Paulo S. Martins

Simulation of AdHoc Networks Including Clustering and Mobility

We introduce a discrete-event simulation model of an AdHoc network considering the presence of clusters and node mobility. The main goal is to study the volume of traffic in relatively large networks of sensor systems and Internet of Things considering fading and network connectivity. We also evaluate relevant parameters such as the mean CPU utilization and the mean queueing time in each node. The model is relatively general in that it combines the traffic from small devices such as sensors as well as more complex intermediate systems such as gateways and Internet nodes. It is also extensible to other types of scenarios and it allows the evaluation of the network under other performance criteria or evaluation metrics. The results show that the model yields simulation values that could be analytically validated by Jackson networks.

Case studies of performance evaluation of cryptographic algorithms for an embedded system and a general purpose computer

Embedded systems are associations between hardware and software designed to perform a specific function. These systems are usually part of a larger system and their wireless communications are a hallmark. Therefore, it is important to guarantee a secure communication by ensuring the confidentiality of information, which is obtained through cryptography. Security has not traditionally been considered a requirement in embedded systems design and the application of specific security techniques to these devices is still incipient. This paper presents a performance evaluation analysis of cryptographic algorithms in embedded systems (namely RC2, AES, Blowfish, DES, 3DES, ECC and RSA). Parameters considered in the analysis are average processor and memory usage, response time and power consumption. The results show that symmetric and asymmetric algorithms such as Blowfish and ECC have a good performance in embedded systems when properly chosen for each situation.

n-Steps ahead software reliability prediction using the Kalman filter

This paper presents KSL, a new software reliability growth model (SRGM) based on the Kalman filter with a sub filter and the Laplace trend test. We applied the model to the Linux operating system kernel as a case study to predict the absolute and relative (per lines of code) number of faults n-steps ahead. The Laplace trend test is applied to detect when the series no longer follows a homogeneous Poisson process, improving the confidence level. An example is provided with a prediction of 13months ahead on the number of faults with 8% error. The results (i.e. predictive capability) indicated that the proposed approach outperforms the S-shaped prediction model, Weibull, and Exponentiated Weibull distributions, as well as typical and OS-ELM Neural networks when the series has a short number of observations.

An Approach to Knowledge Acquisition Based on Verbal Semantics

This work presents an approach to knowledge acquisition based on semantic classification of verbs as a tool to knowledge management. It allows the extraction of propositions, concepts and non-taxonomic relations from a domain. It also allows a systematic understanding of the knowledge construction process, based on systems thinking and energy flows. We argue that this type of extraction may facilitate the understanding of the structure of cognitive processes and contribute to knowledge extraction in several areas that use representation, storage, transfer and flow of knowledge as a resource. Our experiments show that the proposed approach may extract processual knowledge and represent it in a causal concept map, guaranteeing, as much as possible, the accuracy of the acquired knowledge, by minimizing the distance between the knowledge agents domain and what the knowledge engineer is capable of extracting.

Usability heuristics and accessibility guidelines: a comparison of heuristic evaluation and WCAG

The distinction between usability and accessibility becomes convoluted as we focus our analysis in the field of HCI. They were originally conceived as separate concepts and there is no clear comparison between them in the literature. Some authors advocate that these are complementary definitions, while others classify accessibility as a sub-class of usability. Such lack of distinction may pose a challenge to many areas that attempt to implement such concepts such as requirements engineering, possibly leading to redundant implementations or incomplete efforts. Therefore, this work attempts at bridging this gap in the research literature by mapping Nielsens usability heuristics to the W3Cs Web Content Accessibility Guidelines (WCAG). We conclude that there is a strong relationship between them and that it is possible to establish a mapping so that all heuristics are represented by at least one principle and vice versa. Furthermore, we notice that the mapping distribution is non-homogeneous as it focus mainly on three heuristics.

An Approach to Clustering and Sequencing of Textual Requirements

Natural language is often used to write software systems requirements. However, it may be prone to misunderstandings due to its ambiguities. Moreover, it is not easy to modularize these requirements and then find all related ones. In order to find out the impact of requirements in one another, it is necessary to look at every requirement rather than just a group of related requirements. When presented in large numbers, the understanding, organization and sequencing of requirements requires substantial time and effort. In this work, we introduce an approach based both on the clustering of textual requirements and on a data dictionary to organize them, as well as suggest a sequence for their implementation. A case study based on User Stories from Agile processes is introduced to illustrate the approach.

Image segmentation through combined methods: Watershed transform, unsupervised distance learning and Normalized Cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

Automating software validation of a GPON network

This paper presents the automation of a Gigabit Passive Optical Network (GPON) embedded software validation, which improves the coverage of tests and eliminates faults that may compromise system availability. A fault injection (FI) campaign based on a state machine model was performed emulating physical faults that automatically break down the communication between GPON network devices. Consequently, new failures were disclosed even after one-and-a-half year period using conventional test techniques.

Quantitative image analysis of acoustic tomography in woods

The development of acoustic techniques for wood analysis through tomography has enabled the generation of images by means of nondestructive techniques. These images allow for the evaluation of the internal condition of wood trunks. This type of evaluation provides valuable information since the internal defects (e.g. holes) in the wood are difficult to identify—especially in its early stages of development. Whereas there is a substantial body of work that aims to improve these images by applying new interpolation and inspection techniques, the assessment of these techniques has traditionally been carried out via a bare visual analysis or inspection of the real wood trunk. In this work, an approach is proposed to quantitatively assess interpolation methods regarding their ability to correctly detect faults in the wood. This approach is based on a confusion matrix that allows for the computation of accuracy, reliability and recall. An experiment is presented using images from the cross-section of wood trunks generated by two interpolation methods applied for internal-hole detection: (1) an interpolation method using surrounding points and (2) the Ellipse Based Spatial Interpolation. The results demonstrated the effectiveness of the approach in quantitatively assessing and comparing these methods.

Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform.

Stomata are cells mostly found in plant leaves, stems and other organs. They are responsible for controlling the gas exchange process, i.e. the plant absorbs air and water vapor is released through transpiration. Therefore, stomata characteristics such as size and shape are important parameters to be taken into account. In this paper, we present a method (aiming at improved efficiency) to detect and count stomata based on the analysis of the multi-scale properties of the Wavelet, including a spot detection task working in the CIELab colorspace. We also segmented stomata images using the Watershed Transform, assigning each spot initially detected as a marker. Experiments with real and high-quality images were conducted and divided in two phases. In the first, the results were compared to both manual enumeration and another recent method existing in the literature, considering the same dataset. In the second, the segmented results were compared to a gold standard provided by a specialist using the F-Measure. The experimental results demonstrate that the proposed method results in better effectiveness for both stomata detection and segmentation.