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Video telephone systems are convenient media for people to communicate remotely in life. If the video can be analyzed in the process of video chat to extract the relevant physiological indicators of the communicators such as emotions or stress, it can help the communicators to make proper conversations. In this paper, we propose (1) Similar Brightness Area Selection Method (2) and Difference-driven Weight-Setting Method to measure the heart rate in the non-contact image mode that can be adapted to the video signal in the videophone. After the image is compressed, it is transmitted through the network. After being transmitted to the video receiver for image decompression, we also propose that the luminance (Y) instead of Green channel (G) of the image should be utilized based on Photoplethysmography (PPG) to infer the heart rate. In our experiment, we have adjusted the weight range of our Difference-driven Weight-Setting Method for four commonly used video compression codecs including H.264, H.265, VP8, and VP9 together with five different compression bit rates and the results have shown to be effective in improving the accuracy of the heart rate measurement. The root mean square error (RMSE) of the heart rate measurement has also been calculated as evaluation metrics in the experiment. The mean and standard deviation of the overall RMSE is 2.14±0.52 bpm (beats per minute) that have shown the feasibility of our proposed methods to effectively and accurately measure the heart rate in the video telephony system.

Heart rate variability (HRV) is an objective measure of emotional regulation. This study aimed to estimate the accuracy with which an artificial neural network (ANN) algorithm could classify emotions using HRV data that were obtained using wristband heart rate monitors. Four emotions were evoked during gameplay: pleasure, happiness, fear, and anger. Seven normalized HRV features (i.e., 3 time-domain features, 3 frequency-domain features, and heart rate), which yielded 29,727 segments during gameplay, were collected and analyzed first by statistics and then classified by the trained ANN model. General linear model adjusted for individual differences in HRV showed that all HRV features significantly differed across emotions, despite disparities in their magnitudes and associations. When compared to neutral status (i.e., no emotion evoked), the mean of R-R interval was significantly higher for pleasure and fear but lower for happiness and anger. In addition, pleasure evidenced the HRV features that suggested a superior parasympathetic to sympathetic activation. Happiness was associated with a prominent sympathetic activation. These statistical findings suggest that HRV features significantly differ across emotions evoked by gameplay. When further utilizing ANN-based emotion classification, the accuracy rates for prediction were above 75.0% across the 4 emotions with accuracy rates for classification of paired emotions ranging from 82.0% to 93.4%. For classifying emotion in an individual person, the trained ANN model utilizing HRV features yielded a high accuracy rate in our study. ANN is a time-efficient and accurate means to classify emotions using HRV data obtained from wristband heart rate monitors. Thus, this integrated platform can help monitor and quantify human emotions and physiological biometrics.

This paper proposes to infer emotion of game users with a platform that integrates heart rate wristbands, smartphones and emotion inference mechanisms on backend computers. The inferred user emotions can subsequently be utilized by game developers to promptly understand the user reaction to the game content in order to improve the game quality. In this paper, the subjects play four kinds of games that can stimulate emotions such as Pleasure, Happy, Fear and Angry. The subjects' heart rate signals and facial expressions are recorded when they play the games. The facial expressions are then used to determine the emotions of the subjects as the ground truths. The heart rate signals with determined emotions are subsequently extracted and used to calculate the normalized features. Seven normalized features and determined emotions are then used as inputs and outputs to Artificial Neural Network (ANN) for machine learning. The trained ANN model can subsequently be used to classify the emotions of the subjects when they play games. Experimental results show that the average recognition accuracy of the players' emotions of Pleasure, Happy, Fear, Angry and Neutral are 84.41%, 79.08%, 86.81%, 88.64% and 75.30% respectively. We have also selected two, three and four out of the five emotions for recognition as comparison, and the results show that inclusion of more emotions will lead to less recognition accuracy. In the end, data of all the subjects have been merged into a big data set for training and testing. The overall recognition rate falls maybe because of the uniqueness of each subject. It is thus recommended that personal data of a subject should be used as training data for better recognition results.

In this study, we used a microcontroller to develop the blood pressure evaluating devices of dual-PPG and recording two groups of photoplethysmography simultaneously. Cardiac systolic and diastolic change the blood volume and also affect the amount of light reflection. Thus, light sensor will receive the pulse wave changes. After using FIR filtering noise from the signal, the device detects PPG peak and then two groups of PPG can calculate pulse transit time. Multiple regression analysis models for evaluating blood pressure was established by using various parameters such as height, weight, age, and BMI. With this model, we can investigate the feasibility of dual-PPG. The result shows the systolic blood pressure, average deviation and standard deviation is 6.74 ± 8.03mmHg, which closes to the AAMI standard, 5mmHg ± 8mmHg. It means estimating blood pressure using physiological parameters is feasible.

In this study, we have implemented an auscultation sphygmomanometer that can continuously estimate blood pressure via simultaneous measurement of phonocardiogram (PCG) and photoplethysmography (PPG) signals. In PCG, in the process of systole and diastole, the heart will produce "lub" "dub" sounds, called the first (S1) and the second heart sound (S2) respectively. In PPG, according to systole and diastole, the blood volume in the vessel will change, and thus affect the amount of light reflected from the vessel and received in the photosensitive element to detect the heart pulse wave. After using Finite Impulse Response (FIR) and Discrete Wavelet Transform (DWT) to filter out noise from the signal, the device can detect three features, namely S1, S2 and PPG peak, which can be used to calculate Vascular Transit Time (VTT), Ejection Time (ET), and Heart Rate (HR). Multiple regression analysis model for estimating blood pressure is established by using various cardiac parameters such as VTT, ET, HR, and individual's physiological parameters such as gender, age, height, and weight. With this model, auscultation blood pressure measurement can be designed. The results of this study show that for the range of normal blood pressure, the error in systolic blood pressure is 6.67 ± 8.47 mmHg, which is very close to the AAMI standard, 5 ± 8 mmHg. This shows the feasibility of estimating blood pressure using various cardiac and physiological parameters via PCG and PPG measurement.

Hypertension related diseases have highly threatened people's health around the globe. What is even worse is that not only the number of hypertension patients has grown dramatically, their ages of onset have also become younger. Blood pressure monitoring is important for hypertension patients. However the traditional blood pressure measurement is inconvenient which results in less people with habits to measure blood pressure. To improve people's incentive of measuring blood pressure, in this study we have developed a wristband prototype for measuring blood pressure. With our wristband prototype, it is easy for users to establish habits of blood pressure measurement and tracking, and subsequently detect hypertension in the early stage. Our wristband prototypes of wearable sphygmomanometer have been utilized to measure 120 subjects' blood pressures. This wristband prototype can simultaneously measure signals of Photoplethysmogram (PPG) and Electrocardiography (ECG), and then compute the interval of the peak time of these two signals as pulse transit time (PTT). Since PTT has been verified to be highly correlated to blood pressure in numerous researches, PTT is suitable for blood pressure estimation. However, since blood vessels in human body are very complicated and they are highly correlated with blood pressure, we have taken some parameters such as the heart rate (HR) and the subject's physiological differences (e.g., height, weight, age and gender) into account and evaluated the parameters together using multivariate regression analysis. We can subsequently derive testing results from this non-invasive blood pressure measurement method. The results show that in the normal blood pressure range, the error in systolic blood pressure (SBP) is 6.9mmHg ± 8.6mmHg. Although our results have not yet reached the AAMI standard, it has been verified that it is effective to integrate PTT and physiological parameters to estimate blood pressure.

In this paper, we develop a ball-shaped Internet of Things (IoT) device as a smart rehabilitation tool and implement a computer-based game on our device as a rehabilitation tool to maintain user's basic life ability and working memory (WM). In order to validate the workability of the computer-based game ported on the smart rehabilitation tool, this paper focuses on the digital signal processing during game playing by users with our device, as well as on validating the feasibility of the game. The main target population of our rehabilitation tools are users who need rehabilitation like muscle or WM training. In the future, our rehabilitation tools may be applied to more patients or rehabilitation institutes after the completed integration of the hardware structure and software program.

The purpose of this paper is to show the influence of congestive heart failure (CHF) on heart by using different entropies to apply on the group of patients with CHF and normal group. Three different entropies are used: approximate entropy (ApEn), multiscale entropy (MSE), and base-scale entropy (BsEn). We use these three entropies to measure the complexity of the heart rate variability (HRV) and also use analysis of variance (ANOVA) to analyze the result of entropies to discuss the feasibility of recognizing CHF patients by utilizing entropies. With the analysis results of different entropies, the influence of CHF on heart has also been clearly demonstrated. The results on the approximate entropy show that the normal young group has a higher approximate entropy value while the CHF group has a lower value. This can be explained as a healthy, strong heart that can change its heart rate freely to adapt the change of the environment or the needs of the human body, therefore the HRV will be more complex. From the ANOVA results of approximate entropy, it can be observed that the F value is larger than 1, but is still small. In other words, the approximate entropy can be used to distinguish the three groups, the effect is, however, not good. It is hard to recognize a CHF patient by using approximate entropy.

This study employs relational analysis and the GreyART network to identify and study the characteristics of electroencephalogram signals of sleep apnea syndrome (SAS). Seventeen raw electroencephalogram data records from the sleep database compiled by Massachusetts Institute of Technology (MIT) and Beth Israel Hospital (BIH) were used in conjunction with four wavelet decomposition steps to obtain the cD4 wavelet coefficient as input for the GreyART network. The GreyART network was then used for simulation training and testing in order to achieve the best recognition results. This study achieved an average recognition rate of 93.33% for electroencephalogram data record slp01b, and recognition rates during the training and testing stage for this record were 95.80% and 92.12%, respectively. This was the best recognition result for any of the 17 records. The overall average recognition rate for all 17 records was 78.10%. In comparison with past literature, this study's use of the GreyART network to recognize electroencephalogram signal characteristics of SAS possesses excellent reference value. To further reduce the costs and the physiological signal measurement items to make it easier for patients to use, this research also proposes to use a single type of physiological signals, electroencephalographic (EEG) signals, as the sole input as identification information to identify SAS diseases. EEG signal detection is utilized because it is nonintrusive, suitable for long-term monitoring and most importantly, it can be used to detect various types of abnormal physiological conditions in SAS, moods, sleep stages, heart rate abnormalities and mental states. In addition, constant monitoring of users in their familiar home environment can also be utilized as self-screening at home to reduce the burden of sleep medicine centers.

For the elderly and chronic patients with cardiovascular disease who live alone, it is necessary to constantly monitor their physiological parameters, especially the electrocardiogram (ECG), to effectively prevent and control their health condition and even to provide urgent treatment or care while an emergency such as the abnormal variation of heart rate (HR) occurs. In this paper, a wireless in-home physiological monitoring system, based on multi-hop relay communications, which can ubiquitously and continuously monitor the patient's ECG at any time or any place at home without space limit and the ''dead spot'' due to the extended communication coverage by multi-hop wireless connectivity, is proposed. The system consists of a mobile-care device, which is responsible for capturing and wirelessly sending the patient's ECG data, a wireless multi-hop relay network (WMHRN) that is in charge of relaying the data sent by the former, and a residential gateway (RG), which is responsible for gathering and uploading the received ECG data to the remote care server through the Internet to carry out the patient's health condition monitoring and the management of pathological data. However, in order to assure that the ECG data can be effectively and timely forwarded, from the mobile-care device to the RG through the WMHRN, to meet the healthcare quality of service (H-QoS) demand for reliable and real-time end-to-end ECG transmission, the analysis of WMHRN latency in data-forwarding stage and the deployment consideration of wireless relay nodes are investigated in detail in this work. Moreover, an emergency alert service using short message service (SMS), based on the detection of abnormal variation of HR, is also used in the RG to further enhance the healthcare service quality. A prototype of this system has been developed and implemented. Finally, the experimental results are presented to verify the feasibility of the proposed system.

The seismo-ionospheric precursor prior to the Mw7.9 earthquake near Wenchuan, China, on 12 May 2008 was observed by the FORMOSAT-3/COSMIC satellite constellation. By binning radio occultation observations, the three-dimensional ionospheric structure can be obtained to monitor the ionospheric electron density variation prior to the earthquake. It has been determined that near the epicenter the F2-peak height, hmF2, descends about 25 km and the F2-peak electron density, NmF2, decreases about 2 × 105 el/cm3 around noon within 5 days prior to the earthquake. The integrated electron content decreases more than 2 TECU between 250 and 300 km altitude.

Objectives The phenomenon of aging society has derived problems such as shortage of medical resources and reduction of quality in healthcare services. Method This paper presents a system infrastructure for pervasive and long-term healthcare applications, i.e. a ubiquitous network composed of wireless local area network (WLAN) and cable television (CATV) network serving as a platform for monitoring physiological signals. Users can record vital signs including heart rate, blood pressure, and body temperature anytime either at home or at frequently visited public places in order to create a personal health file. Results The whole system was formally implemented in December 2004. Analysis of 2000 questionnaires indicates that 85% of users were satisfied with the provided community-wide healthcare services. Among the services provided by our system, health consultation services offered by family doctors was rated the most important service by 17.9% of respondents, and was followed by control of one's own health condition (16.4% of respondents). Convenience of data access was rated most important by roughly 14.3% of respondents. Discussion/conclusion We proposed and implemented a long-term healthcare system integrating WLAN and CATV networks in the form of a ubiquitous network providing a service platform for physiological monitoring. This system can classify the health levels of the resident according to the variation tendency of his or her physiological signal for important reference of health management.

In this paper, the orthogonality of coefficient matrices of wavelet filters is utilized to derive the energy equation for the relation between time-domain signal and its corresponding wavelet coefficients. Using the energy equation, the relationship between the wavelet coefficient error and the reconstruction error is obtained. The errors considered in this paper include the truncation error and quantization error. This not only helps to control the reconstruction quality but also brings two advantages: (1) It is not necessary to perform inverse transform to obtain the distortion caused by compression using wavelet transform and can thus reduce computation efforts. (2) By using the energy equation, we can search for a threshold value to attain a better compression ratio within the range of a pre-specified percent root-mean-square difference (PRD) value. A compression algorithm with run length encoding is proposed based on the energy equation. In the end, the Matlab software and MIT-BIH database are adopted to perform simulations for verifying the feasibility of our proposed method. The algorithm is also implemented on a DSP chip to examine the practicality and suitability. The required computation time of an ECG segment is less than 0.0786 ,s which is fast enough to process real-time signals. As a result, the proposed algorithm is applicable for implementation on mobile ECG recording devices.

This paper presents the design and implementation of a tele-emergent system to provide constant monitoring of cardiac patients. Our system is consisted of a user integration device attached to the patient, a wireless sensor network (WSN) and a medical gateway installed in the patient's home, and a server set up in the hospital. The user integration device includes Electrocardiogram (ECG) signal acquisition and processing circuit utilizing a "Modified So and Chan" QRS detection algorithm to discriminate the R signal wave and calculate RR intervals to subsequently detect abnormal Heart Rate Variability (HRV), and a Bluetooth transmission module to communicate with WSN. The WSN which is consisted of wireless sensor nodes with power-saving scheme is used to relay the ECG data to the medical gateway for ubiquitous monitoring at home. The medical gateway includes the heart disease symptom recognition system to further analyze the ECG data and recognize the symptoms of serious heart diseases, and a General Packet Radio Service (GPRS) module with Multimedia Messaging Service (MMS) to communicate with the server in the hospital. The server in the hospital can passively process the MMS messages from the medical gateway, and actively send commands to the medical gateway to acquire the needed ECG data from the patient. With our integrated system it is possible to provide real-time and long-term monitoring of physiological data for cardiac patients which is vital for the patients' medical care.

This paper presents the implementation of a low-cost, data access-efficient, sample and easy to deployment, waterproof, and heatproof outdoor cable access point (CAP) device for ubiquitous network applications .The proposed CAP device is consisted of a cable modem (CM) and a wireless local area network (WLAN) access point (AP) to be integrated inside the chassis of an existing outdoor CATV amplifier. The main purpose of the CAP device is to effectively extend the coverage of outdoor wireless access link and to further provide a data access-efficient service for construction of a ubiquitous networking environment in the metropolitan area. Besides, to ensure the safety and validity for data accessing and information exchanging in the wireless broadband network, a security certifying mechanism of authentication, authorization, and accounting (AAA) is also integrated in the CAP device. The experimental results show that the performance and robustness of the CAP devices is suitable for ubiquitous network applications.

Blood pressure is one of the most important indicators of human health condition. A patient with hypertension in long term tends to have various kinds of vessel pathological changes such as coronary heart disease, apoplexy, and nephropathy. Consequently, the blood pressure measurement is important in daily health care for hypertension patients. Traditionally, the design and implementation of blood pressure monitor mostly focuses on the accuracy of pressure measurement and does not deal with the requirement of hypertension health care services with two-way communication via instant messages between the patient and the medical center. In this paper, a mobile-care system integrated with Bluetooth blood pressure and pulse monitor, and cellular phone is designed and implemented to transmit the physiological parameters of the patient in real time via wireless networks. The health care provider can monitor the blood pressure, body temperature, and heart rate of the patient and provide services by intelligent health care systems. The intelligent system constantly manages the health care data and periodically reports the status of the patient to the patient's family members. By the above care mechanism, the patients with hypertension can easily obtain long-term and constant monitoring of the physiological parameters and thus better health care services. A questionnaire survey has been conducted among medical personnel in Taipei municipal Wang-Fang hospital to collect their opinions of using our proposed and implemented blood pressure monitor with Bluetooth transmission function. An average of 95% of the medical personnel agree that long-term monitoring of the patients' physiological information such as blood pressure can be helpful to reduce medical cost, to provide useful reference data, and to provide long-distance health care. This thus shows strong support of the usefulness of our proposed device. Our proposed and implemented device has also been submitted to US FDA for medical device approval.

According to statistics in Taiwan, the proportion of students engaged in regular exercise has declined drastically with the increase in education level. This study thus aims to provide a platform for monitoring of group cardiorespiratory fitness to allow users such as teachers or coaches to easily monitor a group’s exercise condition, intensity and duration to increase exercise efficiency, promote exercise motivation and reduce exercise risk. Based on group measurement concept and wearable chest strap textiles integrated with heart rate monitoring devices, teachers or coaches can immediately acquire and display all heart rate information on a notebook computer together with synchronous field projection display. The acquired heart rate data can also be automatically recorded and analyzed to assist in assessing the physical fitness. Our proposed platform aims to monitor the cardiorespiratory fitness in group mainly for college students and young office worker. To validate the stability of our platform in the long term, we recruited the college students in a physical fitness class, 35 in total, as the subjects for long term observation. In the experiments the subjects are divided into “varsity group” and “sedentary group” according to whether they are with or without regular exercise habits. Subjects wearing chest straps were instructed to take the 3-minute Step Test and the 5-minute constant intensity exercise test. The results show that the “varsity group” has a lower resting heart rate ([Formula: see text][Formula: see text]bpm vs. [Formula: see text][Formula: see text]bpm), a lower exercise heart rate ([Formula: see text][Formula: see text]bpm vs. [Formula: see text][Formula: see text]bpm) and a lower mean heart rate ([Formula: see text][Formula: see text]bpm vs. [Formula: see text][Formula: see text]bpm). The “Varsity group” also has a higher heart rate recovery percentage at the first minute ([Formula: see text] vs. [Formula: see text]) and the second minute ([Formula: see text] vs. [Formula: see text]). Moreover, all these indexes have a high correlation with the fitness index in the 3-minute Step Test, among which the percentage of heart rate recovery in the first minute shows the highest positive correlation ([Formula: see text], [Formula: see text]). Our wearable heart rate monitoring system can thus be deemed as effective to provide a platform for measurement of group heart rates and for assessment of cardiorespiratory fitness.

According to statistics, up to 40% of emergency admissions are due to chest tightness or chest pain. However, merely based on the patient’s current symptoms such as chest pain, it is difficult for a physician to give an instant diagnosis as most cardiovascular diseases are chronic. To address this issue, it is necessary to provide a set of tools to indicate the patient’s status during hospitalization to help the physician in diagnosis. It is thus our primary objective to design and develop a wearable heart rate monitoring system and prediction tool that can measure the patient’s heart rate parameters, allow him/her to move around easily, and which also can effectively improve the medical personnel’s working efficiency. This research utilizes conductive filament to design textile to integrate electric circuit with clothing. Using a conductive vest and chest belt that can be worn comfortably, our system can continuously record patients’ physiological index parameters during their hospitalization. Physiological index parameters of multiple patients can then be transmitted wirelessly and recorded in a physician-end computer. At the end of their hospitalization, the patient’s original physiological indices together with the recorded heart rate variability (HRV) parameters can then be summarized to assess the risk score of their discharging from hospital. This paper adopts the concept of TIMI risk score, while adding every index of HRV measured when subjects are hospitalized. The risk score can hence be used to provide emergency physicians as a basis for an early prognosis and subsequently a better hospital-discharging assessment of patients with chest pain. The accuracy of the proposed prognosis has been verified with the 3-day and 30-day recall rate of the patients and the result has been shown to be promising for chest pain patients in emergency admission units.

In this paper we focus on the deployment strategy of sensor nodes for Wireless Sensor Network (WSN) for Sequence-based Localization (SBL) algorithm in order to effectively reduce location error. To investigate the effects of locations of deployed sensor nodes on the location error, we first develop a node deployment analysis toolkit for node-sequence localization. Based on the observation via the toolkit we then develop our node deployment methodology. When deploying sensor nodes, we discover that in order to reduce location error (1) the standard deviation of the polygon area cut by the perpendicular bisectors of the sensor nodes should be kept as small as possible, (2) certain amount of space should be maintained between the sensor nodes and (3) optimization with the angle between the perpendicular bisectors should be utilized. We use the ns2 network simulator for evaluation, and the ns2 simulation results show that our proposed node deployment methodology can indeed reduce location error of the SBL algorithm.

[1] The Imager for Sprites and Upper Atmospheric Lightning (ISUAL) payload on board the FORMOSAT-2 satellite carried out the first limb imaging observation of 630 nm airglow for the purpose of studying physical processes in the F region ionosphere. For a total of 14 nights in 2006–2008, ISUAL scanned the midnight latitude-altitude distribution of 630 nm airglow in the Asian sector. On two nights of relatively active conditions (ΣKp = 26, 30+) we found several bright airglow regions, which were highly variable each night in terms of luminosity and location. In relatively quiet conditions (ΣKp = 4–20) near May/June we found two bright regions which were stably located in the midlatitude region of 40°S–10°S (50°S–20°S magnetic latitude (MLAT)) and in the equatorial region of 0°–10°N (10°S–0° MLAT). On one of the quiet nights, FORMOSAT-3/COSMIC and CHAMP simultaneously measured the plasma density in the same region where ISUAL observed airglow. The plasma density data generally show good agreement, suggesting that plasma enhancements were the primary source of these two bright airglow regions. From detailed comparison with past studies we explain that the airglow in the equatorial region was due to the midnight brightness wave produced in association with the midnight temperature maximum, while that in the midlatitude region was due to the typical plasma distribution usually formed in the midnight sector. The fact that the equatorial airglow was much brighter than the midlatitude airglow and was observed on most nights during the campaign period strongly suggests the importance of further studies on the MTM/MBW phenomenology, which is not well reproduced in the current general circulation model.

The FORMOSAT-3/COSMIC mission consisting of six satellites is the world's first demonstration of near real-time operational GPS Radio Occultation (RO) constellation mission. The success of the mission expected to operate through 2011 has initiated a new era for operational GPS RO soundings. The World Meteorological Organization had recommended continuing RO observations operationally and urges planning for a complementary follow-on mission. The follow-on mission will be a new constellation of 12 to 18 satellites equipped with the GNSS RO payload and collect over 9000 soundings per day. This higher density of sounding profiles will be useful for global and mesoscale models, and also severe weather forecasting. The follow-on mission will establish international standards so that future RO missions deployed by any country can be used together for operations and research. In this paper, we present the state-of-the-art achievements and the results of the current mission; as well as the follow-on mission planning progress.

A new era of studying the ionospheric space weather effects has come after launch of the innovative satellite constellation, named as Formosa Satellite 3 or Constellation Observing System for Meteorology, Ionosphere, and Climate (abbreviated as FORMOSAT-3/COSMTC or F3/C in short), performing a radio occultation experiment capable of observing the global ionosphere three-dimensionally. This is the first time that a satellite constellation provides instantaneously both the lower and upper parts of the ionospheric electron density up to the satellite altitude. With more than 2500 soundings of the ionospheric vertical electron density profiles every day, ionospheric plasma structures over many continents and most of oceans, where ground-based observation is limited, are now observed continuously. Important ionospheric research topics, such as space weather effects to the ionosphere, variations of ionospheric plasma structure and dynamics produced by solar outputs, and atmosphere-ionosphere coupling processes, can be widely studied and modeled based on the three-dimensional ionospheric images constructed by the F3/C observations. After one year in orbit, a great amount of radio occultation soundings allow us to construct global ionospheric maps to study the ionospheric seasonal effects and atmosphere-ionosphere interactions. Taking advantage of the uniqueness of dense global coverage, the major physical mechanisms of the two studies are given. For study of the seasonal variation during solstice, electron density images of the mid- and low-latitude ionosphere show a clear north-to-south asymmetry which may be affected by the summer-to-winter neutral wind. Meanwhile a significant longitudinal variation at midnight is also seen in the solstitial season. Another interesting result is the four stronger equatorial ionization anomaly (ETA) regions located at different longitudes. This four-peaked ETA structure may result from upward propagating nonmigrating tides originated from troposphere. F3/C's observation of the daytime four-peaked structure provides an important evidence to support the proposed forming mechanism.

In this paper, an active RFID system integrated public key encryption, embedded computation, and wireless communication technology that is implemented with cryptography and authentication mechanisms. In the system, a secure RFID Tag intermittently transmits cipher text to a RFID Reader which then transmits to a back-end platform to perform data comparison for authentication. Besides, the digital signature scheme-Tame Transformation Signatures (TTS) is used in the system to protect the plain text. There are two major features in the proposed RFID system. The first is to fully design and implement an active RFID system which includes active Tags and Readers. The second is to successfully implement an active RFID system with TTS cryptography and authentication mechanisms.

[Abstract] The FORMOSAT-3 mission, also known as COSMIC, Constellation Observing System for Meteorology, Ionosphere, and Climate, is the third major project of the FORMOSAT series implemented by National Space Organization (NSPO) in Taiwan. The mission is jointly developed by Taiwan’s NSPO, the US’s UCAR in collaboration with NSF, USAF, NOAA, NASA, JPL and NRL. The FORMOSAT-3 consisting of six LEO satellites is the world’s first demonstration of GPS radio occultation (RO) near real-time operational constellation mission representing a revolution in atmospheric sounding from space, with precise, accurate, and all-weather global observations useful for weather, climate, and space weather research and operations. The FORMOSAT-3 has processed over 1800 good atmospheric sounding profiles per day, which exceeds the number of worldwide radiosondes launched per day. Since the successful launch in April 2006, FORMOSAT-3 has provided over 1 million GPS RO soundings. The goal of the follow-on mission is to transfer the mission from research to real-time operational, which will steadily provide about 9000 GPS RO soundings per day. The planning mission is complementary to the current mission with a new constellation of 12 to 18 satellites. Currently, the atmospheric radio soundings data are assimilated into the Numerical Weather Prediction (NWP) models for weather prediction and typhoon/hurricane forecast. The data assimilations into the operational systems have shown positive impact on weather prediction to global and national weather centers, such as Denmark Meteorological Institute (DMI), European Centre for MediumRange Weather Forecasts (ECMWF), Institut D'Estudis Espacials de Catalunya (IEEC), United Kingdom Met Office (UKMO), National Environmental Satellite, Data, and Information Service (NESDIS), Joint Center for Satellite Data Assimilation (JCSDA), and many other weather forecast centers in Japan, Korea, China, India, and Taiwan. This paper

Hypertension and arrhythmia are chronic diseases, which can be effectively prevented and controlled only if the physiological parameters of the patient are constantly monitored, along with the full support of the health education and professional medical care. In this paper, a role-based intelligent mobile care system with alert mechanism in chronic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and healthcare providers. Each of the roles represents a person that uses a mobile device such as a mobile phone to communicate with the server setup in the care center such that he or she can go around without restrictions. For commercial mobile phones with Bluetooth communication capability attached to chronic patients, we have developed physiological signal recognition algorithms that were implemented and built-in in the mobile phone without affecting its original communication functions. It is thus possible to integrate several front-end mobile care devices with Bluetooth communication capability to extract patients' various physiological parameters [such as blood pressure, pulse, saturation of haemoglobin (SpO2), and electrocardiogram (ECG)], to monitor multiple physiological signals without space limit, and to upload important or abnormal physiological information to healthcare center for storage and analysis or transmit the information to physicians and healthcare providers for further processing. Thus, the physiological signal extraction devices only have to deal with signal extraction and wireless transmission. Since they do not have to do signal processing, their form factor can be further reduced to reach the goal of microminiaturization and power saving. An alert management mechanism has been included in back-end healthcare center to initiate various strategies for automatic emergency alerts after receiving emergency messages or after automatically recognizing emergency messages. Within the time intervals in system setting, according to the medical history of a specific patient, our prototype system can inform various healthcare providers in sequence to provide healthcare service with their reply to ensure the accuracy of alert information and the completeness of early warning notification to further improve the healthcare quality. In the end, with the testing results and performance evaluation of our implemented system prototype, we conclude that it is possible to set up a complete intelligent health care chain with mobile monitoring and healthcare service via the assistance of our system.

The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members.

This paper presents the implementation of a low-cost, data access-efficient, sample and easy to deployment, waterproof, and heatproof outdoor cable access point (CAP) device for ubiquitous network applications. The proposed CAP device is consisted of a cable modem (CM) and a wireless local area network (WLAN) access point (AP) to be integrated inside the chassis of an existing outdoor CATV amplifier. The main purpose of the CAP device is to effectively extend the coverage of outdoor wireless access link and to further provide a data access-efficient service for construction of a ubiquitous networking environment in the metropolitan area. Besides, to ensure the safety and validity for data accessing and information exchanging in the wireless broadband network, a security certifying mechanism of authentication, authorization, and accounting (AAA) is also integrated in the CAP device. The experimental results show that the performance and robustness of the CAP devices is suitable for ubiquitous network applications.

Intelligent surveillance system has become an important research topic in the field of computer vision. The authors propose a monitoring method based on the cellular model to monitor human activities in the indoor environment. The measured area of an indoor room is divided into several unit areas in which each unit area is considered as a simple cell in the cellular model. A rectangular box is then used to group those neighbouring active cells into a unit to represent a moving object. Since people usually walk without a fixed style and the colour of objects may be similar to that of the background, the distribution of active cells is often uncertain and incomplete. The authors therefore apply the gray relational analysis to detect and track multiple moving objects. Several experiments have been conducted to evaluate the performance of the proposed system. The experimental results show that the proposed system is highly effective in verifying and tracking multiple objects in real time.

Six identical micro-satellites of the FORMOSAT-3/COSMIC (Formosa Satellite #3 and Constellation Observing System for Meteorology, Ionosphere and Climate: FS-3/C) mission were successfully launched on 14 April 2006. The FS-3/C mission provides the first satellite constellation for monitoring global weather using the Global Positioning System (GPS) radio occultation (RO) technique. The mission¡¦s primary scientificific goal is to obtain near-real time profiles of the bending angle and refractivity in the neutral atmosphere and in the ionosphere. In April, 2007 the FS-3/C mission provide about RO soundings of 2000 atmospheric vertical profiles per day in a nearly uniform distribution around the globe. The lowest altitude penetration for more than 80% of RO soundings reached below 1 kmin altitude. Most soundings have penetration below 800m altitude in the equatorial region and below 200 m altitude in polar regions. The quality and accuracy of the RO sounding profiles are in good agreement with the CHAMP(CHAllenging Minisatellite Payload) RO soundings and direct measurements using dropsondes. The FS-3/C RO sounding observations are used to support operational global weather prediction, climate monitoring and research, space weather forecasting, and ionosphere and gravity research.