An efficient micro control unit VLSI design for wearable electronics and sensor networks

In this paper, an efficient micro control unit (MCU) design is proposed for wearable electronics and wireless sensor networks. It consists of an asynchronous interface, a register bank, a reconfigurable filter, a lossless data encoder, an encryption encoder, an error correct coding (ECC) encoder, a power management, a resolution controller and a multi-sensor controller. The asynchronous interface is used to exchange data in different frequency. The reconfigurable filter cooperates with the register bank to provide functions of high-pass, low-pass and band-pass filters according to various signals. The lossless data encoder consists of an adaptive predictor and a hybrid entropy encoder, which can use different methods to compress different characteristics of signals adaptively. The encryption and ECC encoders are added to improve the security of data and transmission, respectively. For, long-term usage, the power management is developed for reducing the power consumption of the whole system. The resolution and multi-sensor controllers are designed to adjust the resolution and select different sensors, respectively, according to the characteristic of various signals. In addition, the proposed wearable electronics and wireless sensor network systems include image sensors and processor for the applications of special education, autism children assistance and healthcare. The proposed MCU design was synthesized by a 0.18-μm CMOS process and it can operate at 100-MHz processing rate. This design contains 4.29-K gate counts and its core area is 43k-μm2. Compared with previous designs, this design achieved higher performance, higher security, higher reliability, more functions, more flexibility, higher compatibility and lower cost than previous designs. It is suitable for developing wearable electronics and wireless sensor network systems.

[1]  James M. Rehg,et al.  Behavioral Imaging and Autism , 2014, IEEE Pervasive Computing.

[2]  James M. Rehg,et al.  Social interactions: A first-person perspective , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  T. Meydan,et al.  Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG , 2006, IEEE Sensors Journal.

[4]  James M. Rehg Behavior Imaging: Using Computer Vision to Study Autism , 2011, MVA.

[5]  Ting-Lan Lin,et al.  Efficient fuzzy-controlled and hybrid entropy coding strategy lossless ECG encoder VLSI design for wireless body sensor networks , 2013 .

[6]  James M. Rehg,et al.  Temporal causality for the analysis of visual events , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Hai Phuong. Le,et al.  Ultra-low-power variable-resolution successive approximation ADC for biomedical application , 2005 .

[8]  James M. Rehg,et al.  Video Segmentation by Tracking Many Figure-Ground Segments , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  James M. Rehg,et al.  Decoding Children's Social Behavior , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Nicos Maglaveras,et al.  A Methodology for Reliability Analysis in Health Networks , 2008, IEEE Transactions on Information Technology in Biomedicine.

[11]  James M. Rehg,et al.  Quasi-periodic event analysis for social game retrieval , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Álvaro Alesanco Iglesias,et al.  An Integrated Healthcare Information System for End-to-End Standardized Exchange and Homogeneous Management of Digital ECG Formats , 2012, IEEE Transactions on Information Technology in Biomedicine.

[13]  Shih-Lun Chen,et al.  A Power-Efficient Adaptive Fuzzy Resolution Control System for Wireless Body Sensor Networks , 2015, IEEE Access.

[14]  A. Murray,et al.  Toward a miniature wireless integrated multisensor microsystem for industrial and biomedical applications , 2002 .

[15]  Shih-Lun Chen,et al.  An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs) , 2011, Sensors.

[16]  James M. Rehg,et al.  Learning to Predict Gaze in Egocentric Video , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Ivan Laptev,et al.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

[18]  Shih-Lun Chen,et al.  A CMOS Smart Thermal Sensor for Biomedical Application , 2008, IEICE Trans. Electron..

[19]  James M. Rehg Behavior imaging and the study of autism , 2013, ICMI '13.

[20]  Soyoun Jung,et al.  Drain Current Centric Modality: Instrumentation and Evaluation of ISFET for Monitoring Myocardial Ischemia Like Variations in pH and Potassium Ion Concentration , 2009, IEEE Sensors Journal.

[21]  J. Nicolics,et al.  A Low-Cost Wireless Sensor System and Its Application in Dental Retainers , 2009, IEEE Sensors Journal.

[22]  Ericson Chua,et al.  Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems , 2011, IEEE Transactions on Consumer Electronics.

[23]  C. Toumazou,et al.  Ultra-low-power semiconductors for wireless vital signs early warning systems , 2011 .

[24]  Shih-Lun Chen,et al.  Wireless Body Sensor Network With Adaptive Low-Power Design for Biometrics and Healthcare Applications , 2009, IEEE Systems Journal.

[25]  James M. Rehg,et al.  Modeling Actions through State Changes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Shih-Lun Chen,et al.  An Efficient Micro Control Unit with a Reconfigurable Filter Design for Wireless Body Sensor Networks (WBSNs) , 2012, Sensors.