In this work, a follow-up study of estimating optimal operating points by an improved parameter-based model of three-phase induction machines (IMs) is conducted and presented. The main objective is to examine the representation of IMs' parameters within a specified operating boundary. The paper begins by reviewing the theory behind the inverse$\Gamma$-form model (IGFM) and maximum torque control. This review serves as a foundation for developing methods for estimating operating points. Finally, the study compares the results obtained using a 2D electromagnetic finite element method (FEM) model of a 15kW IM with those derived using a parameter-based model obtained through the conventional method, demonstrating the practical utility of the improved parameter-based model.

This research investigates the design space of combining touchscreens with passive rich-ID building block systems to support the physical construction of contexts in touchscreen interactions. With two proof-of-concept systems, RFIPillars and RFITiles, we explore various schemes for using tangible inputs for context enrichment in touchscreen interactions. Instead of incorporating an electronic touchscreen module that requires per-module maintenance, this work intentionally makes each tangible object passive. We explore rear-projection solutions to integrate touchscreen interactions into these passive building blocks with capacitive touch sensing techniques and deliberate physical forgiving to retain the merits of being both batteryless and wireless. The presented research artifacts embody the interaction designs and elucidate scalability challenges in integrating touchscreen interactions into this emerging tangible user interface.

This paper presents RFTouchPads, a system of batteryless and wireless modular hardware designs of two-dimensional (2D) touch sensor pads based on the ultra-high frequency (UHF) radio-frequency identification (RFID) technology. In this system, multiple RFID IC chips are connected to an antenna in parallel. Each chip connects only one of its endpoints to the antenna; hence, the module normally turns off when it gets insufficient energy to operate. When a finger touches the circuit trace attached to another endpoint of the chip, the finger functions as part of the antenna that turns the connected chip on, while the finger touch location is determined according to the chip's ID. Based on this principle, we propose two hardware designs, namely, StickerPad and TilePad. StickerPad is a flexible 3×3 touch-sensing pad suitable for applications on curved surfaces such as the human body. TilePad is a modular 3×3 touch-sensing pad that supports the modular area expansion by tiling and provides a more flexible deployment because its antenna is folded. Our implementation allows 2D touch inputs to be reliability detected 2 m away from a remote antenna of an RFID reader. The proposed batteryless, wireless, and modular hardware design enables fine-grained and less-constrained 2D touch inputs in various ubiquitous computing applications.

We present RFIBricks, an interactive building block system based on ultrahigh frequency radio-frequency identification (RFID) sensing. The system enables geometry resolution based on a simple yet highly generalizable mechanism: an RFID contact switch, which is made by cutting each RFID tag into two parts, namely antenna and chip. A magnetic connector is then coupled with each part. When the antenna and chip connect, an interaction event with an ID is transmitted to the reader. On the basis of our design of RFID contact switch patterns, we present a system of interactive physical building blocks that resolves the stacking order and orientation when one block is stacked upon another, determines a three-dimensional (3D) geometry built on a two-dimensional base plate, and detects user inputs by incorporating electromechanical sensors. Because it is calibration-free and does not require batteries in each block, it facilitates straightforward maintenance when deployed at scale. Compared with other approaches, this RFID-based system resolves several critical challenges in human-computer interaction, such as 1) determining the identity and the built 3D geometry of passive building blocks, 2) enabling stackable token+constraint interaction on a tabletop, and 3) tracking in-hand assembly.

This paper presents a technique enabling distributed batteryless near-field identification (ID) between two passive radio frequency ID (RFID) tags. Each conventional ultra-high-frequency (UHF) RFID tag is modified by connecting its antenna and chip to a reed switch and then attaching a magnet to one of the reed switch's terminals, thus transforming it into an always-on switch. When the two modules approach each other, the magnets counteract each other and turn off both switches at the same time. The coabsence of IDs thus indicates a unique interaction event. In addition to sensing, the module also provides native haptic feedback through magnetic repulsion force, enabling users to perceive the system's state eyes-free, without physical constraints. Additional visual feedback can be provided through an energy-harvesting module and a light emitting diode. This specific hardware design supports contactless, orientation-invariant sensing, with a form factor compact enough for embedded and wearable use in ubiquitous computing applications.

This paper investigates the feasibility of using a nail-mounted array of tactors, NailTactors, as an eyes-free output device. By rim-attached eccentric-rotating-mass (ERM) vibrators to artificial nails, miniature high-resolution tactile displays were realized as an eyes-free output device. To understand how to deliver rich signals to users for valid signal perception, three user studies were conducted. The results suggest that users can not only recognized absolute and relative directional cues, but also recognized numerical characters in EdgeWrite format with an overall 89% recognition rate. Experiments also identified the optimal placement of ERM actuators for maximizing information transfer.

With the ever increasing size of data sets, traditional parallel relational database solution can be prohibitively expensive and may suffer limited scalability. To perform large-scale data processing in a cost-effective manner, several NoSQL data processing systems have been proposed. In this paper, we devise a new, distributed B-tree column indexing scheme for HBase, which can support indexing for non-row-key columns, as well as parallel B-tree search in large data table. Our experiment results demonstrate both performance and scalability advantage of our indexing scheme on point queries, range queries, and aggregation operations, compared with HBase.

As the size of data set in cloud increases rapidly, how to process large amount of data efficiently has become a critical issue. MapReduce provides a framework for large data processing and is shown to be scalable and fault-tolerant on commondity machines. However, it has higher learning curve than SQL-like language and the codes are hard to maintain and reuse. On the other hand, traditional SQL-based data processing is familiar to user but is limited in scalability. In this paper, we propose a hybrid approach to fill the gap between SQL-based and MapReduce data processing. We develop a data management system for cloud, named SQLMR. SQLMR complies SQL-like queries to a sequence of MapReduce jobs. Existing SQL-based applications are compatible seamlessly with SQLMR and users can manage Tera to PataByte scale of data with SQL-like queries instead of writing MapReduce codes. We also devise a number of optimization techniques to improve the performance of SQLMR. The experiment results demonstrate both performance and scalability advantage of SQLMR compared to MySQL and two NoSQL data processing systems, Hive and HadoopDB.