Topic
Blackfin
About: Blackfin is a research topic. Over the lifetime, 135 publications have been published within this topic receiving 512 citations.
Papers published on a yearly basis
Papers
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22 Oct 2007TL;DR: This paper reports work on benchmarking the performance and cost of scale invariant feature transform (SIFT) for visual classification on a Blackfin DSP processor and discusses implications to other application domains.
Abstract: Advances in DSP technology create important avenues of research for embedded vision. One such avenue is the investigation of tradeoffs amongst system parameters which affect the energy, accuracy, and latency of the overall system. This paper reports work on benchmarking the performance and cost of scale invariant feature transform (SIFT) for visual classification on a Blackfin DSP processor. Through measurements and modeling of the camera sensor node, we investigate system performance (classification accuracy, latency, energy consumption) in light of image resolution, arithmetic precision, location of processing (local vs. server-side), and processor speed. A case study on counting eggs during avian nesting season is used to experimentally determine the tradeoffs of different design parameters and discuss implications to other application domains.
20 citations
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01 Jan 2010TL;DR: The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis, and the complexity of the various stages of the REACT algorithm on the Blackfin processor is analysed; in particular the EEG feature extraction stages.
Abstract: REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis. Furthermore, the complexity of the various stages of the REACT algorithm on the Blackfin processor is analysed; in particular the EEG feature extraction stages. This hardware profile is used to select a reduced, platform-aware feature set, in order to evaluate the seizure classification accuracy of a lower-complexity, lower-power REACT system.
13 citations
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TL;DR: The proposed technique is well suited for implementation in field pest classification on-line for precision agriculture and can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP.
Abstract: This paper presents a feasibility study on a real-time in field pest classification system design based on Blackfin DSP and 3G wireless communication technology. This prototype system is composed of remote on-line classification platform (ROCP), which uses a digital signal processor (DSP) as a core CPU, and a host control platform (HCP). The ROCP is in charge of acquiring the pest image, extracting image features and detecting the class of pest using an Artificial Neural Network (ANN) classifier. It sends the image data, which is encoded using JPEG 2000 in DSP, to the HCP through the 3G network at the same time for further identification. The image transmission and communication are accomplished using 3G technology. Our system transmits the data via a commercial base station. The system can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP. In the HCP, the image data is decoded and the pest image displayed in real-time for further identification. Authentication and performance tests of the prototype system were conducted. The authentication test showed that the image data were transmitted correctly. Based on the performance test results on six classes of pests, the average accuracy is 82%. Considering the different live pests’ pose and different field lighting conditions, the result is satisfactory. The proposed technique is well suited for implementation in field pest classification on-line for precision agriculture.
11 citations
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17 Jun 2006TL;DR: This paper describes the use of the embedded DSP platform based on the Analog Devices Blackfin digital signal processor in the Microprocessor-based Design Laboratory and discusses how this platform can be used in a range of classes.
Abstract: At Northeastern University we are building a number of courses upon a common embedded systems platform. The goal is to reduce the learning curve associated with new architectures and programming environments. The platform selected is based on the Analog Devices Blackfin digital signal processor.In this paper we discuss our recent experience developing anew undergraduate embedded systems lab. Students learn to utilize the embedded DSP platform to address a number of different applications, including controller design, RS-232 communication, encryption, and image processing. This platform provides a rich design exploration sandbox replete with programming and simulation tools. We describe our use of this platform in our Microprocessor-based Design Laboratory and discuss how this platform can be used in a range of classes.
11 citations
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21 Jun 2012TL;DR: It is demonstrated that the LwIP stack has good performance, comparable with the TCP/IP stack implementation in operating systems like Linux or Windows.
Abstract: The paper has the purpose to evaluate the performance of the lightweight TCP/IP protocol stack using the digital signal processor Blackfin BF537. The applications of such embedded system are in sensor networking, voice over IP (VoIP) communications and process control in industrial environments. The performance of LwIP stack has been evaluated using the evaluation board EZ-KIT. In this paper we demonstrated that the LwIP stack has good performance, comparable with the TCP/IP stack implementation in operating systems like Linux or Windows. Furthermore, a framework for developing embedded networking applications is provided.
11 citations