scholarly journals A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Daejin Park ◽  
Jonghee M. Youn ◽  
Jeonghun Cho
Keyword(s):  
Signal Processing ◽  
Data Processing ◽  
Energy Efficient ◽  
Rare Event ◽  
Sensor Data ◽  
Processing Unit ◽  
Sensing Applications ◽  
Event Driven ◽  
Iot Devices ◽  
Activity Sensing

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.

scholarly journals Accuracy-Energy Configurable Sensor Processor and IoT Device for Long-Term Activity Monitoring in Rare-Event Sensing Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Daejin Park ◽  
Jeonghun Cho
Keyword(s):  
Rare Event ◽  
Sensor Data ◽  
Event Generator ◽  
Signal Acquisition ◽  
Processing Unit ◽  
Sensor Signal ◽  
Single Chip ◽  
Sensing Applications ◽  
Tightly Coupled ◽  
Sensor Processor

A specially designed sensor processor used as a main processor in IoT (internet-of-thing) device for the rare-event sensing applications is proposed. The IoT device including the proposed sensor processor performs the event-driven sensor data processing based on an accuracy-energy configurable event-quantization in architectural level. The received sensor signal is converted into a sequence of atomic events, which is extracted by the signal-to-atomic-event generator (AEG). Using an event signal processing unit (EPU) as an accelerator, the extracted atomic events are analyzed to build the final event. Instead of the sampled raw data transmission via internet, the proposed method delays the communication with a host system until a semantic pattern of the signal is identified as a final event. The proposed processor is implemented on a single chip, which is tightly coupled in bus connection level with a microcontroller using a 0.18 μm CMOS embedded-flash process. For experimental results, we evaluated the proposed sensor processor by using an IR- (infrared radio-) based signal reflection and sensor signal acquisition system. We successfully demonstrated that the expected power consumption is in the range of 20% to 50% compared to the result of the basement in case of allowing 10% accuracy error.

scholarly journals Design and Testing of a Sensor Data Processing Unit for Dual-Control Missile Demonstrator

2019 ◽  
Vol 10 (2) ◽  
pp. 131-142
Author(s):  
Witold BUŻANTOWICZ ◽  
Jan PIETRASIEŃSKI
Keyword(s):  
Mathematical Model ◽  
Data Processing ◽  
Angular Rate ◽  
Feedback Loops ◽  
Sensor Data ◽  
Processing Unit ◽  
Dual Control ◽  
Electronic Components ◽  
Data Processing Unit ◽  
Design And Testing

The article discusses aspects of the design and testing of a sensor data processing unit whose function relates to the static and dynamic stabilization of a missile airframe. The authors present a mathematical model of dual-control missile dynamics, along with the autopilot implemented on the basis of two feedback loops – from acceleration and from angular rate of the airframe. The draft of the sensor data processing unit is presented in the form of three PCB packages, with connections for the installation of electronic components. In addition, a laboratory stand used in the experimental research, as well as selected results for the device are described.

Internet of Things and Fog Computing Applications in Intelligent Transportation Systems

2020 ◽  
pp. 131-150 ◽  
Author(s):  
Korupalli V. Rajesh Kumar ◽  
K. Dinesh Kumar ◽  
Ravi Kumar Poluru ◽  
Syed Muzamil Basha ◽  
M Praveen Kumar Reddy
Keyword(s):  
Intelligent Transportation Systems ◽  
Fog Computing ◽  
Analytical Techniques ◽  
Transportation Systems ◽  
Sensor Data ◽  
Smart Sensors ◽  
Processing Unit ◽  
Stream Data ◽  
Autonomous Cars ◽  
Iot Devices

Self-driving vehicles such as autonomous cars are manufactured mostly with smart sensors and IoT devices with artificial intelligence (AI) techniques. In most of the cases, smart sensors are networked with IoT devices to transmit the data in real-time. IoT devices transmit the sensor data to the processing unit to do necessary actions based on sensor output data. The processing unit executes the tasks based on pre-defined instructions given to the processor with embedded and AI coding techniques. Continuous streaming of sensors raw data to the processing unit and for cloud storage are creating a huge load on cloud devices or on servers. In order to reduce the amount of stream data load on the cloud, fog computing, or fogging technology, helps a lot. Fogging is nothing but the pre-processing of the data before deploying it into the cloud. In fog environment, data optimization and analytical techniques take place as a part of data processing in a data hub on IoT devices or in a gateway.

scholarly journals IWSNs with On-Sensor Data Processing for Energy Efficient Machine Fault Diagnosis

2019 ◽  
Vol 15 (08) ◽  
pp. 42
Author(s):  
Liqun Hou ◽  
Junteng Hao ◽  
Yongguang Ma ◽  
Neil Bergmann
Keyword(s):  
Energy Consumption ◽  
Fault Diagnosis ◽  
Data Processing ◽  
Energy Efficient ◽  
Sensor Data ◽  
Support Vector ◽  
Font Size ◽  
Machine Fault Diagnosis ◽  
Transmission Data ◽  
Machine Fault

<span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">Machine fault diagnosis systems need to collect and transmit dynamic signals, like vibration and current, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. Large amounts of transmission data will </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">increase the energy consumption and </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">shorten the lifetime of energy-constrained IWSN node</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s as well</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">.</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">To address th</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> tension</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> when implementing machine fault diagnosis applications in </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">IWSNs</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">, this paper proposes a</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">n</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">energy efficient </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.</span>

A 0.05pJ/Pixel 70fps FHD 1Meps Event-Driven Visual Data Processing Unit

2020 ◽  
Author(s):  
S. Paul ◽  
T. Majumder ◽  
C. Augustine ◽  
A. F. Malavasi ◽  
S. Usirikayala ◽  
...  
Keyword(s):  
Data Processing ◽  
Processing Unit ◽  
Visual Data ◽  
Data Processing Unit ◽  
Event Driven

FPGA Based Vehicle to Vehicle Communication in Spartan 3E

2020 ◽  
Vol 8 ◽  
pp. 14-21
Author(s):  
Surya Man Koju ◽  
Nikil Thapa
Keyword(s):  
Signal Processing ◽  
Autonomous Vehicle ◽  
Processing Unit ◽  
Warning Signals ◽  
Vehicle Communication ◽  
Digital Vlsi ◽  
Vehicle To Vehicle ◽  
Vehicle Operation ◽  
Vehicle Technology ◽  
The Voice

This paper presents economic and reconfigurable RF based wireless communication at 2.4 GHz between two vehicles. It implements digital VLSI using two Spartan 3E FPGAs, where one vehicle receives the information of another vehicle and shares its own information to another vehicle. The information includes vehicle’s speed, location, heading and its operation, such as braking status and turning status. It implements autonomous vehicle technology. In this work, FPGA is used as central signal processing unit which is interfaced with two microcontrollers (ATmega328P). Microcontroller-1 is interfaced with compass module, GPS module, DF Player mini and nRF24L01 module. This microcontroller determines the relative position and the relative heading as seen from one vehicle to another. Microcontroller-2 is used to measure the speed of vehicle digitally. The resulting data from these microcontrollers are transmitted separately and serially through UART interface to FPGA. At FPGA, different signal processing such as speed comparison, turn comparison, distance range measurement and vehicle operation processing, are carried out to generate the voice announcement command, warning signals, event signals, and such outputs are utilized to warn drivers about potential accidents and prevent crashes before event happens.

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