scholarly journals Real-time Frame Buffer Implementation based on External Memory using FPGA

2018 ◽  
Vol 131 ◽  
pp. 641-646
Author(s):  
Dogancan Davutoglu ◽  
Nerhun Yildiz ◽  
Umut Engin Ayten ◽  
Vedat Tavsanoglu
Neurosurgery ◽  
2004 ◽  
Vol 55 (3) ◽  
pp. 551-561 ◽  
Author(s):  
Ali H. Mesiwala ◽  
Louis D. Scampavia ◽  
Peter S. Rabinovitch ◽  
Jaromir Ruzicka ◽  
Robert C. Rostomily

Abstract OBJECTIVE: This study tests the feasibility of using on-line analysis of tissue during surgical resection of brain tumors to provide biologically relevant information in a clinically relevant time frame to augment surgical decision making. For the purposes of establishing feasibility, we used measurement of deoxyribonucleic acid (DNA) content as the end point for analysis. METHODS: We investigated the feasibility of interfacing an ultrasonic aspiration (USA) system with a flow cytometer (FC) capable of analyzing DNA content (DNA-FC). The sampling system design, tissue preparation requirements, and time requirements for each step of the on-line analysis system were determined using fresh beef brain tissue samples. We also compared DNA-FC measurements in 28 nonneoplastic human brain samples with DNA-FC measurements in specimens of 11 glioma patients obtained from central tumor regions and surgical margins after macroscopically gross total tumor removal to estimate the potential for analysis of a biological marker to influence surgical decision making. RESULTS: With minimal modification, modern FC systems are fully capable of real-time, intraoperative analysis of USA specimens. The total time required for on-line analysis of USA specimens varies between 36 and 63 seconds; this time includes delivery from the tip of the USA to complete analysis of the specimen. Approximately 60% of this time is required for equilibration of the DNA stain. When compared with values for nonneoplastic human brain samples, 50% of samples (10 of 20) from macroscopically normal glioma surgical margins contained DNA-FC abnormalities potentially indicating residual tumor. CONCLUSION: With an interface of existing technologies, DNA content of brain tissue samples can be analyzed in a meaningful time frame that has the potential to provide real-time information for surgical guidance. The identification of DNA content abnormalities in macroscopically normal tumor resection margins by DNA-FC supports the practical potential for on-line analysis of a tumor marker to guide surgical resections. The development of such a device would provide neurosurgeons with an objective method for intraoperative analysis of a clinically relevant biological parameter that can be measured in real time.


Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1174 ◽  
Author(s):  
Jian Luo ◽  
Chang Lin

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.


2018 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Mounir Hafsa ◽  
Farah Jemili

Cybersecurity ventures expect that cyber-attack damage costs will rise to $11.5 billion in 2019 and that a business will fall victim to a cyber-attack every 14 seconds. Notice here that the time frame for such an event is seconds. With petabytes of data generated each day, this is a challenging task for traditional intrusion detection systems (IDSs). Protecting sensitive information is a major concern for both businesses and governments. Therefore, the need for a real-time, large-scale and effective IDS is a must. In this work, we present a cloud-based, fault tolerant, scalable and distributed IDS that uses Apache Spark Structured Streaming and its Machine Learning library (MLlib) to detect intrusions in real-time. To demonstrate the efficacy and effectivity of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities. A decision tree algorithm is used to predict the nature of incoming data. For this task, the use of the MAWILab dataset as a data source will give better insights about the system capabilities against cyber-attacks. The experimental results showed a 99.95% accuracy and more than 55,175 events per second were processed by the proposed system on a small cluster.


2015 ◽  
Vol 27 (2) ◽  
pp. 246-265 ◽  
Author(s):  
Caroline Whiting ◽  
Yury Shtyrov ◽  
William Marslen-Wilson

Despite a century of research into visual word recognition, basic questions remain unresolved about the functional architecture of the process that maps visual inputs from orthographic analysis onto lexical form and meaning and about the units of analysis in terms of which these processes are conducted. Here we use magnetoencephalography, supported by a masked priming behavioral study, to address these questions using contrasting sets of simple (walk), complex (swimmer), and pseudo-complex (corner) forms. Early analyses of orthographic structure, detectable in bilateral posterior temporal regions within a 150–230 msec time frame, are shown to segment the visual input into linguistic substrings (words and morphemes) that trigger lexical access in left middle temporal locations from 300 msec. These are primarily feedforward processes and are not initially constrained by lexical-level variables. Lexical constraints become significant from 390 msec, in both simple and complex words, with increased processing of pseudowords and pseudo-complex forms. These results, consistent with morpho-orthographic models based on masked priming data, map out the real-time functional architecture of visual word recognition, establishing basic feedforward processing relationships between orthographic form, morphological structure, and lexical meaning.


Author(s):  
J.G. Bosch ◽  
J.H.C. Reiber ◽  
G. van Burken ◽  
J.J. Gerbrands ◽  
A. Kostov ◽  
...  

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