Previous quality-of-service (QoS) routing/multicasting protocols in mobile ad hoc networks determined bandwidth-satisfied routes for QoS applications. However, they suffer from two bandwidth-violation problems, namely, the hidden route problem (HRP) and the hidden multicast route problem (HMRP). HRP may arise when a new flow is permitted and only the bandwidth consumption of the hosts in the neighborhood of the route is computed. Similarly, HMRP may arise when multiple flows are permitted concurrently. Not considering the bandwidth consumption of two-hop neighbors is the reason that the two problems are introduced. In this paper, a novel algorithm that can avoid the two problems is proposed to construct bandwidth-satisfied multicast trees for QoS applications. Furthermore, it also aims at minimizing the number of forwarders so as to reduce bandwidth and power consumption. Simulation results show that the proposed algorithm can improve the network throughput.
Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.
This paper describes the development of a signal and image processing system for the hyperspectral biomedical imager (HBI). The HBI forms hyperspectral images of human tissue with high spatial and spectral resolution. The final goal of the research project is to develop a fully functional HBI image formation/processing system based on Texas Instrument's TMS320C6X floating-point digital signal processing (DSP) chip. This would permit fast and efficient data processing, enhancing the utility of the HBI.
The problem of correlated data gathering in wireless sensor networks is studied in this paper. For the sake of efficiency, tree transmission structures are often used for data gathering. Previously, the problem of minimizing the total communication cost with a single-tree transmission structure was shown to be NP-hard. However, when the explicit communication approach is used, the total communication cost can be further reduced, provided a double-tree transmission structure is used and inverse links are allowed. This motivates us to devise a double-tree routing scheme in which two trees are used for data transmission, one carrying raw data and the other carrying encoded data. We show that with the double-tree routing scheme, the problem of minimizing the total communication cost remains NP-hard. A distributed algorithm for solving it is suggested. We show that under the simple correlation model, the algorithm has an approximation ratio of two. Extensive simulations are conducted to verify the effectiveness of the double-tree routing scheme.
In this study, we focus on the electronic bus (E-Bus) system developed for Taiwan's mountainous areas.The rural area in Taiwan with its towering mountains has become a sightseeing hot spot.The radio propagation of a (cloud-based) 4G/LTE network may be severely limited by the mountainous geography, leading to unacceptable connection quality.We have built a smart (fog-based) local wireless E-Bus system using a feature-enhanced long-range widearea network (LoRaWAN, also known as GloRa in Taiwan).Since there is no electricity on the mountain roads, the bus stops of this E-Bus system are operated by a dynamo.Furthermore, we present a virtual direction and position algorithm for multiple E-Bus lines.A novel sensing method using the dynamos is proposed to estimate the feasibility of the E-Bus optimization problem.Recently, Boshita et al. (1) developed an IoT-based bus location system using a long-range wide-area network (LoRaWAN).A prototype system was evaluated in Nisshin City, Japan.Its bus stops consisted of a LoRa device, a microcomputer, and e-paper as the display medium, powered by a solar panel.With the usage of LoRaWAN (920 MHz), they compressed the time and position information acquired from the GPS to enable it to be effectively transmitted.They confirmed that an IoT-based bus location system can be realized at a lower cost than a bus location system using 4G/LTE.An intelligent transportation system on rural roads requires both a sustainable and extendable communication infrastructure to deliver smart and safe services.The rapid growth of low-power wide-area network (LPWAN) technologies has provided a remedy for rural communication.Specifically, when radio propagation of the (cloud-based) 4G/LTE network is severely limited by path loss, shadowing, or a Doppler shift due to the mountainous geography, the connection quality becomes unacceptable.To build a robust rural E-Bus system, a feature-enhanced LoRaWAN (also known as GloRa in Taiwan) will be used to report bus locations in the rural area.Note that rural areas have a limited power supply due to terrain constraints. (2) In addition to the global energy crisis and the issue of global warming, smart rural area development should support self-generation and zero-emission technology as part of the transition to a green economy.As well as having no electricity, roads in Taiwan's mountainous areas may also lack sunlight due to trees and foggy weather.Instead of a solar panel, we use a dynamo to activate bus stops for radio communication. (3)A dynamo charger is a hand-cranked power generator for power generation when cycling indoors or outdoors.Since there is no electricity on mountain roads in Taiwan, the bus stops of the rural E-Bus system are powered by generators to receive information on the arrival of buses in a bitwise data transmission format.The rural E-Bus bus stops use energy-saving mini LED lights to display bus locations.Additionally, passengers waiting at the bus stop may crank the generator to obtain bus arrival information.Owing to unacceptable delays in 4G/LTE services in mountainous terrain, seamless internet connectivity does not exist in rural mountainous regions of Taiwan.Using GloRa for reliable communication and storing the transportation data at rural data centers, the rural E-Bus system can run the bus operation without the internet.The rural E-Bus system creates a low-latency network connection and enables a quick response time. (4)Innovative IoT applications are expected to include not only transportation but also those in the home, health, agriculture, retail, and forestry.Driven by emerging new mobile technologies (such as 5G, fog computing, etc.), new machine-type communication will move the world towards a responsive smart service entity. (5)Responsive machine-type communications are moving IoT scenarios towards a superconnected world.Future rural networks need to be location-aware, energy-efficient, costeffective, sustainable, and extensible for IoT applications.The proposed framework of the rural E-Bus system can be further enhanced in the future with appropriate devices to create a sustainable smart village.The United Nations' Economic and Social Commission for Asia and the Pacific (ESCAP) (6) is calling for enhanced rural transport connectivity to regional and international transport networks in Asia and the Pacific; Australia's New South Wales state (7) is conducting surveys of
Today, Google Maps has become one of the most important tools for exploring maps. In addition, Google Street View further provides images for improving users' cognition of new smart cities. Many route recommendation services are available online, but most provide recommended routes on a 2D planar map. This can create a gap between a user's perception and reality. It is not easy for users to understand an unfamiliar place through linked line-segments on a 2D map only. Many users find themselves lost while they attempt to virtually walkthrough an area in Google Street View via a mouse. Moreover, these street images are static and do not depict current weather conditions. Therefore, in this paper, we propose a novel approach for synthesizing cartoon-like animation for street navigation (CANavi). To achieve our goal, we utilized the abundant resources from Google Maps and Google Street View. The results show that our street-navigation animations create interesting and vivid sceneries that can successfully improve a user's cognition and experience when exploring new smart cities.
Recent advances of AI applications in various of industries have led to remarkable performance and efficiency. Driven by the great success of datasets and experience sharing, people are exploring more precious datasets with diverse features and longer time range. The promising reasoning information of well-curated student grade datasets is expected to assist young students to find the best of themselves and then improve their learning outcome and study experience. Through data and experience sharing, young students can have a better understanding of their learning condition and possible learning outcomes. Existing course selection systems in Taiwan which offer limited basic enrolling functions fail to provide performance prediction and course arrangement guidance based on their own learning condition. Students now selecting courses with unawareness of their expecting performance. A personalized guide for students on course selection is crucial for how they structure professional knowledge and arrange study schedule. In this paper, we first analyzed what factors can be used on defining learning curve, and discovered the difference between students with different properties and background. Second, we developed a recommendation system based on great amount of grade datasets of past students, and the system can give students suggestions on how to assign their credits based on their own learning curve and students that had similar learning curve. The result of our research demonstrates the feasibility of a new approach on applying big data and AI technology on learning analysis and course selection.
In stroke rehabilitation systems and applications, reliability, accuracy, and occlusion should be taken into consideration. Unfortunately, most existing approaches focus primarily on the first two issues. However, during the stroke rehabilitation process, occlusion leads to incorrect judgements even for medical staff. In order to tackle these three important issues simultaneously, we propose a heterogeneous sensor fusion framework composed of an RGB-D camera and a wearable device to consider occlusion and provide robust joint locations for rehabilitation. To fuse multiple sensor measurements when compensating for occlusion, we apply heterogeneous sensor simultaneous localization, tracking, and modeling to estimate the locations of joints and sensors and construct an upper extremity model for occlusion situation. Virtual measurements based on this model are used to estimate the joint's location during occlusion, and a virtual relative orientation technique is applied to relax system limitations regarding orientation. Experimental results using the proposed approach with synthetic data and data collected from ten subjects show a 4.6 cm error on average and about 15 cm error on average during occlusion. This constitutes a more robust approach for stroke patients which takes into account these three important issues.
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