6,635 research outputs found

    Mobile forensic triage for damaged phones using M_Triage

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    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

    Get PDF
    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

    Collecting ground truth annotations for drum detection in polyphonic music

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    In order to train and test algorithms that can automatically detect drum events in polyphonic music, ground truth data is needed. This paper describes a setup used for gathering manual annotations for 49 real-world music fragments containing different drum event types. Apart from the drum events, the beat was also annotated. The annotators were experienced drummers or percussionists. This paper is primarily aimed towards other drum detection researchers, but might also be of interest to others dealing with automatic music analysis, manual annotation and data gathering. Its purpose is threefold: providing annotation data for algorithm training and evaluation, describing a practical way of setting up a drum annotation task, and reporting issues that came up during the annotation sessions while at the same time providing some thoughts on important points that could be taken into account when setting up similar tasks in the future

    Index to NASA Tech Briefs, 1975

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    This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

    Oceanus.

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    v. 38, no.1 (1995

    A tesselated probabilistic representation for spatial robot perception and navigation

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    The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations

    A sensor fusion layer to cope with reduced visibility in SLAM

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    Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

    A sensor fusion layer to cope with reduced visibility in SLAM

    Get PDF
    Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained
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