Fully-Differential Multichannel Integrated Neural Signal Recording Front-End

Neural signal can be used for clinical disease diagnosis,data analysis and real-time life signal monitoring.Its analysis requires high-performance signal processors.Based on the 180 nm standard CMOS technology,a16-channel fully-differential neural recording chip is designed.The chip consists of 16-channel low-noise pre-amplifiers,a multiplexer and a successive approximation register（SAR）ADC.The result shows that the equivalent input-referred noise of recording amplifier is 3.63μV,bringing down noise efficiency factor to 4.24.At 8.5 bits effective number of bit（ENOB）,the analog-to-digital converter（ADC）has an SNR of 52.6dB.The core area of the proposed neural recording front-end is about 2.46 mm^2.

Online Detection of State Estimator Performance Degradation via Efficient Numerical Observability Analysis

An efficient observability analysis method is proposed to enable online detection of performance degradation of an optimization-based sliding window visual-inertial state estimation framework.The proposed methodology leverages numerical techniques in nonlinear observability analysis to enable online evaluation of the system observability and indication of the state estimation performance.Specifically,an empirical observability Gramian based approach is introduced to efficiently measure the observability condition of the windowed nonlinear system,and a scalar index is proposed to quantify the average system observability.The proposed approach is specialized to a challenging optimizationbased sliding window monocular visual-inertial state estimation formulation and evaluated through simulation and experiments to assess the efficacy of the methodology.The analysis result shows that the proposed approach can correctly indicate degradation of the state estimation accuracy with real-time performance.

Ultra-Low Power 1 Volt Small Size 2.4 GHz CMOS RF Transceiver Design for Wireless Sensor Node

Ultra-low power transceiver design is proposed for wireless sensor node used in the wireless sensor network（WSN）.Typically,each sensor node contains a transceiver so it is required that both hardware and software designs of WSN node must take care of energy consumption during all modes of operation including active/sleep modes so that the operational life of each node can be increased in order to increase the lifetime of network.The current declared size of the wireless sensor node is of millimeter order,excluding the power source and crystal oscillator.We have proposed a new 2.4 GHz transceiver that has five blocks namely XO,PLL,PA,LNA and IF.The proposed transceiver incorporates less number of low-drop outs（LDOs）regulators.The size of the transceiver is reduced by decreasing the area of beneficiary components up to 0.41 mm;of core area in such a way that some functions are optimally distributed among other components.The proposed design is smaller in size and consumes less power,<1 mW,compared to other transceivers.The operating voltage has also been reduced to 1 V.This transceiver is most efficient and will be fruitful for the wireless networks as it has been designed by considering modern requirements.

Anticipating Degradation in State Estimation Accuracy via Online Nonlinear Observability Analysis

A methodology is proposed to enable real-time evaluation of the observability of local motions,and generate a local observability cost map to enable informed local motion planning in order to avoid potential degradation or degeneracy in state estimator performance.The proposed approach leverages efficient numerical techniques in nonlinear observability analysis and motion primitive-based planning technique to realize the local observability prediction with real-time performance.The degradation of the state estimation performance can be readily predicted with the local observability evaluation result.The proposed approach is specialized to a representative optimization-based monocular visual-inertial state estimation formulation and evaluated through simulation and experiments.The experimental results demonstrated the ability of the proposed methodology to correctly anticipate the potential state estimation degradation.

Optimized Implementation for Wave Digital Filter Based Circuit Emulation on FPGA

A binary tree representation is designed in this paper for optimization of wave digital filter（WDF）implementation.To achieve this,an equivalent WDF model of the original circuit is converted into abinary tree representation at first.This WDF binary tree can then be transformed to several topologies with the same implication,since the WDF adaptors have a symmetrical behavior on their ports.Because the WDF implementation is related to field programmable gate array（FPGA）resource usage and the cycle time of emulation,choosing aproper binary tree topology for WDF implementation can help balance the complexity and performance quality of an emulation system.Both WDF-FPGA emulation and HSpice simulation on the same circuit are tested.There is no significant difference between these two simulations.However,in terms of time consumption,the WDF-FPGA emulation has an advantage over the other.Our experiment also demonstrates that the optimized WDF-FPGA emulation has an acceptable accuracy and feasibility.

Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis

An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.