Search results for " hardware"
showing 10 items of 422 documents
A Hardware and Secure Pseudorandom Generator for Constrained Devices
2018
Hardware security for an Internet of Things or cyber physical system drives the need for ubiquitous cryptography to different sensing infrastructures in these fields. In particular, generating strong cryptographic keys on such resource-constrained device depends on a lightweight and cryptographically secure random number generator. In this research work, we have introduced a new hardware chaos-based pseudorandom number generator, which is mainly based on the deletion of an Hamilton cycle within the $N$ -cube (or on the vectorial negation), plus one single permutation. We have rigorously proven the chaotic behavior and cryptographically secure property of the whole proposal: the mid-term eff…
DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
2021
Abstract Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named De…
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
2019
Abstract Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we…
Support Tool for the Combined Software/Hardware Design of On-Chip ELM Training for SLFF Neural Networks
2016
Typically, hardware implemented neural networks are trained before implementation. Extreme learning machine (ELM) is a noniterative training method for single-layer feed-forward (SLFF) neural networks well suited for hardware implementation. It provides fixed-time learning and simplifies retraining of a neural network once implemented, which is very important in applications demanding on-chip training. This study proposes the data flow of a software support tool in the design process of a hardware implementation of on-chip ELM learning for SLFF neural networks. The software tool allows the user to obtain the optimal definition of functional and hardware parameters for any application, and e…
A 4K-Input High-Speed Winner-Take-All (WTA) Circuit with Single-Winner Selection for Change-Driven Vision Sensors
2019
Winner-Take-All (WTA) circuits play an important role in applications where a single element must be selected according to its relevance. They have been successfully applied in neural networks and vision sensors. These applications usually require a large number of inputs for the WTA circuit, especially for vision applications where thousands to millions of pixels may compete to be selected. WTA circuits usually exhibit poor response-time scaling with the number of competitors, and most of the current WTA implementations are designed to work with less than 100 inputs. Another problem related to the large number of inputs is the difficulty to select just one winner, since many competitors ma…
Efficient MLP Digital Implementation on FPGA
2005
The efficiency and the accuracy of a digital feed-forward neural networks must be optimized to obtain both high classification rate and minimum area on chip. In this paper an efficient MLP digital implementation. The key features of the hardware implementation are the virtual neuron based architecture and the use of the sinusoidal activation function for the hidden layer. The effectiveness of the proposed solutions has been evaluated developing different FPGA based neural prototypes for the High Energy Physics domain and the automatic Road Sign Recognition domain. The use of the sinusoidal activation function decreases hardware resource employment of about 32% when compared with the standar…
Efficient techniques for energy saving in data center networks
2018
Data centers are constructed with a huge number of network devices to support the expanding cloud based services. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. Consequently, many devices are set into idle state and cause a huge energy waste leading to a non-proportionality between the network load and the energy consumed. In this paper, we propose a new approach to improve the efficiency of data centers in terms of energy consumption. Our approach exploits the correlation in time of the inter-node communication traffic and some topological features to maximize energy saving w…
High temperature solid-catalized transesterification for biodiesel production
2010
Biodiesel has become more attractive recently because of its environmental benefits and the fact that it is made from renewable resources. Biodiesel is a mixture of monoalkyl esters of long chain fatty acids derived from renewable feed stock like vegetable oils and animal fats, mainly made of fatty acid glycerides. It is produced by transesterification processes in which oil or fat are reacted with a monohydric alcohol in the presence of a catalyst. The transesterification process is affected by reaction conditions, alcohol to oil molar ratio, type of alcohol, type and amount of catalysts, temperature and purity of reactants. Heterogeneous acid catalysts are quite efficient in promoting the…
A Formal Model for Developing of the self-Diagosing and Self-Repairing 8-Bits Microprocessor, and Its Investigation Using Simulation
1986
Abstract The complete model of functional diagnostics is theoretically described. It specifies the conditions, which must be satisfied if the system to be self-diagnosable. The general principles of constructing self-diagnosable systems are enumerated. The model enables the realization of self-renewal, too. The model has been developed on the basis of the works by Preparata, Metze, Chien (1967) and Hakimi, Amin (1974) . The model contains a method of diagnostics completely separeted from the physical structure of the system. Recent results (Gruber, 1978; Swiatek, 1982) indicate that it is only necessary to know the set of transformations realized by the circuit. The model has been applied t…
On-board Energy Consumption Assessment for Symbolic Execution Models on Embedded Devices
2020
Internet of Things (IoT) applications operate in several domains while requiring seamless integration among heterogeneous objects. Regardless of the specific platform and context, IoT applications demand high energy efficiency. Adopting resource-constrained embedded devices for IoT applications means ensuring low power consumption, low maintenance costs and possibly longer battery life. Meeting these requirements is particularly arduous as programmers are not able to monitor the energy consumption of their own software during development or when applications are finally deployed. In this paper, we discuss on-board real-time energy evaluation of both hardware and software during the developm…