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Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people's daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented. [ABSTRACT FROM AUTHOR]

A study of geographic information has become a significant field of concentrate in software engineering because of the expansion of much information created by electronic gadgets fit together geographic data from people, like advanced mobile phones and GPS gadgets. Area facts allow a deeper understanding of users' options and actions by bridging the gap between the physical and digital worlds. This expansion of wide geo-spatial datasets has inspired studies into novel recommender systems that aim to make users' travels and social interactions easier. This huge amount of data generated by these devices has prompted an increase in the number of research activities and procedures aimed at breaking down and recovering useful data based on these large datasets. The aim of this challenge is to study GPS directions from a variety of people, as well as examine and apply computational strategies to recover useful data from those directions, useful data from GPS directions, including areas of interest and people's proximity, and then create an instrument for information representation. This paper demonstrates how data mining techniques is used to recover valuable information from spatial data. As well as how such data can be useful in understanding people and areas within a district. Depending on the outcomes, we recommend a HITS (Hypertext Induced Topic Search) based POI recommendation calculation that can take into account the effect of social connections when recommending POIs to individual users. [ABSTRACT FROM AUTHOR]

Traditional ice and snow path planning methods still have internal environmental problems in intelligent path planning, such as weak innovation ability, imperfect management, long planning path, unreasonable security structure, and low degree of specialization. Therefore, more and more ice and snow sports lovers are eager to solve this problem. This paper designs a path planning method based on three-dimensional ice and snow model. The path planning method of moving snow and ice based on MNN (Multiclass Neural Networks) algorithm is studied from many aspects. MNN algorithm is used for comprehensive analysis and evaluation. The mobile phone provides data information on key nodes, air resistance, momentum change, ice and snow movement track, and so on. The results show that the ice and snow path planning system based on MNN algorithm designed in this paper has the advantages of high feasibility, high data accuracy, and good prediction effect and can effectively improve the efficiency of ice and snow path planning. [ABSTRACT FROM AUTHOR]

In recent years, the research of indoor positioning technology has mainly focused on WI-FI, Bluetooth, ultra-wideband, and pseudo-satellites. Each of these indoor positioning technologies has different advantages, but also has various limitations, especially in indoor areas such as corridors, tunnels, and stairs. Due to the limitations of application scenarios, navigation and positioning accuracy and efficiency, it is difficult to form a satisfactory navigation and positioning solution for indoor special environment. In order to solve the positioning problem of indoor long and narrow areas, a multi-source fusion navigation and positioning algorithm based on pseudo-satellite assistance is proposed in this paper. Under certain map information constraints, it can use the direct arrival signal of a single pseudo-satellite to correct the location information output by mobile phone PDR(Pedestrian Dead Reckoning), and improve the navigation and positioning performance of indoor passageway and corridor areas. To realize the system, it is necessary to ensure that a direct pseudo-satellite signal can be received in any area of the corridor. The position of the pseudo-satellite can be determined according to the captured pseudo-satellite signal, and the pseudo-range change rate can be estimated by calculating the carrier phase change rate. In this paper, we deduce the relationship between the pseudo-range change rate and the change of the mobile phone PDR position, and build an optimization model based on map information constraints, which improves the positioning accuracy of the system for indoor narrow and long areas. A large number of experimental results show that the system has high positioning accuracy in the real indoor long and narrow environment. The average positioning accuracy of the algorithm in indoor environments is better than 0.4 m, and there is no significant change in the performance after a long time of cumulative positioning. [ABSTRACT FROM AUTHOR]

With the rapid development and popularization of the Internet, the dependency of human life on the Internet is increasing by degrees. The demand for video transmission is also rising with the emergence of portable and handheld devices such as mobile phones, tablets, laptops, etc. The scalability of Scalable Video Coding (SVC) determines that it can meet the needs of the newly emerged devices or terminals. Despite this advancement, security is still one of the major challenges. Based on the research of network security, this paper deeply discusses the encryption technology, and puts forward a new point multiplication algorithm using SVC and sliding window, which makes the algorithm more flexible, improves the efficiency of the algorithm to a certain extent, greatly reduces the expected calculation and storage of the algorithm, and makes the algorithm to adapt the memory limited environment such as smart card. Critical analysis of the proposed DES algorithm is carried out and different fragile keys are recommended which is not safe for secure encryption. This study also present the complete overview of the SVC technology and its multi-layered stream representation in the form of temporal, spatial, and SNR resolution. In addition, an encryption method based on the DES algorithm is also proposed. [ABSTRACT FROM AUTHOR]

Touch is one of the most important human senses. With the popularization of touch-screen mobile phones, tablet computers, and other devices, touch-screen interactive technology has become a norm in people's daily lives, and advertisements that were once dominated by vision and hearing have added an interactive experience in the dimension of touch. Traditional advertising media screens can only complete simple information dissemination functions and cannot interact with users in a two-way manner. They can only receive information one-way and passively and lack interactivity. Touch-screen interactive advertising forms a good interaction with the target audience, thereby disseminating advertising information to achieve the purpose of promotion or brand image building. This paper designs a set of advertising media screen interaction systems based on smart sensors, including a gesture interaction module, a remote interaction module, and a touch interaction module. The gesture interaction module can recognize 5 static gestures and send gesture commands to control the advertising media screen. The remote interaction module can remotely control the advertising media screen, and the touch interaction module can control the advertising media screen through the touch screen. According to the functional requirements, the overall design of software and hardware is given, and the technical background of each module of the software is introduced. Next, the depth image-based gesture recognition method is studied. The number of fingers and the center distance feature are fused as feature vectors, and the weighted template matching method is used to classify and recognize gestures. Finally, the design and implementation of the interactive system are introduced. [ABSTRACT FROM AUTHOR]

Environmental monitoring is a process for observing around with various conditions. Recently, internet of things (IoT) and wireless sensor network (WSN) technologies support to solve these problems. In this paper, we implemented a system to monitor environmental conditions using IoT and WSN technology. The data measure is temperature, humidity, carbon monoxide (CO) and carbon dioxide (CO2) sensors. All sensor data will be sent and stored to the cloud through the internet in real-time. We provide applications for monitoring website and mobile phone-based environmental conditions, so users can access wherever and whenever. Furthermore, we also confirm the evaluation of analyst data that used k-NN method is better than other methods with an accuracy rate of 99.0657%. [ABSTRACT FROM AUTHOR]

Precise travel route identification is the guiding basis of feasible urban traffic planning and modeling. A range of map matching (MM) algorithms have been studied in previous research to integrate coordinate-based GPS data with digital road network information to identify personal travel route. However, these methods significantly depend on the high-density positioning data, which are usually unavailable or costly for transportation planning projects. Fortunately, mobile phones under 3 G, 4 G and 5 G networks can generate massive cell-based travel trajectory data with almost no additional cost. This data is potentially valuable for travel route identification if proper MM algorithms can be developed. This paper proposes an MM method to detect travel route by using handoff trajectory data from the cellular phone network. First, a wireless communication simulation model for dynamic cellular handoff-based traffic monitoring is developed for handoff data collection. Second, the Earth Mover's Distance (EMD) algorithm is used to identify travel route by finding the time-space relationship among the cellular handoff patterns of the road network. Finally, by comparing the performance of the proposed method with classical sequence similarity algorithms, results indicate that the EMD-based MM algorithm is much more efficient for travel route identification, and it can detect small spacing parallel roads with a high accuracy. [ABSTRACT FROM AUTHOR]

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture. [ABSTRACT FROM AUTHOR]

With the continuous advancement of science and technology, the social network based on the Internet has gradually penetrated into people's daily lives. The image data shared on social media using platforms such as mobile phones has exploded, and hundreds of millions of dollars are generated every day with picture information. In the past, the traditional ways of expressing textual information that people are familiar with have begun to be gradually replaced by image information that is not subject to regional culture such as language and script. In modern warfare, with the continuous development and equipment of highly sophisticated weapons and equipment, the amount of information that the entire combat system needs to process will also increase. In particular, the air defense system needs to quickly and accurately identify the aircraft targets that are coming. It mainly uses computer to extract the feature information of the acquired image and converts the content in the image into a feature expression that can be processed by the computer. After the appropriate classification algorithm, the image is the target object is classified by category. In this paper, we propose a complex image recognition algorithm based on immune random forest model. The experimental results show that the proposed algorithm has high recognition efficiency and higher robustness. [ABSTRACT FROM AUTHOR]

Non-contact identification of cattle is the basis for cattle management, insurance claims, and traceability of milk sources and meat products. In order to lower the threshold of deep learning technology in practical application and improve the recognition accuracy, this paper uses the localization and classification paradigm, fills the image and proposes the LKA (Locate Key Area) module, which further improves the identification accuracy of cattle without using any labeling information. To avoid the impact of different collection devices, we use three brands of mobile phones and two cameras to collect data. Due to the fact that CNN features do not have scale invariance, the image is filled as a square, and the filling value is discussed. Finally, (0,0,0) is selected for filling. In order to reduce the labeling of new data, the face, torso, and body of the cattle are first detected, and then automatically trimmed to build a classification dataset. In order to further improve the accuracy of classification, LKA module is proposed. This module locates key areas based on the results of class activation map and uses shallow features for monitoring. Thereby completing fine-grained feature extraction without supervision information. Finally, the features of different parts are adjusted for fusion recognition. Experiments were conducted on the head, torso, and whole of cattle. Based on the ResNet50 network architecture, after filling the image into a square, the recognition accuracy has been improved by 0.30%, 0.21%, and 0.43%, respectively. After further adding LKA modules, the accuracy rates were improved by 0.59%, 0.59%, and 0.45%, respectively. In the fusion experiment, ResNet50_LKA and MobileNetV3_LKA achieves 100% accuracy. Experiments have realized dataset construction and fine-grained feature extraction without labeled information, and achieved high recognition accuracy, proving the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved. [ABSTRACT FROM AUTHOR]

Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

Digital public displays installed in various locations provide valuable information for the passers-by. However, the static characteristic of the digital public display limits the consumption of the displayed content to a small area. Personal mobile devices such as smartphones are now capable of interacting with digital public displays, which enables the passers-by to “take-away” the content and consume it on-the-go. This process requires device binding, content selection, and transfer between the two devices. In this paper, we propose a device binding method which utilizes the content brightness changing pattern as a unique content ID on the public display and an illuminance sensor on the mobile to bind and transfer between two devices. We conducted performance evaluations for binding algorithm robustness in different conditions. Also comparative studies among other binding interaction methods were conducted. Our results show that our proposed method performed stably across the various conditions and overall performance in interaction completion time and error rate was similar or superior to the existing methods. [ABSTRACT FROM AUTHOR]

The ubiquity of wireless technologies in the Internet of Things (IoT) concept enforces utilization of power-optimized wireless techniques. Furthermore, a specially tailored mesh-routing algorithm is required in order to achieve battery longevity and node accessibility. In the paper, we propose an improved BLE (Bluetooth Low Energy) mesh-routing algorithm for an IoT Smart Home application. The proposed algorithm is based on a time-slotted medium-access scheme, which enables communication nodes to sleep and/or exchange information. In order to achieve compatibility with any BLE-enabled device, such as mobile phones/tablets, routing and data information is transmitted via Eddystone beacons. Performance analysis of the proposed BLE mesh-routing algorithm is carried out using an OMNeT++ discrete simulation environment and Mixim framework. Validation of the proposed algorithm is completed on the basis of measurements from a real-life test network. The results show that the proposed algorithm is suitable for the IoT applications where the energy efficiency of the communication nodes is a key priority and propagation delays are not critical. [ABSTRACT FROM AUTHOR]

Recently, the ubiquitousness of smartphones and tablet computers have changed the style of people’s daily life. With this tendency, location based service (LBS) has become one of the prosperous types of service along with the wireless and positioning technology development. However, as the LBS server needs precise location information about the user to provide service result, the procedure of LBS may reveal location privacy, especially when a user is utilizing continuous query along the road. In continuous query, attributes of the user are released inadvertently with per-query, and the information can be collected by an adversary as background knowledge to correlate the location trajectory and infer the personal privacy. Although, a user can employ a central server (CS) to provide privacy preservation for his location, the trustfulness of CS still is without testified and it is usually considered as an un-trusted entity. Thus, in this paper, the trustfulness of CS is verified by a game tree, and then with the result we propose a hash based attribute anonymous scheme (short for HBAA) to obfuscate the attributes released in each query along the road. With the help of HBAA, the CS has no opportunity to get any information about the user who sends his query for generalization service. Furthermore, as the set of attributes is transmitted into a fixed length of hash value, the processing time that spent in attribute generalization is stripped down and the performance of executive efficiency is improved. At last, security analysis and simulation experiment are proposed, and then results of security proving as well as simulation experiments further reflect the superiority of our proposed scheme. [ABSTRACT FROM AUTHOR]

In mobile health care monitoring, compression is an essential tool for solving storage and transmission problems. The important issue is able to recover the original signal from the compressed signal. The main purpose of this paper is compressing the ECG signal with no loss of essential data and also encrypting the signal to keep it confidential from everyone, except for physicians. In this paper, mobile processors are used and there is no need for any computers to serve this purpose. After initial preprocessing such as removal of the baseline noise, Gaussian noise, peak detection and determination of heart rate, the ECG signal is compressed. In compression stage, after 3 steps of wavelet transform (db04), thresholding techniques are used. Then, Huffman coding with chaos for compression and encryption of the ECG signal are used. The compression rates of proposed algorithm is 97.72 %. Then, the ECG signals are sent to a telemedicine center to acquire specialist diagnosis by TCP/IP protocol. [ABSTRACT FROM AUTHOR]

Due to recent developments in technologies associated with the Internet of Things (IoT), a large number of people now regularly use smart devices, such as smartwatches and smartphones. However, these devices are prone to data leaks because of security vulnerabilities. In particular, Android devices use permission-based security, which allows users to directly approve permissions requested by an app when installing it. As a result, many malicious apps can obtain and leak private user data by requesting more permissions than are needed. However, it is difficult to identify malicious apps based solely on the requested permissions. A system is hence needed to accurately identify malicious apps and protect private data from them. In this paper, we propose a system for hiding data related to a user’s contacts or providing virtual data according to preconfigured policies when an Android app requests access to them. By hiding data related to the contacts, the proposed system can protect them from malicious apps. By using virtual data, it can even detect malicious apps that leak private data. The system requires less storage and provides faster access to user contacts than prevalent solutions to similar problems. [ABSTRACT FROM AUTHOR]

Fingerprint Recognition System is widely deployed in variety of application domain, ranging from forensic to mobile phones. Its widespread deployment in various applications were person authentication are required, has caused concern that a leaked fingerprint template may be used to reconstruct the original fingerprint and the reconstructed fingerprint can be used to circumvent all the applications the person is enrolled. In this paper, a non-invertible fingerprint template that stores only the relative geometric information about the minutiae points is proposed. The spatial location of the minutiae points in original fingerprint and its orientations are not available in the proposed template which makes it impossible to estimate the orientation of fingerprint from the template. The proposed template is invariant to rotation, translation and distortion and immune to reconstruction algorithm. The proposed system is experimented using standard FVC2000 database and yields better results in terms of EER and FMR as compared with latest techniques. [ABSTRACT FROM AUTHOR]

Identity-based proxy re-signature (IDPRS) is a novel cryptographic primitive that allows a semi-trusted proxy to convert a signature under one identity into another signature under another identity on the same message by using a re-signature key. Due to this transformation function, IDPRS is very useful in constructing privacy-preserving schemes for various information systems. Key revocation functionality is important in practical IDPRS for managing users dynamically; however, the existing IDPRS schemes do not provide revocation mechanisms that allow the removal of misbehaving or compromised users from the system. In this paper, we first introduce a notion called revocable identity-based proxy re-signature (RIDPRS) to achieve the revocation functionality. We provide a formal definition of RIDPRS as well as its security model. Then, we present a concrete RIDPRS scheme that can resist signing key exposure and prove that the proposed scheme is existentially unforgeable against adaptive chosen identity and message attacks in the standard model. To further improve the performance of signature verification in RIDPRS, we introduce a notion called server-aided revocable identity-based proxy re-signature (SA-RIDPRS). Moreover, we extend the proposed RIDPRS scheme to the SA-RIDPRS scheme and prove that this extended scheme is secure against adaptive chosen message and collusion attacks. The analysis results show that our two schemes remain efficient in terms of computational complexity when implementing user revocation procedures. In particular, in the SA-RIDPRS scheme, the verifier needs to perform only a bilinear pairing and four exponentiation operations to verify the validity of the signature. Compared with other IDPRS schemes in the standard model, our SA-RIDPRS scheme greatly reduces the computation overhead of verification. [ABSTRACT FROM AUTHOR]

Passively-generated mobile phone data is emerging as a potential data source for transportation research and applications. Despite the large amount of studies based on the mobile phone data, only a few have reported the properties of such data, and documented how they have processed the data. In this paper, we describe two types of common mobile phone data: Call Details Record (CDR) data and sightings data, and propose a data processing framework and the associated algorithms to address two key issues associated with the sightings data: locational uncertainty and oscillation. We show the effectiveness of our proposed methods in addressing these two issues compared to the state of art algorithms in the field. We also demonstrate that without proper processing applied to the data, the statistical regularity of human mobility patterns—a key, significant trait identified for human mobility—is over-estimated. We hope this study will stimulate more studies in examining the properties of such data and developing methods to address them. Though not as glamorous as those directly deriving insights on mobility patterns (such as statistical regularity), understanding properties of such data and developing methods to address them is a fundamental research topic on which important insights are derived on mobility patterns. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the popularity of smart phones has made exponential growth in mobile phone application market. How to help users to select applications they prefer has become a hot topic in recommendation algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay more attention to permissions of those applications, because of some privacy and security reasons. There are few recommendation algorithms which take account of apps' permissions, functionalities and users' interests altogether. Some of them only consider permissions while neglecting the users' interests, others just perform linear combination of apps' permissions, functionalities and users' interests to implement top-N recommendation. In this paper, we devise a recommendation method based on both permissions and functionalities. After demonstrating the correlation of apps' permissions and users' interests, we design an app risk score calculating method ARSM based on app-permission bipartite graph model. Furthermore, we propose a novel matrix factorization algorithm MFPF based on users' interests, apps' permissions and functionalities to handle personalized app recommendation. We compare our work with some of the state-of-the-art recommendation algorithms, and the results indicate that our work can improve the recommendation accuracy remarkably. [ABSTRACT FROM AUTHOR]

Due to the explosive proliferation of mobile cloud computing applications, much data needs to be transmitted between mobile users and clouds, incurring a huge traffic demand on cellular networks. Mobile offloading is a promising approach to address this challenge. In this paper, we focus on the problem of offloading many deadline-sensitive data items to some WiFi networks with capacity constraints; that is, how to schedule each data item to the WiFi networks, so that we can offload as many data items before their deadlines as possible, while taking the constraints of transmission capacity into consideration. This problem involves a probabilistic combination of multiple 0-1 knapsack constraints, which differs from existing problems. To solve this problem, we propose a greedy oFfline Data Offloading (FDO) algorithm, achieving an approximation ratio of 2. Also, we propose an oNline Data Offloading (NDO) algorithm, which has a competitive ratio of 2. Additionally, we extend our problem to a more general scenario where WiFi transmission costs are heterogeneous. We design a Heterogeneous Data Offloading (HDO) algorithm to solve the extended problem, and give its performance analysis. Finally, we demonstrate the significant performances of our algorithms through extensive simulations based on some real-world and synthetic WiFi datasets. [ABSTRACT FROM AUTHOR]

Based on the traditional Fast Retina Keypoint (FREAK) feature description algorithm, this paper proposed a Gravity-FREAK feature description algorithm based on Micro-electromechanical Systems (MEMS) sensor to overcome the limited computing performance and memory resources of mobile devices and further improve the reality interaction experience of clients through digital information added to the real world by augmented reality technology. The algorithm takes the gravity projection vector corresponding to the feature point as its feature orientation, which saved the time of calculating the neighborhood gray gradient of each feature point, reduced the cost of calculation and improved the accuracy of feature extraction. In the case of registration method of matching and tracking natural features, the adaptive and generic corner detection based on the Gravity-FREAK matching purification algorithm was used to eliminate abnormal matches, and Gravity Kaneda-Lucas Tracking (KLT) algorithm based on MEMS sensor can be used for the tracking registration of the targets and robustness improvement of tracking registration algorithm under mobile environment. [ABSTRACT FROM AUTHOR]

Abstract: As the number of smartphone users has grown recently, many context-aware services have been studied and launched. Activity recognition becomes one of the important issues for user adaptive services on the mobile phones. Even though many researchers have attempted to recognize a user's activities on a mobile device, it is still difficult to infer human activities from uncertain, incomplete and insufficient mobile sensor data. We present a method to recognize a person's activities from sensors in a mobile phone using mixture-of-experts (ME) model. In order to train the ME model, we have applied global–local co-training (GLCT) algorithm with both labeled and unlabeled data to improve the performance. The GLCT is a variation of co-training that uses a global model and a local model together. To evaluate the usefulness of the proposed method, we have conducted experiments using real datasets collected from Google Android smartphones. This paper is a revised and extended version of a paper that was presented at HAIS 2011. [Copyright &y& Elsevier]

This paper describes the design, acquisition process and baseline evaluation of the new e-BioSign database, which includes dynamic signature and handwriting information. Data is acquired from 5 different COTS devices: three Wacom devices (STU-500, STU-530 and DTU-1031) specifically designed to capture dynamic signatures and handwriting, and two general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV 7). For the two Samsung tablets, data is collected using both pen stylus and also the finger in order to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 65 subjects, and includes dynamic information of the signature, the full name and alpha numeric sequences. Skilled forgeries were also performed for signatures and full names. We also report a benchmark evaluation based on e-BioSign for person verification under three different real scenarios: 1) intra-device, 2) inter-device, and 3) mixed writing-tool. We have experimented the proposed benchmark using the main existing approaches for signature verification: feature- and time functions-based. As a result, new insights into the problem of signature biometrics in sensor-interoperable scenarios have been obtained, namely: the importance of specific methods for dealing with device interoperability, and the necessity of a deeper analysis on signatures acquired using the finger as the writing tool. This e-BioSign public database allows the research community to: 1) further analyse and develop signature verification systems in realistic scenarios, and 2) investigate towards a better understanding of the nature of the human handwriting when captured using electronic COTS devices in realistic conditions. [ABSTRACT FROM AUTHOR]

Decision support systems (DSS) are increasingly demanded due that diagnosis is one of the main activities that physicians accomplish every day. This fact seems critical when primary care physicians deal with uncommon problems belonging to specialized areas. The main objective of this paper is the development and user evaluation of a mobile DSS for iOS named OphthalDSS. This app has as purpose helping in anterior segment ocular diseases' diagnosis, besides offering educative content about ophthalmic diseases to users. For the deployment of this work, firstly it has been used the Apple IDE, Xcode, to develop the OphthalDSS mobile application using Objective-C as programming language. The core of the decision support system implemented by OphthalDSS is a decision tree developed by expert ophthalmologists. In order to evaluate the Quality of Experience (QoE) of primary care physicians after having tried the OphthalDSS app, a written inquiry based on the Likert scale was used. A total of 50 physicians answered to it, after trying the app during 1 month in their medical consultation. OphthalDSS is capable of helping to make diagnoses of diseases related to the anterior segment of the eye. Other features of OphthalDSS are a guide of each disease and an educational section. A 70% of the physicians answered in the survey that OphthalDSS performs in the way that they expected, and a 95% assures their trust in the reliability of the clinical information. Moreover, a 75% of them think that the decision system has a proper performance. Most of the primary care physicians agree with that OphthalDSS does the function that they expected, it is a user-friendly and the contents and structure are adequate. We can conclude that OphthalDSS is a practical tool but physicians require extra content that makes it a really useful one. [ABSTRACT FROM AUTHOR]

Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement. [ABSTRACT FROM AUTHOR]

During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient’s mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture. [ABSTRACT FROM AUTHOR]

In this paper, a numerical exposure assessment method is presented for a quasi-static analysis by the use of finite-difference time-domain (FDTD) algorithm. The proposed method is composed of scattered field FDTD method and quasi-static approximation for analyzing of the low frequency band electromagnetic problems. The proposed method provides an effective tool to compute induced electric fields in an anatomically realistic human voxel model exposed to an arbitrary non-uniform field source in the low frequency ranges. The method is verified, and excellent agreement with theoretical solutions is found for a dielectric sphere model exposed to a magnetic dipole source. The assessment method serves a practical example of the electric fields, current densities, and specific absorption rates induced in a human head and body in close proximity to a 150-kHz wireless power transfer system for cell phone charging. The results are compared to the limits recommended by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the IEEE standard guidelines. [ABSTRACT FROM AUTHOR]

Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger’s kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone. [ABSTRACT FROM PUBLISHER]

In the existing centralized location services system structure, the server is easily attracted and be the communication bottleneck. It caused the disclosure of users’ location. For this, we presented a new distributed collaborative recommendation strategy that is based on the distributed system. In this strategy, each node establishes profiles of their own location information. When requests for location services appear, the user can obtain the corresponding location services according to the recommendation of the neighboring users’ location information profiles. If no suitable recommended location service results are obtained, then the user can send a service request to the server according to the construction of a k-anonymous data set with a centroid position of the neighbors. In this strategy, we designed a new model of distributed collaborative recommendation location service based on the users’ location information profiles and used generalization and encryption to ensure the safety of the user’s location information privacy. Finally, we used the real location data set to make theoretical and experimental analysis. And the results show that the strategy proposed in this paper is capable of reducing the frequency of access to the location server, providing better location services and protecting better the user’s location privacy. [ABSTRACT FROM AUTHOR]

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm. [ABSTRACT FROM PUBLISHER]

The increasing densification of telecommunications mobile networks raises the compelling problem of exposure to electromagnetic fields (EMF). In this paper, we address the particular concept of heterogeneous networks, where small cells (SCs) are deployed to build up a second layer to the existing macrocell network. SC deployment is shown to be particularly effective in reducing EMF exposure, in addition to capacity enhancement. Previous studies on EMF have focused on the uplink (UL) and downlink (DL) components separately, although they are strongly correlated. Relying on the definition of the exposure index (EI) from the European FP7 Lexnet project, we evaluate the impact on EMF exposure due to SC deployment. Guidelines for EMF-efficient deployment of SCs are first presented and evaluated. Then, a new enhanced intercell interference coordination (eICIC) algorithm using UL and DL metrics to optimize capacity and exposure to EMF is proposed. Static and self-optimizing dynamic implementations of the solution are considered, using a utility function related to the sojourn time of the users. Time-domain and frequency-domain self-optimized eICIC are compared in terms of EMF exposure reduction and capacity enhancement. The eICIC algorithms are combined with a self-optimized coverage range expansion function leading to further reduction of the EI. The proposed solution for optimizing both capacity and exposure to EMF is compared with a capacity-driven solution based on the DL metrics only. Simulation results illustrate the performance gains obtained for the different SC deployment scenarios. [ABSTRACT FROM PUBLISHER]

Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. [ABSTRACT FROM AUTHOR]

Product aspect recognition is a key task in fine-grained opinion mining. Current methods primarily focus on the extraction of aspects from the product reviews. However, it is also important to cluster synonymous extracted aspects into the same category. In this paper, we focus on the problem of product aspect clustering. The primary challenge is to properly cluster and generalize aspects that have similar meanings but different representations. To address this problem, we learn two types of background knowledge for each extracted aspect based on two types of effective aspect relations: relevant aspect relations and irrelevant aspect relations, which describe two different types of relationships between two aspects. Based on these two types of relationships, we can assign many relevant and irrelevant aspects into two different sets as the background knowledge to describe each product aspect. To obtain abundant background knowledge for each product aspect, we can enrich the available information with background knowledge from the Web. Then, we design a hierarchical clustering algorithm to cluster these aspects into different groups, in which aspect similarity is computed using the relevant and irrelevant aspect sets for each product aspect. Experimental results obtained in both camera and mobile phone domains demonstrate that the proposed product aspect clustering method based on two types of background knowledge performs better than the baseline approach without the use of background knowledge. Moreover, the experimental results also indicate that expanding the available background knowledge using the Web is feasible. [ABSTRACT FROM AUTHOR]

Smartphones have become the major communication and portable computing devices that access the Internet through Wi-Fi or mobile networks. Unfortunately, users without a mobile data subscription can only access the Internet at limited locations, such as hotspots. In this paper, we propose a collaborative bandwidth sharing protocol (CBSP) built on top of MultiPath TCP (MPTCP). CBSP enables users to buy bandwidth on demand from neighbors (called Helpers) and uses virtual interfaces to bind the subflows of MPTCP to avoid modifying the implementation of MPTCP. However, although MPTCP provides the required multi-homing functionality for bandwidth sharing, the current packet scheduling in collaborative MPTCP (e.g., Co-MPTCP) leads to the so-called biased-feeding problem. In this problem, the fastest link might always be selected to send packets whenever it has available cwnd, which results in other links not being fully utilized. In this work, we set out to design an algorithm, called Scheduled Window-based Transmission Control (SWTC), to improve the performance of packet scheduling in MPTCP, and we perform extensive simulations to evaluate its performance. [ABSTRACT FROM AUTHOR]

In this paper, a dual-microphone speech enhancement algorithm for the mobile phones is proposed. The adopted method exploits the coherence function algorithm and the Kalman filter. This algorithm has a simple implementation that does not need a prediction of interfering signals statistics. In addition, this algorithm can be used in small devices with so closely distance between the two microphones. Moreover, the use of such algorithm allows reducing multiple noise sources at many azimuths positions. Finally, the new algorithm proves its performances referring to the perceptual evaluation of speech quality and the time domain waveforms. [ABSTRACT FROM AUTHOR]

Due to advances of network enabled wireless devices, there is a growing trend of the use of networked and distributed mobile devices. As a result, deployment of day to day applications in MANET for information services is also rapidly evolving in ad hoc mobile environments, with the growing number of mobile users. The application running on different devices can be accessed as a service by different users on an on demand basis. These scenario demands deployment of Service Oriented Architecture (SOA) in MANET for efficient access and usage of applications. Since nodes in MANET does not have any prior knowledge about other nodes, in many cases, the publish/subscribe communication paradigm that has the decoupling and asynchrony properties can be useful to share services between nodes. Unfortunately, none of the currently available content-based publish-subscribe system fit the requirements of such dynamic environments. In this paper several distributed publish/subscribe mechanisms for improving the reliability of the service discovery process is analyzed. Nodes in MANET are constrained by limited processing power, battery power, memory etc, so there lays a need to find appropriate nodes for service brokering. To select a broker for publishing services, parameters like available battery power, CPU power, memory capacity, nodes mobility etc. needs to be considered. In this paper a broker selection model based on the above mentioned parameters is proposed. Unlike other mobility prediction algorithms in terms of speed which require devices to be equipped with GPS to predict the mobility, mobility in the proposed algorithm is detected using frequent change of neighborhood. [ABSTRACT FROM AUTHOR]

Although iris recognition technology has been reported to be more stable and reliable than other biometric systems, performance can be degraded due to many factors such as small eyes, camera defocusing, eyelash occlusions and specular reflections on the surface of glasses. In this paper, we propose a new multi-unit iris authentication method that uses score level fusion based on a support vector machine (SVM) and a quality assessment method for mobile phones. Compared to previous research, this paper presents the following two contributions. First, we reduced the false rejection rate and improved iris recognition accuracy by using iris quality assessment. Second, if even two iris images were determined to be of bad quality, we captured the iris images again without using a recognition process. If only one iris image among the left and right irises was regarded as a good one, it was used for recognition. However, if both the left and right iris images were good, we performed multi-unit iris recognition using score level fusion based on a SVM. Experimental results showed that the accuracy of the proposed method was superior to previous methods that used only one good iris image or those methods that used conventional fusion methods. [ABSTRACT FROM AUTHOR]

In a companion paper (see "Resource Allocation for Downlink Cellular OFDMA Systems-Part I: Optimal Allocation," IEEE Trans. Signal Process., vol. 58, no. 2, pp. 720-734, Feb. 2010), we characterized the optimal resource allocation in terms of power control and subcarrier assignment, for a downlink sectorized OFDMA system impaired by multicell interference. In our model, the network is assumed to be one dimensional (linear) for the sake of analysis. We also assume that a certain part of the available bandwidth is likely to be reused by different base stations while that the other part of the bandwidth is shared in an orthogonal way between these base stations. The optimal resource allocation characterized in Part I is obtained by minimizing the total power spent by the network under the constraint that all users' rate requirements are satisfied. It is worth noting that when optimal resource allocation is used, any user receives data either in the reused bandwidth or in the protected bandwidth, but not in both (except for at most one pivot-user in each cell). We also proposed an algorithm that determines the optimal values of users' resource allocation parameters. As a matter of fact, the optimal allocation algorithm proposed in Part! requires a large number of operations. In the present paper, we propose a distributed practical resource allocation algorithm with low complexity. We study the asymptotic behavior of both this simplified resource allocation algorithm and the optimal resource allocation algorithm of Part I as the number of users in each cell tends to infinity. Our analysis allows to prove that the proposed simplified algorithm is asymptotically optimal, i.e., it achieves the same asymptotic transmit power as the optimal algorithm as the number of users in each cell tends to infinity. As a byproduct of our analysis, we characterize the optimal value of the frequency reuse factor. Simulations sustain our claims and show that substantial performance improvements are obtained when the optimal value of the frequency reuse factor is used. [ABSTRACT FROM AUTHOR]

Mobile services differ from other services because of their temporal and spatial attributes. Mobile services additionally differ from each other in their value-added to the end-user. Some services—such as emailing and voice—are more business oriented. On the other hand, various free-time oriented services are provided in new smartphones, such as imaging and music playback. The present paper studies how mobile services are used in different contexts. For this, the paper develops a specialized algorithm that can be used with handset-based usage data acquired straight from end-users in an established panel study process. Educated guesses can be drawn on the user context based on the developed algorithm. In the present exercise usage contexts were divided into home, office and “on the move”. The algorithm is used with exemplary data from Finland and the UK covering 324 consumers in 2006. More than 70% of contextual use cases are correctly classified based on raw data. According to exemplary results particularly multimedia services are used “on the move”, whereas legacy mobile services experience more evenly distributed usage across all contexts. The algorithm that identifies context based on raw data provides a new angle to mobile end-user research. In the future, the accuracy of the algorithm will be improved with the integration of seamless cell-id logging and GPS data. [ABSTRACT FROM AUTHOR]

In wireless location, nonline-of-sight (NLOS) errors are a major error source. To improve positional performance, these errors should be identified and mitigated before measurements are used in position computations. A novel NLOS mitigation algorithm is proposed in this paper. Employing system redundancy, the algorithm derives from the distribution function of hyperbolic intersections, which is an intermediate mobile location. Cost functions based on the intermediate position are then created for each time-of-arrival measurement. NLOS errors are detected and mitigated via hypothesis tests performed on the cost functions. Simulation tests are presented in this paper, and the results verify the effectiveness of this NLOS-error-mitigation method. This paper also demonstrates that algorithm performance depends closely on receiver noise. Reducing receiver noise or providing the intermediate mobile location via external aiding can improve performance. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy-based dynamic channel-borrowing scheme (FDCBS) is presented to maximize the number of served calls in a distributed wireless cellular network. The uneven traffic load may create hot-spot cells and possibly causes a high blocking rate in hot-spot cells. Most conventional methods use load indices with a threshold value to determine the load status of a cell. However, those exists a ping–pong effect, as loads are around the threshold value. This result causes an unstable system and unnecessary message passing overhead. In addition, the estimation of traffic load is difficult and time-consuming. Thus, an intelligent prediction mechanism is needed. In this paper, we develop a method to predict the cell load and to solve the channel-borrowing problem based on the fuzzy logic control. A new channel-borrowing algorithm with multi-channels borrowing is also presented in this paper. A borrowing mechanism supporting the present facility has been built on the application-level of wireless cellular networks. The FDCBS exhibits better adaptability, robustness, and fault-tolerant capability thus yielding better performance compared with other algorithms. Through simulations, we evaluate the blocking rate, update overhead, and channel acquisition delay time of the proposed method. The results demonstrate that our algorithm has lower blocking rate, less updated overhead, and shorter channel acquisition delays. [Copyright &y& Elsevier]

As a result of the greatly increased use of mobile devices, the disadvantages of portable devices have gradually begun to emerge. To solve these problems, the use of mobile cloud computing assisted by cloud data centers has been proposed. However, cloud data centers are always very far from the mobile requesters. In this paper, we propose an improved multi-objective local mobile cloud model: Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri). This new architecture could briefly store jobs that arrive simultaneously at the cloudlet in different priority positions according to the result of auction processing, and then execute partitioning tasks on capable helpers. In the Scheduling Module, NSGA-II is employed as the scheduling algorithm to shorten processing time and decrease requester cost relative to PSO and sequential scheduling. The simulation results show that the number of iteration times that is defined to 30 is the best choice of the system. In addition, comparing with LMCque, LMCpri is able to effectively accommodate a requester who would like his job to be executed in advance and shorten execution time. Finally, we make a comparing experiment between LMCpri and cloud assisting architecture, and the results reveal that LMCpri presents a better performance advantage than cloud assisting architecture. [ABSTRACT FROM AUTHOR]

Due to the ever-growing popularity mobile devices of various kinds have received worldwide, the demands on large-scale wireless network infrastructure development and enhancement have been rapidly swelling in recent years. A mobile device holder can get online at a wireless network access point, which covers a limited area. When the client leaves the access point, there will be a temporary disconnection until he/she enters the coverage of another access point. Even when the coverages of two neighboring access points overlap, there is still work to do to make the wireless connection smoothly continue. The action of one wireless network access point passing a client to another access point is referred to as the handover. During handover, for security concerns, the client and the new access point should perform mutual authentication before any Internet access service is practically gained/provided. If the handover protocol is inefficient, in some cases discontinued Internet service will happen. In 2013, Li et al. proposed a fast handover authentication mechanism for wireless mesh network (WMN) based on tickets. Unfortunately, Li et al.’s work came with some weaknesses. For one thing, some sensitive information such as the time and date of expiration is sent in plaintext, which increases security risks. For another, Li et al.’s protocol includes the use of high-quality tamper-proof devices (TPDs), and this unreasonably high equipment requirement limits its applicability. In this paper, we shall propose a new efficient handover authentication mechanism. The new mechanism offers a higher level of security on a more scalable ground with the client’s privacy better preserved. The results of our performance analysis suggest that our new mechanism is superior to some similar mechanisms in terms of authentication delay. [ABSTRACT FROM AUTHOR]

This paper presents ERELT (Enhanced Radial Edgeless Tree), a tree visualization approach on modern mobile devices. ERELT is designed to offer a clear visualization of any tree structure with intuitive interaction. Such visualization can assist users in interacting with a hierarchical structure such as a media collection, file system, etc. In the ERELT visualization, a subset of the tree is displayed at a time. The displayed tree size depends on the maximum number of tree elements that can be put on the screen while maintaining clarity. Users can quickly navigate to the hidden parts of the tree through touch-based gestures. We have conducted a user study to evaluate this visualization for a music collection. The study results show that this approach reduces the time and effort in navigating tree structures for exploration and search tasks. [ABSTRACT FROM AUTHOR]

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, multi-user computation partitioning problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10 percent on average in terms of application delay. Based on SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces. [ABSTRACT FROM AUTHOR]

One of the challenges faced by visually impaired persons is the recognition of currency. Mobile phones are becoming more affordable and are being used by the visually impaired community to help them in their everyday lives. This paper investigates the Local Binary Patterns (LBP) as a method to recognise Trinidad and Tobago currency notes. A mobile application was developed and the effectiveness of the LBP algorithm was tested in terms of speed, robustness to illumination, scale and rotation. The LBP algorithm realised a recognition rate of at least 95 percent for Trinidad and Tobago currency. The recognition rates on mobile devices were compared for the LBP and the ORB (Oriented FAST and Rotated BRIEF) methods. [ABSTRACT FROM AUTHOR]