like charge-coupled device (CCD) cameras but on a much larger scale. To generate the classification maps, usually, labeled RS image pixels should be provided by RS experts to train a classification system. Traditionally, labeled samples are obtained according to ground surveys, image photo interpretation or a combination of the aforementioned strategies. All the strategies should be taken care of by domain experts, in a means which is costly, time consuming, and sometimes of a low quality due to reasons such as photo interpretation based on RS images only. These practices and constraints make it more challenging to classify land-cover RS images using big RS data. In this paper, a new methodology is proposed to classify urban RS images by exploiting the semantics of location-based social media photos (SMPs). To validate the effectiveness of this methodology, an automatic classification system is developed based on RS images as well as SMPs via big data analysis techniques including active learning, crowdsourcing, shallow machine learning, and deep learning. As the labels of RS training data are given by ordinary people with a crowdsourcing technique, the developed system is named Crowd4RS. The quantitative and qualitative experiments confirm the effectiveness of the proposed Crowd4RS system as well as the proposed methodology for automatically generating RS image maps in terms of classification results based on big RS data made up of multispectral RS images in a high spatial resolution and a large amount of photos from social media sites, such as Flickr and Panoramio. |
Potentials of Active and Passive Geospatial Crowdsourcing in Complementing Sentinel Data and Supporting Copernicus Service Portfolio
| Pengarang | : | Wei, Juncheng,Ren, Xiaofeng |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1913-1925 |
| Abstrak | : | 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. Even 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. |
Social Media: New Perspectives to Improve Remote Sensing for Emergency Response
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 105 (No. 10) |
| Halaman | : | 1900-1912 |
| Abstrak | : | Remote sensing is a powerful technology for Earth observation (EO), and it plays an essential role in many applications, including environmental monitoring, precision agriculture, resource managing, urban characterization, disaster and emergency response, etc. However, due to limitations in the spectral, spatial, and temporal resolution of EO sensors, there are many situations in which remote sensing data cannot be fully exploited, particularly in the context of emergency response (i.e., applications in which real/near-real-time response is needed). Recently, with the rapid development and availability of social media data, new opportunities have become available to complement and fill the gaps in remote sensing data for emergency response. In this paper, we provide an overview on the integration of social media and remote sensing in time-critical applications. First, we revisit the most recent advances in the integration of social media and remote sensing data. Then, we describe several practical case studies and examples addressing the use of social media data to improve remote sensing data and/or techniques for emergency response. |