Mobile forensic triage for damaged phones using M_Triage

Mobile forensics triage is a useful technique in a digital forensics investigation for recovering lost or purposely deleted and hidden files from digital storage. It is particularly useful, especially when solving a very sensitive crime, for example, kidnapping, in a timely manner. However, the existing mobile forensics triage tools do not consider performing a triage examination on damaged mobile phones. This research addressed the issues of performing triage examination on damaged Android mobile phones and reduction of false positive result generated by the current mobile forensics triage tools. Furthermore, the research addressed the issues of ignoring possible evidence residing in a bad block memory location. In this research a new forensics triage tool called M_Triage was introduced by extending Decode’s framework to handle data retrieval challenges on damaged Android mobile phones. The tool was designed to obtain evidence quickly and accurately (i.e. valid address book, call logs, SMS, images, and, videos, etc.) on Android damaged mobile phones. The tool was developed using C#, while back end engines was done using C programming and tested using five data sets. Based on the computational time processing comparison with Dec0de, Lifter, XRY and Xaver, the result showed that there was 75% improvement over Dec0de, 36% over Lifter, 28% over XRY and finally 71% over Xaver. Again, based on the experiment done on five data sets, M_Triage was capable of carving valid address book, call logs, SMS, images and videos as compared to Dec0de, Lifter, XRY and Xaver. With the average improvement of 90% over DEC0DE, 30% over Lifter, 40% over XRY and lastly 61% over Xaver. This shows that M_Triage is a better tool to be used because it saves time, carve more relevant files and less false positive result are achieved with the tool

Mobile forensic triage for damaged phones using M_Triage

Mobile forensics triage is a useful technique in a digital forensics investigation for recovering lost or purposely deleted and hidden files from digital storage. It is particularly useful, especially when solving a very sensitive crime, for example, kidnapping, in a timely manner. However, the existing mobile forensics triage tools do not consider performing a triage examination on damaged mobile phones. This research addressed the issues of performing triage examination on damaged Android mobile phones and reduction of false positive result generated by the current mobile forensics triage tools. Furthermore, the research addressed the issues of ignoring possible evidence residing in a bad block memory location. In this research a new forensics triage tool called M_Triage was introduced by extending Decode’s framework to handle data retrieval challenges on damaged Android mobile phones. The tool was designed to obtain evidence quickly and accurately (i.e. valid address book, call logs, SMS, images, and, videos, etc.) on Android damaged mobile phones. The tool was developed using C#, while back end engines was done using C programming and tested using five data sets. Based on the computational time processing comparison with Dec0de, Lifter, XRY and Xaver, the result showed that there was 75% improvement over Dec0de, 36% over Lifter, 28% over XRY and finally 71% over Xaver. Again, based on the experiment done on five data sets, M_Triage was capable of carving valid address book, call logs, SMS, images and videos as compared to Dec0de, Lifter, XRY and Xaver. With the average improvement of 90% over DEC0DE, 30% over Lifter, 40% over XRY and lastly 61% over Xaver. This shows that M_Triage is a better tool to be used because it saves time, carve more relevant files and less false positive result are achieved with the tool

CINet: A Learning Based Approach to Incremental Context Modeling in Robots

There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to increment the number of contexts. In this paper, we pose the task of when to increment as a learning problem, which we solve using a Recurrent Neural Network. We show that the network successfully (with 98\% testing accuracy) learns to predict when to increment, and demonstrate, in a scene modeling problem (where the correct number of contexts is not known), that the robot increments the number of contexts in an expected manner (i.e., the entropy of the system is reduced). We also present how the incremental model can be used for various scene reasoning tasks.Comment: The first two authors have contributed equally, 6 pages, 8 figures, International Conference on Intelligent Robots (IROS 2018

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

An Open-Source Benchmark Simulator: Control of a BlueROV2 Underwater Robot

This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances

Cable Driven Robot to Simulate Low Gravity and Its Applications in Underwater Humanoid Robots

[Abstract] This paper addresses the main results obtained during the design and analysis of a cable-driven robot able to simulate the dynamic conditions existing in underwater environment. This work includes the kinematic and dynamic modeling as well as the analysis of the tension of the cables along different trajectories. The low-gravity simulator application is novel in the context of cable-driven robots and it is aimed to be implemented in an underwater humanoid robot. Therefore, this work can be seen as a test case of the complementary research contributions of the group of Robotics and Intelligent Machines at CAR in the recent years.The research leading to these results has received funding from the Spanish Government CICYT project Ref. DPI2014-57220-C2-1-P, DPI2013-49527-EXP, the Universidad Politécnica de Madrid project Ref. AL14-PID-15, and the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EUUniversidad Politécnica de Madrid; AL14-PID-15Comunidad de Madrid; S2013/MIT-2748https://doi.org/10.17979/spudc.978849749808

METAL LINE DETECTION: A NEW SENSORY SYSTEM FOR LINE FOLLOWING MOBILE ROBOT

This paper presents a new type of line following robot that uses metal sensor to detect metal line and maneuver around based on that line. The paper focused on developing the hardware model of automated guide vehicle (AGV) system and integrating it with metal detection sensor. The system performance is measured in a straight line movement and when the robot turns at specific degrees.The metal line following robot can be used to move objects in daily life operation, warehouse operations or manufacturing facility to any desired location automatically. A metal line with certain length can be placed on the desired floor to indicate the path that the robot requires to move. Based on the experimental studies, it showed that the mobile robot can maneuver and track the metal line that is attached on the floor by utilizing three inductive proximity sensor located in front of the robot. This sensory system can be used as alternative sensor instead of using line following sensor which normally based on the infrared proximity detector. The line following robot that operates based on the metal line capable to overcome the problem of different light intensity reflection

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.Eurpean Commission, H2020, 66210