We estimated the world’s technological capacity to store, communicate, and compute information, tracking 60 analog and digital technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 10 20 optimally compressed bytes, communicate almost 2 × 10 21 bytes, and carry out 6.4 × 10 18 instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The world’s capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored information (23%). Humankind’s capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94% digital in 2007).

/pdf/the-world-s-technological-capacity-to-store-communicate-and-39dybc0mey.pdf

The World’s Technological Capacity to Store, Communicate, and Compute Information

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

/pdf/big-data-survey-technologies-opportunities-and-challenges-2538s9eunj.pdf

Big Data: Survey, Technologies, Opportunities, and Challenges

Steady advances in VLSI technology and design tools have extensively expanded the application domain of digital signal processing over the past decade. While application-specific integrated circuits (ASICs) and programmable digital signal processors (PDSPs) remain the implementation mechanisms of choice for many DSP applications, increasingly new system implementations based on reconfigurable computing are being considered. These flexible platforms, which offer the functional efficiency of hardware and the programmability of software, are quickly maturing as the logic capacity of programmable devices follows Moore's Law and advanced automated design techniques become available. As initial reconfigurable technologies have emerged, new academic and commercial efforts have been initiated to support power optimization, cost reduction, and enhanced run-time performance.

This paper presents a survey of academic research and commercial development in reconfigurable computing for DSP systems over the past fifteen years. This work is placed in the context of other available DSP implementation media including ASICs and PDSPs to fully document the range of design choices available to system engineers. It is shown that while contemporary reconfigurable computing can be applied to a variety of DSP applications including video, audio, speech, and control, much work remains to realize its full potential. While individual implementations of PDSP, ASIC, and reconfigurable resources each offer distinct advantages, it is likely that integrated combinations of these technologies will provide more complete solutions.

http://www.umiacs.umd.edu/~joseph/classes/enee640/fpga-dsp-survey.pdf

Reconfigurable Computing for Digital Signal Processing: A Survey

This report describes the prototype design, development, analysis and performance of a traffic surveillance system that is based on wireless sensor networks. Vehicle classification and reidentification schemes for low-cost, low-power platforms with limited computation resources were developed and tested. Both acoustic and magnetic sensors were tested. A robust real-time vehicle detection algorithm for both sensors was developed, and the magnetic sensor was shown to be superior to the acoustic sensor. The detection accuracy was shown to be comparable to that of inductive loop detectors while also having a much higher configuration flexibility, thus allowing the wireless sensor network system to be scalable and deployable everywhere in the traffic networks.

Traffic Surveillance by Wireless Sensor Networks: Final Report

From the Publisher: 
This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

数字信号处理 = Understanding digital signal processing

The number and variety of products that include some form of digital signal processing (DSP) has grown dramatically. DSP has become a key component in many consumer, communications, medical, and industrial products, which use a variety of hardware approaches to implement DSP, ranging from the use of off-the-shelf microprocessors to field-programmable gate arrays (FPGAs) to custom integrated circuits (ICs). Programmable "DSP processors", a class of microprocessors optimized for DSP, are a popular solution for several reasons: they can potentially be reprogrammed in the field, allowing product upgrades or fixes. They are often more cost-effective (and less risky) than custom hardware, particularly for low-volume applications, where the development cost of custom ICs may be prohibitive. In comparison to other types of microprocessors, DSP processors often have an advantage in terms of speed, cost, and energy efficiency. In this article, we trace the evolution of DSP processors from early architectures to current state-of-the-art devices. We highlight some of the key differences among architectures and compare their strengths and weaknesses. Finally, we discuss the growing class of general-purpose processors that have been enhanced to address the needs of DSP applications.

J. Eyre

Papers

The evolution of DSP processors