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showing 10 items of 2747 documents
Versatile Direct and Transpose Matrix Multiplication with Chained Operations: An Optimized Architecture Using Circulant Matrices
2016
With growing demands in real-time control, classification or prediction, algorithms become more complex while low power and small size devices are required. Matrix multiplication (direct or transpose) is common for such computation algorithms. In numerous algorithms, it is also required to perform matrix multiplication repeatedly, where the result of a multiplication is further multiplied again. This work describes a versatile computation procedure and architecture: one of the matrices is stored in internal memory in its circulant form, then, a sequence of direct or transpose multiplications can be performed without timing penalty. The architecture proposes a RAM-ALU block for each matrix c…
Neurosurgical resident training in Czech Republic
2018
Introduction: Resident training is essential to be able and offer high-quality medical care. Neurosurgical training in its traditional form is currently challenged by law-enforced working hour restrictions and general re-structuring within Europe. We aimed to evaluate the current situation of resident training in the Czech Republic. Methods: An electronic survey was sent to European neurosurgical trainees between 06/2014 and 03/2015. The responses of Czech trainees were compared to those of trainees from other European countries. Logistic regression analysis was used to assess the effect size of the relationship between a trainee being from Czech Republic and the outcomes (e.g. satisfaction…
Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)
2021
Forest fires are undesirable situations with tremendous impacts on wildlife and people&rsquo
DNS Tunneling Detection Techniques – Classification, and Theoretical Comparison in Case of a Real APT Campaign
2017
Domain Name System (DNS) plays an important role as a translation protocol in everyday use of the Internet. The purpose of DNS is to translate domain names into IP addresses and vice versa. However, its simple architecture can easily be misused for malicious activities. One huge security threat concerning DNS is tunneling, which helps attackers bypass the security systems unnoticed. A DNS tunnel can be used for three purposes: as a command and control channel, for data exfiltration or even for tunneling another protocol through it. In this paper, we surveyed different techniques for DNS tunneling detection. We classified those first based on the type of data and then within the categories b…
Criminal networks analysis in missing data scenarios through graph distances.
2021
Data collected in criminal investigations may suffer from: (i) incompleteness, due to the covert nature of criminal organisations; (ii) incorrectness, caused by either unintentional data collection errors and intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyse nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data and to determine which network type is most affected by it. The networks are firstly pruned following two specific methods: …
Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems
2015
Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many …
A Special Issue on Advances in Machine Learning for Remote Sensing and Geosciences [From the Guest Editors]
2016
Machine learning has become a standard paradigm for the analysis of remote sensing and geoscience data at both local and global scales. In the upcoming years, with the advent of new satellite constellations, machine learning will have a fundamental role in processing large and heterogeneous data sources. Machine learning will move from mere statistical data processing to actual learning, understanding, and knowledge extraction. The ambitious goal is to provide responses to the challenging scientific questions about the earth system. This special issue aims at providing an updated, refreshing view of current developments in the field. For this special issue, we have collected five articles t…
A Selective Change Driven System for High-Speed Motion Analysis.
2016
Vision-based sensing algorithms are computationally-demanding tasks due to the large amount of data acquired and processed. Visual sensors deliver much information, even if data are redundant, and do not give any additional information. A Selective Change Driven (SCD) sensing system is based on a sensor that delivers, ordered by the magnitude of its change, only those pixels that have changed most since the last read-out. This allows the information stream to be adjusted to the computation capabilities. Following this strategy, a new SCD processing architecture for high-speed motion analysis, based on processing pixels instead of full frames, has been developed and implemented into a Field …
Toward fast and accurate emergency cases detection in BSNs
2020
International audience; In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data v…
Essential Enabling Technologies
2016
The previous chapter contained presentations of several relevant collaboration methods. Section 3.1 indicated that the evolution in e-collaboration was associated with a number of technological breakthroughs.