Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing server shutdowns because of overheating and improving a data center’s energy efficiency. This article presents a novel cyber-physical approach for temperature forecasting in data centers, one that integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach reduces not only the computational complexity of online temperature modeling and prediction, but also the number of deployed sensors, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of a monitored data center. We extensively evaluated the proposed system on a rack of 15 servers and a testbed of five racks and 229 servers in a small-scale production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52○C, within a duration up to 10 minutes. Moreover, our approach can reduce the required number of sensors by 67% while maintaining desirable prediction fidelity.

Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing overheating-induced server shutdowns and improving a data center's energy efficiency. This paper presents a novel cyber-physical approach for temperature forecasting in data centers, which integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure the forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach significantly reduces the computational complexity of online temperature modeling and prediction, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of monitored data center. We extensively evaluated our system on a rack of 15 servers and a test bed of five racks and 229 servers in a production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52C, within a duration up to 10 minutes.

The act of note-taking is a key component of learning in secondary and post-secondary classrooms. Students who take notes retain information from classroom lectures better, even if they never refer to those notes afterward. However, students who are legally blind, and who wish to take notes in their classrooms are at a disadvantage. Simply equipping classrooms with lecture recording systems does not substitute for note taking, since it does not actively engage the student in note-taking during the lecture. In this paper we detail the problems encountered by one math and computer science student who is legally blind, and we present our proposed solution: the CUbiC Note-Taker, which is a highly portable device that requires no prior classroom setup, and does not require lecturers to adapt their presentations. We also present results from two case studies of the Note-Taker, totaling more than 200 hours of in-class use.

Classroom note-taking has been shown to be beneficial, even if the student never reviews his/her own notes. Students that are legally blind are thus at a disadvantage because they face significant barriers to note-taking in the classroom. This presentation demonstrates a working prototype of the CUbiC Note Taker, which is a highly portable device that allows students who are legally blind to take their own notes in class without any special in-classroom accommodations, and without requiring lecturers to adapt their presentation in any way.

The canonical face depth map (CFDM) is a standardized representation for storing and manipulating 3D data from human faces. Our algorithm automates the process of transforming a 3D face scan into its canonical representation, eliminating the need for hand-labeled anchor points. The presented algorithm is designed to be a robust, fully automatic preprocessor for any 3D face recognition algorithm. The experimental results presented here demonstrate that our CFDM is robust to noise and occlusion, and we show that using such a canonical representation can improve the efficiency efface recognition algorithms and reduce memory requirements. Producing the CFDM takes, on average, 0.85 seconds for 320 times 240 pixel scans, and 3.8 seconds for 640 times 480 pixel scans (using a dual AMD Opteron 275, 2.2 GHz, with 2 MB Cache, and 1 GIG RAM). The CFDM enables both 2D and 3D image processing methods - such as convolution and PCA - to be readily used for feature localization and face recognition.

The 3DID system has been developed to verify the identity of a person by matching a sensed 3D surface of the face to a face model stored during an enrollment process. Anchor point detection is performed based on shape index; then, a rigid alignment is determined between the observed and model face anchor points. The alignment is refined using a modified ICP algorithm allowing for trimming of 10% noise. Trimmed RMS error for the same person is almost always smaller than 1.3mm; whereas for different persons, it is almost always larger. Performance analysis shows that the 3DID system is fast enough (< 2 sec on a 3.2 MHz P4), reliable enough (1% EER with 1.5% reject rate), and user friendly enough (handles 30 degrees of yaw and 10 degrees of roll and pitch) to be practical in several applications. The current system provides several displays of value to human agents either in online or delayed analysis mode. An inexpensive scanner is needed for widespread use

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.