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The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%.

In this paper, we propose an intelligent item searching framework for smart handheld devices through mobile cloud computing based on the Internet of Things (IoT) architecture. The proposed framework enables mobile users to drag a website/software icon over interested/unknown items for learning details, comparing prices, purchasing goods, etc. Our framework explores the IoT architecture, mobile cloud computing, and gesture-based interacting interfaces to alleviate the complexity and difficulty of device manipulation encountered by mobile users. In addition, we integrate our framework with an item caching mechanism that can reduce the total searching time as much as possible for popular items. Furthermore, we implement an Android-based item searching system to verify the feasibility and superiority of the proposed framework. Experimental results show that our implemented system outperforms existing methods and can significantly reduce the total consumption time of mobile item searching.

In today's society, the number of people rearing pets has increased and their awareness of the need to protect pets' health has increased. Pet posture behaviour analysis and prediction are providing assistance in the medical treatment of pets. Hence, the demand for pet skeleton drawing applications has risen dramatically. Our proposed system predicts pet posture using smart camera networks powered by the artificial intelligence of things. This system is built on a platform using a Raspberry Pi embedded system. The system can determine from an image whether there is a detection target and generate a contour mask based on Mask R-CNN Technology. According to object detection, poses and key parts can be identified to predict and draw pet skeletons. Simultaneously, the behavioural action of a pet can be determined according to continuous skeleton data and then the system will actively inform the owner to perform subsequent processing.

In this paper, we propose a crowdsourced children monitoring and finding (CCMF) framework to detect holding-up behaviors and find missing children using wearable devices and surrounding smartphones based on Internet of Things (IoT) technologies. In the monitoring mode, the CCMF framework can prevent young children from taking away by strangers/people with bad intentions. In the finding mode, the CCMF framework can cooperatively find missing children equipped with wearable devices consisting of mobile iBeacon and 3-axis accelerometer modules through crowdsourced sensing networks formed by smartphone users with outdoor GPS and indoor IoT localization. According to our review of relevant research, CCMF is the first children monitoring and finding solution that can detect holding-up postures of a target child and provide the guiding path to a lost child through crowdsourced sensing networks. An iOS-based prototype with Arduino wearable devices and mobile/static iBeacon nodes is implemented to verify the feasibility and superiority of our framework. Experimental results show that CCMF outperforms existing methods and can significantly increase recognition success rates and efficiently reduce false alarm rates of holding up detection.

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

Attendance monitoring is a basic system that is required for the operation of a company. However, traditional paper-based clock-in devices and radio frequency identification methods cannot avoid consigning others to the punch card problem. This paper therefore presents an attendance monitoring system based on Artificial Intelligence (AI) facial recognition technology. The proposed system uses a deep learning Tensorflow framework to build a face recognition model that can detect images in real time and upload this information to a cloud server. Our face recognition system can be used to store ‘punch card’ records as part of an attendance monitoring system.

Public Bike Sharing Systems (PBSSs) have become popular as mass transportation tools. Along with the growth of the population using them, disadvantages of PBSS are emerging, for example, when there are no bikes available for users to rent or no vacant parking lots to which bikes can be returned. To make up for this disadvantage, users are allowed to apply for fee discounts and obtain real-time information in advance from the Internet. The two main problems are full stations or lack of available bikes. But in hot spots or at specific hot times, even when users know the current station information, it is not possible to guarantee whether there will be parking lots or available bikes in the next second. To prevent the decline of the reference value of real-time bike information caused by large numbers of users, we collect the number of bikes near stations and real-time information in bike stations to form return anxiety information. When a station is nearly full or there are many bikes near the station, the return anxiety information value will increase. When users enquire about returning bikes to stations, they can refer to the return anxiety information. The higher the return anxiety information value, the lower the user’s tendency to return the bike. In the implementation, our study develops an android application to collect basic data and uses a Petri Net to simulate the PBSS scenario. Various rates of user trust are used to verify the reference value of return anxiety information.

This research paper proposes sentiment analysis of pets using deep learning technologies in the artificial intelligence of things system. Mask R-CNN is used to detect image objects and generate contour mask maps, pose analysis algorithms are used to obtain object posture information, and at the same time object sound signals are converted into spectrograms as features, using deep learning image recognition technology to obtain object emotion information. By using the fusion of object posture and emotional characteristics as the basis for pet emotion identification and analysis, the detected specific pet behaviour states will be actively notified to the owner for processing. Compared with traditional speech recognition, which uses mel-frequency cepstral coefficients for feature extraction, coupled with a Gaussian mixture model-hidden Markov model for voice recognition, the experimental method of this research paper effectively improves the accuracy by 70%. Prior work on the implementation of smart pet surveillance systems has used the pet's tail and mouth as features, and has combined these with sound features to analyse the pet's emotions. This research paper proposes a new method of sentiment analysis in pets, and our method is compared with previous related work. Experimental results show that our approach increases the accuracy rate by 70%.

This paper proposes a smart vending machine system combined with deep learning and machine learning technologies. The proposed system is combined with temperature and camera sensor to obtain consumer without individual information and upload this information to cloud server. The system uses face recognition with deep learning to obtain the gender information. It uses the k nearest neighbors (KNN) machine learning method to group based on temperature, time, price and gender information. The proposed system relies on grouping information to dynamically adjust price in real time.

The object recognition system for machine learning has become widely known recently, and each algorithm of the object recognition system has its own advantages and disadvantages. The disadvantages are that the speed of identification is slow and the accuracy of identification is poor. The purpose of this paper is to identify birds through the YOLO and Custom Vision algorithms. When the user uploads bird photos to the two systems through a smart mobile device, the system will identify the objects according to the algorithm. It judges which bird it may be and, after a final comparison, the result with a higher percentage of accuracy is transmitted back to the user, thereby improving the accuracy of the identification system.

With the rapid development of the wireless network, IEEE 802.11 wireless network access technology has become a widely used technology, in which the contention channel of the distributed coordination function (DCF) is the default mechanism. Through the contention window, waiting time required before sending data can be determined. Therefore, the adjustment of the competition window is an important key that affects network transmission performance. At present, most related researches anchored on wireless local area networks (WLAN) to design channel competition backoff algorithms. These methods all consider the transmission collision problem caused by the increase of the number of nodes to improve the performance, which it is uncertain whether these methods can be applied on Wireless Home Area Network (WHAN) that contains fewer nodes. Through experimentation with different numbers of nodes and different contention window sizes, this study found a collision rate range with superior efficiency, and thus proposed a backoff algorithm for Home Area Network (BA-HAN). The size of the competition window is adjusted based on the size of the contention window to achieve the performance improvement and reduction of the collision rate. According to the experimental result, the BA-HAN algorithm is superior to related channel competition mechanisms (EIED, LILD, ELBA, and CRBA).

Along with the flourishing development of Internet of Things, vehicles which assist move goods are developed as well, for example, automatic guided vehicle applied in manufacturing plants. Vehicles vary with pattern of goods to be moved. If it moves along magnetic tapes, it would lose its flexibility in moving directions. To make vehicles more dynamic and convenient, this study designs and implements automatic following technology of vehicles. Through relative position between vehicles and objects to be followed positioned by satellite and laser radar installed on vehicles which can detect relative distance, vehicles are able to automatically follow objects to be followed.

In this paper, we propose a solution for implementing a real-time image-streaming system for vehicle networks. Our proposed system organizes each vehicle as a local Internet of Things network. In each network, nodes are connected to cameras to capture images of the surrounding environment of the road. Then, the captured data are published to a streaming server via a 4G internet connection. In order to adapt to the change in bandwidth channel and vehicle speed, we propose algorithms to control the quality of image capture and the number of images taken. Our algorithms are based on a prediction method of the available bandwidth of the channel to control the rate of sending data from each local node in subsequent transmissions. Also in this paper, we present the relationships between channel bandwidth, vehicle velocity, and image quality. The experimental results and our simulation show that the proposed system significantly reduces end-to-end delay when the number of nodes increases. This offers a capability for high-quality image-streaming applications over vehicle networks. The system is also successfully implemented in a real-world application, and the results show that the collected images are of high quality.

Construction of multifunctional nanoparticles (NPs) with near-infrared (NIR) plasmonic responses is considered a versatile and multifaceted platform for several biomedical applications. Herein, a double layer of Au/Ag alloy on the surface of truncated octahedral iron oxide NPs (IONPs) was prepared and the distance between the layers was controlled to exhibit broad and strong NIR absorption. The rattle-shaped IONP@shell-in-shell nanostructure showed light-response to the NIR biological window from 650 to 1300 nm for photothermal therapy (PTT) and magnetic guidance for hyperthermia and magnetic resonance imaging (MRI) diagnosis. Exposing the aqueous solution of IONP@shell-in-shell to a 1064 nm diode laser, its heat conversion efficiency was ∼28.3%. The in vitro cell viability at a gold concentration of 100 ppm was ∼85%, and decreased to ∼16% when the cells were treated with the NIR irradiation and magnetic attraction. T2-weighted MRI images showed a clear accumulation of IONP@shell-in-shell at the tumor site with magnetic attraction. In vivo luminescence tumor images explained that the IONP@shell-in-shell could reduce the U87MG-luc2 cancer cell proliferation in mice with the NIR irradiation and magnetic attraction. These results indicate the IONP@shell-in-shell as a promising nanomedicine for PTT, magnetic targeting, and magnetic resonance imaging (MRI).

In this paper, we propose a smart campus care and guiding framework with deep learning-based face recognition, called DeepGuiding, for students through Internet of Things technologies. The DeepGuiding framework can construct the dedicated video trajectory of a campus student, where the recorded video for each student can be automatically classified to achieve efficient footprint review as necessary. In addition, DeepGuiding can provide time-efficient indoor and outdoor guiding in a campus to quickly reach places, meet friends, and find students. To the best of our knowledge, DeepGuiding is the first campus care and guiding system which provides the following features: 1) it achieves the seamless outdoor and indoor navigation between buildings in a campus; 2) it keeps additional construction cost low by utilizing existing surveillance cameras in a campus; and 3) it reduces the total searching time for finding a specific event/target in a campus by alleviating time-consuming labor overhead to review a huge amount of video data. An Android-based prototype using iBeacon indoor localization and global positioning system outdoor positioning with surveillance cameras is implemented to verify the feasibility and superiority of our DeepGuiding framework. The Experimental results show that DeepGuiding outperforms existing face recognition methods and can achieve high recognition accuracy for students not close to surveillance cameras.

Photodynamic therapy (PDT) provides a potential therapeutic approach for killing malignant cell/solid tumors, but currently approved photosensitizers (PSs) are generally excited by visible light, limiting the penetration depth in tissues. It is necessary to develop a near-infrared (NIR) responsive photodynamic platform, providing maximum tissue penetration. Here, we present a gold nanopeanut platform exhibiting dual functions of NIR PDT and two-photon luminescence imaging. The nanopeanut with a size less than 100 nm exhibits two distinct NIR surface plasmon absorption bands at approximately 1110 and 1300 nm. To perform PDT, we conjugated commercial toluidine blue O (TBO) PS on the surface of the nanopeanuts. With spectral overlap, the 1230-nm femtosecond Cr: forsterite laser can excite the surface plasmons of nanopeanuts, transfer energy to TBO, and generate singlet oxygen to kill cells. Moreover, the plasmon resonance-enhanced two-photon luminescence of nanopeanuts can be used to map their delivery in vivo. These results demonstrate that the PS-conjugated gold nanopeanut is an effective theranostic system for NIR PDT.

Along with the blooming development and rapid change of Internet of Things technology, implementing its related application in everyday life can improve living quality. When people go out, they usually need to use public toilets. The hygiene would affect experience of people using toilets. To enhance user experience using toilets and reduce waiting time, this study proposes to use Internet of Things to monitor the using status of public toilets for the reference of people using these toilets. Combining Ar-duino with ultrasonic sensor and button module, it would report the using status of toilets to cloud management system via wireless communication so that users can refer to the provided information including using status and hygiene state of toilets to decide whether they want to go to these toilets. Moreover, the Raspberry Pi system is used to process the information returned by each public toilet, and to enable cleaning personnel to understand the environmental sanitation of the public toilet in order to decide whether to go to clean it.

Cloud technology uses MapReduce to make computers quickly process a huge amount of data with plentiful resources in clouds, but requires that applications should be developed or reprogrammed to process data in a batch mode of MapReduce. By our solution addressed in this paper, cloud technology no longer poses application writers because our ASPECT solution can: (1) free application writers from burdens of developing or reprogramming an application in the MapReduce programming model; (2) keep the existing application processing data as usual without switching to the batch mode of processing data in MapReduce, and (3) speed up the execution of the application in clouds by reusing or sharing run-time data with other instances of the application.

A large enhancement in the performance of solar cells, arising from plasmonic nanomaterials with a broad band absorption spectrum, is proposed. Gold nanorod-in-shell (Au NR-in-shell) nanomaterials can absorb photons covering the UV-visible-NIR region in contrast to the two distinct narrow peaks for bare Au NRs. The broad localized surface plasmon resonance (LSPR) induced light absorption enhancement as well as series resistance, carrier lifetime and device mobility for devices containing Au NR-in-shells are superior than those for Au NR devices. To illustrate our working principle, we implemented Au NR-in-shells in polymer solar cells, which resulted in high efficiency. This led to the realization of polythieno[3,4-b]-thiophene/benzodithiophene:[6,6]-phenyl C71 butyric acid methyl ester (PTB7:PC71BM) polymer solar cells (PSCs) with a power-conversion-efficiency (PCE) of 9.01%, which is more than twice the enhancement for that of Au NRs incorporated cells and sets the highest record reported to date for PTB7 based inverted devices adopting the plasmonic light trapping strategy. Our approach shown here is a key step for the further development of high performance optoelectronic devices.

In this paper, we propose a cyber-physical signage interacting framework for the interaction between digital signage and mobile users using smart handheld devices, such as smartphones and tablets. The proposed framework can provide diverse multimedia/feedback services to mobile users interacting with digital signage through face detection, classification, and recognition techniques based on mobile cloud computing. Mobile users only need to click and drag the interested service over the face of advertising celebrities/endorsers displayed on digital signage and can obtain supplementary multimedia information or feedback related comments. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage through their handheld devices. Our framework reveals an innovative human–machine interface for signage interacting between digital signage and mobile users. In addition, we integrate our framework with a face cache mechanism that can make the interaction delay as small as possible for popular signage. Furthermore, an Android-based signage interacting system is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms the existing methods and can significantly reduce the average consumption time of obtaining interested multimedia contents from digital signage.

In this article, we proposed an infrastructureless cooperative lane positioning (ICLP) framework using vehicle-to-vehicle (V2V) communications. The ICLP framework applies vehicular sensor networks (VSNs) to localize lane positions of vehicles on roads. ICLP allows the vehicles equipped with image sensors to detect the current located lane without utilizing global positioning system (GPS) locations and roadside infrastructures. Through image sensors, ICLP can keep recognizing the lane position for a vehicle. In addition, ICLP can request lane position information from other vehicles in the same lane as necessary, based on V2V communications. ICLP makes innovative applications of intelligent transportation systems relying on accurate positioning possible, such as lane-level cooperative collision avoidance, dynamic traffic control, and intelligent personal navigation. An Android-based prototype is implemented to verify the feasibility and superiority of our framework. The experimental results show that ICLP can achieve high positioning success ratios and provide accurate lane positions for vehicles.

Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet&rsquo, s bark (growl and cry) to identify the pet&rsquo, s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identification module subsystem to respond to changes of pet hair and ages in real time. After successfully identifying images of featured parts each time, our system captures images of the identified featured parts and stores them as effective samples for subsequent training and improving the identification ability of the system. When the identification result is transmitted to the owner each time, the owner can get the current mood and state of the pet in real time. According to the experimental results, our system can use a faster R-CNN model to improve 27.47%, 68.17% and 26.23% accuracy of traditional image identification in the mood of happy, angry and sad respectively.

Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.

This paper implements an automatic bird data capture system which uploads data captured from social network websites to the server and stores them in the database. When using the Facebook API to capture data of interest, the system needs to filter the captured data to avoid false data storage. This paper takes building an automatic system that includes reinforcing the training database of the bird community as a top priority.

In this study, we explored solutions to improve the quality of real-time image transmission in vehicle networks. We deployed multiple cameras in each vehicle to collect scene data on the road. Then, the collected data was transmitted to a streaming server through a gateway and using a 4G internet connection. We use the MQTT protocol to implement our system since this is a protocol designed specifically for Internet of Things technologies and has several advantages in terms of image streaming. In addition, in order to adapt to the change in bandwidth channel due to the movement of vehicles, we propose an algorithm to control the quality of image capture which is based on threshold levels. This algorithm is based on the current throughput of local network nodes, as compared with threshold values, to control the rate of sending data from each local node in subsequent transmissions. The results of simulation show that our proposed network significantly reduces both end-to-end delay and the delay in arrival of messages in the network when the number of nodes increases. The experimental results showed that the collected images are of high quality and allow accurate analysis of the surrounding environment of the moving vehicles.

Trashcans are the basic unit in the garbage-collecting procedure. This paper proposes an intelligent trashcan with flexible features, which is associated with a smart device application. The trashcan is able to detect RFID tags to decide whether to open its lid or not. The trashcan is also capable of moving around when it receives instructions from the remote application, and it has the ability to move according to a planned path. Different settings of the smart trashcan can be configured via the remote application. The proposed system has been implemented, and it is shown that the proposal is applicable.

This paper introduces a novel algorithm that increases the efficiency of the current cloud-based smart-parking system and develops a network architecture based on the Internet-of-Things technology. This paper proposed a system that helps users automatically find a free parking space at the least cost based on new performance metrics to calculate the user parking cost by considering the distance and the total number of free places in each car park. This cost will be used to offer a solution of finding an available parking space upon a request by the user and a solution of suggesting a new car park if the current car park is full. The simulation results show that the algorithm helps improve the probability of successful parking and minimizes the user waiting time. We also successfully implemented the proposed system in the real world.

With advances in technology and the economy, people are more inclined to buy cars. However, as a result, people forfeit their lives and property through accidents due to traffic congestion and traffic chaos. To ensure safe driving, anti-collision warning applications have been widely discussed. Most vehicular safety applications use vehicular positioning and vehicular communication technologies to avoid car accidents. With Vehicular Ad Hoc Networks, it is common to use the global positioning system (GPS) as the vehicular positioning tool owing to its comprehensive development in vehicular navigation. However, there are factors that affect GPS positioning accuracy (e.g., multipath effect, atmospheric effect, etc.). To improve GPS vehicular positioning accuracy, we propose a cooperative positioning algorithm that can improve localization accuracy. We also demonstrate that our algorithm can work well in a real city environment.

Taxi plays a crucial role in the transportation system because of the characteristic that can be hailed conveniently. Most of the taxi drivers obtain passengers by hunting on the road or waiting in a fixed taxi queuing point; however these methods have poor performance, high vacancy rate, and several critical problems such as air pollution and foul up traffic. This study proposed a taxi carrying management system by using location based services and zone queuing techniques on Internet of things. The proposed system allows drivers to both hunt on the road and wait in a queuing zone. A queuing table is used in the control center and neighbor tables are used in RSUs for zone queuing establishment. Joining and leaving mechanisms are developed for zone queuing management. To enhance service efficiency and quality, we present a scheme to prevent the ping-pong effect which is based on the location based services, a hunting rate calculation scheme, and a path planning service for taxi drivers according to the history carrying record. PRISM is used to simulate the proposed system, and the results indicated that our scheme outperforms the waiting and hunting models in terms of number of customers, vacancy rate, and profit.

The Forward Collision Warning System (FCWS) has become an important research topic in recent years. Many FCWS products are used in the real world. However, these FCWS products cannot provide emergency braking events warning from the vehicle in front. Hence, this paper proposes a Dedicated Short Range Communications (DSRC) system combined with radar detection for an FCWS mechanism. The mechanism proposed by this paper will actively probe the emergency brakes of the vehicle in front and broadcast warning information with the Global Positioning System (GPS) position. Moreover, this mechanism uses warning information based on the GPS position to calculate the time of collision in order to alert the driver.

Video streaming over wireless network poses a great challenge because the high packet error rate usually decreases the quality of video streaming. Forward error correction (FEC) mechanism is generally used to protect the video quality. However, the recovery performance of the FEC mechanism decreases when burst packet loss is larger than the added FEC redundant packets. In this paper, the forward-looking forward error correction (FL-FEC) mechanism is proposed to recover lost packets for video streaming over wireless networks. The FL-FEC mechanism recovers not only the lost packet from its FEC block but also the previous FEC block from the recovered packet, repeating the recovery procedure until recovering the first FEC block. If the play-out buffer at the receiver is large, the FL-FEC mechanism can execute a chain of recovery procedures to ultimately recover all lost packets without any negative impact on application performance. The FL-FEC mechanism selects non-continuous source packets in previous FEC blocks to generate FEC redundancy with the FEC block. Hence, the FL-FEC mechanism can significantly disperse burst packet loss into different FEC blocks. The FL-FEC mechanism uses an analytical model to decide the number of FEC redundant packets in order to obtain the minimum recovery overhead. The FL-FEC mechanism is tested to show the benefits of high recovery performance and low recovery overhead in improving the peak signal-to-noise ratio and the decodable frame rate of video streaming over wireless networks.

Video streaming over vehicular networks is an attractive feature to many applications, such as emergency video transmission and inter-vehicle video transmission. Vehicles accessing road-side units have a few seconds to download information and experience a high packet loss rate. Hence, this paper proposes Cooperative Error Control (CEC) mechanism combining Cognitive Technology (CT) for video streaming over wireless vehicular networks. CEC mechanism combining CT uses a cooperative error recover scheme to recover lost packets not only from a road-side unit which uses the primary channel but also from the other vehicles using a free channel. Hence, CEC mechanism with CT can enhance error recovery performance and quality of video streaming over vehicular networks. Simulation results show the error recover performance of the CEC mechanism combining CT performs better than the other related mechanisms.

Video streaming is a popular application on next generation networks (NGNs). However, video streaming over NGNs has many challenges due to the high bit error rates of these networks. Forward error correction (FEC) is often applied to improve the quality of video streaming. However, continuous lost packets decrease the recovery performance of FEC protection in NGNs. To disperse continuous lost packets to different FEC blocks, we propose a concurrent multipath transmission that combines FEC with path interleaving. Our proposed control scheme adaptively adjusts the FEC block length and concurrently sends data interleaved over multiple paths. Experimental results with our approach show improved packet loss and signal to noise ratio performance.

Wireless transmission suffers from poor wireless channel conditions, such as high bit error rate and error bursts resulting from channel fading and radio interference. The MAC-level Forward Error Correction (MFEC) mechanism is proposed to solve these problems. The MFEC mechanism cannot only adaptively adjust Forward Error Correction (FEC) redundancy, depending on the network condition, but can also retransmit whole packets even if the error packet includes several bit errors. In this paper, an Adaptive MAC-level FEC (AMFEC) mechanism is proposed to improve wireless transmission quality. The AMFEC mechanism relies on the effective packet loss rate model to dynamically adjust MFEC redundancy in order to minimize error recovery overhead. The AMFEC mechanism will retransmit the error of sub-packets and increase FEC redundancy to reduce transmission redundancy. The AMFEC mechanism not only enhances the quality of wireless transmission but also reduces the overhead of retransmission. The experimental results show the AMFEC mechanism is better than conventional MFEC mechanisms.

Most thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in ultrasound images. Numerous textural feature extraction methods are used to characterize these patterns to reduce the misdiagnosis rate. Thyroid nodules can be classified using the corresponding textural features. In this paper, six support vector machines (SVMs) are adopted to select significant textural features and to classify the nodular lesions of a thyroid. Experiment results show that the proposed method can correctly and efficiently classify thyroid nodules. A comparison with existing methods shows that the feature-selection capability of the proposed method is similar to that of the sequential-floating-forward-selection (SFFS) method, while the execution time is about 3-37 times faster. In addition, the proposed criterion function achieves higher accuracy than those of the F-score, T-test, entropy, and Bhattacharyya distance methods.

Mobile communication and handheld devices are currently extremely popular, and provide people with convenient and instant platforms for social networking. However, existing social networking services cannot offer efficient human-machine interfaces or intuitive user experiences. Mobile users must manually input account information and find targets from search results when attempting to add someone to their friend list on social networking sites, such as Facebook and Twitter. Additionally, mobile users may not be able to identify correct targets because some usernames are identical. Typos may occur during the input process due to unfamiliar identifiers, further increasing the total operation time. To encourage social initiation between mobile users, we design an instant social networking framework, called SocialYou, to minimize the startup time based on mobile cloud computing. SocialYou proposes an efficient architecture and innovative human-machine interfaces to alleviate the complexity and difficulty for mobile users using handheld devices. In particular, we implement an Android-based prototype to verify the feasibility and superiority of SocialYou. The experimental results show that SocialYou outperforms the existing methods and saves substantial amounts of operation time for mobile social networking.

Video streaming over wireless networks is a challenging task due to its high error rate. Forward error correction (FEC) is a popular mechanism to recover lost packets for video streaming. Conventional FEC mechanisms use a whole redundant packet to recover the error source packet, when the packet error occurs with only a few bit errors inside. In this paper, we propose an Adaptive packet and block length FEC (APB-FEC) control mechanism. In order to overcome the high bit error rate, a small packet length reduces the packet error rate and a large FEC block length will enhance the recovery performance. Our proposed APB-FEC can obtain better recovery performance than conventional FEC mechanisms. Hence, APB-FEC can also reduce retransmission overhead. Using extensive emulations, we validate the efficiency of APB-FEC mechanism for video streaming over wireless networks.

Bone scintigraphy helps to detect sacroiliitis before the radiographic changes. Nuclear medicine physicians interpret lesion on sacroiliac joint by the aid of sacroiliac joint-to-sacrum uptake ratios (sacroiliac joint index, SII). Usually, the SII is measured by manually drawing regions of interest (ROI) over the sacroiliac joints and sacrum, which is a tedious, time consuming, and highly operator dependent procedure. In this approach, we developed an automatic SII measurement program based on fuzzy sets histogram thresholding, anatomy-based image segmentation method and pelvis reference points located by morphological operations. To validate the program, the results of automatic SII measurement from 33 patients were compared with the results of manual SII measurement. For each patient, six SIIs representing the upper, middle, and low portions of the right and left SIIs were obtained. Totally, 198 SIIs were included for linear regression, bias, and precision analyses. The coefficient of determination (R(2)) between our approach and 2 medical experts' manual measurements are 0.923 and 0.917 respectively. The predictive performance (bias, precision) shows a good agreement result. From the result of this study, we expect that this automatic computer-aided approach may be applied to help nuclear medicine physicians in sacroiliitis interpretation.

Traditional Forward Error Correction (FEC) mechanisms can be divided into Packet level FEC (PFEC) mechanisms and Byte level FEC (BFEC) mechanisms. The PFEC mechanism of recovering from errors in a source packet requires an entire FEC redundant packet even though the error involves a few bit errors. The recovery capability of the BFEC mechanism is only half of the FEC redundancy. Accordingly, an adaptive Sub-Packet FEC (SPFEC) mechanism is proposed in this paper to improve the quality of video streaming data over wireless networks, simultaneously enhancing the recovery performance and reducing the end-to-end delay jitter. The SPFEC mechanism divides a packet into n sub-packets by means of the concept of a virtual packet. The SPFEC mechanism uses a checksum in each sub-packet to identify the position of the error sub-packet. Simulation experiments show the adaptive SPFEC mechanism achieves high recovery performance and low end-to-end delay jitter. The SPFEC mechanism outperforms traditional FEC mechanism in terms of packet loss rate and video Peak Signal-to-Noise Ratio (PSNR). SPFEC offers an alternative for improved efficiency video streaming that will be of interest to the designers of the next generation environments.

MapReduce is a programming model used to develop applications on cloud computing. However, MapReduce strongly relies on the runtime system to handle issues of task managements in order to get a better performance. While most research works focus on scheduling Map tasks to improve performances, MapReduce is identified by this paper to have the potential for performance improvements through the Speed-based Load Balancer (SLB) for scheduling Reduce tasks. According to observations on experiments of Inverted Index, Radix Sort and Word Count, MapReduce can use SLB to outperform the native scheduler used by Hadoop in the runtime system.

Safety applications in vehicular networks have been popular research topics in recent years, such as forward collision warning, emergency braking warning and intersection collision warning systems. The basic safety message broadcast from each car transmits the position, car speed and car heading information. Neighbouring cars receiving this information can decide if there is any danger within the next second. However, the safety application message has positioning accuracy and time-critical problems. Accurate positioning of the car provides a more effective warning to the driver. Moreover, the reliable and efficient delivery of safety information needs to be improved when the penetration rate increases. Hence, this paper proposes a cooperative emergency braking warning system to solve the above problems. This is a system that integrates a camera sensor to construct the state of neighbourhood cars in order to solve the first problem. It then proposes to reduce the repeat and derivable information between the broadcast messages in order to solve the second problem. The proposed system has been implemented to provide a safer driving environment.

On-line analytical processing (OLAP) provides analysis of multi-dimensional data stored in a database and achieves great success in many applications such as sales, marketing, financial data analysis. OLAP operation is a dominant part of data analysis especially when addressing a large amount of data. With the emergence of the MapReduce paradigm and cloud technology, OLAP operation can be processed on big data that resides in scalable, distributed storage. However, current MapReduce implementations of OLAP operation processing have a major performance drawback caused by improper processing procedure. This is crucial when dimension or dependent attributes are large, which is a common case for most data warehouses hold nowadays. To solve this issue, this paper proposes a methodology to accelerate the performance of OLAP operation processing on big data. We have conducted the experiments on the basic algebra of OLAP operation with different data sizes to demonstrate the effectiveness of our system.

Cloud computing is the emerging and attractive technology and provides users with various services in a pay-as-you-go manner. Cloud computing nowadays does not limit resources of the services in a cloud to the computers that are far away from users and connected to each other in a data center with high speed networks at the same geographic location. Cloud computing may present a cloud to users by connecting resources at multiple geographic locations. By connecting resources at multiple geographic locations to organize a cloud, cloud computing may meet problems of communication interception, congestion, and interruption. Cloud computing should have a way to supply extra processing on demand for certain links between computers separated geographically. Since a MapReduce cloud is the key to the success of the large-scale computation, cloud computing can use the Smart MapReduce Cloud (SMRC) proposed in this paper to apply extra processing to intermediate data on demand while intermediate data is delivered among computers in the MapReduce cloud. In experiments, cloud computing is tested with several popular MapReduce applications to observe performances of data encryption and compression via XOR and GZIP functions in SMRC.

Big data refers to large datasets which are huge, complex and hard to be stored and analysed by traditional data processing tools. Linked data is one of the approaches to deal with big data which are stored and processed in TripleStore. For huge dataset, TripleStore requires more scalable techniques. 'MapReduce' programming model is the most representative of cloud technology. There are several approaches using MapReduce to serve SPARQL query but still exhibit unacceptable performance in complex queries. In this paper, we propose a map-phase-multi-join algorithm for processing SPARQL queries. Using multi-join, job initialisation time is reduced by avoiding iterative of MapReduce jobs. Furthermore, map-phase join can save bandwidth by preventing join-less data to be transferred among computing nodes. We also design a storage schema and a join-order rule which enhance the performance of our system. The evaluation results show that our system outperforms traditional join approaches in most queries.

Safety applications have been popular research topics in recent years, such as forward collision warning, emergency electronic brake lights and intersection collision warning. The basic safety message is used to broadcast from each car that transmits the position, car speed and car heading information. Neighbourhood cars receiving this information can decide on the danger or not in the next second. However, the safety application message has a time-critical problem. The reliable and efficient delivery of safety information needs to improve when the penetration rate increases. Many research works focus on two angles: one is the control of transmission power, and the other is the control of transmission frequency. This paper proposes a novel concept to reduce the broadcast message method in vehicular networks. The proposed method will not change the transmission power and transmission frequency, but reduces the repeat and derivable information between the broadcast messages.

Due to the rise of cloud computing, many cloud services have been developed. Google proposed a programming model called MapReduce for processing large amounts of data. After YAHOO! proposed Hadoop, many companies and enterprises have started using this programming model to establish their own cluster for handling large amounts of data. Computing resources within a cluster are often not all be used. Therefore, many researches about cluster-scaling are presented. These studies were proposed to reduce the size of the cluster to achieve power saving or to add more computing nodes in order to obtain better performance. However, there is always a trade-off between performance and power saving. Therefore, taking both performance and energy saving into account, we propose a simple policy which can effectively identify how many computing nodes can be inactivated from a cluster without affecting the execution time. We evaluate our policy in many cases to prove that it is well-performed in different configurations and achieves performance and power saving both.

Photothermal cancer therapy using near-infrared (NIR) laser radiation is an emerging treatment. In the NIR region, two biological transparency windows are located in 650-950 nm (first NIR window) and 1000-1350 nm (second NIR window) with optimal tissue transmission obtained from low scattering and energy absorption, thus providing maximum radiation penetration through tissue and minimizing autofluorescence. To date, intensive effort has resulted in the generation of various methods that can be used to shift the absorbance of nanomaterials to the 650-950 nm NIR regions for studying photoinduced therapy. However, NIR light absorbers smaller than 100 nm in the second NIR region have been scant. We report that a Au nanorod (NR) can be designed with a rod-in-shell (rattle-like) structure smaller than 100 nm that is tailored to be responsive to the first and second NIR windows, in which we can perform hyperthermia-based therapy. In vitro performance clearly displays high efficacy in the NIR photothermal destruction of cancer cells, showing large cell-damaged area beyond the laser-irradiated area. This marked phenomenon has made the rod-in-shell structure a promising hyperthermia agent for the in vivo photothermal ablation of solid tumors when activated using a continuous-wave 808 m (first NIR window) or a 1064 nm (second NIR window) diode laser. We tailored the UV-vis-NIR spectrum of the rod-in-shell structure by changing the gap distance between the Au NR core and the AuAg nanoshell, to evaluate the therapeutic effect of using a 1064 nm diode laser. Regarding the first NIR window with the use of an 808 nm diode laser, rod-in-shell particles exhibit a more effective anticancer efficacy in the laser ablation of solid tumors compared to Au NRs.

In the highway environment, many applications require localization. It is common to use the Global Positioning System (GPS) to find the current position, but GPS is easily influenced by the external environment and has many internal errors. Dead Reckoning (DR) is a common method for correcting the GPS position. However, the related applications do not have a notice system that can show the number of and distances to neighboring vehicles. In this paper, we design a map application on the Android operating system for vehicular networks. In this map application, the road map would be shown on a monitor, and the neighboring vehicles could also be drawn on this map. Users can easily understand the road conditions. Moreover, the application can exchange traffic information with other vehicles, and the map can also show the vehicles which are out of range. Furthermore, the application can show the time to collision between two vehicles. The experimental results will show the adjustment of position corrected by DR on Google Map, and the radar map on the Android operating system.

Summary As more and more well-known companies, such as Twitter, Yahoo, and Alibaba, start to focus on real-time big data applications, how to build a platform for processing real-time data becomes an important issue. Among all the real-time processing systems, Apache Storm is the most well-known and representative open-source, distributed, real-time computation system. In Storm, the computation is implemented by a topology such as a graph where nodes are operators and edges represent the data flows between operators. In big data processing and analysis systems, scalability is an important issue. Storm provides rebalance mechanism for its scalability property, which can adjust the parallelism of a running topology. However, there are some drawbacks in rebalance command, such as resource usage restriction and topology execution suspension. In this paper, we propose a topology-based scaling mechanism for Apache Storm. When a topology is overloaded, it scales by adjusting the number of the cloned topologies or replaced by another new topology with more tasks. When scaling by topology-based mechanism, it eliminates resource usage restriction and execution suspension in the topology, and the procedure is automatically launched. The experimental results show that our topology-based scaling mechanism can improve the scaling performance of Storm. Copyright © 2016 John Wiley & Sons, Ltd.

We investigated salivary function using quantitative scintigraphy and sought to identify functional correlations between parotid dose and quality of life (QoL) for head and neck cancer (HNC) patients receiving intensity-modulated radiotherapy (IMRT).Between August, 2007 and June, 2008, 31 patients treated IMRT for HNC were enrolled in this prospective study. Salivary excretion function (SEF) was previously measured by salivary scintigraphy at annual intervals for 2 years after IMRT. A dose-volume histogram of each parotid gland was calculated, and the normal tissue complication probability (NTCP) was used to determine the tolerance dose. QoL was longitudinally assessed by the EORTC QLQ-C30 and HN35 questionnaires prior to RT, and at one, three, 12 and 24 months after RT.A significant correlation was found between the reduction of SEF and the mean parotid dose measured at 1 year (correlation coefficient, R(2)=0.651) and 2 years (R(2)=0.310) after IMRT (p0.001). The TD(50) of the parotid gland at 1 year after IMRT is 43.6 Gy, comparable to results from western countries. We further found that contralateral parotid and submandibular gland function preservation was correlated with reduced sticky saliva and a better QoL compared to the functional preservation of both parotid glands, as determined by the EORTC QLQ-HN35 questionnaire.A significant correlation was found between the reduction of SEF and the mean parotid dose. Preservation of contralateral parotid and submandibular gland function predicts a better QoL compared to preservation of the function of both parotid glands.