Realizzazione di un controller su piattaforma Android per la movimentazione di manipolatori industriali

Nell'ottica di trovare modalità sempre più intuitive per movimentare manipolatori industriali l’obiettivo della tesi è quello di realizzare una mobile app su piattaforma Android in grado appunto di movimentare un generico manipolatore industriale. L'applicazione sviluppata fornisce all'utente un’interfaccia semplice e intuitiva che permette, dopo un’opportuna configurazione iniziale, di controllare il moto di un manipolatore industriale attraverso l’uso del touch screen e degli elementi grafici dell’interfaccia. Oltre a istruire un manipolatore l’applicazione offre anche delle funzionalità per il salvataggio e la gestione di determinate configurazioni che il manipolatore può assumere nello spazio. Il grande vantaggio dell’applicazione è quello di fornire un’interfaccia universale per la movimentazione di qualsiasi manipolatore. Si può affermare quindi che essa fornisce un livello di astrazione superiore. In questo progetto di tesi è stato effettuato il testing dell'applicazione sviluppata sia con il manipolatore industriale Comau Smart Six, robot antropomorfo a 6 gradi di libertà, sia con un manipolatore simulato in Unity 3D. Sono stati raccolti dei dati, in particolare dei grafici, che mettono in relazione i comandi impartiti al manipolatore e i dati ricevuti da questo, in modo da ricavarne dei parametri che misurano l'efficienza e la correttezza dell'applicazione.

Estudo da usabilidade do portal do aluno do SIGAA Mobile Android da UFRN: identificando diretrizes de interface a serem utilizadas como recomendações no processo de desenvolvimento das funcionalidades deste sistema

Avaliação de usabilidade é um processo importante durante o desenvolvimento de um software, seja ele para um sistema web ou mobile. No caso de um sistema mobile, o custo é bastante oneroso, tanto no que se refere à mão de obra especializada, como de recursos tecnológicos utilizados, tornando essa avaliação de usabilidade ainda mais importante. Além disso, as versões webdos sistemas SIG da UFRN já estão consolidadas e com uma grande aceitação, fazendo com que suas versões mobile tenham, ainda mais, a preocupação de lançar um produto de boa qualidade para manter essa credibilidade dos sistemas SIG , tanto na UFRN, como nas demais instituições que utilizam esses sistemas. Com este trabalho, buscou-se identificar algumas diretrizes de interface que possam ser utilizadas no processo de desenvolvimento dos sistemas SIG Mobile, mais especificamente o SIGAA Mobile, de modo a facilitar o desenvolvimento de novas funcionalidades voltadas para estes sistemas. Para isso, foi realizada uma avaliação de usabilidade no Portal do Aluno do SIGAA Mobile Android, tomando como base padrões de interface já existentes na literatura. Posteriormente, foi aplicado um questionário com os usuários do sistema para coletar as opiniões e sugestões dos mesmos. De posse de todos esses dados coletados, foi possível definir algumas diretrizes de interface a serem seguidas como recomendações no processo de desenvolvimento dos sistemas SIG Mobile.

A Comparison of Geo-tagging in Mobile Internet Browsing Applications on iOS and Android.

Nowadays there is almost no crime committed without a trace of digital evidence, and since the advanced functionality of mobile devices today can be exploited to assist in crime, the need for mobile forensics is imperative. Many of the mobile applications available today, including internet browsers, will request the user’s permission to access their current location when in use. This geolocation data is subsequently stored and managed by that application's underlying database files. If recovered from a device during a forensic investigation, such GPS evidence and track points could hold major evidentiary value for a case. The aim of this paper is to examine and compare to what extent geolocation data is available from the iOS and Android operating systems. We focus particularly on geolocation data recovered from internet browsing applications, comparing the native Safari and Browser apps with Google Chrome, downloaded on to both platforms. All browsers were used over a period of several days at various locations to generate comparable test data for analysis. Results show considerable differences not only in the storage locations and formats, but also in the amount of geolocation data stored by different browsers and on different operating systems.

An Android application sandbox system for suspicious software detection

Smartphones are steadily gaining popularity, creating new application areas as their capabilities increase in terms of computational power, sensors and communication. Emerging new features of mobile devices give opportunity to new threats. Android is one of the newer operating systems targeting smartphones. While being based on a Linux kernel, Android has unique properties and specific limitations due to its mobile nature. This makes it harder to detect and react upon malware attacks if using conventional techniques. In this paper, we propose an Android Application Sandbox (AASandbox) which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications. Static analysis scans the software for malicious patterns without installing it. Dynamic analysis executes the application in a fully isolated environment, i.e. sandbox, which intervenes and logs low-level interactions with the system for further analysis. Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market. Additionally, AASandbox might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.

Using static analysis for automatic assessment and mitigation of unwanted and malicious activities within Android applications

In the last decade, smartphones have gained widespread usage. Since the advent of online application stores, hundreds of thousands of applications have become instantly available to millions of smart-phone users. Within the Android ecosystem, application security is governed by digital signatures and a list of coarse-grained permissions. However, this mechanism is not fine-grained enough to provide the user with a sufficient means of control of the applications' activities. Abuse of highly sensible private information such as phone numbers without users' notice is the result. We show that there is a high frequency of privacy leaks even among widely popular applications. Together with the fact that the majority of the users are not proficient in computer security, this presents a challenge to the engineers developing security solutions for the platform. Our contribution is twofold: first, we propose a service which is able to assess Android Market applications via static analysis and provide detailed, but readable reports to the user. Second, we describe a means to mitigate security and privacy threats by automated reverse-engineering and refactoring binary application packages according to the users' security preferences.

Developing and benchmarking native Linux applications on Android

Smartphones get increasingly popular where more and more smartphone platforms emerge. Special attention was gained by the open source platform Android which was presented by the Open Handset Alliance (OHA) hosting members like Google, Motorola, and HTC. Android uses a Linux kernel and a stripped-down userland with a custom Java VM set on top. The resulting system joins the advantages of both environments, while third-parties are intended to develop only Java applications at the moment. In this work, we present the benefit of using native applications in Android. Android includes a fully functional Linux, and using it for heavy computational tasks when developing applications can bring in substantional performance increase. We present how to develop native applications and software components, as well as how to let Linux applications and components communicate with Java programs. Additionally, we present performance measurements of native and Java applications executing identical tasks. The results show that native C applications can be up to 30 times as fast as an identical algorithm running in Dalvik VM. Java applications can become a speed-up of up to 10 times if utilizing JNI.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

Google Android : a comprehensive introduction

Google Android, Google's new product and its first attempt to enter the mobile market, might have an equal impact on mobile users like Apple's hyped product, the iPhone. In this Technical report we are going to present the Google Android platform, what Android is, describe why it might be considered as a worthy rival to Apple's iPhone. We will describe parts of its internals, take a look "under the hood" while explaining components of the underlying operating system. We will show how to develop applications for this platform, which difficulties a developer might have to face, and how developers can possibly use other programming languages to develop for Android than the propagated language Java.

Reuse of software components orientated to iOS mobile devices

Within the next few pages, I will try to give a wide description of the project that I have been doing for IK4-Ikerlan. For the last six months, I have been working in developing a socket-based application for Apple devices. These devices work under the iOS operative system, which is programmed in Objective-C, a language similar to C. Although I did not have the chance to develop this application for Apple TV, I was able to create an application for iPhone and another one for iPad. The only difference between both applications was the screen resolution, but we decided to make them separately, as it would be really hard to combine both resolutions, and wallpapers, everything in the same workspace. Finally, it is necessary to add that the main goal was not to create a new application for iOS, but to translate an Android application into iOS. To achieve this, it is required to translate Java code into Objective- C, which is the language used to develop applications for all kinds of Apple devices. Fortunately, there is a tool created by Google, which helped us with this exercise. This tool is called j2ObjC, and it is still being developed.

Desarrollo de cliente OMA PoC para Android

[ES]Se presenta el siguiente proyecto de desarrollo, enfocado en el contexto de suplir las necesidades de los equipos de emergencias. Actualmente los equipos de emergencias, hacen uso de walkie talkies para comunicarse entre sí, es decir, hacen uso de la tecnología PTT (Push-to-talk). Es una comunicación unidireccional de un usuario a muchos, de manera que pueden transmitir la información necesaria a varios usuarios simultáneamente. Por otra parte, la comunicación mediante esta tecnología hace posible que sea en tiempo real sin prácticamente ningún retraso. Sin embargo el uso de esta forma de comunicación trae consigo algunos inconvenientes. Por ejemplo, los dispositivos tienen un rango de alcance de transmisión-recepción limitado. Además la banda de frecuencia para poder comunicarse debe ser reservada a fin de evitar posibles problemas de comunicación debido a que el canal estuviera ocupado por otros usuarios, posibles interferencias, etc. La reserva de este rango de frecuencia, supone un coste añadido para el uso de esta tecnología. Con el desarrollo de las redes móviles que ofrecen grandes velocidades de transmisión, se hace posible el uso de la tecnología PTT mediante la red móvil existente. De esta manera, surge el siguiente proyecto de desarrollo de una aplicación móvil Android, como cliente que haga uso de la tecnología PTT sobre la red móvil PoC (Push-to-talk Over Cellular).

Evaluation of Setup Procedures on Mobile Devices Based on Users’ Initial Experience

Users’ initial perceptions of their competence are key motivational factors for further use. However, initial tasks on a mobile operating system (OS) require setup procedures, which are currently largely inconsistent, do not provide users with clear, visible and immediate feedback on their actions, and require significant adjustment time for first-time users. This paper reports on a study with ten users, carried out to better understand how both prior experience and initial interaction with two touchscreen mobile interfaces (Apple iOS and Google Android) affected setup task performance and motivation. The results show that the reactions to setup on mobile interfaces appear to be partially dependent on which device was experienced first. Initial experience with lower-complexity devices improves performance on higher-complexity devices, but not vice versa. Based on these results, the paper proposes six guidelines for designers to design more intuitive and motivating user interfaces (UI) for setup procedures. The preliminary results indicate that these guidelines can contribute to the design of more inclusive mobile platforms and further work to validate these findings is proposed.

A cycle route planner mobile-app for Dublin city

In a road network, cyclists are the group exposed to the maximum amount of risk. Route choice of a cyclist is often based on level of expertise, perceived or actual road risks, personal decisions, weather conditions and a number of other factors. Consequently, cycling tends to be the only significant travel mode where optimised route choice is not based on least-path or least-time. This paper presents an Android platform based mobile-app for personalised route planning of cyclists in Dublin. The mobile-app, apart from its immediate advantage to the cyclists, acts as the departure point for a number of research projects and aids in establishing some critical calibration values for the cycling network in Dublin. 

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.