This PDF file contains the front matter associated with SPIE Proceedings Volume 9411 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

This paper deals with the usefulness and accuracy of point clouds obtained by different consumer cameras out of Structure from motion (SfM) algorithms. It summarizes some research results of the practical usage of the SfM method in applications where highly accurate point clouds are required.

Remote health monitoring systems involve energy-constrained devices, such as sensors and mobile gateways. Current data formats for communication of health data, such as DICOM and HL7, were not designed for multi-sensor applications or to enable the management of power-constrained devices in health monitoring processes. In this paper, a data format suitable for collection of multiple sensor data, including readings and other operational parameters is presented. By using the data format, the system management can assess energy consumptions and plan realistic monitoring scenarios. The proposed data format not only outperforms other known data formats in terms of readability, flexibility, interoperability and validation of compliant documents, but also enables energy assessment capability for realistic data collection scenarios and maintains or even reduces the overhead introduced due to formatting. Additionally, we provide analytical methods to estimate incremental energy consumption by various sensors and experiments to measure the actual battery drain on smartphones.

We describe the design of a video streaming system using adaptation to viewing conditions to reduce the bitrate needed for delivery of video content. A visual model is used to determine sufficient resolution needed under various viewing conditions. Sensors on a mobile device estimate properties of the viewing conditions, particularly the distance to the viewer. We leverage the framework of existing adaptive bitrate streaming systems such as HLS, Smooth Streaming or MPEG-DASH. The client rate selection logic is modified to include a sufficient resolution computed using the visual model and the estimated viewing conditions. Our experiments demonstrate significant bitrate savings compare to conventional streaming methods which do not exploit viewing conditions.

Measuring water quality of bays, estuaries, and gulfs is a complicated and time-consuming process. YSI Sonde is an instrument used to measure water quality parameters such as pH, temperature, salinity, and dissolved oxygen. This instrument is taken to water bodies in a boat trip and researchers note down different parameters displayed by the instrument’s display monitor. In this project, a mobile application is developed for Android platform that allows a user to take a picture of the YSI Sonde monitor, extract text from the image and store it in a file on the phone. The image captured by the application is first processed to remove perspective distortion. Probabilistic Hough line transform is used to identify lines in the image and the corner of the image is then obtained by determining the intersection of the detected horizontal and vertical lines. The image is warped using the perspective transformation matrix, obtained from the corner points of the source image and the destination image, hence, removing the perspective distortion. Mathematical morphology operation, black-hat is used to correct the shading of the image. The image is binarized using Otsu’s binarization technique and is then passed to the Optical Character Recognition (OCR) software for character recognition. The extracted information is stored in a file on the phone and can be retrieved later for analysis. The algorithm was tested on 60 different images of YSI Sonde with different perspective features and shading. Experimental results, in comparison to ground-truth results, demonstrate the effectiveness of the proposed method.

We propose a system that uses depth information for video stabilization. The system uses 2D-homographies as frame pair transforms that are estimated with keypoints at the depth of interest. This makes the estimation more robust as the points lie on a plane. The depth of interest can be determined automatically from the depth histogram, inferred from user input such as tap-to-focus, or selected by the user; i.e., tap-to-stabilize. The proposed system can stabilize videos on the fly in a single pass and is especially suited for mobile phones with multiple cameras that can compute depth maps automatically during image acquisition.

Aim of the paper is to show that the colorimetric characterization of optically clear colored liquids can be performed with different measurement methods and their application specific multichannel spectral sensors. The possible measurement methods are differentiated by the applied types of multichannel spectral sensors and therefore by their spectral resolution, measurement speed, measurement accuracy and measurement costs. The paper describes how different types of multichannel spectral sensors are calibrated with different types of calibration methods and how the measurement values can be used for further colorimetric calculations. The different measurement methods and the different application specific calibration methods will be explained methodically and theoretically. The paper proofs that and how different multichannel spectral sensor modules with different calibration methods can be applied with smartpads for the calculation of measurement results both in laboratory and in field. A given practical example is the application of different multichannel spectral sensors for the colorimetric characterization of petroleum oils and fuels and their colorimetric characterization by the Saybolt color scale.

The present paper demonstrates the application of quadcopters as educational material for students in aerospace computer science, as it is already in usage today. The work with quadrotors teaches students theoretical and practical knowledge in the fields of robotics, control theory, aerospace and electrical engineering as well as embedded programming and computer science. For this the material, concept, realization and future view of such a course is discussed in this paper. Besides that, the paper gives a brief overview of student research projects following the course, which are related to the research and development of fully autonomous quadrotors.

In the context of password-based authentication, a user can only memorize limited number of usernames and passwords. They are generally referred to as user-credentials. Longer character length of passwords further adds complication in mastering them. The expansion of the Internet and our growing dependency on it, has made it almost impossible for us to handle the big pool of user-credentials. Using simple, same or similar passwords is considered a poor practice, as it can easily be compromised by password cracking tools and social engineering attacks. Therefore, a robust and painless technique to manage personal credentials for websites is desirable. In this paper, a novel technique for user-credentials management via a smart mobile device such as a smartphone in a local network is proposed. We present a secure user-credential management scheme in which user’s account login (username) and password associated with websites domain name is saved into the mobile device’s database using a mobile application. We develop a custom browser extension application for client and use it to import user’s credentials linked with the corresponding website from the mobile device via the local Wi-Fi network connection. The browser extension imports and identifies the authentication credentials and pushes them into the target TextBox locations in the webpage, ready for the user to execute. This scheme is suitably demonstrated between two personal devices in a local network.

We present a system for door and window image-based measurement using an Android mobile device. In this system a user takes an image of a door or window that needs to be measured and using interaction measures specific dimensions of the object. The existing object is removed from the image and a 3D model of a replacement is rendered onto the image. The visualization provides a 3D model with which the user can interact. When tested on a mobile Android platform with an 8MP camera we obtain an average measurement error of roughly 0.5%. This error rate is stable across a range of view angles, distances from the object, and image resolutions. The main advantages of our mobile device application for image measurement include measuring objects for which physical access is not readily available, documenting in a precise manner the locations in the scene where the measurements were taken, and visualizing a new object with custom selections inside the original view.

Recently, a new concept of device-to-device (D2D) communication, which is called “point-and-link communication” has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user’s own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.

In our paper we demonstrate the development of an Android Application (AndroidSfM) for photogrammetric 3D reconstruction that works on smartphones and tablets likewise. The photos are taken with mobile devices, and can thereafter directly be calibrated using standard calibration algorithms of photogrammetry and computer vision, on that device. Due to still limited computing resources on mobile devices, a client-server handshake using Dropbox transfers the photos to the sever to run AndroidSfM for the pose estimation of all photos by Structure-from-Motion and, thereafter, uses the oriented bunch of photos for dense point cloud estimation by dense image matching algorithms. The result is transferred back to the mobile device for visualization and ad-hoc on-screen measurements.

In the area of computer graphics the design of hardware and software has primarily been driven by the need to achieve maximum performance. Energy efficiency was usually neglected, assuming that a stable always-on power source was available. However, the advent of the mobile era has brought into question these ideas and designs in computer graphics since mobile devices are both limited by their computational capabilities and their energy sources. Aligned to this emerging need in computer graphics for energy efficiency analysis we have setup a software framework to obtain power measurements from 3D scenes using off-the-shelf hardware that allows for sampling the energy consumption over the power rails of the CPU and GPU. Our experiments include geometric complexity, texture resolution and common CPU and GPU workloads. The goal of this work is to combine the knowledge obtained from these measurements into a prototype energy-aware balancer of processing resources. The balancer dynamically selects the rendering parameters and uses a simple framerate-based dynamic frequency scaling strategy. Our experimental results demonstrate that our power saving framework can achieve savings of approximately 40%.

A content addressable memory system includes CAM cells, each having a compare circuit and a memory bit cell that stores complementary bits. The main CAM design challenge is to reduce power consumption associated with large amount of parallel switching circuitry, without sacrificing speed or density. In this paper, we present a new technique to eliminate crowbar current during bit-cell write operation (saving 0.0114mA per cell in 22nm process), reduce average current consumption during cam operation and eliminate the need for routing the complementary data to every cam cell, saving routing track in smaller node technology where wire cap is dominant.

There exist various implementations of the Retinex algorithm first developed by Edwin H. Land and Mcann, all of which allow for varying amounts of user control of specifications, intermediary steps and filters, and different forms of application. The purpose of this project is to study various existing algorithms implementing multiscale Retinex (and color restoration) in order to understand how they differ, their various advantages and limitations, and overall which are the most powerful methods. From this study, we attempted to improve upon an existing algorithm in order to greatly reduce processing time while still achieving a good result which we would judge using a new measure of color constancy.

By combining four different open standards belonging to the ISO/IEC JTC1/SC29 WG11 (a.k.a. MPEG) and W3C, this paper advances an architecture for mobile, medical oriented virtual collaborative environments. The various users are represented according to MPEG-UD (MPEG User Description) while the security issues are dealt with by deploying the WebID principles. On the server side, irrespective of their elementary types (text, image, video, 3D, …), the medical data are aggregated into hierarchical, interactive multimedia scenes which are alternatively represented into MPEG-4 BiFS or HTML5 standards. This way, each type of content can be optimally encoded according to its particular constraints (semantic, medical practice, network conditions, etc.). The mobile device should ensure only the displaying of the content (inside an MPEG player or an HTML5 browser) and the capturing of the user interaction. The overall architecture is implemented and tested under the framework of the MEDUSA European project, in partnership with medical institutions. The testbed considers a server emulated by a PC and heterogeneous user devices (tablets, smartphones, laptops) running under iOS, Android and Windows operating systems. The connection between the users and the server is alternatively ensured by WiFi and 3G/4G networks.

Proactive monitoring, control and protection of critical companies data - across devices, applications, data and network, are of increasing importance in today's society. Organizations and companies are committed Enterprise Mobility Management solution to meet new and demanding requirements. The aim of this paper is to show, based on certain specific scenarios, which benefits or risks for IT infrastructures exist. The investigation shows a significant step in assessing these applications with respect to the level of protection required in enterprise networks.

Nowadays, wearable and implantable medical devices are being increasingly deployed to improve diagnosis, monitoring, and therapy for various medical conditions. Compared to other types of electronics and computing systems, security attacks on these medical devices have extreme consequences and must be carefully analyzed and prevented with strongest efforts. Often, the security vulnerabilities of such systems are not well understood or underestimated. The aim of this paper is to demonstrate security attacks that can easily be done in the laboratory on a popular infusion pump on the market, and also propose defenses against such attacks.

Modern mobile devices contain integrated sensors that enable multitude of applications in such fields as mobile health (mHealth), entertainment, sports, etc. Human physical activity monitoring is one of such the emerging applications. There exists a range of challenges that relate to activity monitoring tasks, and, particularly, exploiting optimal solutions and architectures for respective mobile software application development.

This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption. Energy consumption significantly varies as sensor data acquisition is followed by data analysis including various transformations and signal processing algorithms. This paper will address several aspects of more optimal activity monitoring implementations exploiting state-of-the-art capabilities of modern platforms.

Video quality assessment (VQA) has been a hot topic due to the rapidly increasing demands in related video applications. The existing state-of-art full reference (FR) VQA metric ViS3 uses adapted the Most Apparent Distortion (MAD) algorithm to capture spatial distortion first, and then quantifies the spatiotemporal distortion by spatiotemporal correlation and a HVS-based model from the spatiotemporal slices (STS) images. In this paper we argue that the STS images can provide enough information for measuring video distortion. Taking advantage of an effective and easy-applied FR image quality model GMSD, we propose to measure video quality by analysing the structural changes between the STS images of the reference videos and their distorted counterparts. This new VQA model is denoted as STS-GMSD. To further investigate the influence spatial dissimilarity, we also combine the frame-by-frame spatial GMSD factor with the STS-GMSD and propose another VQA model, named SSTS-GMSD. Extensive experimental evaluations on two benchmark video quality databases demonstrate that the proposed STS-GMSD outperforms the existing state-of-the-art FR-VQA methods. While STS-GMSD works all square with SSTS-GMSD, which validates that STS images contain enough information for FR-VQA model design.

In this paper, we describe a new look on the application of Givens rotations to the QR-decomposition problem, which is similar to the method of Householder transformations. We apply the concept of the discrete heap transform, or signal-induced unitary transforms which had been introduced by Grigoryan (2006) and used in signal and image processing. Both cases of real and complex nonsingular matrices are considered and examples of performing QR-decomposition of square matrices are given. The proposed method of QR-decomposition for the complex matrix is novel and differs from the known method of complex Givens rotation and is based on analytical equations for the heap transforms. Many examples illustrated the proposed heap transform method of QR-decomposition are given, algorithms are described in detail, and MATLAB-based codes are included.

The aim of this paper is to show the recent progress in the design and prototypical development of a software suite Copra Breeder* for semi-automatic generation of test methodologies and security checklists for IT vulnerability assessment in small and medium-sized enterprises.

In this paper, we consider the model of quaternion signal degradation when the signal is convoluted and an additive noise is added. The classical model of such a model leads to the solution of the optimal Wiener filter, where the optimality with respect to the mean square error. The characteristic of this filter can be found in the frequency domain by using the Fourier transform. For quaternion signals, the inverse problem is complicated by the fact that the quaternion arithmetic is not commutative. The quaternion Fourier transform does not map the convolution to the operation of multiplication. In this paper, we analyze the linear model of the signal and image degradation with an additive independent noise and the optimal filtration of the signal and images in the frequency domain and in the quaternion space.

Fast unitary transforms are widely used in different areas such as data compression, pattern recognition and image reconstruction, interpolation, linear filtering, and spectral analysis. In this paper, we analyze the general concept of rotation and processing of data around not only circles but ellipses, in general. For that, we describe and analyze the general concept of the elliptic Fourier transform which was developed by Grigoryan in 2009. The block-wise representation of the discrete Fourier transform is considered in the real space, which is effective and that can be generalized to obtain new methods in spectral analysis. The N-point Elliptic discrete Fourier transform (EDFT) uses as a basic 2 × 2 transformation the rotations around ellipses. The EDFT distinguishes well from the carrying frequencies of the signal in both real and imaginary parts. It also has a simple inverse matrix. It is parameterized and includes also the DFT. Our preliminary results show that by using different parameters, the EDFT can be used effectively for solving many problems in signal and image processing field, in which includes problems such as image enhancement, filtration, encryption and many others.

With the growing integration of location based services (LBS) such as GPS in mobile devices, indoor position systems (IPS) have become an important role for research. There are several IPS methods such as AOA, TOA, TDOA, which use trilateration for indoor location estimation but are generally based on line-of-sight. Other methods rely on classification such as fingerprinting which uses WLAN indoor signals. This paper re-examines the classical WLAN fingerprinting accuracy which uses received signal strength (RSS) measurements by introducing channel estimates for improvements in the classification of indoor locations. The purpose of this paper is to improve existing classification algorithms used in fingerprinting by introducing channel estimates when there are a low number of APs available. The channel impulse response, or in this case the channel estimation from the receiver, should characterize a complex indoor area which usually has multipath, thus providing a unique signature for each location which proves useful for better pattern recognition. In this experiment, channel estimates are extracted from a Software-Defined Radio (SDR) environment, thus exploiting the benefits of SDR from a NI-USRP model and LabVIEW software. Measurements are taken from a known building, and several scenarios with one and two access points (APs) are used in this experiment. Also, three granularities in distance between locations are analyzed. A Support Vector Machine (SVM) is used as the algorithm for pattern recognition of different locations based on the samples taken from RSS and channel estimation coefficients.

In this paper, we propose a new software system that employs features that help the organization to comply with USA HIPAA regulations. The system uses SMS as the primary way of communication to transfer information. Lack of knowledge about some diseases is still a major reason for some harmful diseases spreading. The developed system includes different features that may help to communicate amongst low income people who don’t even have access to the internet. Since the software system deals with Personal Health Information (PHI) it is equipped with an access control authentication system mechanism to protect privacy. The system is analyzed for performance to identify how much overhead the privacy rules impose.

This paper describes a web service for video identification using an API provided by Videntifier Technologies. The main contribution of the study is an analysis of the requirements and implementation for such a web service. Possible fields of use are discussed as well as difficulties that are important to address for a successful implementation.

For most of mobile devices with a large screen, image quality and power consumption are both of the major factors affecting the consumers’ preference. Contents-adaptive backlight control (CABC) method can be utilized to adjust the backlight and promote the performance of mobile devices. Unlike the previous works mostly focusing on the reduction of power consumption, both of image quality and power consumption are taken into account in the proposed method. Firstly, region of interest (ROI) is detected to divide image into two parts: ROI and non-ROI. Then, three attributes including entropy, luminance, and saturation information in ROI are calculated. To achieve high perceived image quality in mobile devices, optimal value of backlight can be calculated by a linear combination of the aforementioned attributes. Coefficients of the linear combination are determined by applying the linear regression to the subjective scores of human visual experiments and objective values of the attributes. Based on the optimal value of backlight, displayed image data are processed brightly and backlight is darkened to reduce the power consumption of backlight later. Here, the ratios of increasing image data and decreasing backlight functionally depend on the luminance information of displayed image. Also, the proposed method is hardware implemented. Experimental results indicate that the proposed technique exhibits better performance compared to the conventional methods.

As new technologies like High Dynamic Range cameras, AMOLED and high resolution displays emerge on consumer electronics market, it becomes very important to deliver the best picture quality for mobile devices. Tone Mapping (TM) is a popular technique to enhance visual quality. However, the traditional implementation of Tone Mapping procedure is limited by pixel’s value to value mapping, and the performance is restricted in terms of local sharpness and colorfulness. To overcome the drawbacks of traditional TM, we propose a spatial-frequency based framework in this paper.

In the proposed solution, intensity component of an input video/image signal is split on low pass filtered (LPF) and high pass filtered (HPF) bands. Tone Mapping (TM) function is applied to LPF band to improve the global contrast/brightness, and HPF band is added back afterwards to keep the local contrast. The HPF band may be adjusted by a coring function to avoid noise boosting and signal overshooting. Colorfulness of an original image may be preserved or enhanced by chroma components correction by means of saturation function. Localized content adaptation is further improved by dividing an image to a set of non-overlapped regions and modifying each region individually.

The suggested framework allows users to implement a wide range of tone mapping applications with perceptional local sharpness and colorfulness preserved or enhanced. Corresponding hardware circuit may be integrated in camera, video or display pipeline with minimal hardware budget