A 0.18µm CMOS UWB wireless transceiver for medical sensing applications

Recently, there is a new trend of demand of a biomedical device that can continuously monitor patient’s vital life index such as heart rate variability (HRV) and respiration rate. This desired device would be compact, wearable, wireless, networkable and low-power to enable proactive home monitoring of vital signs. This device should have a radar sensor portion and a wireless communication link all integrated in one small set. The promising technology that can satisfy these requirements is the impulse radio based Ultra-wideband (IR-UWB) technology. Since Federal Communications Commission (FCC) released the 3.1GHz-10.6GHz frequency band for UWB applications in 2002 [1], IR-UWB has received significant attention for applications in target positioning and wireless communications. IR-UWB employs extremely narrow Gaussian monocycle pulses or any other forms of short RF pulses to represent information. In this project, an integrated wireless UWB transceiver for the 3.1GHz-10.6GHz IR-UWB medical sensor was developed in the 0.18µm CMOS technology. This UWB transceiver can be employed for both radar sensing and communication purposes. The transceiver applies the On-Off Keying (OOK) modulation scheme to transmit short Gaussian pulse signals. The transmitter output power level is adjustable. The fully integrated UWB transceiver occupies a core area of 0.752mm^2 and the total die area of 1.274mm^2 with the pad ring inserted. The transceiver was simulated with overall power consumption of 40mW for radar sensing. The receiver is very sensitive to weak signals with a sensitivity of -73.01dBm. The average power of a single pulse is 9.8µW. The pulses are not posing any harm to human tissues. The sensing resolution and the target positioning precision are presumably sufficient for heart movement detection purpose in medical applications. This transceiver can also be used for high speed wireless data communications. The data transmission rate of 200 Mbps was achieved with an overall power consumption of 57mW. A combination of sensing and communications can be used to build a low power sensor

Ultra-Wideband CMOS Transceiver Front-End for Bio-Medical Radar Sensing

Since the Federal Communication Commission released the unlicensed 3.1-10.6 GHz frequency band for commercial use in early 2002, the ultra wideband (UWB) has developed from an emerging technology into a mainstream research area. The UWB technology, which utilizes wide spectrum, opens a new era of possibility for practical applications in radar sensing, one of which is the human vital sign monitoring. The aim of this thesis is to study and research the possibility of a new generation humanrespiration monitoring sensor using UWB radar technology and to develop a new prototype of UWB radar sensor for system-on-chip solutions in CMOS technology. In this thesis, a lowpower Gaussian impulse UWB mono-static radar transceiver architecture is presented. The UWB Gaussian pulse transmitter and receiver are implemented and fabricated using 90nm CMOS technology. Since the energy of low order Gaussian pulse is mostly condensed at lower frequency, in order to transmit the pulse in a very efficient way, higher order Gaussian derivative pulses are desired as the baseband signal. This motivates the advancement of the design into UWB high-order pulse transmitter. Both the Gaussian impulse UWB transmitter and Gaussian higher-order impulse UWB transmitter take the low-power and high-speed advantage of digital circuit to generate different waveforms. The measurement results are analyzed and discussed. This thesis also presents a low-power UWB mono-static radar transceiver architecture exploiting the full benefit of UWB bandwidth in radar sensing applications. The transceiver includes a full UWB band transmitter, an UWB receiver front-end, and an on-chip diplexer. The non-coherent UWB transmitter generates pulse modulated baseband signals at different carrier frequencies within the designated 3-10 GHz band using a digitally controlled pulse generator. The test shows the proposed radar transceiver can detect the human respiration pattern within 50 cm distance. The applications of this UWB radar sensing solution in commercialized standard CMOS technology include constant breathing pattern monitoring for gated radiation therapy, realtime monitoring of patients, and any other breathing monitoring. The research paves the way to wireless technology integration with health care and bio-sensor network

Effective p-wave interaction and topological superfluids in s-wave quantum gases

P-wave interaction in cold atoms may give rise to exotic topological superfluids. However, the realization of p-wave interaction in cold atom system is experimentally challenging. Here we propose a simple scheme to synthesize effective $p$-wave interaction in conventional $s$-wave interacting quantum gases. The key idea is to load atoms into spin-dependent optical lattice potential. Using two concrete examples involving spin-1/2 fermions, we show how the original system can be mapped into a model describing spinless fermions with nearest neighbor p-wave interaction, whose ground state can be a topological superfluid that supports Majorana fermions under proper conditions. Our proposal has the advantage that it does not require spin-orbit coupling or loading atoms onto higher orbitals, which is the key in earlier proposals to synthesize effective $p$-wave interaction in $s$-wave quantum gases, and may provide a completely new route for realizing $p$-wave topological superfluids.Comment: 5 pages, 4 figure

Head Pose Estimation via Manifold Learning

For the last decades, manifold learning has shown its advantage of efficient non-linear dimensionality reduction in data analysis. Based on the assumption that informative and discriminative representation of the data lies on a low-dimensional smooth manifold which implicitly embedded in the original high-dimensional space, manifold learning aims to learn the low-dimensional representation following some geometrical protocols, such as preserving piecewise local structure of the original data. Manifold learning also plays an important role in the applications of computer vision, i.e., face image analysis. According to the observations that many face-related research is benefitted by the head pose estimation, and the continuous variation of head pose can be modelled and interpreted as a low-dimensional smooth manifold, we will focus on the head pose estimation via manifold learning in this chapter. Generally, head pose is hard to directly explore from the high-dimensional space interpreted as face images, which is, however, can be efficiently represented in low-dimensional manifold. Therefore, in this chapter, classical manifold learning algorithms are introduced and the corresponding application on head pose estimation are elaborated. Several extensions of manifold learning algorithms which are developed especially for head pose estimation are also discussed and compared

An Intelligent Social Learning-based Optimization Strategy for Black-box Robotic Control with Reinforcement Learning

Implementing intelligent control of robots is a difficult task, especially when dealing with complex black-box systems, because of the lack of visibility and understanding of how these robots work internally. This paper proposes an Intelligent Social Learning (ISL) algorithm to enable intelligent control of black-box robotic systems. Inspired by mutual learning among individuals in human social groups, ISL includes learning, imitation, and self-study styles. Individuals in the learning style use the Levy flight search strategy to learn from the best performer and form the closest relationships. In the imitation style, individuals mimic the best performer with a second-level rapport by employing a random perturbation strategy. In the self-study style, individuals learn independently using a normal distribution sampling method while maintaining a distant relationship with the best performer. Individuals in the population are regarded as autonomous intelligent agents in each style. Neural networks perform strategic actions in three styles to interact with the environment and the robot and iteratively optimize the network policy. Overall, ISL builds on the principles of intelligent optimization, incorporating ideas from reinforcement learning, and possesses strong search capabilities, fast computation speed, fewer hyperparameters, and insensitivity to sparse rewards. The proposed ISL algorithm is compared with four state-of-the-art methods on six continuous control benchmark cases in MuJoCo to verify its effectiveness and advantages. Furthermore, ISL is adopted in the simulation and experimental grasping tasks of the UR3 robot for validations, and satisfactory solutions are yielded

Ontology-aware Learning and Evaluation for Audio Tagging

This study defines a new evaluation metric for audio tagging tasks to overcome the limitation of the conventional mean average precision (mAP) metric, which treats different kinds of sound as independent classes without considering their relations. Also, due to the ambiguities in sound labeling, the labels in the training and evaluation set are not guaranteed to be accurate and exhaustive, which poses challenges for robust evaluation with mAP. The proposed metric, ontology-aware mean average precision (OmAP) addresses the weaknesses of mAP by utilizing the AudioSet ontology information during the evaluation. Specifically, we reweight the false positive events in the model prediction based on the ontology graph distance to the target classes. The OmAP measure also provides more insights into model performance by evaluations with different coarse-grained levels in the ontology graph. We conduct human evaluations and demonstrate that OmAP is more consistent with human perception than mAP. To further verify the importance of utilizing the ontology information, we also propose a novel loss function (OBCE) that reweights binary cross entropy (BCE) loss based on the ontology distance. Our experiment shows that OBCE can improve both mAP and OmAP metrics on the AudioSet tagging task.Comment: Submitted to ICASSP 2023. The code is open-sourced at https://github.com/haoheliu/ontology-aware-audio-taggin

Low-complexity CNNs for Acoustic Scene Classification

This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K parameters, and (b) there should be a maximum of 30 millions multiply-accumulate operations (MACs) per inference. In this report, we present low-complexity systems for ASC that follow the rules intended for the task.Comment: Technical Report DCASE 2022 TASK 1. arXiv admin note: substantial text overlap with arXiv:2207.1152

Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning

Automated audio captioning (AAC) which generates textual descriptions of audio content. Existing AAC models achieve good results but only use the high-dimensional representation of the encoder. There is always insufficient information learning of high-dimensional methods owing to high-dimensional representations having a large amount of information. In this paper, a new encoder-decoder model called the Low- and High-Dimensional Feature Fusion (LHDFF) is proposed. LHDFF uses a new PANNs encoder called Residual PANNs (RPANNs) to fuse low- and high-dimensional features. Low-dimensional features contain limited information about specific audio scenes. The fusion of low- and high-dimensional features can improve model performance by repeatedly emphasizing specific audio scene information. To fully exploit the fused features, LHDFF uses a dual transformer decoder structure to generate captions in parallel. Experimental results show that LHDFF outperforms existing audio captioning models.Comment: INTERSPEECH 2023. arXiv admin note: substantial text overlap with arXiv:2210.0503