Owing to their tremendous adaptability to free-form objects, soft grippers with fin-ray structure have a wide range of applications. However, kinetostatics analysis and contact sensing for such soft grippers are quite a challenge due to large structural deformations. In this article, a model-based method for intrinsic contact sensing, object perception, and interactive manipulation, is proposed for this kind of adaptive grippers. The contributions arise from the integration of compact strain-gauge sensors, that are particularly fabricated for slender flexible beams undergoing large deformations. Using a discretization-based approach, the contact condition can be identified efficiently in light of the local deformations gathered via the deflection sensors. Prototypes are developed using simple materials and manufacturing methods, on which various validation experiments are conducted. Owing to the contact sensing capability, the developed adaptive fin-ray gripper is able to perceive the boundary geometry and structural compliance of unstructured objects. Moreover, sensor-based feed-back control can be accomplished to perform interactive manipulation, in which the contact force between the finger and object can be regulated precisely (around$5\,\%$RMS error) in real-time.

Reducing Coflow Completion Time (CCT) has a significant impact on application performance in data-parallel frameworks. Most existing works assume that the endpoints of constituent flows in each coflow are predetermined. We argue that CCT can be further optimized by treating flows' destinations as an additional optimization dimension via reducer placement. In this article, we propose and implement RPC, a joint online Reducer Placement and Coflow bandwidth scheduling framework, to minimize the average CCT in cloud clusters. We first develop a 2-approximation algorithm to minimize the CCT of a single coflow, and then schedule all the coflows following the Shortest Remaining Time First (SRTF) principle. We use real testbed experiments and extensive large-scale simulations to demonstrate that RPC can reduce the average CCT by 64.98% compared with the state-of-the-art technologies.

This paper investigates how to estimate instantaneous fuel consumption using smartphones and OBD-II (On-board Diagnostics) adapters. Although most of the new cars have instant miles per gallon readout feature, the readings from those dashboard displays are usually proprietary and are difficult to record. Not to mention older cars do not have this feature. In this paper, we describe a system and associated algorithms to monitor fuel consumption of gasoline-powered vehicles in real time at second-level granularity. Specifically, we propose two algorithms: 1) Powertrain-based Model, which is derived from estimating an engine's fuel injection rate, and 2) Vehicle Dynamics-based Model, which considers fuel consumption in terms of the mechanical work applied to a vehicle. They are designed for vehicles with and without OBD-II adaptors respectively. The proposed system is compatible with most of the passenger vehicles and can be easily deployed. We evaluate our system in a field test and show that it can successfully estimate instantaneous fuel consumption, the average difference between estimation results and the ground truth is about 6%.

Reducing Coflow Completion Time (CCT) has a significant impact on application performance in data-parallel frameworks. Most existing works assume that the endpoints of constituent flows in each coflow are predetermined. We argue that CCT can be further optimized by treating flows' destinations as an additional optimization dimension via reducer placement. In this paper, we propose and implement RPC, a joint online Reducer Placement and Coflow bandwidth scheduling framework, to minimize the average CCT in cloud clusters. We first develop a 2-approximation algorithm to minimize the CCT of a single coflow, then schedule all the coflows following the Shortest Remaining Time First (SRTF) principle. We use a real testbed implementation and extensive large-scale simulations to demonstrate that RPC can reduce the average CCT by 64.98% compared with state-of-the-art technologies.

Network function virtualization is a promising technique to greatly improve the effectiveness and flexibility of network services through a process named service function chain (SFC) mapping, with which network functions are deployed over virtualized and shared platforms in data centers. However, failures are quite common in data centers. Therefore, a practical and yet theoretically challenging issue in SFC mapping in such an environment is to manage the availability of the requests. In this paper, we present a framework to provision availability of SFC requests in a data center with multiple layers of connected devices, and the devices follow heterogeneous failure processes with the objective of minimizing resource usage. To expedite the process, we further propose an optimization problem of request mapping and backup estimation and solve it efficiently with an approximation algorithm. With simulations, we demonstrate the effectiveness of our proposed framework.

In the electronic system manufacturing process, the board-level functional test is recognized as the most significant step to prevent defective products from entering the market. In recent years, machine learning and data mining have proven to be efficient techniques in determining root cause from the problematic functional test result, especially when the integrated circuits (IC) are becoming increasingly highly-integrated. However, the test results are sometimes unavailable due to either abnormal ending of the test sequence or occasional system failures, which results in a decreased performance of data-driven diagnosis systems. In this paper, we propose a data imputation algorithm to predict the missing entries in the functional test result, by considering the correlation between test items with conditional specification. We evaluate our data imputation algorithm over the test results collected from three different stages of functional test on a line card used in the telecommunication system. The result shows that our proposed data imputation algorithm consistently outperforms other imputation techniques with various data-driven approaches in terms of diagnosing the root cause, increasing the diagnosis accuracy by an average of 28.13% compared to none data imputation, and 9.74% compared to the naive pass imputation.

Location information is playing a significant role in nowadays mobile applications. Performing indoor localization with existing WiFi infrastructure and smartphone motion sensor through statistical filtering has been proven to be a feasible solution. Many literature have resorted to particle filters to deal with the multi-modal and non-Gaussian problem associated with the filtering process. Although grid-based filters can approximate the true densities better compared with particle filters, their computational cost is extremely expensive, especially for large areas. In this paper, we develop a novel asymmetric grid-based filter to accommodate both high-resolution requirement and computational cost-efficiency. The evaluation over an indoor area of 3750m2has shown that our proposed method can achieve a median error of only 2.72m, which is 0.17m more accurate with only 24% of the computation cost compared to particle filters.

Technological breakthroughs allow us to collect data with increasing spatio-temporal resolution from complex interaction systems. The combination of high-resolution observations, expressive dynamic models, and efficient machine learning algorithms can lead to crucial insights into complex interaction dynamics and the functions of these systems. In this paper, we formulate the dynamics of a complex interacting network as a stochastic process driven by a sequence of events, and develop expectation propagation algorithms to make inferences from noisy observations. To avoid getting stuck at a local optimum, we formulate the problem of minimizing Bethe free energy as a constrained primal problem and take advantage of the concavity of dual problem in the feasible domain of dual variables guaranteed by duality theorem. Our expectation propagation algorithms demonstrate better performance in inferring the interaction dynamics in complex transportation networks than competing models such as particle filter, extended Kalman filter, and deep neural networks.

This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehicle skidding events using smartphones and OBD-II (On board Diagnostics) adapters. This system, which we call the VehSense, gathers data from smartphone inertial sensors and vehicle wheel speed sensors, and processes the data to monitor slippery road conditions in real-time. Specifically, two speed readings are collected: 1) ground speed, which is estimated by vehicle acceleration and rotation, and 2) wheel speed, which is retrieved from the OBD-II interface. The mismatch between these two speeds is used to infer a skidding event. Without tapping into vehicle manufactures' proprietary data (e.g., antilock braking system), VehSense is compatible with most of the passenger vehicles, and thus can be easily deployed. We evaluate our system on snow-covered roads at Buffalo, and show that it can detect vehicle skidding effectively.

Location information has become a key component of many applications in mobile and pervasive computing, and the ability to accurately predict the mobility of clients allows these applications to provide better service. However, existing location predictors rely heavily on a significant amount of empirical knowledge to function well. In this paper, we develop a novel framework to predict unknown locations when only little location information is available. Specifically, we first extract WiFi locations from WiFi scan results, then a mobility model is built based on the resulted WiFi location graph with connectivity information, finally, we make location predictions with little known location data with Gibbs sampling over the mobility model. Using a data set containing 31 fairly complete WiFi traces collected over three months as ground truth, we compare our proposed approach with other existing state-of-the-art location predictors. The experimental results show that our framework can achieve 83% location prediction accuracy with only three location samples each day, 15% better than Markov and Bayesian predictors which heavily rely on empirical knowledge.

Indoor location information plays a fundamental role in supporting various interesting location- aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach takes the error from the indoor radio signal propagation model into consideration. It also exploits information of unobserved APs at a given location and an optimal clustering method in the location searching phase. Our real-world experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost even for a large indoor area. In addition, the results show that our scheme can also be effectively applied to Bluetooth networks with sparse signal coverage.