The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution. The operation of such platforms could be driven by the event oriented nature of mobility changes, and could support the execution of location analysis techniques for full on-device and on-line detection of stay points.Such alternative mechanism might also opportunistically label the identified stay points for adding semantic meaning using information from other sensors . Once computed, the semantic meaning and specific location and time attributes of stay points could be transmitted to the cloud, quite sparse instead of densely sampled raw location data, for large-scale or population-wise analytics. Hybrid solutions are also possible with consequences and tradeoffs in energy, accuracy, delays, etc. Moreover, far beyond of efficient on-device implementations, research is still needed for improving the power-efficiency of GPS position sensing using intelligent sensor management strategies . We arguThe design of a full on-device middleware for autonomous location data collection and event-driven stay points detection in smartphones in a non-intrusive manner, which addresses power saving mechanisms of mobile OS and offers duty cycling support for a flexible sampling process.A quantitative evaluation through experiments in real mobile devices using the proposed middleware, which highlights the possibility of obtaining power savings when detecting stay points, in comparison with an MCC oriented solution, even under variable stress levels expressed as different fixed duty cycles.Attending the aforementioned motivation, in this article we propose and evaluate an event-driven and fully on-device stay points detection middleware, with support for duty cycled access to GPS receiver in a continuous manner. We adopted an event-driven design as it allows to interact transparently with the built-in location facilities of mobile devices. Similarly, it also acts in a power-aware manner as, out-of-the-box, it only activates its internal modules upon reception of a significant event notification, instead of performing a permanent data polling among its layers. Finally, the event-driven design eases the development and extension of the proposed middleware, as it favors a low coupling between its modules. The development of such middleware is not straightforward, as it must overcome several technical barriers imposed by the complex software stack of modern smartphones. For instance, although aggressive power saving mechanisms, changes in the power-states, and a too generic management of sensors aid the smartphone to obtain the most of its battery , they alFrom experimental results, we observe that only a minor degradation in the accuracy of detected stay points is produced, due to the employment of fixed duty cycles for accessing the GPS receiver. The middleware produces a minor computational impact that does not degrade the operation of the mobile device and, instead, reduces the energy consumption in comparison to GPS location transmission costs. The rest of this article is organized as follows. In order to provide a self-contained description, we first present the definition of stay point, followed by a brief review of existing related work.ts).A stay point refers to a geographical zone a user holds a special interest with. Formally, a stay point is a virtual location defined by latitude and longitude coordinates, as well as arrival time, and departure time. It is calculated from a set of time consecutive GPS fixes From the provided definition, stay points are constrained by a geographical region size and a stay time, so that in practice a distance ts) of the first location fix (lat) and longitude (lon) coordinates are computed according to Equations (at) and departure time (dt) are obtained from the timestamp (ts) of the Equation  constraiEquation  defines quations  and 4),,P must bClustering-based strategies: Clustering algorithms are employed for grouping raw coordinates into places. These algorithms could only consider spatial distance aspects  or alsolgorithm  and CB-Slgorithm ).Differential-based strategies: Algorithms are based on the analysis of time and space differences between individual GPS fixes for finding centroids that represent a stay point , Bayesis (as in ), and Cos (as in ).Most of the existing MCC oriented solutions are powered by off-line algorithms on which the input is a full trajectory of time-sequenced GPS fixes and the output is the list of identified stay points . In thisNotice that since full trajectories are the input for most of these strategies, they are unable to incrementally compute stay points after each location update is received, which could enable on-line stay points detection. Similarly, their design often disregards the technical barriers for implementing them in mobile platforms, which are constrained by a limited battery, as well as by the availability and accuracy variations associated to GPS infrastructure. As a consequence, many of the existing MCC techniques only use the smartphone as a simple medium for collecting location data and, occasionally, for performing light filtering tasks.TRAC-IT application [A rich body of research has been conducted for reducing the impact of MCC solutions on energy consumption due to the large amount of location data generated by the smartphone, and also for partially supporting on-device stay point detection. For instance, Barbeau et al., presented the lication , which iViBN, by Miluzzo et al.  is a conPerez-Torres and Torres-Huitzil proposed in  a middleLoseto et al., proposed a mobile agent  for profsuperstate model [superstate, which is modeled as the user being at all states simultaneously, each of them holding a likelihood; the state with the highest probability is selected as the current state. Across time, user mobility will describe transitions between superstates, which are hold in an HMM transition matrix. From these states, the system is able to identify stay points if, for instance, the special sequence of walking, indoors, staying indoors, and transportation states is detected. Nonetheless, the required learning stage is complex to achieve and its implementation in a mobile device was not demonstrated. Despite the quality of the results in stay points detection and behavior recognition of this work, the energy constraint was not considered in its overall design.Lee et al., evaluated several algorithms for off-line trajectory analysis, and proposed an alternative method for stay points detection that employs a te model , as an eThe on-line detection of stay points on-device has been partially addressed in consideration of its energy constraints. Hu et al.  presenteIn this regard, existing work in the literature has been mostly focused in off-line detection of stay points, and the exploration of complementary schemes to the MCC oriented approach has been only partially analyzed. By studying such on-device alternative strategies, we consider that energy savings could be boosted (as densely data transmission is avoided), privacy concerns could be lessen (as data is kept in the mobile device) and autonomous cognitive features could be achieved by the smartphone itself.The overall design of the proposed middleware is described according to its two main features. The first one refers to the support for event-driven differential algorithms for stay points detection. The second one consists in an event-driven layered architecture that embeds the event-driven algorithms, autonomously manages the collection of location data (supporting duty cycling and policy-driven sampling), and notifies each identified stay point to LBS subscribers. Such design fits transparently with the way on which mobile platforms access to sensors. Both features are described in the following sections.The asynchronous nature of GPS data acquisition promotes an event-driven design of the software stack of mobile platforms, or at least, as in the case of Android, a mock event-driven design that wraps the continuous polling between its layers ,46. SuchAmong the different off-line trajectory analysis algorithms for stay points detection, the algorithm proposed by Li et al. ,31 belonFirst, the algorithm keeps two pointers if comparison of line 6 is false, meaning that user is still inside the geographical region.Second, the Equation  is met, if comparison of line 6 is true), it would mean that user has exited from the geographical zone, so that the time constraint defined in Equation (if comparison of line 7) must be evaluated.Third, if the distance constraint is not met  is processed, and how the accumulated sub-trajectory data is reset, respectively. For instance, depending on algorithm design aspects, the Line 2 could represent the storing of the incoming location updates into an internal buffer, or just simply the accumulation of their coordinate values in the Mobile platforms usually define a complex and rigid software stack as a way to overcome the constraints of their hardware components and to ensure the continuous execution of diverse computing, communication and sensing tasks. Such a design approach makes the power efficient collection of sensors data cumbersome, since a full understanding of the smartphone’s operation is needed. As a rule of thumb, it cannot be assumed that sensors data are immediately available upon request. In fact, there is an intrinsic asynchrony in the operation of sensors, since the collection of data requires to perform an actual measurement of the physical environment, situation that is augmented by the complexity of the software stack. Additionally, the measurement might depend on external infrastructure, as in the case of GPS location updates, which involves communicating with GPS satellites , introduevent source; (ii) event processing; and (iii) event handling [An event-driven system is one that works by only sending asynchronous notifications (or message passing) between its components for triggering computing tasks ,48. Thanevent source is a system component that is capable of producing a stream of observations corresponding to events occurring in a given environment, for instance hardware sensors. As such, the GPS receiver is considered an event source, which continuously produces a stream of location update events. The next fundamental component is an event processing engine that incrementally processes the stream of incoming events and produces more complex events or actions with a valuable semantic meaning. In the proposed approach, the detection of stay points is kept in background, triggering processing tasks only when a GPS fix is received from the stream of location updates, and serving to concurrent applications, even during the smartphone’s stand-by power mode. More specifically, such processing considers the space-time relationship, transforming the simple low-level events (raw GPS coordinates) into stay points. Finally, an event handling component consumes the detected event and reacts accordingly by triggering a specific-domain action. The event-handling components must be registered in the driven system as event-subscribers in order to receive messages of incoming events. Such subscription-notification insight is usually supported by the mobile OS. For our middleware, the handling components refers to the subscribed mobile applications requesting stay points notifications.Under the event-driven viewpoint, an Although not implemented in the presented work, the event handling or event processing could be improved to enrich stay points data using additional information, for instance: soliciting a semantic meaning of the location from user feedback ; including activity information from accelerometer data; and performing further processing for detecting top stay points. These tasks could be also implemented by the smartphone itself following a personal sensing paradigm, having always the possibility to transmit this reduced amount of high level information to external servers for conducting population-wide analysis as in the community sensing paradigm .The proposed solution was implemented as a middleware for the Android platform, which provides fine-grained control for sensing operations and a flexible development environment. Nevertheless, the underlying principles of our middleware still are valid for other mobile platforms that also follow an asynchronous access to sensors. Specific actions were taken for ensuring the actual execution of the middleware in the smartphone, as described in next paragraphs.Taking the generic event-driven algorithm provided in firstElement); (b) Assigning collectedData variable  Assigning buffered algorithm only keeps in memory the minimum amount of location fixes needed for calculating stay points, instead of a full location trajectory.The call to calculate centroid in Line 5 is a strict implementation of the stay point’s definition provided in buffered variant, as well as the original off-line Li’s algorithm, holds explicit memory requirements for storing the location fixes of current sub-trajectory. As such, it could be argued that a large amount of memory might be needed if user remains in the same place for a long time. A rough estimation of these memory requirements in the Android OS, considering a GPS sampling frequency of 1 fix per minute, is as follows. An instance of the native Location class (which represents a location fix) occupies 132 bytes, thus a full day of GPS readings could be allocated in around 185 kB. The memory provided by Android varies according to software version, mobile application and hardware constraints, and could be dynamically re-allocated when a mobile App reaches a low memory condition [buffered algorithm variant, we performed a slight modification, herein after referred to as sigma, which avoids the need for buffering GPS location fixes.The event-driven ondition . During sigma version, the Line 2 for incorporating a location update is implemented by accumulating its latitude and longitude coordinate values, and incrementing the amount of accumulated fixes , as a way to perform an incremental calculation of stay points. The call to Line 5 is implemented as a simple arithmetical division of the accumulated coordinate values by the amount of fixes, attaching the timestamp of the first sub-trajectory fix and the received location update as the arrival and departure time of the stay point, respectively. Finally, the implementation of Line 8 resets the accumulated data by assigning the latitude and longitude coordinates of On this Observer pattern throughout our middleware to register subscribers for receiving notification about changes in the platform [Our middleware abstracts the complexity of the different layers and technical mechanisms that must be addressed for enabling continuous GPS access, like reacting to changes in power-level modes and energy management of Android OS. Its architecture provides significant enhancements of our previous work (described in ), improvplatform , specifiRunning location-based mobile application requests the detection of stay points to the Background service component, using an implementation of a Java interface that describes the policy to follow for subsequent GPS accesses. As shown in scheduleNextReading, where the lastPeriod parameter refers to the last time interval employed for scheduling GPS readings, while previousLocations contains a list of the last location fixes reported by the GPS receiver. The interface implementation is evaluated by the Policy executor once a new location update is received, allowing the middleware to define the moment on which next reading must be scheduled.A detailed description of how the middleware employs such Android low-level components and the interaction between its modules is as follows. Initially, at the top of the Android software stack , the Activity of the hosted mobile app, which establishes a connection with a Service (the Background service component) using an Intent when calling the bindService method. After such method call, the Background service is started in foreground mode, marking it with high priority so that Android OS does not kill it under low memory scenarios.By using the interface, the hosted mobile app is able to inject this functionality according to its own needs. For instance, it could study the distance and time changes of last obtained locations, generating speed-based policies (as in ), or simBackground service component includes several modules built from different Android API Framework elements, in particular the alarm, power, and location system services. After the Policy executor evaluates the interface implementation, the next GPS reading is scheduled by registering alarms into the mobile OS using the AlarmManager system service of the Android software stack. Among the different task scheduling strategies provided by Android, the AlarmManager approach is the only control point for addressing the aggressive power-saving mechanisms aimed at extending battery life. In this way, the smartphone could be on its active mode, even reactivating it from sleep mode  if needed. The scheduling of alarms is followed by the registration of a BroadcastReceiver component that will receive event notifications of alarms going off, so that the middleware is reactivated for continuing location data collection.The POWER_SERVICE system service provides a specific token denominated partial WakeLock for explicitly controlling the power state of the mobile device. When acquired, a WakeLock forces the CPU and low-level hardware components to be kept in active power mode. We employed a specific type of WakeLock denominated PartialWakeLock, as it is able to turn off the screen while keeping the CPU and sensors awake. In this way, the WakeLock manager component ensures that the smartphone will be kept awake when accessing to GPS and processing location data [Although scheduled alarms are guaranteed to be triggered, the smartphone still can return to sleep mode while collecting and processing GPS data. The Android OS’s ion data .Background service also embeds the Stay points detection algorithm module, which executes the event-driven buffered and sigma algorithm variants for on-line detection of stay points. This module is aware of the limitations of smartphone’s battery, so that after notifying the detected stay point to the subscribed mobile app, it also notifies to the Policy executor the end of location processing for the immediate release of the active WakeLock. The proper management of WakeLocks is essential for the continuous operation of mobile apps, and their unexpected retention and missuses are common causes of a poor management of energy resources [The esources . AnotherGPS Administrator module employs the LocationManager Android API component for accessing to the native GPS location provider, using the sampling scheme instructed by the Policy executor. The GPS Listener module, upon GPS Administrator request, registers for location updates with the Android HAL (Hardware Abstraction Layer) using a LocationListener implementation. Upon location update reception, the GPS Listener unregisters from HAL so that GPS receiver could be turned off, and notifies the location update to subscribers in upper layers of the middleware. It is important to recall that the acquisition of a GPS location fix is highly dependent on the visibility of GPS satellite infrastructure, which is affected in indoors locations, and by other external factors like meteorological phenomena. As a consequence, it is possible that location updates could not be retrieved. For this issue, the GPS Administrator includes a TimerTask mechanism that provides a time interval on which the location update must be obtained. In the case a time out is reached, the GPS Listener is unregistered from the native location provider and a notification of not found location is delivered to upper layers. The subscribers of location updates, i.e. the Policy executor and the Stay points detection algorithm modules, must be aware of this issue and prepared to react accordingly.The GPS administrator and listener layers control the registration - unregistration with the GPS hardware. The Despite the fact Android offers a mock event-driven API for sensors access, its actual implementation instructs layers to pull data from underneath layers ,56. Unli*), the Running location-based mobile app requests the notification of location and stay point updates to the Background service in step 0. Then, the Policy executor evaluates the specified policy (Step 1) for scheduling the time at which the mobile device must be woken up (Step 2) to perform location updates. After the location update (or a time out) is obtained from the event source components (GPS Administrator and GPS Listener), it is notified to upper layers (Step 3), including the hosted mobile App (Step 4). The location update is received by the Background service, for launching the event processing in the Stay points detection algorithm module (Step 5), which would notify the detection of a stay point to the hosted mobile App (Step 6). The location update is also processed by the Policy executor, which after storing it inside the previousLocations list, will trigger the Evaluation of the specified policy, restarting the sampling process.Policy executor block could analyze a historical window of GPS data to infer context information about user mobility and generate an adequate schedule for the next GPS access. Although in this article the analysis of the previousLocations list is discarded and only fixed sampling periods are explored, such scheduling could be based on more complex mechanisms that employ information learned from multi-sources of context data [Thus, the ext data ,29. It iExperimental trials were performed in order to evaluate two main aspects of the proposed middleware. On the one hand, we evaluated the feasibility and reliability of employing it for conducting stay points detection completely on-device. On the other hand, we analyzed the energy savings produced by its event-driven design when compared against an MCC approach that transmits location data for offloading the detection of stay points.For all of the outdoor experiments, the smartphones employed were the Google Nexus 6, which are equipped with a 2.7 GHz quad-core processor, 3 GB RAM, and a new 3220 mAh battery, running Android 6. As part of fair experimental conditions, the smartphones were only employed for running the experimentation, discarding any other uses , no additional mobile apps were installed on them, and only the mobile app corresponding to the experiments was kept in foreground, using the GPS as the unique location provider (wireless networks and cellular location providers were disabled). The parameters values for running the stay point detection algorithms were set to 45 min for The main goal of the first experiment was to ensure the feasibility of performing on-device stay points detection using the proposed middleware. In this regard, the feasibility is understood as the ability for executing the middleware in the mobile device under different stress levels without a premature finalization caused by CPU and memory consumption usage issues. For this purpose, the experiment consisted on developing a sample Android location-based mobile application that would request stay points detection to our middleware. The referred stress levels were implemented by the sampling periods in the set {30, 60, 90, 120, 150 s}, which produce different workloads in the CPU, memory, and battery consumption of the smartphone.The selected sampling periods defined fixed duty cycling schemes for the GPS receiver that, although simple, are still valid for the purpose of the experiment. Note that in this article we do not focus on a deep quantitative evaluation of the correctness of duty cycling schemes towards accurate location tracking and stay points detection (as performed in ). Insteabuffered algorithm variant, and the other mobile device running the sigma variant, both of them using the same sampling period at once.The experimentation was performed assuming that typical speeds from transportation modes are  (a) stopsampling period indicates the sampling period employed for accessing the GPS receiver, while the algorithm variant column indicates the version of the algorithm executed by the mobile device. Column obtained GPS fixes refers to the amount of GPS fixes that were requested by the middleware to the mobile OS and that were actually obtained. It is important to mention that a 1 min timeout was established for obtaining each location fix; if the timeout was reached, the GPS location fix was considered not valid and a new GPS reading was scheduled. The column average GPS fixes per SP indicates the average amount of location fixes involved in all of the calculated stay points. Such amount gives an idea of the amount of memory that the smartphone should manage for executing the buffered algorithm variant. Finally, the running time indicates how long (min) the experiment was executed by the smartphones, starting from a full-charged battery and ending with a 4% battery level .The results of the first experiment are summarized in buffered version with the 60 s sampling period, which describes a running time even higher than the obtained in the 150 s sampling trial. Such outlier could be caused by the multiple background tasks that smartphones perform and that affect the transitions to the different internal power states of the mobile platform. Similarly, as could be intuitively inferred, shorter sampling periods could obtain higher amounts of GPS fixes in each algorithm variant, again with the same outlier of 60 s sampling trial. Also, it is noteworthy that the running time does not exactly correspond to obtained GPS fixes × sampling period value since each fix is not obtained immediately and because of the aggressive internal mechanisms that the Android OS implements for reducing energy consumption in the smartphone’s idle state like the doze and app standby features [For all of the sampling periods, both smartphones were able to calculate stay points without being affected by their memory and computing capabilities, ensuring its feasibility. By analyzing the performance of each algorithm during the trials, it is noteworthy that the usage of larger sampling periods incremented the total running time of each trial, with the exception of the outlier represented by the features .running time even though both smartphones were kept under the same described environment. Additionally, an extra energy overhead is generated when a GPS fix is requested by the mobile app to the mobile OS, but it is not obtained due to severely attenuated signals. Such weak satellite signals are highly dependent to factors like the location of user  or mobility conditions that, although identical for both smartphones, are out of control in the experimentation’s scenario. As a result of the previous factors, there is no a unique algorithm variant that improves the running time for all of the sampling periods of the conducted experimentation. As such, despite the fact that memory requirements of the buffered algorithm version are higher than those of the sigma variant, it was not possible to elaborate conclusive remarks from the conducted trials about the energy performance of the algorithms. More experiments are needed for detecting a confident tendency in the energy performance improvements of one algorithm variant respect to another. Nonetheless, it is important to recall that the main objective of this experiment was to ensure the feasibility of employing the proposed middleware towards stay points detection, which was successfully proved.In this regard, any attempt for replicating the presented results must consider such encapsulated power saving mechanisms that do not expose control points to developers and that hinder the execution of continuous GPS sampling. The effect of the power saving mechanisms is evident in the experiment’s results, since for all of the tested sampling periods one algorithm implementation depleted the battery faster than the other one, describing a shorter i fixes collected from ground-truth data, and let j fixes collected through a specific sampling policy, and also let In addition to exploring the feasibility of performing on-device stay points detection, the first experiment also allows to evaluate the impact of non-continuous GPS sampling on the spatial and temporal accuracy of detected stay points. In this matter, it is important to consider that there is no restriction in the density of the GPS trajectories employed for detecting stay points, so that stay points calculated from non-continuous trajectories could be compared against stay points generated from ground-truth  trajectories as follows. Let On the other hand, the spatial accuracy is evaluated by a direct measurement of the distance between stay points.detected SP’s indicates the amount of stay points obtained by the algorithm variants in each experiment round, while the average SP stay time difference and average SP distance difference columns show the time and spatial differences from ground-truth location data. As it could be expected, the shortest sampling periods calculated stay points more accurately, in terms of distance to the ground-truth stay points, when compared against largest sampling periods.sigma algorithm variant with a 150 s sampling period, caused by accuracy issues of the smartphone’s GPS receiver. It is important to highlight that the accuracy results shown here are highly dependent on the actual mobility of smartphone user, which is one of the common issues that hinders the comparison of power-aware frameworks for LBS [Note that for some of the experiment rounds, the algorithm variants detected less stay points than those according to ground-truth values, which was due to the depletion of battery that caused the finalization of the mobile app. The fact that battery ran out faster for one smartphone is, again, attributable to several factors like the different tasks that mobile platforms run in the background (which represent an energy overhead), and the power saving mechanisms implemented by the mobile OS. In any case, regardless of a lower running time, the semantic meaning of the stay points detected by both smartphones was equivalent. There was only one actual missed stay point that occurred when executing the  for LBS .30 s sampling period that denotes the mobility pattern of a user that leaves home in the morning, stays around 9 h at work, and then arrives to a sport facility for returning home later. On such sub-trajectory, different motion speeds (in the range of a vehicle transportation mode) were described by user, which produced stay points with different spatial granularity. The We also validated the correctness of the obtained stay points by analyzing the graphical representation of part of the outcomes from the first experiment. In particular, we considered a sub-trajectory obtained using a From these results, we also identified the possibility of performing an on-line and on-device stay points detection using location data collected through duty cycling schemes that, in conjunction with the event-driven design of the proposed solution, could reduce energy consumption without producing a significant impact on the spatial and time accuracy of calculated stay points.The objective of the second experiment was to bring the smartphone under different stress levels for evaluating the energy consumption of the proposed middleware with respect to an MCC oriented solution that offloads location data processing. For this experiment, in the first smartphone we implemented the on-device middleware architecture, whereas in the second one we deployed a location application based on an MCC approach.sigma algorithm for performing the on-device discovery of stay points. In the case of the MCC oriented solution, it also subscribed for location updates to our middleware, but translated each received location fix into a string representation  for its immediate transmission to an application server using the available cellular data network. As in the previous experiment, both smartphones were carried always together, following the same trajectory. Regarding wireless interfaces, only the smartphone running the MCC oriented mobile application was allowed to access mobile data network. Also, both smartphones had the Wi-Fi interface enabled, but were not allowed to connect to any access point during the trials.The stress levels were the same sampling periods in the set {30, 60, 90, 120, 150 s}. The smartphone running the mobile app hosted by our middleware executed the event-driven average acquisition time per fix indicates the average amount of seconds invested for acquiring each location fix, which is calculated as the quotient between GPS-on time and the amount of obtained GPS fixes. Finally, the data sent and data received columns, applicable only to the MCC oriented tests, include the total amount of bytes requested by the mobile app to be sent as payload and the total amount of bytes received as payload in the smartphone by the server side, respectively.The results of the experiment are summarized in x to 1.52x. This produces a more efficient usage of energy resources and allows to conduct location tracking and stay points detection for a longer time. On the other hand, the MCC oriented strategies achieve shorter running times and obtain a lower amount of GPS fixes than corresponding local trials, which is caused by the energy overhead generated by the data transmission that contributes to a faster depletion of battery. It is also worth to note how the average time for obtaining a fix in the local trials is increased when longer sampling periods are employed, which could be explained by the different types of power states in the operation of GPS receiver . Longer sampling periods might represent a heavier energy demand since GPS receiver should perform a cold-start for obtaining a GPS fix. The average acquisition time of GPS fixes in the MCC oriented implementations is lower than the on-device strategy, which is attributable to the assistance provided by the mobile data network to the A-GPS receiver of the smartphone [From these results, it could be observed that all of the local executions, regardless of the employed duty cycle, outperform the running time of the corresponding remote trials, increasing the battery lifetime in factors within the range 1.26artphone ,59. Howe120 s test, which ends very close to the 90 s trial, but 15 min before. In the case of the local tests, a similar situation is found, where tests end following an increasing order of their sampling period, again with the exception of the 120 s sampling trial that finishes 739 min before the 60 s trial. The unexpected early finalization of the 120 s trials, both in remote and local tests, suggests that this specific sampling period could represent a breaking point in the operation of the GPS receiver of the employed smartphones. A pragmatic interpretation is that location update requests in a 120 s sampling period produce cold and/or warm starts in the operation of GPS receiver. This means that for some of the sampling periods, the ephemeris and almanac information of GPS satellites must be updated, producing an additional energy overhead that increases energy consumption, up to the point of finishing the execution of the trial even before than shorter sampling periods.30 s sampling period experiment corresponds to 1080 min; while for the 60 s sampling period such percentage is equivalent to 1581 min. The battery gains shown in From the energy performance results, it is possible to explore the battery gains obtained by the on-device implementation. 30 s sampling period experiment, is as follows. 80% of elapsed time in the remote experiment corresponds to approximately 4321 min; at that time, the level of the smartphone’s battery running the on-device implementation is about 49% and for the MCC oriented strategy is around 20%. From Equation  than wireless transmission in the tested mobile platforms and under the discussed scenario. Since the proposed on-device approach avoids the high energy consumption generated by the wireless transmission of densely sampled locations, it is able to calculate stay points with a good precision and without jeopardizing energy resources of the mobile device. Moreover, it was outlined that collecting and analyzing stay points and mobility patterns completely on-device provide an opportunity for optimizing diverse system design requirements, such as the energy consumption that could be saved when reducing the data traffic, and the performance of location-based mobile apps that could infer and learn the stay points without delays caused by computation offloading.Policy executor component of our middleware could be improved by supporting policies that involve multiple sources of context information as well as machine learning techniques that provide a characterization of the places where an individual spends most of the time, building an expanded spatial-time model of user mobility. Such a mobility model could be exploited for improving the management of GPS receiver and produce an adaptive duty cycling that performs accurate location tracking with power efficiency. Aligned with this insight, it is important to remark the current trends that visualize the smartphone as a device capable, on its own, of performing context information discovery through adapted machine learning and pattern recognition techniques. Such perspective increases the variety of application niches and the substantial benefits for people on their daily activities.As future work, the fluctuations in the accuracy of GPS data collected by smartphones can be addressed through preprocessing techniques that smooth such inherent error in subsequent location updates, or by including data from additional context information sources, like inertial sensors and wireless communication interfaces. Similarly, the exploration of more alternatives for full on-device discovery of stay points is needed, which could be achieved by adapting more classic trajectory analysis algorithms to an event-driven perspective. As previously mentioned, the


The relationships between the fatigue crack growth rate  As the damage tolerance concept has been widely accepted and applied in the aerospace engineering, it becomes necessary and important to calculate the fatigue crack growth. As is known, s region ,2,3,4. Fum alloy , 7055-T7um alloy , the strR), the threshold stress intensity factor range R and To fit the nonlinear relationship between he model , which h’s model  can be aR.Based on the mechanism of fatigue crack growth, Elber ,12 propo McClung  proved tα is correlation parameter, and R range, which makes it superior to other analytic formulas. However, α for different materials and cannot effectively handle the materials whose stress ratio effects on the Further studies by Kujawski , Donald Kujawski  suggesteR, Many researchers have been making efforts to find an explicit mathematic formula to match the nonlinearities between the crack growth rate and the driving force parameters by employing more related parameters, such as To overcome the shortcomings of classical models in fatigue crack growth estimation, more and more interdisciplinary methods are introduced. The numerical approach and the machine learning method are the most widely used which have been demonstrated to be effective. Additionally, the combination of numerical approach and machine learning algorithms (MLA) is also an evaluable investigation direction. For example, the knowledge-based neural network (KBNN) associates with the finite element method and optimization algorithms . The numIn this paper, a fatigue crack growth calculation method is proposed based on three MLAs: Extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithm optimized back propagation network (GABP). The rest paper is organized as following: First, the basic theories of the three MLAs for nonlinear learning are introduced, and the MLA based fatigue crack growth calculation method is established. After that, the three MLAs are validated and compared by using testing data of different materials. The classical Back propagation (BP) network is one of the most widely used MLAs because of its excellent learning ability and flexible structure. Genetic algorithm (GA) is a parallel computing optimization method by simulating the natural genetic mechanism and theory of biological evolution. The weights and thresholds of the BP network should be randomly initialized whenever it is trained, which will lead to the inaccuracy and instability of the training. The GA optimized BP network (GABP) is to use GA to improve the accuracy and stability by optimizing the initial weights and thresholds.BP network is the core of multi-layer feed-forward neural networks (FFNNs). The structure of the three layers BP network is shown in GA applies the principle of reproduction and survival of the fittest to the populations whose chromosomes  are encoded by parameters to be optimized. The chromosomes with better fitness are chosen by repeatedly using genetic operations , until the best chromosome is determined.In the GABP, the initial weights and thresholds of BP network are used to encode the chromosomes of GA as shown in Radial basis function network (RBFN) is one of the MLAs using multi-dimensional spatial interpolation technique. The structure of RBFN is similar with the three-layer FFNN as shown in The RBFN can use different learning algorithms depending on the different methods for selecting the center of activation function. In this paper, the self-organizing selection center method is utilized. The hidden layer uses nonlinear strategy to tune the parameters of RBFN, while the output layer uses linear optimal strategy to adjust the linear weights. Therefore, the RBFN can be trained quicker than BP network and can handle nonlinear problem with complex mappings ,34. It hExtreme learning machine (ELM) is an emerging MLA, which is a single-hidden layer feed-forward neural network (SLFN) . The strR are selected as the inputs, and the R and The MATLAB R2014 of MathWorks is utilized to design and train the machine learning based algorithms for fatigue crack growth. To develop the well-trained MLAs used for fatigue crack growth, the following procedures should be done as shown in x-axis and y-axis are the stress intensity factor range and fatigue crack growth rate, respectively. The different markers represent different stress ratios. It can be observed that the experimental data with different stress ratios show obvious nonlinear tendencies even in the Paris region.To clearly describe the forms of the output results, the experimental data of 2024-T351 aluminum alloy  with difR are used to train the MLAs and output the The preprocessed R) on the surface can be calculated by using the Equation (3). Additionally, the tendency of the surface along a certain R is nonlinear, which is more consistent with the experimental data than the results of classical formula methods. Therefore, the MLAs have significant advantages in predicting and extrapolating the fatigue crack growth rate.As the experimental data are discrete, they cannot be used to calculate the fatigue crack growth rate. However, the continuous function is suitable for fatigue life prediction in mathematic approach. Furthermore, the continuous function can be shown as a smooth surface by interpolating and extrapolating da) in one loading cycle. The basis theory is to calculate the crack length (a) at the a0). The equation can be expressed asw is the width of the specimen. According to the above functions, the relationship between loading information and the crack increment in every cycle is established. The fatigue crack growth equation can be integrated asAfter the MLAs are established and trained, the predicted results of MLAs will be used for fatigue life prediction in this section. The fatigue life prediction is based on the crack length increment (ca) and determine whether to update the geometric factor and There are three main steps to calculate the fatigue crack growth using the MLA based method as shown in α value in A feasible theory and methodology for using the three MLAs for fatigue crack growth rate calculation has been introduced. In this section, various experimental data of different materials and characteristics are employed to validate the practical effectiveness of MLAs. The three MLAs are compared with each other to demonstrate the advantages and disadvantages. The training results of MLAs listed in this paper are the best choosing from a number of trial and error. Additionally, the predictions of classical approach ,43.The experimental data of 2024-T351 aluminum alloy  with fouTo clearly check the effectiveness and accuracy of the predicted results, −7, 9.91 × 10−8 and 1.33 × 10−8, respectively.In this part, the cross-validation is conducted to investigate the predictive capacity of the MLAs by using the same experimental data of 2024-T351 aluminum alloy . This tiTo further validate the globally optimizing characteristics of the MLAs, the experimental data of 6013-T651 aluminum alloy [The corresponding 2D graphs of 6013-T651 aluminum alloy with different stress ratios are plotted in In order to more clearly contrast the extrapolating performance of each MLA, the experimental data of 4340 steel  with nar−5, 8.89 × 10−8 and 3.12 × 10−8, respectively, which indicates that the three MLAs all can match the experimental data very well, and the ELM is the best one. The R = 0 and 0.1 cross with each other and the RBFN curves of R = 0, 0.05 and 0.1 almost converge to one in the extrapolating range, which is impossible in experiments for 4340 steel. Therefore, ELM is the only one of the three MLAs not only can excellently fit the experimental data, but also extrapolate the nonlinear tendencies very well. In other words, when dealing with small size fatigue crack growth data such as for 4340 steel, ELM is the best one, although all the three MLAs can learn and predict the nonlinearities of experimental data very well.To validate the accuracy and effectiveness of the three MLAs, the predicted curves and experimental data of 4340 steel in 2D graphs are shown in In the following part, the flexibility and applicability of the three MLAs are further validated and compared with um alloy . The preA comparison study of the three MLAs using different kinds of fatigue crack growth rate data indicates that it is feasible to apply the MLAs to fatigue crack growth calculation. The accuracy and effectiveness of MLAs compared well to those of In order to validate the MLA based fatigue life prediction method, experimental data of 7050-T7451 aluminum alloy are employed. The experimental information is listed in x-axis is the cycle number, and the y-axis is the crack length. By comparing to the experimental data, it can be seen that the predictions of MLAs are superior to those of The predicted a-N curves of 7050-T7451 aluminum alloy by MLAs and The fatigue life can be calculated once the failure criterion is determined by using the predictions. To compare the prediction accuracy of MLAs with that ofTo validate the flexibility and applicability of MLAs to different materials, ADB610 steel  is used The predicted a-N curves of ADB610 steel are shown in The prediction errors of ADB610 steel when the critical crack length is 25 mm and 32 mm are listed in Furthermore, different experimental data of D16 aluminum alloy  are alsoThe prediction errors of D16 aluminum alloy when the critical crack length is 15 mm, 18 mm and 22 mm are calculated in In this paper, a fatigue crack growth calculation method based on MLA is proposed, and three MLAs are used for comparison study. The MLA based method is validated by using the testing data of different materials. The results indicate that the MLAs can fit the nonlinearities of fatigue crack growth rate very well, and the MLA based fatigue crack growth calculation method show fairly good performance for different experimental data. Additionally, the classical R. Therefore, the optimization of the training data combination need further study to guide the design of fatigue crack growth experiments, and then improve the MLAs’ efficiency and effectiveness.The above study demonstrates that the MLA based method has many advantages in handling the nonlinearities of fatigue crack growth. However, the performance of this method highly depends on the sufficiency of the experimental data, which are expected to include all the nonlinear information among all the possible ranges of


The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system. For decades, ultrasound image has been extensively applied in the detection of different diseases because of its high safety and high efficiency –3, such There are two important aspects of CAD research which are “Detection” and “Diagnosis,” respectively . “DetectThe ultrasound CAD system also consists of “Detection” and “Diagnosis.” The ultrasound CAD system can be divided into four phases: image preprocessing, image segmentation, feature extraction, and lesion classification. In this article, we present an overview of recent developments in ultrasound CAD to support future studies. There have been many studies which summarized the research of ultrasound CAD , 12, 13.As for the traditional ultrasound CAD system, the feature selection and extraction are indispensable steps . The effThe texture is one of the most common features in the ultrasound CAD system. Texture features can reflect the character of the lesion surface. A few general utilized features are shown as follows.Laws Texture Energy (LTE). This feature utilizes the local masks to detect the texture types [re types . In geneContrast of Gray Level Values. This feature is a measure of local variations in the image. It can be defined asPd is the probability of the pixel value  lying at distance d in the image.Gray Level Cooccurrence Matrix (GLCM). GLCM reflects the distribution of cooccurring pixel grayscale values at a given offset. GLCM is a common feature in CAD system. The methods in [mx, my, Sx2, and Sy2 are defined asthods in , 33 haveLocal Binary Pattern (LBP). LBP is proposed by T. Ojala, M. Pietikäinen, and D. Harwood. It can reflect the local texture of ultrasound image. The LBP is defined in a 3 × 3 neighborhood. The center of the neighborhood is taken as the threshold. The other 8 gray values are compared to the threshold. If the value is larger than the threshold, that pixel will be marked by 1; otherwise, it will be marked by 0. In this approach, every 3 × 3 neighborhood will be transformed into an 8-bit binary number [y number . LBP posWavelet Features. This feature is derived from the wavelet transform of the ultrasound image. The wavelet transform is a generally used method in ultrasound image processing. The method in [ethod in  utilizedCompared with the texture feature, the morphologic feature is more focused on the lesion. We summarized some common morphologic features as follows. Spiculation. This feature reflects the smoothness of lesion margin. Reference [eference  proposedDepth-to-Width Ratio. Depth-to-width ratio is an active feature for the classification of many tumors, which has been widely employed by many studies [y-axis values of two points on the margin of the tumor. The width means the largest difference between the x-axis values of two points on the margin of the tumor. As for the malignant tumor, the depth-to-width ratio is usually larger than 1, and the ratio of benign tumor is usually smaller than 1.  studies , 36. TheElliptic-Normalized Circumference (ENC). ENC is the circumference ratio of the equivalent ellipse of the tumor which is defined as the ratio of the circumference of the ellipse to its diameter [diameter .  FigureElliptic-Normalized Skeleton (ENS). ENS is the number of skeleton points which are normalized by the perimeter of the equivalent ellipse. The larger the ENS is, the higher the possibility of malignancy is [nancy is . Long Axis-to-Short Axis Ratio (L : S). This feature is defined as the ratio of long axis to short axis. The long axis is the major axis of the equivalent ellipse, and the short axis is the minor axis of the ellipse [ ellipse .The model-based feature is one of the unique features of ultrasound images. It reflects the character of the backscattered echo from tissues. Scholars utilized different models to simulate the echo of backscatter. The parameters of these models are employed as tools to classify the tumors. Nakagami Model-Based Features. Nakagami model is one of the most common models of backscattered echo, which can be utilized to simulate different backscattered distributions. The parameter of Nakagami model is defined by the statistics of the backscattered echoes. The authors in [thors in  have attK-Distribution Model-Based Features. The feature based on K-distribution model is also widely used in ultrasound CAD system. Reference [K distribution to classify the breast tumor. The experiment in [K-α feature to the method without K-α feature. The result shows that the performance of method utilizing K-α feature is higher than the method without K-α feature.eference  utilizediment in  comparedThe descriptor feature is usually summarized from the experience of clinicians. As for different applications, the descriptor feature is different. For example, as for the breast tumor, most of the descriptor features come from the breast imaging reporting and data system (BI-RADS) lexicon. But as for the thyroid nodules, most of the descriptors are attributes in thyroid imaging reporting and data system (TI-RADS) lexicon. Shape . Shape is a universal descriptor feature for classification of many tumors [y tumors . The regCalcifications (Absent or Present). In general, there are more calcifications or microcalcifications in malignant tumor than in benign tumor. Posterior Shadow or Posterior Echo. The posterior shadow or posterior echo reflects the characteristic of the posterior region of the tumor, where gray value is smaller than the region of the surrounding. Echo Characteristic. This feature reflects the model of echo in the ultrasound image including hypoechoic, isoechoic, hyperechoic, and complex. The echo signal of different tissues shows different characteristic in the ultrasound image [nd image .Most of ultrasound CAD systems are designed to classify the lesion such as the breast tumor, liver fibrosis, and thyroid nodules. The classifier is one of the most important parts in the lesion classification. After the selection and extraction of features, many classifiers are adopted to classify the ultrasound images. This section introduced the major classifiers employed by the ultrasound CAD system.Linear discrimination analysis (LDA)  and logiThe Bayesian classifier is one of the most frequently used methods in the machine learning field. It can utilize the prior information of data to estimate the posterior information. The most famous Bayesian classifier is the Naïve Bayesian Classifier (NBC). NBC is based on the Bayesian theorem. It hypothesizes that the feature of samples is conditionally independent. There are only a few parameters of NBC which are required for estimation through the statistics of samples. Due to the advantage of insensitivity to data, NBC is widely applied in social information analysis and medical field. Reference  utilizedThe support vector machine (SVM) is a method in statistics and computer science to analyze data and recognize pattern. It is a supervised learning method which can be applied in both of classification and regression. The target of SVM is to build a hyperplane to divide the sample into different categories . It utilThe decision tree is an effective algorithm for classification of ultrasound images , 50. It Artificial neural network (ANN) is the machine learning model which is designed according to the human nervous system. In general, the architecture of ANN can be divided into three layers: the input layer, the hidden layer, and the output layer. The layer consists of the neuron. The number of the hidden layers and the number of the neurons in each layer are flexible. One of the most famous ANN is the back-propagation neural network (BPNN) . BPNN isAdaBoost is one of the most popularly used ensemble methods proposed and has the ability to improve the classification accuracy by integrating multiple weak classifiers. AdaBoost method generates a series of weak classifiers firstly and builds a powerful classifier through weighted majority voting of the classes predicted by weak classifiers. Reference  utilizedIn 2006, the professor of the University of Toronto, Hinton, and his student published the paper which utilized the neural network to reduce the dimensionality of data . This paThe largest change from the traditional ultrasound CAD to deep learning ultrasound CAD is that the feature employed by deep learning ultrasound CAD system is not artificial. In the traditional ultrasound CAD system, most of the features are human-crafted, such as gray features and texture features. However, with the development of deep learning, the researchers noted that the feature extracted by the deep neural network is sometimes more effective than the feature designed by the human.In this section, the newest applications of deep learning on the ultrasound CAD system are introduced. The major application field includes the breast lesion diagnosis, the liver lesion diagnosis, the fetal ultrasound standard plane detection, the thyroid nodule diagnosis, and the carotid ultrasound image classification.The breast tumor is one of the most common cancers for women. Thousands of women suffer from breast tumor all over the world. The early detection can decrease the death rate of the breast cancer significantly . The ultThe deep learning technology is widely applied in the breast ultrasound image. However, most of the studies are limited by the number of samples. Methods adopted by these studies usually utilized the deep learning technology as a tool to generate the representation of images. Only  utilizedThe liver disease has been a menace to humans for a long time. The incidence and mortality of the liver disease grow yearly. The ultrasonography is one of the most common techniques to detect the liver disease. Many researchers have attempted to employ deep learning technology to support the doctor diagnosis by liver ultrasound image. Reference  utilizedLiver fibrosis classification is also a high profile field of research. Meng et al. utilized the VGGNet and fully connected network (FCN) to differentiate the level of liver fibrosis . To addrThe deep learning is a powerful tool to detect the liver diseases from ultrasound liver images. According to the experiment result of , 24, theThe ultrasound imaging is one of the most common technologies in the prenatal examination for being economic and safe. Standard plane selection is one of the necessary phases in the ultrasound examination , 60. TheThe study of Yu et al. focuses on one type of the fetal ultrasound standard plane. Their method cannot distinguish other types of the fetal ultrasound standard plane. Chen et al. proposed a deep learning framework which can detect different types of the fetal ultrasound standard plane . Chen etBesides the fetal ultrasound standard plane, the deep learning was also applied in the detection of fetal neurosonographic diagnostic plane. The fetal neurosonographic diagnostic plane can help the clinician to estimate the growth of fetal head and detect the serious central nervous system anomalies. Reference  proposedThe fetal ultrasound standard plane detection is one of the research fields where the deep learning can be applied. Unlike the breast lesions diagnosis and liver diseases diagnosis, the collection of the fetal ultrasound standard plane samples is more convenient. There are sufficient samples which can be utilized to train the deep learning network.The thyroid nodule is a common disease upon a world scale. The ultrasound imaging is a widely employed scheme to detect the thyroid nodule. To support the doctor to diagnose the thyroid nodule, many CAD systems were proposed. With the breakthrough of deep learning, many scholars focus on the method which employs the deep learning to classify the thyroid nodule. Chi et al. employed the GoogLeNet to classify the thyroid nodule . To addrThe deep learning can improve the performance of thyroid nodule diagnosis significantly. However, the time consumption of train the deep learning network is also enormous. In , the traThe mortality of cardiovascular diseases increases yearly. The atherosclerotic plaque is the major reason of cardiovascular diseases. In the early detection of atherosclerosis, the intima-media thickness (IMT) of the carotid artery is an important indicator. IMT is the distance between the lumen-intima interface (LII) and the media-adventitia interface (MAI). The doctor usually utilized the ultrasound image to measure the IMT. To support the diagnosis of doctors, the researcher has attempted to utilize the deep learning to acquire the IMT automatically. Reference  utilizedBesides the calculation of IMT, the deep learning method also is applied to detect the composition of plaque. Reference  utilizedBesides the application mentioned above, there are some other applications of deep learning on the ultrasound CAD system. The study in  applied In this section, we summarized the performance of various techniques in the different application fields. In this study, we summarized the literature about the ultrasound CAD system. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employs the manmade feature. The major feature and major classifier adopted by the traditional ultrasound CAD system are introduced. Another category is the deep learning ultrasound CAD system which employs the deep neural network to extract features and classify them. The newest applications of deep learning on the ultrasound CAD system were summarized.K-distribution model-based features are two common model-based features. The descriptor feature is usually summarized from clinical experience. As for the different application, the descriptor feature is different.As for the traditional ultrasound CAD system, the selection of feature impacts the performance of final diagnosis. The common feature employed by traditional ultrasound CAD system can be divided into four categories: textural features, morphologic features, model-based features, and descriptor features. The textural feature is one of the earliest adopted features in the ultrasound CAD system. TEM, GLDS, GLCM, and other textual features are widely applied in the classification of liver diseases and breast lesions. The morphologic feature is a powerful feature in the traditional ultrasound CAD system. It contains the prior knowledge of clinicians. Morphologic features like spiculation and depth-to-width ratio are designed according to the experience of clinicians. These features are extracted from the ultrasound image automatically and are extensively adopted in the ultrasound CAD system. Model-based features are based on the backscattered echo of ultrasound images. Nakagami model-based features and The classifiers employed by traditional ultrasound CAD system are divided into 6 categories: linear classifier, Bayesian method, SVM, decision tree, ANN, and AdaBoost. Both of the linear classifier and Bayesian method are common classifiers in the machine learning field. These two classifiers are convenient to use. However, the performance of them is not stable on all of the data. The decision tree is also a simple algorithm, and the complexity of it is low. The SVM is a powerful classifier. It can perform well even in the small dataset. As for ANN, there is no certain rule in the design of ANN. It is flexible and widely applicable. The AdaBoost can integrate the output of weak classifiers to get a robust classification result.The largest difference between the deep learning ultrasound CAD system and traditional ultrasound CAD system is the approach of extracting features. In the traditional ultrasound CAD system, the feature is designed by the human. But in the deep learning ultrasound CAD system, the feature is extracted by deep learning network automatically. This paper introduced the newest application of deep learning on the ultrasound CAD system. The application field includes the breast lesion diagnosis, the liver lesion diagnosis, the fetal ultrasound standard plane detection, the thyroid nodule diagnosis, and the carotid ultrasound image classification.This study summarized the performance of ultrasound CAD in three fields including breast tumor classification, liver diseases, and thyroid nodule diagnosis. It can be seen that the dataset employed by these studies is different. There are huge differences in the size and the modality of the dataset employed by different methods. It is hard to fairly evaluate the performance of different methods utilizing different datasets. The construction of standard dataset for different ultrasound CAD applications is an important task in further studies.On the other hand, the collection of ultrasound data is also a problem. Deep learning methods require plenty of samples to train the network. However, the size of the dataset employed by most of the studies mentioned above is still small. The shortage of ultrasound samples is one of the obstacles in the way of applying deep learning.


In 2007, the Food and Agriculture Organization of the United Nations (FAO) initiated the Global plan of action for Farm Animal Genetic Resources (FAnGR). The main goal of this plan is to reduce further loss of genetic diversity in farm animals, so as to protect and promote the diversity of farm animal resources. An important step to reach this goal is to monitor and prioritize endangered breeds in the context of conservation programs.http://lasigsrv2.epfl.ch/genmon-ch.The GENMON WebGIS platform is able to monitor FAnGR and to evaluate the degree of endangerment of livestock breeds. The system takes into account pedigree and introgression information, the geographical concentration of animals, the cryo-conservation plan and the sustainability of breeding activities based on socio-economic data as well as present and future land use conditions. A multi-criteria decision tool supports the aggregation of the multi-thematic indices mentioned above using the MACBETH method, which is based on a weighted average using satisfaction thresholds. GENMON is a monitoring tool to reach subjective decisions made by a government agency. It relies on open source software and is available at GENMON allows users to upload pedigree-information  from a specific livestock breed and to define species and/or region-specific weighting parameters and thresholds. The program then completes a pedigree analysis and derives several indices that are used to calculate an integrated score of conservation prioritization for the breeds under investigation. The score can be visualized on a geographic map and allows a fast, intuitive and regional identification of breeds in danger. Appropriate conservation actions and breeding programs can thus be undertaken in order to promote the recovery of the genetic diversity in livestock breeds in need. The use of the platform is illustrated by means of an example based on three local livestock breeds from different species in Switzerland. Agricultural biodiversity is the basis of the functioning and the productivity of agricultural systems and is thus essential, for example to satisfy human nutritional needs. Nowadays, agriculture is facing increasing stress due to structural changes and modern farming methods aimed at improving productivity. In the livestock sector, locally adapted breeds have been gradually substituted with a limited number of highly specialized transboundary breeds (such as. the Holstein Friesian cattle selected for milk production)  requirinIn order to counteract the current trend of erosion and underutilization of animal genetic resources, the Food and Agriculture Organization of the United Nations (FAO) initiated a global strategy for the management of Farm Animal Genetic Resources (FAnGR) in 2007 . This stOne of the major challenges is the definition of meaningful criteria to identify endangered breeds. FAO created a scale of endangerment based on the number of breeding females and males ; this apIn accordance with the FAO Global Plan, the top strategic priority is given to the characterization of animal genetic resources (AnGR), to the monitoring of trends and risks to these resources, and to the establishment of breed endangerment Early Warning Systems (EWS) . In ordeAnimal genetic resources have to be monitored and conserved at local, regional and global levels  and thusHere we propose the application of a GIS-based multi-criteria analysis for the integration of multi-disciplinary data in order to monitor animal genetic resources at different scales. Geographic Information Systems (GIS) offer an appropriate basis in a monitoring perspective as it integrates different categories of information  on different geographical scales  . FurtherWhether combined with the use of GIS or not, multi-criteria decision analysis (MCDA) has often been applied to support decisions involving environmental issues and biodiversity conservation ,25–27. HIn parallel, when dealing with multi-criteria analysis, we are often confronted to the problem of non-commensurability, which occurs in situations where criteria are assessed onto different and incomparable scales of measure . This diTo cope with the challenge of the identification of endangered breeds, we propose an easy-to-use WebGIS platform (GENMON), designed to facilitate decision-making which should favor sustainable use and conservation of livestock breeds via the integration of five important categories of information: pedigree analysis, introgression, geographical concentration, cryo-conserved material and agriculture sustainability . Investigated breeds are then ranked in order to identify the most endangered ones using weighting of the various criteria. The GENMON application has been designed in the Swiss context and uses data available in this country; however, the system can easily be adapted to the data available in other countries.The usefulness of the GENMON approach is demonstrated here through its application to three local livestock breeds, the Swiss Original Braunvieh cattle (OBV), the Valais Blacknose sheep (VBN) and the Franches-Montagnes horse (FM). The OBV is from central Switzerland, not ranked among endangered breeds but under supervision because of its international interest due to valuable genetic heritage . The VBNThe process implemented in GENMON is based on multiple stages and its overview is represented in In this section we list and describe the different input data (see http://www.post.ch/). With this file, the animals to be geo-referenced according to the unique identifier (ZIP-code) of the mail delivery area where they are bred. The shapefile contains 4,191 polygons covering the whole country. In parallel, a shapefile with 2,564 municipalities (2013) is used [www.statistics.admin.ch/).The GENMON application has been developed to integrate different categories of information in order to monitor animal genetic resources. All categories have a geographical component and the GIS-based multi-criteria analysis platform developed allows the different data sources to be linked and compared according to geographic coordinates . Thus geographical units are key to this platform. Here we use a shapefile containing the geographic coordinates of Swiss ZIP-code areas corresponding to the situation in June 2013  as defin is used  to geo-rThe data used to characterize investigated animals are summarized in From The ZIP-code (input I) is used for two purposes. On the one hand it makes it possible to link animals with socio-economic variables at their respective locations, and on the other hand it enables the determination of the geographical concentration of breeds . This parameter is computed with the help of spatial SQL (structured query language). Given the lack of a precise position for each individual, the centroid of the ZIP-code polygons containing the animals is used as an approximation.The introgression rate of each animal corresponds to the fraction of animals from other breeds in the pedigree. This individual rate is used to calculate the average introgression rate of the breed over the last generation interval. GENMON requires the introgression rate of each animal to be computed before being uploaded onto the database.http://doi.org/10.5281/zenodo.220887The Swiss Original Braunvieh data were provided by the Swiss Brown Cattle Breeders' Federation  and consAdditionally, the breeding associations are also asked to provide general information about the breeding activities listed in The information on the cryo-conservation (input J in The current and past number of farms (input K and L) are used to compute the trend of the number of farms, while the current and past cultural value (input M and N) will give a cultural value score  and all four inputs are used in the BAS index. Cultural value include criteria such as antiquity, role of the agricultural system, farming techniques, role in landscape, gastronomy, folklore and artistic expression Data used for the socio-economic and environmental characterization of breeding locations are summarized in http://www.bfs.admin.ch/bfs/portal/en/index.html).Unless specified in the “Remark” column, these variables were obtained from Swiss Statistics   were alsNote that the Local Agriculture Sustainability (LAS) Index is calculated for all municipalities (including those without breeding activities). A description of how the information at the municipality level is linked to information at the breed level is given in the section Multi-criteria aggregation.The data input described above shows various geometry types. Therefore, a succinct description of the method used to match the different spatial units is proposed in In this section we describe the criteria included in GENMON in order to quantify aspects that are important to evaluate and monitor the conservation status of the breeds under study. They belong to five categories: 1) pedigree-related issues and genetic diversity, 2) introgression, 3) geographical concentration 4) cryo-conservation plan and 5) agriculture sustainability. The selection of criteria has been discussed with a group of 12 experts, scientists and professionals whose activities are related to livestock breeding and management, within a workshop organized for this purpose. The size of the group is justified by the need to favor discussion and emergence of ideas between experts. The description of the discussion course is made available in the supporting information .F) is a measure of the relatedness of ancestors [F and Ne) are calculated and taken into consideration as criteria in GENMON. Given that the inbreeding coefficient is computed at the individual level, it is necessary to perform an average of this coefficient. In order to alleviate problems arising from missing or incomplete pedigree data in certain years, here we compute the average inbreeding coefficient over the last Generation Interval . Thencestors . In consffspring ), as an As regards the effective population size (Ne), several methods have been proposed and output different values with the same data . This isWe also included the pedigree completeness as a criterion to maximize, in order to counterbalance the fact that breeds with incomplete pedigree will artificially achieve a low level of inbreeding. While it is true that such breeds are not necessarily endangered, GENMON will lower their final score to emphasize potential problems concealed by their incomplete pedigree. This criterion is computed as the average pedigree completeness at the sixth generation (output by poprep) over the last generation interval. It is necessary to take deep pedigree completeness because the first generations are usually almost 100% complete, which does not discriminate between complete versus incomplete pedigree.In parallel to the PopRep analysis, the trend of the number of females and males over the last five years is also computed, giving an insight into the evolution of the breeding practices.The introgression rate is entered by the user for each animal. It corresponds to the percentage of foreign blood (based on the fraction of ancestors in the pedigree belonging to other breeds) per individual. Like the inbreeding coefficient, the average computation takes place over the last generation interval.To quantify the geographical concentration of breeds, Alderson  proposedThe presence of a cryo-conservation plan is also taken into account via a score bounded between 0 and 1. If such plan exists and follows the FAO guidelines for the given specie , the scoet al. [et al. [et al. [To calculate the agricultural sustainability for each breed, we take two components into account: statistics on regions where the animals are reared  and general breed information. The first component, i.e. local agricultural sustainability index , is assessed by an approach inspired by Bertaglia et al.  who quan [et al. ) with co [et al. ) and to  [et al. ). The unRegions with a too negative demographic balance  are often marginal regions where cattle breeding activities can be difficult to pursue . NegativAs regards the criterion used to translate employment trend in agriculture, the difference of the percentage of people working in agriculture among years (here between 2010 and 2012) informs about current employment dynamics in this field of activity. Finally, as a last criterion, the agricultural land loss projection from Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) for 2050 is used . This isThe second component at the breed level assesses breeding practices including the evolution in time of the number of farms in which animals are reared. This criterion adds another relevant dimension to the criterion considering the trend of the number of animals . Indeed, if a large number of animals are still alive but are reared in a sole farm, the breed can be considered as endangered, since the future of the breed practically depends on the decisions of a single person. Additionally, the cultural value of the breed and its evolution is also considered, as by definition a breed being culturally significant will be better sustained by local people, so as to prevent its extinction .In the following section, we explain how these two components  are aggregated and how they relate to other criteria.j . This value is then aggregated (according to the MACBETH method) with the criteria at the breed level . This aggregated parameter is then named “Breed Agriculture Sustainability” (BAS) Index. This score gives an insight into the sustainability of breeding conditions for a specific breed.In this section we describe the technology used to develop the GENMON application while we will argue the reasons supporting these choices in the discussion section. All technologies presented here are open-source.The main part of the interface is built in Hypertext Markup Language (HTML) and Hypertext Preprocessor (PHP) language. The upload of a file  is made through an HTML-form. The file is then stored in a database management system (DBMS). Here we use PostgreSQL  with itsFor the visualization part, the interface is built on OpenLayers , a javasIn this section, we give an overview of the outputs produced by GENMON, illustrated by means of three Swiss breeds: the Valais Blacknose sheep (VBN), the Franches-Montagnes horse (FM) and the Swiss Original Braunvieh cattle (OBV).The main output of GENMON is a summary table enabling the comparison between breeds according to the different criteria mentioned above, which are then aggregated in a global index see . Each liOnce the main problems are identified, more detailed information can be obtained on the breeds of interest. Here we show the details for the breed with the lowest global index, namely the VBN; it is of interest to visualize the geographical distribution of inbreeding coefficients  and its The VBN is very concentrated in a small portion of the Swiss territory  is shown in The Swiss territory shows different regional sustainability trends. Low sustainability values are found mainly in the Plateau region located between Lausanne, Berne and Zurich .The best conditions for breeding activities are found primarily in lowland/mid-altitude areas located in the region between the Plateau and Jura . These mGENMON offers a ranking evaluation of the level of endangerments based on a straightforward application. This service has not been implemented anywhere else and might serve as a good basis in order to initiate, support and supervise prioritization and conservation programs as required by the FAO protocol . Being sThe use of georeferenced animal data as proposed in GENMON is also unique in FAnGR. Indeed, the English system of livestock endangerment scale only uses geography to assess geographic concentration of breeds. The integration of different data type such as socio-economic and environmental factors is made possible using georeferenced data that is often ignored in livestock conservation. The idea of assessing sustainability of breeding condition has already been proposed , but hasBoth in the English system and in GENMON, the estimation of the geographic concentration is a rough approximation. It is mainly used to assess the ability of diseases to spread between flocks, but does not take the barriers or paths of the environment into account. A better approach could be inspired by the assessment of the geographic range and the area of occupancy in wildlife conservation (see for example ).GENMON also offers flexibility, since the weights and thresholds can be adjusted, which enables an adequate modelling of the situation depending on the species and the country. The selection of variables, weights and threshold parameters used to build the LAS and BAS sustainability indices is part of a participatory process involving experts, in order to select the proper social, demographic and economic factors affecting the development of specific livestock breeds. Care must be taken when deciding the weights and thresholds. Indeed these parameters will have a considerable influence on the final output. Here we undoubtedly face a heuristic problem with unknown properties, so that we have no way of assessing how good the final score is. As a result, the panel of experts must be representative and diverse enough to represent different backgrounds, breeding associations and professional activities related to the livestock sector. Indeed its role is to select a robust set of parameters translating the policy the government agency wants to apply. A key role will be played by specialists of the surveyed breeds to fine-tune these parameters. In the case we present here, the selection of parameters and the tuning of weights was carried out by a panel of experts working for the Swiss Federal Office for Agriculture (FOAG), and involved in the sector of animal production responsible for the monitoring of livestock breeds.When confronted to GENMON, breeders reacted positively and actively gave their opinions to improve the application. They expressed their satisfaction in being able to step back and discover in more detail the context of breed monitoring that considers various criteria, to evaluate the status of their breed when compared to other and to diagnose which components of their breed could be improved.GENMON has been designed for the specific case of Switzerland and relies on the data availability of this country. Nonetheless, the methodology could be used in other places, with inevitable modifications to adapt to the accessible data. For example, it is not mandatory to use ZIP codes, but one could use other administrative divisions. In addition, DNA instead of pedigree could be used to assess the level of inbreeding. The weights and thresholds should be discussed in each country individually, depending on the specific environmental conditions, breeding practices, policy implemented and data available.Finally, GENMON quickly assesses the conservation status of breeds and to identify and prioritize vulnerable ones. Moreover, a rapid identification of factors affecting the conservation of breeds is possible through the detailed results , it is of interest to perform the computation on the server-side, which frees the user’s computer for other tasks.With regards to the software used in GENMON, the DBMS PostgreSQL was chosen since it is one of the most efficient open source DBMS (notably due to its capacity to handle georeferenced data efficiently ) and becGENMON was developed to satisfy requirements of the “Global Plan of Action for Farm Animal Genetic Resources” launched by FAO, which still needs to be set up in many countries. This application has been developed as a useful tool for FAnGR monitoring that could assist many countries in this task, provided they have sufficient data (including pedigree and socio-economic variables).It is an easy-to-use WebGIS application relying on open source software solutions and provides a multi-criteria approach for monitoring endangered breeds based on subjective thresholds of a government agency. By means of geographic coordinates, the application integrates different types of criteria, including pedigree data, genetic introgression, socio-economic and environmental aspects and geographical concentration of the breeds under study to evaluate the local context in which they are reared. GENMON computes a global sustainability index for each breed, making it possible to compare the endangerment level of several species and/or breeds, while enabling the identification of the most important problems and their geographical location for breeds separately, on the basis of sub-indices. Based on these outputs, a detailed examination of the conservation status of breeds can be carried out, which might serve as a firm basis for proposing prioritization policies. An important contribution of GENMON is to provide decision-makers with a clear identification of breeds, municipalities and corresponding breeders that should be supported with special policies in order to maintain a lively and sustainable breeding sector.et al. [The GENMON application will be expanded with new features. A relevant example is the potential future use of genetic data following the methodology described by vanRaden et al. , so as thttps://github.com/SolangeD/GENMON.The GENMON application is functional and can be accessed using the following link: lasigsrv2.epfl.ch/genmon-ch. A sample file is available for users interested in testing the upload of a file. The code is available on GitHub: S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.


Background: Cardiac resynchronization therapy is an effective device therapy for heart failure patients with conduction block. However, a problem with this invasive technique is the nearly 30% of non-responders. A number of studies have reported a functional line of block of cardiac excitation propagation in responders. However, this can only be detected using non-contact endocardial mapping. Further, although the line of block is considered a sign of responders to therapy, the mechanism remains unclear.Methods: Herein, we created two patient-specific heart models with conduction block and simulated the propagation of excitation based on a cellmodel of electrophysiology. In one model with a relatively narrow QRS width (176 ms), we modeled the Purkinje network using a thin endocardial layer with rapid conduction. To reproduce a wider QRS complex (200 ms) in the second model, we eliminated the Purkinje network, and we simulated the endocardial mapping by solving the inverse problem according to the actual mapping system.Results: We successfully observed the line of block using non-contact mapping in the model without the rapid propagation of excitation through the Purkinje network, although the excitation in the wall propagated smoothly. This model of slow conduction also reproduced the characteristic properties of the line of block, including dense isochronal lines and fractionated local electrocardiograms. Further, simulation of ventricular pacing from the lateral wall shifted the location of the line of block. By contrast, in the model with the Purkinje network, propagation of excitation in the endocardial map faithfully followed the actual propagation in the wall, without showing the line of block. Finally, switching the mode of propagation between the two models completely reversed these findings.Conclusions: Our simulation data suggest that the absence of rapid propagation of excitation through the Purkinje network is the major cause of the functional line of block recorded by non-contact endocardial mapping. The line of block can be used to identify responders as these patients loose rapid propagation through the Purkinje network. Although the therapeutic effects of cardiac resynchronization therapy (CRT) for patients with heart failure with conduction block have been confirmed by number of clinical trials, the existence of non-responders remains a major health and economic problem  and blood inside the ventricular cavity containing blood (ΩB). From this we obtained the membrane potential (∅V), and the potential in the extracellular space (∅E) in ΩH and the potential in ΩB (∅B)  were sampled at the corresponding position in the blood domain, and were expressed by the following matrix equation.We simulated the unipolar non-contact mapping of the LV endocardium using a 64-electrode array, as used clinically  Figure , left. Pwhere N is the number of nodes in the heart domain. It is important to note that these potentials recorded by the electrode array reflected the electrical activity across the whole transmural thickness, not the mirror image of the endocardial potential. Finally, endocardial potentials were obtained as the inverse solution Figure .L is the Laplacian operator. The second term on the right-hand side is a regularization term for stabilizing the minimization.where All program code was written in FORTRAN in our laboratory. Simulations were performed using a 2.6-GHz HP Blade System c7000 . With the total number of degrees of freedom set to approximately 60 million, it took 50 min to compute a single cardiac cycle using 127 cores. Numerical data were visualized using commercial software .The activation sequence in the entire ventricular wall (upper row) and the results of endocardial non-contact mapping (lower row) in the patient with a relatively narrow QRS complex (176 ms Figure To reproduce the ECG with a wide QRS complex (200 ms; Figure Auricchio et al. reported several findings supporting the functional nature of the line of block (Auricchio et al., The activation sequence in the ventricle can be modified by multiple factors, including the electrophysiological property of myocytes, anisotropic conduction properties of tissue, and gross morphology of the ventricle. Our multi-scale heart simulator reproduces these properties, some of which can be personalized. To exclude the possibility that these confounding factors may contribute to the genesis of the line of block, we switched the mode of conduction between the two models shown in Figures As unipolar non-contact mapping system can measure the global pattern of the left ventricular activation process, it has been previously used to assess the pathophysiology of heart failure patients with electromechanical abnormalities (Auricchio et al., We previously developed a multi-scale heart simulator in which the function and structure of the heart are reproduced at the microscopic to macroscopic levels (Sugiura et al., What then, is the underlying mechanisms of the line of block only observed by non-contact mapping, and why does it appears in a specific position? As shown in Figure When the same heart was paced from the LV free wall, apparently symmetrical patterns were observed in both the activation sequence and extracellular field potential Figure . HoweverIf our proposed mechanism is correct, the line of block may be simply an artifact. Nevertheless, the line of block provides an indication of slow conduction occurring outside of the endocardial Purkinje network, consistent with a prior clinical report (Fung et al., Although the heart models used in this study were based on clinical data, endocardial mapping was not performed in any of the patients. Thus, direct comparisons with endocardial mapping data should be performed in future studies to confirm our findings. The Purkinje network was modeled as an endocardial layer with a rapid propagation velocity and the distributions of the earliest activation sites, instead of explicitly modeling it. Although the good agreement with clinical ECG data confirmed in our previous study may rationalize this approach (Okada et al., JO, TH, and SS designed the study analyzed data and wrote the paper. JO and TW wrote the programs. MN, MW, and YK performed data processing. TK, HY, YY, SM, and RN collected data and were involved in study design.JO, TW, TH, and SS have received grant support from Fujitsu Ltd. MW, YK, and MN was employed by company Fujitsu Ltd. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.