Computer Science

Abstract In many robotics applications, knowing the material properties around a robot is often critical for the robot's successful performance. For example, in mobility, knowledge about the ground surface may determine the success of a robot's gait. In manipulation, the physical properties of an object may dictate the results of a grasping strategy. Thus, a reliable surface identification system would be invaluable for these applications.

Abstract In many camera-based robotics applications, stabilizing video images in real-time is often critical for successful performance. In particular vision-based navigation, localization and tracking tasks cannot be performed reliably when landmarks are blurry, poorly focused or disappear from the camera view due to strong vibrations. Thus a reliable video image stabilization system would be invaluable for these applications. This paper presents a real-time video image stabilization system (VISS) primarily developed for aerial robots.

ABSTRACT—New smartphone technologies are emerging which combine head-mounted displays (HMD) with standard functions such as receiving phone calls, emails, and helping with navigation. This opens new opportunities to explore cyber robotics algorithms (robotics sensors and human motor plant). To make these devices more adaptive to the environmental conditions, user behavior, and user preferences, it is important to allow the sensor-equipped devices to efficiently adapt and respond to user activities (e.g., disable incoming phone calls in an elevator, activate video recording while car driving). This paper hence presents a situation awareness system (SAS) for head-mounted smartphones. After collecting data from inertial sensors (accelerometers, gyroscopes), and video data (camera), SAS performs activity classification in three steps. Step 1 transforms inertial sensor data into a head orientation-independent and stable normalized coordinate system. Step 2 extracts critical features (sta...

Smart coaching platforms are emerging which combine Body-Sensor-Networks with AI-based training software to monitor and analyze body motions of athletes, workers, or medical patients. This allows for new opportunities to explore algorithms to interpret body sensor data and provide analytical feedback for learning a physical task, refining body motions, or to protect from work-related injuries. This paper presents a solution to non-invasively equip a person with sensors of a Smart Training System (STS) to improve training efficiency during sport activities. Our system calculates the significance of each body part during physical activities and provides targeted feedback on which body locations are under-performing. In experiments, the system collected data from 13 inertial sensors attached to the entire body of inexperienced golf learners. Using an indoor golf training net with a central target with 3 concentric zones, 1,080 real-world golf swings of 11 participants were analyzed. During the first 30 swings of each participant, the system learned distributions of motions from each sensor, conditioned on swing performance reported by users from their hitting location on the target. In the later 70 swings, feedback was provided to a subgroup of 8 participants, by computing, for an optimal set of features determined during training, the largest discrepancy. The remaining 3 (control) participants received no feedback. From only 100 golf swings for each participant, our system led to significantly improved scores by on average 3.7x (t-test, $\mathrm{p}\lt 0.0001)$ over the latter 70 swings. Our results suggest that the combination of motion sensors and processing developed here was able to yield significantly improved golf swing training.

Abstract Multiple transmission rates are supported in the current 802.11 WLANs, which allows stations to exploit different rates in an adaptive manner to cope with the variability of wireless channels and achieve the higher system throughput. Many efforts have been made at rate adaptation in the literature. The design of rate adaptation algorithms needs to take four basic issues into account.

Abstract Underwater communications is a very challenging topic due to its singular channel characteristics. Most protocols used in terrestrial wireless communications can not be directly applied in the underwater world. A high bit error rate and low propagation delay make the design of reliable transport protocols especially awkward. In this paper, we first propose four schemes that combine forward error correction mechanisms at the bit and/or packet level to increase the reliability in a non-cooperative scenario.

Abstract The mobility of underwater sensor nodes makes the network topology inconveniently controlled and slowly changed. Thus, in order to enable underwater sensor networks to work more effectively, it is necessary for us to periodically detect the coverage rate and redeploy nodes to non-coverage areas. In this paper, we take the lead in introducing the redeployment issue in underwater sensor networks. In our opinion, the key point of the redeployment issue is coverage.

Abstract In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured.

Abstract The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security.

Abstract Underwater communication is a very challenging topic. Protocols used in terrestrial sensor networks cannot be directly applied in the underwater world. High-bit error rate and large propagation delay make the design of transport protocols especially awkward. ARQ-based reliable transport schemes are not appropriate in underwater environments due to large propagation delay, low communication bandwidth, and high error probability. Thus, we focus on redundancy-based transport schemes in this paper.

Abstract Underwater sensor networks (UWSNs) have been proposed for many location-dependent applications such as oceanographic data collection, pollution monitoring, mine reconnaissance, etc. Accurate node localization plays an important role in realizing the potential gains of these applications.

Abstract—Due to the lossy nature of wireless links, it is difficult to determine if packet losses are due to wireless-induced effects or from malicious discarding. Many prior efforts on detecting malicious packet drops rely on evidence collected via passive monitoring by neighbor nodes; however, they do not analyze the cause of packet losses. In this paper, we ask:(a) Given certain macroscopic parameters of the network (like traffic intensity and node density) what is the likelihood that evidence exists with respect to a transmission?