Low-power All-analog Circuit for Rectangular-type Analog Joint Source Channel Coding

A low-complexity all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC), which can compress two or more sensor signals into one with controlled distortion while also being robust against wireless channel impairments. The idea is to realize the rectangular-type AJSCC using Voltage Controlled Voltage Sources (VCVS). The proposal is verified by Spice simulations as well as breadboard and Printed Circuit Board (PCB) implementations. Results indicate that the design is feasible for low-complexity systems like persistent wireless sensor networks requiring low circuit power.Comment: 4 pages ISCAS 2016. arXiv admin note: text overlap with arXiv:1701.05599, arXiv:1907.0144

Towards Low-power Wearable Wireless Sensors for Molecular Biomarker and Physiological Signal Monitoring

A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power consumption of the sensor is achieved by an all-analog circuit implementing Analog Joint Source-Channel Coding (AJSCC) compression. The sensor is applicable to a wide range of biomedical applications that require real-time concurrent molecular biomarker and physiological signal monitoring.Comment: 4 pages ISCAS 2017. arXiv admin note: substantial text overlap with arXiv:1907.0032

On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).Comment: IEEE International Conference on Robotics and Automation (ICRA), May 2020, 6+1 page

Towards Ultra-low-power Realization of Analog Joint Source-Channel Coding using MOSFETs

Certain sensing applications such as Internet of Things (IoTs), where the sensing phenomenon may change rapidly in both time and space, requires sensors that consume ultra-low power (so that they do not need to be put to sleep leading to loss of temporal and spatial resolution) and have low costs (for high density deployment). A novel encoding based on Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) is proposed to realize Analog Joint Source Channel Coding (AJSCC), a low-complexity technique to compress two (or more) signals into one with controlled distortion. In AJSCC, the y-axis is quantized while the x-axis is continuously captured. A power-efficient design to support multiple quantization levels is presented so that the digital receiver can decide the optimum quantization and the analog transmitter circuit is able to realize that. The approach is verified via Spice and MATLAB simulations.Comment: 5 pages, IEEE ISCAS 2019. arXiv admin note: text overlap with arXiv:1907.0096

Improved Circuit Design of Analog Joint Source Channel Coding for Low-power and Low-complexity Wireless Sensors

To enable low-power and low-complexity wireless monitoring, an improved circuit design of Analog Joint Source Channel Coding (AJSCC) is proposed for wireless sensor nodes. This innovative design is based on Analog Divider Blocks (ADB) with tunable spacing between AJSCC levels. The ADB controls the switching between two types of Voltage Controlled Voltage Sources (VCVS). LTSpice simulations were performed to evaluate the performance of the circuit, and the power consumption and circuit complexity of this new ADB-based design were compared with our previous parallel-VCVS design. It is found that this improved circuit design based on ADB outperforms the design based on parallel VCVS for a large number of AJSCC levels (>= 16), both in terms of power consumption as well as circuit complexity, thus enabling persistent and higher temporal/spatial resolution environmental sensing.Comment: 8 pages, IEEE Sensor Journa

Analog Signal Compression and Multiplexing Techniques for Healthcare Internet of Things

Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and/or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages---it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.Comment: 9 pages, IEEE MASS 201