A CMOS Analog Front-End for Tunnelling Magnetoresistive Spintronic Sensing Systems

This paper presents a CMOS readout circuit for an integrated and highly-sensitive tunnel-magnetoresistive (TMR) sensor. Based on the characterization of the TMR sensor in the finite-element simulation, using COMSOL Multiphysics, the circuit including a Wheatstone bridge and an analogue front-end (AFE) circuit, were designed to achieve low-noise and low-power sensing. We present a transimpedance amplifier (TIA) that biases and amplifies a TMR sensor array using switched-capacitors external noise filtering and allows the integration of TMR sensors on CMOS without decreasing the measurement resolution. Designed using TSMC 0.18 μm 1V technology, the amplifier consumes 160 nA at 1.8 V supply to achieve a dc gain of 118 dB and a bandwidth of 3.8 MHz. The results confirm that the full system is able to detect the magnetic field in the pico-Tesla range with low circuit noise (2.297 pA/√Hz) and low power consumption (86 μW). A concurrent reduction in the power consumption and attenuation of noise in TMR sensors makes them suitable for long-term deployment in spintronic sensing systems

Stochastic Electron Acceleration in Shell-Type Supernova Remnants II

We discuss the generic characteristics of stochastic particle acceleration by a fully developed turbulence spectrum and show that resonant interactions of particles with high speed waves dominate the acceleration process. To produce the relativistic electrons inferred from the broadband spectrum of a few well-observed shell-type supernova remnants in the leptonic scenario for the TeV emission, fast mode waves must be excited effectively in the downstream and dominate the turbulence in the subsonic phase. Strong collisionless non-relativistic astrophysical shocks are studied with the assumption of a constant Aflven speed. The energy density of non-thermal electrons is found to be comparable to that of the magnetic field. With reasonable parameters, the model explains observations of shell-type supernova remnants. More detailed studies are warranted to better understand the nature of supernova shocks.Comment: 5 pages, 7 figures, submitted to Proceedings of the Conference on "2008 Heidelberg International Symposium on High Energy Gamma-Ray Astronomy

Experimental investigation of a novel vertical loop-heat-pipe PV/T heat and power system under different height differences

For a novel vertical solar loop-heat-pipe photovoltaic/thermal system, the height difference between evaporator and condenser plays an important role in the heat transport capacity, which has significant impact on the solar thermal efficiency and parametrical optimization of this system. Therefore, based on the results derived from the authors’ previous analytical investigation and computer modelling studies, a prototype of this novel system was designed, constructed, and an experimental investigation under different height difference was undertaken to study the impact of height difference on the system performance. It was found that the relationship between the solar thermal efficiency of this vertical system and the height difference is nonlinear. In present study, the optimal height difference is around 1.1 m, which was selected as an optimal value for the following experimental investigations, and below 1.1 m, the PV module surface temperature decreased with the increase of the height difference. Furthermore, the transient solar thermal and electrical performance of this system with the selected optimal height difference were investigated under outdoor real weather condition. These results of this experimentation can help optimize the system construction and thus help to develop the high thermal performance and low-cost solar PV/T system for space heating and power generation

A CMOS hall sensor modelling with readout circuitry and microcontroller processing for magnetic detection

A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 μm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array. The system puts forward a complete solution from Hall sensors, analog front-end circuit, analog-to-digital converter (ADC) to microcontroller unit. Using Ansoft Maxwell and COMSOL Multiphysics software for simulation verification, the minimum diameter of magnetic particles that can be detected in the system is 2 μm. The measured signal to noise and distortion ratio, spurious free dynamic range, and effective number of bits of the proposed ADC are 70.61 dB, 90.08 dB, and 11.44-bit, respectively. The microsystem based on STM32 combines hardware and software design, which can effectively adjust the motion parameters and realize the real-time display in the LCD screen of the magnetic field and voltage information. Compared to the prior system, the portability, cost, and efficiency have been considerably improved, which is aimed at the rapid measurement of heavy metal particles such as Fe, Co, and Ni in ambient air and blood

Detection techniques of biological and chemical Hall sensors

Integrated magnetic Hall effect sensors have been widely used in people's daily life over the past decades, and still are gaining enormous attention from researchers to establish novel applications, especially in biochemistry and biomedical healthcare. This paper reviews, classifies, compares and concludes state-of-the-art integrated Hall magnetic sensors in terms of cost, power, area, performance and application. Current applications of the Hall sensors such as detecting magnetic nanoparticles (MNPs) labeled on biomolecule, monitoring blood pulse wave velocity, characterizing soft biological materials, controlling syringe injection rate and eye surgery by training systems, and assisting magnetic resonance imaging (MRI) will be discussed comprehensively and future applications and trends will be highlighted. This review paper will introduce Hall sensor's advantages such as simple design and technology of manufacturing, low cost, low power consumption, possibility of the miniaturizing, noninvasive and room temperature measurement, with respect to the other magnetic sensing systems and methods

Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning

In the field of multi-task reinforcement learning, the modular principle, which involves specializing functionalities into different modules and combining them appropriately, has been widely adopted as a promising approach to prevent the negative transfer problem that performance degradation due to conflicts between tasks. However, most of the existing multi-task RL methods only combine shared modules at the task level, ignoring that there may be conflicts within the task. In addition, these methods do not take into account that without constraints, some modules may learn similar functions, resulting in restricting the model's expressiveness and generalization capability of modular methods. In this paper, we propose the Contrastive Modules with Temporal Attention(CMTA) method to address these limitations. CMTA constrains the modules to be different from each other by contrastive learning and combining shared modules at a finer granularity than the task level with temporal attention, alleviating the negative transfer within the task and improving the generalization ability and the performance for multi-task RL. We conducted the experiment on Meta-World, a multi-task RL benchmark containing various robotics manipulation tasks. Experimental results show that CMTA outperforms learning each task individually for the first time and achieves substantial performance improvements over the baselines.Comment: This paper has been accepted at NeurIPS 2023 as a poste

Stochastic Acceleration in the Western Hotspot of Pictor A

Chandra's high resolution observations of radio galaxies require a revisit of the relevant electron acceleration processes. Although the diffusive shock particle acceleration model may explain spectra of spatially unresolved sources, it encounters difficulties in explaining the structure and spectral properties of recently discovered Chandra X-ray features in several low-power radio sources. We argue that these observations strongly suggest stochastic electron acceleration by magnetized turbulence, and show that the simplest stochastic particle acceleration model with energy independent acceleration and escape timescales can overcome most of these difficulties. We use the bright core of the western hotspot of Pictor A as an example to demonstrate the model characteristics, which may be tested with high energy observations.Comment: 12 pages, 2 figures. Accepted by ApJ Letter

RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars

Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still face great obstacles in real-world scenarios. One of the vital causes is inadequate datasets -- 1) current public datasets can only support researchers to explore high-fidelity head avatars in one or two task directions; 2) these datasets usually contain digital head assets with limited data volume, and narrow distribution over different attributes. In this paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive advance in head avatar research. It contains massive data assets, with 243+ million complete head frames, and over 800k video sequences from 500 different identities captured by synchronized multi-view cameras at 30 FPS. It is a large-scale digital library for head avatars with three key attributes: 1) High Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K cameras in 360 degrees. 2) High Diversity: The collected subjects vary from different ages, eras, ethnicities, and cultures, providing abundant materials with distinctive styles in appearance and geometry. Moreover, each subject is asked to perform various motions, such as expressions and head rotations, which further extend the richness of assets. 3) Rich Annotations: we provide annotations with different granularities: cameras' parameters, matting, scan, 2D/3D facial landmarks, FLAME fitting, and text description. Based on the dataset, we build a comprehensive benchmark for head avatar research, with 16 state-of-the-art methods performed on five main tasks: novel view synthesis, novel expression synthesis, hair rendering, hair editing, and talking head generation. Our experiments uncover the strengths and weaknesses of current methods. RenderMe-360 opens the door for future exploration in head avatars.Comment: Technical Report; Project Page: 36; Github Link: https://github.com/RenderMe-360/RenderMe-36

An Efficient Method to Extract Surface-Wave Poles of Green\u27s Functions near Branch Cut in Lossy Layered Media

Calculating the Green\u27s functions in lossy layered media using the discrete complex image method (DCIM) is challenging, due to the difficulties in extracting the surface-wave poles that are very close to a branch cut. An efficient algorithm based on the contour method is proposed in this communication to locate these poles and calculate the residues. The proposed method is robust for both the lossless and lossy media. With the proposed approach, it is shown in numerical examples that some poles, very close to a branch cut, are successfully extracted in lossy media. The accurate calculation of the Green\u27s functions in lossy layered media enables the accurate and efficient modeling of complex structures in lossy semiconductor substrates and new 3D IC structures including through-silicon vias (TSVs)