Implementation of the AES algorithm using a Reconfigurable Functional Unit

Cardarilli, G.C.; Nunzio, Luca Di; Fazzolari, Rocco; Pontarelli, Salvatore; Re, M.; Salsano, A.

doi:10.1109/isscs.2011.5978668

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…In the applications where computational complexity and time consuming are important constraints, it is possible to implement alternative algorithms with comparable performance but lesser complexity than Pan & Tompkins algorithm. A further improvement for algorithm performance can be obtained using a different hardware approach, based, for example, on hardware accelerators as shown in [22], [23], [24], [25], [26], [27] and [28].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Silvestri

Carlo

Nunzio

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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Today, thanks to the development of advanced wearable devices, it is possible to track patient conditions outside hospital setting for several days. One of the most important bio-signals used for health analysis is the electrocardiographic (ECG) signal. It provides information about the heart rate, rhythm, and morphology of heart. Many algorithms are proposed over years for automated ECG analysis. Due to their computational complexity, not all these techniques can be implemented on wearable devices for real-time ECG detection. In this frame, a particular interest is toward the algorithms for automatic QRS detection. Different algorithms have been presented in the literature. Among all, more suitable class for the implementation on embedded systems is based on the use of signal derivatives and thresholds. These algorithms are composed by pre-processing stage, for the noise removal, and decision stage for the QRS detection. In literature, the different algorithms were discriminated only with respect to their preprocessing stages. Furthermore, not all algorithms were tested with standard database: this makes the results difficult to compare and evaluate. Moreover, the algorithms performance in case of pathological behaviours was not compared. This paper is motivated by the need to perform a comparison of the whole algorithms, both pre-processing and decision stages, under a standard database (MIT-BIH ECG database of Physionet), either for non-pathological and pathological signals. The results confirm that the Pan & Tompkins algorithm has the best performance in terms of QRS complex detection. However, in some cases, its performance is comparable with the other algorithms ones. For this reason, in the applications in which the reduced of computational complexity is an important constraint, it is possible to implemented algorithms with comparable performance but with lesser complexity with respect to P&T algorithm.

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Section: Discussionmentioning

confidence: 99%

Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Silvestri

Carlo

Nunzio

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

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“…All the above-mentioned complex systems, often require complex algorithm execution that could be optimised by using distributed systems [20]- [22] or other complex techniques [23]- [26]. However, in embedded systems, weight and power consumption [27], does not allow such solutions and consequently, different approaches based on the using of an embedded microprocessor and hardware accelerators will be used [28], [29]. The potential to integrate Lean Manufacturing to Industry 4.0 has been debated [30], and, more specifically, Lean Six Sigma (LSS) has been investigated in its applications to accelerate the process of extracting key insights from Big Data, and how Big Data processing can help to innovate and cast a new light on the projects requiring the use of Lean Six Sigma [31], [32].…”

Section: Introductionmentioning

confidence: 99%

The Revolution Lean Six Sigma 4.0

Arcidiacono

Pieroni

2018

International Journal on Advanced Science, Engineering and Information Technology

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Industry 4.0 makes a factory smart by applying advanced information systems and future-oriented technologies. Today, thanks to the application of the most innovative digital technologies offered by the new Industry 4.0 paradigm, in this Fourth Industrial Revolution, there is a significant "evolution" of many methodologies of Continuous Improvement, such as, e.g., Lean Six Sigma (LSS). Most of the tools of Lean Six Sigma relies on data to know in depth problems: data is necessary to drive any process improvement. The key issue is based on data integrity and on real time data. The aim of this paper consists of proving the efficiency of the so called "Lean Six Sigma 4.0". This paper deals with engineering approaches, here applied in HealthCare environment, in order to optimise the services supply process and to reduce the waste of resources (human and/or material), while improving the Quality of Experience (QoE) of the patients. Indeed, it has been proved that the huge growth in the HealthCare costs is due to inefficient use of available resources and not-optimised service processes. Applying Lean Six Sigma 4.0 it is possible to reduce HealthCare costs, improving at the same time the QoE perceived by the patient.

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“…For this reason, when high performances are required, their implementation cannot be realized on standard microprocessors. In facts, microprocessors are inefficient because they are not optimized for parallel processing [21]. In these cases, different hardware architectures are required; among these architectures, the most interesting is the Graphics Processing Units (GPUs) and the Field Programmable Gate Arrays (FPGAs).…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Hand-written Number Recognition Based on CNN

Giardino

Matta

Silvestri

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a handwritten number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%.

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Implementation of the AES algorithm using a Reconfigurable Functional Unit

Cited by 12 publications

References 11 publications

Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

The Revolution Lean Six Sigma 4.0

FPGA Implementation of Hand-written Number Recognition Based on CNN

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