Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION

This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 92×92µm for the implemented digital-backend

A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION

This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 9292m for the implemented digital-backend

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words