Lightweight and Efficient Architecture for AES Algorithm based on FPGA

Different platforms, such as resource limited devices and high-performance processors, are used in IoT networks, each with its own set of resource, performance, and security needs. It is critical to optimize existing standard cryptographic algorithms to meet the needs of today's networks, yet this is a difficult undertaking. In this paper, a compact and efficient architecture for the Advanced Encryption Standard (AES) is developed and implemented using several FPGA platforms, with the goal of addressing both restricted and high-performance platforms in IoT networks. To create compact and efficient AES based on FPGA, a hybrid optimization technique is applied. The implementation makes advantage of FPGA embedded resources such as BRAMs and DSP slices. To synthesize and implement it on the Xilinx Virtex-7 device, the Vivado HLS tool 2019.1 is utilized. Similarly, as devices older than the Xilinx 7-series platforms are not directly supported by Vivado HLS tool, Xilinx 14.5 ISE tool is used to synthesize and implement it. Smaller resources, such as 572 slices, 8 BRAMs, and 32 DSP slices, are used in comparison to the implementation outcomes found in literature. Additionally, improved throughput performance (112.399 Gbps) was achieved by satisfying the current work's optimization targets

An Approach to Estimate Electric Vehicle Driving Range

The use of electric vehicle (EV) has grown rapidly over the past few years. The EV is now accepted as a reliable and eco-friendly means of transportation. When choosing an EV, usually one of the key parameters of choice for the customer is its driving range (DR) capability. This is a decisive factor since it minimizes the drivers anxiety on a trip. The DR depends on many factors that must be taken into account when attempting its prediction.In this paper, we explore the use of machine learning (ML) techniques to estimate the DR prediction.We use regression techniques on models trained with publicly available datasets, evaluated with standard metrics.The prediction results are better than those provided by statistical techniques, thus being quite encouraging.As the end result, we also provide a ML benchmark written in Python, aiming to advance future research on this topic

Simulation of plasmonic effects of Metal (Au,Ag and Al) NPs and rGO embedded in aqueous solutions

Graphene [1] is a material that has been extensively explored in recent years as a material with optical properties that enable its application as active material in sensing devices.In this work we will study plasmonic effects and optical properties of graphene and metal nanoparticles (AuNPs), comparing its results, whenever possible, with results obtained in previous studies. Analysis will be supported by simulation results obtained with Matlab (“Mie analysis”)

A Fatigue and Drowsiness Detection System Using Inertial Sensors and Electrocardiogram Signals

The interest in monitoring a drivers conditions and performance has increased in the past years, to make the roads safer both for drivers and pedestrians. This raised the idea of developing a system to monitor the drivers conditions to prevent road disasters. In this paper, we propose a system to monitor the drivers fatigue and drowsiness, based on the Car- dioWheel system, developed by CardioID. The proposed system records both the persons ECG signal and the motion of the steering wheel during the driving session. The amount of data acquired demands a compression stage for transmission with the goal to reduce the required bandwidth. The transmission of the compressed data is done via Bluetooth Low Energy, with an exclusive profile developed for this system. To detect fatigue and drowsiness patterns, a machine learning approach was taken. Among the evaluated classifiers, the Support Vector Machines technique proved to be the best classification method with the highest accuracy. Thus, the developed prototype has the ability to warn the driver about his physiological and physical states, increasing the safety in the roads

Combined detection of nitrite and bioelectrical activity using microelectrode arrays and a phosphate buffered saline solution

This paper presents the manufacturing of amicroelectrode array, on printed circuit boards (PCB) and onsilicon substrate, composed of 60 to 64 gold microelectrodes(between 4 µm and 70 µm each in diameter). The sensor has ameasurement total area of 0.5 cm in radius, one referenceelectrode (1 mm2 in area) and 28.4 mm wide and 28.4 mm long.These microelectrodes were used for checking and logging ofextracelullar local field potential of cell culture and nitritemeasurement in a phosphate buffered saline solution(electrolytical aqueous medium). In addition, an apparatus toshield from electromagnetic interference for connecting thearrays was designed to allow the capture of electrochemicalreactions or electronic signals by the microelectrodes, forexample: nitrite or cardiac potential measurement, respectively.Finally, biocompatibility tests of the array structures wereperformed. The preliminary electrical and biocompatibilitytesting, along with the collected data, has shown promisingresults pointing to the development of an accurate sensor afterthe completion of this study. The sensor has potentially abroader range of applications with only a few adaptations anddue its good accuracy it can be a very useful resource for manychemical and biological applications

A primer on understanding Google Earth Engine APIs

This article is build on the experience of using Google Earth Engine as a development framework for a previous work by the same authors.Being primarily a distributed parallel computing platform, it is designed around a functional language pattern, even though supported on an object model, and a map / reduce distributed workload paradigm.Leveraging the sheer computing power delivered by the Google infrastructure and a multi petabyte remote sensing data repository, GoogleEarth Engine is an efficient development framework that presents itself in two basic flavors: one online integrated development environment which uses the browser Javascript engine; two APIs that can be deployed to a Python or a NodeJS environment.This work emphasizes the comparison between the Javascript browserbased implementation and the Python environment packages

A Deep Learning Approach to Identify Not Suitable for Work Images

Web Archiving (WA) deals with the preservation of portions of the World Wide Web (WWW) allowing their availability for future access. Arquivo.pt is a WA initiativeholding a huge amount of content, including image files.However, some of these images contain nudity and pornography, that can be offensive for the users, and thus being Not SuitableFor Work (NSFW). This work proposes a methodology to classify NSFW images available at Arquivo.pt, using deep neural network approaches. A large dataset of images is built using Arquivo.pt data and two pre-trained neural network models, namely ResNet and SqueezeNet, are evaluated and improved for the NSFW classification task, using the dataset.The evaluation of these models reported an accuracy of 93% and 72%, respectively. After a fine tuning stage, the accuracy of these models improved to 94% and 89%, respectively.The proposed solution is integrated into the Arquivo.pt Image Search System, enabling the filtering of the problematic NSFW images. At the time of this writing, the proposed solution is in production at https://arquivo.pt/images.js

APC speech coding techniques applied to ECG signals

This paper describes an ECG signal coding with an adaptive predictive coding scheme using speech techniques like linear predictive coding and long-term prediction

Urban Sound Event Detection and Classification

The ability to automatically detect/classify complex and dynamic urban sounds is important tool for urban planning such as building efficient noise monitoring and control, surveillance systems, urban soundscape mappings, and could be a foundation for improvement of life in the cities of tomorrow.We present a proof of concept for a sound detection, monitoring and classification system for urban environments in the context of Smart Cities. Ultimately, the proposed system be deployed in numerous urban locations for long periods of time, allowing for the collection of urban acoustic data

ELECTROSPINNING PAN/PdCl2 NANOFIBER FOR HIDROGEN STORAGE – THE INFLUENCE OF THE PREPARATION METHOD

The search for materials for hydrogen storage that are able to adsorb and desorb hydrogen molecules has received attention. Nanofibers have been studied because it has some interesting characteristics like high surface to volume ratio and the ease of producing by means of a low-cost setup. Polyacrylonitrile (PAN) has allowed the possibility of obtaining carbon nanofibers from the electrospinning process followed by a thermal treatment; moreover Pd has the capability of store hydrogen and can form a complex with dimethylformamide (DMF) so that, during the preparation of the solution of PAN and PdCl2, both present high solubility in the solvent.  Thus, the results demonstrated that as Pd can form complex with DMF the preparation method of the PAN+PdCl2 solutions influences the interaction between Pd and PAN. The decrease in dispersion viscosity and an unexpected increase in the diameter of the fibers confirm the hypothesis. The FTIRS and XPS analysis confirm that the Pd complexes with DMF by means of bonds to N