Integrated Sensing and Communications (ISAC) is emerged as one of the key technologies in next generation wireless systems. However, ISAC systems have been commonly explored neglecting mutual coupling. This paper investigates the mutual coupling exploitation to further improve the performance of ISAC systems. We aim to maximize the sensing beampatern gain by optimizing the tunable loads and the dual-functional beamforming while satisfying the minimum signal-to-interference-plus-noise (SINR) per user and the hardware constraint of the tunable loads. To solve the non-convex problem, we propose a penaltybased iterative algorithm to obtain a stationary point. Specifically, in each iteration we adopt the block coordinate descent (BCD) method where the dual-functional beamforming is obtained by using Lagrange duality, and the tunable loads are solved with closed forms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms compared to the baseline schemes. To the best of our knowledge, this is the first study utilizing the MC effect to improve system performance in an ISAC system with tunable loads.

Exact position control of the flexible-joint manipulator (FJM) is a challenging task due to the manipulator’s nonlinearity and underactuated characteristic. For the three-dimensional (3-D) rigid-link FJM, the image-based visual servoing (IBVS) approach with link-position output constraint and unknown camera parameters is investigated in this article. The controller is designed to deal with three problems. First, the visual servoing control law is divided into two domains based on the singular perturbation method, where the control input for the fast subsystem is developed to damp out the flexible joint’s vibration. Second, the adaptive updating law for estimating the unknown camera parameters is presented in the slow subsystem. Third, to guarantee that the rigid-link position keep in set constraints, the controller for the slow subsystem is designed by introducing a barrier Lyapunov function. According to the Lyapunov theorem of stability, the proposed controller for the FJM is theoretically proved to be asymptotically stable. Several numerical simulation experiments are provided to illustrate that the presented control scheme is effective.

In this article, to solve trajectory tracing problem and vibration suppression for a planar two-link rigid-flexible manipulator subject to joint-velocity measurement noise, a novel uncalibrated visual servoing control is proposed. To begin with, the manipulator’s dynamic model is established by the assumed mode method (AMM). On this basis, based on the singular perturbation theory, two subsystem controllers are designed, one is slow subsystem controller, and the other one is fast subsystem controller. In the slow subsystem, to cope with the complication of the camera calibration, an adaptive algorithm is formulated to evaluate the parameters of a fixed camera online. Aiming to overcome the challenge that exact joint-velocity measurement may be disturbed by external noise, a nonlinear sliding observer is developed to estimate the state of joint velocity accurately. The asymptotic convergence of image tracking error is proved by means of Lyapunov analysis. Additionally, for the purpose of restraining the flexible beam’s elastic vibration, a linear quadratic regulator (LQR) approach is adopted in the fast subsystem control design. The realistic comparing simulation experiments are presented to demonstrate the performance of the proposed controller.

By learning from the existing family mealtime activities, family members can be motivated to make the positive changes towards better relationships, which are important for the physical and mental health of children. Moreover, the details of family mealtime activities provide rich information for study in sociology and culture. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing, etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family with a CRFs-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. FamilyLog can detect those events with high accuracy across different families and home environments.

The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

PPG-based heart rate estimation has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user's physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios (e.g., running or biking), making them impractical in real-world settings where a user may perform a wide range of physical activities. In this paper, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from ECG signals based on a training data set. Then a denoising convolutional neural network (DnCNN) is trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by the DnCNN and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup (SPC) training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.98 bpm, outperforming two state-of-the-art methods TROIKA and Deep PPG.

In recent years, there has been growing interest in the use of fitness trackers and smartphone applications for promoting physical activity. Many of these applications use accelerometers to estimate the level of activity that users engage in and provide visual reports of a user's step counts. When provided, most recommendations are limited to popular general health advice. In our study, we develop an approach for providing data-driven and personalized recommendations for intraday activity planning. We generate an hour-by-hour activity plan that is based on the user's probability of adhering to the plan. The user's probability of adherence to the plan is personalized, based on his/her past activity patterns and current activity target. Using this approach, we can tailor notifications (e.g., reminders, encouragement) to each user. We can also dynamically update the user's activity plan at mid-day, if his/her actual activity deviates sufficiently from the original plan. In this paper, we describe an implementation of our approach and report our technical findings with respect to identifying typical activity patterns from historical data, predicting whether an activity target will be achieved, and adapting an activity plan based on a user's actual performance throughout the day.

Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

As one of the most popular exercises, running is accomplished through a tight cooperation between the respiratory and locomotor systems. Research has suggested that a proper running rhythm -- the coordination between breathing and strides -- helps improve exercise efficiency and postpone fatigue. This paper presents RunBuddy -- the first smartphone-based system for continuous running rhythm monitoring. RunBuddy is designed to be a convenient and unobtrusive exercise feedback system, and only utilizes commodity devices including smartphone and Bluetooth headset. A key challenge in designing RunBuddy is that the sound of breathing typically has very low intensity and is susceptible to interference. To reliably measure running rhythm, we propose a novel approach that integrates ambient sensing based on accelerometer and microphone, and a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. We evaluate RunBuddy through experiments involving 13 subjects and 39 runs. Our results show that, by leveraging the LRC model, RunBuddy correctly measures the running rhythm for indoor/outdoor running 92:7% of the time. Moreover, RunBuddy also provides detailed physiological profile of running that can help users better understand their running process and improve exercise self-efficacy.

Time synchronization is a fundamental service for wireless sensor networks (WSNs). Although a number of message passing protocols can achieve satisfactory synchronization accuracy, they suffer poor scalability and high transmission overhead. An alternative approach is to utilize the global time references such as those induced by GPS and timekeeping radios. However, they require the hardware receiver to decode the out of band clock signal, which introduces extra cost and design complexity. This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that 802.15.4 sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a 802.15.4 node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). A key advantage of our approach is that, due to the long communication range of Wi-Fi, a large number of 802.15.4 sensors can synchronize clock rates to the same beacons without any message exchange. This paper makes several key contributions. First, we experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs digital signal processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1.1 and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

The quality of sleep is an important factor in maintaining a healthy life style. To date, technology has not enabled personalized, in-place sleep quality monitoring and analysis. Current sleep monitoring systems are often difficult to use and hence limited to sleep clinics, or invasive to users, e.g., requiring users to wear a device during sleep. This paper presents iSleep -- a practical system to monitor an individual's sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, including body movement, couch and snore, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events based on carefully selected acoustic features, and tracks the dynamic ambient noise characteristics to improve the robustness of classification. We have evaluated iSleep based on the experiment that involves 7 participants and total 51 nights of sleep, as well the data collected from real iSleep users. Our results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

This paper presents COBRA - a visible light communication (VLC) system for off-the-shelf smartphones. COBRA encodes information into specially designed 2D color barcodes and streams them between screen and camera of smartphones. Due to the directionality and short range of visible light, COBRA can preserve user privacy and security in many near field communication scenarios such as opportunistic data exchange between smartphones. We develop a new 2D color barcode for COBRA that is optimized for streaming between small-size screen and low-speed camera of smartphones. COBRA adapts the size and layout of code blocks in streamed barcodes to deal with the significant image blur in mobile environments, and adopts new image processing techniques to achieve real-time barcode stream decoding. Our approach is evaluated through extensive experiments on Android smartphones.

No abstract available.

Time synchronization is a fundamental service for Wireless Sensor Networks (WSNs). This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that ZigBee sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a ZigBee node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). We experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs advanced Digital Signal Processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1x and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

The first author has recently received a 3 million RMB, 4 year grant to build Chinapsilas first artificial brain, starting in 2008, that will consist of approximately 15,000 interconnected neural net modules, evolved one at a time in a special accelerator board (which is 50 times faster than using an ordinary PC) to control the hundreds of behaviors of an autonomous robot. The approach taken in building this artificial brain is fast and cheap (e.g. $1500 for the FPGA board, $1000 for the robot, and $500 for the PC, a total of $3000), so we hope that other brain building groups around the world will copy this evolutionary engineering approach.