Determining the deposited energy of a gamma interaction in a scintillator is of vital importance for PET imaging. In this paper, a peak-based PET detector that simultaneously attain a high count rate is proposed. This detector exploits a known fact that a pulse peak is roughly linear with its energy. Therefore, a fast peak detector is designed for energy characterization using peaks directly acquired from scintillation pulses without any pulse shaping techniques. The key design of this peak detector is the utilization of comparators rather than amplifiers, along with a turbocharged current source, so as to increase the slew rate and equivalently curtail the time it requires to catch the peak of a fast scintillation pulse in as few as around 40 ns. A PET detector prototype is implemented based on the designed circuit and experiments are carried out extensively to evaluate its performance. The prototype achieves an energy resolution of 14.8%@511KeV and a coincidence resolution time of 345 ps.
Proliferation of smart environments entails the need for real-time and ubiquitous human-machine interactions through, mostly likely, hand/arm motions. Though a few recent efforts attempt to track hand/arm motions in real-time with COTS devices, they either obtain a rather low accuracy or have to rely on a carefully designed infrastructure and some heavy signal processing. To this end, we propose SoM (Sound of Motion) as a lightweight system for wrist tracking. Requiring only a smart watch-phone pair, SoM entails very light computations that can operate in resource constrained smartwatches. SoM uses embedded IMU sensors to perform basic motion tracking in the smartwatch, and it depends on the fixed smartphone to act as an "acoustic anchor": regular beacons sent by the phone are received in an irregular manner due to the watch motion, and such variances provide useful hints to adjust the drifting of IMU tracking. Using extensive experiments on our SoM prototype, we demonstrate that the delicately engineered system achieves a satisfactory wrist tracking accuracy and strikes a good balance between complexity and performance.
Recent years have witnessed significant advances in indoor positioning. Existing approaches either require cumbersome site surveys or customized hardware, thus hindering their popularity. In this paper, we propose Stationary peers-Assisted indoor Positioning (SAP), a practical indoor positioning solution that is highly scalable and easy-to-deploy. SAP greatly alleviates the pain of fingerprint collection by smartly leveraging the relatively stationary people, namely stationary peers that are largely available in common indoor environments to assist positioning. Once SPs’ locations and the relative distance between SPs and a target are obtained, SAP can apply trilateration to locate the target. SAP incorporates three key modules: a novel accelerometer-based filter that can accurately identify SP, an enhanced fingerprint-based positioning method that can accurately pinpoint SPs’ locations, and a robust acoustic ranging method. We implement a prototype of SAP on the Android platform and evaluate its performance in representative real-world environments. SAP achieves an 80% positioning error of 2.2 m, which is comparable to the most existing smartphone-assisted approaches.
Peak detection is useful in a wide range of applications. To achieve this task, conventional approaches [including dedicated application specific integrated circuit-based designs] often demand analog readout chains and compulsory add-ons [e.g., additional analogy-to-digital converter (ADC) for peak sampling], rendering them neither compact nor flexible. In this article, we propose a field programmable gate array (FPGA)-only solution to fulfill this task without any external components. Specifically, we achieve peak detection with only a low-voltage differential signaling (LVDS) comparator and two general-purpose input/outputs (GPIOs). Therefore, one can flexibly make a practical deployment by a simple routing process, a nontrivial task even for inexperienced engineers. Meanwhile, to further compact the design for peak sampling, we leverage an FPGA-based slope ADC within which, we propose a new resource-efficient and calibration-free time-to-digital converter architecture. The underlying rational is to minimize the nonlinearity problem by squeezing all resources within a single clock region. We have implemented a prototype using Xilinx FPGA and extensively evaluated its performance in rather challenging scenarios. Results demonstrate a peak digitization error of only about 30 mV in a full 1.8 V range for impulses with as few as 10ns pulsewidth. Meanwhile, in a practical deployment that requires a wide range of peak sampling, the achieved energy resolution is only 1.3% worse than a dedicated high-speed oscilloscope.
Finite network lifetime severely limits the usage of sensor networks. To prolong the lifetime, researchers adopt mobile chargers to recharge sensors with external power sources. Prior studies customarily assumed that a mobile charger sequentially visits and charges sensors. However, this method is inefficient, since it normally incurs considerable charging waiting time. To address the limitation, in this paper, we propose to replenish sensors with a mobile worker carrying multiple portable chargers. The worker simultaneously charges multiple sensors in a small region by deploying the chargers onto the sensors. We claim that this parallel charging mode significantly reduces waiting time incurred per sensor and thus improves charging efficiency. Based on the novel design, this paper contributes two novel approaches called periodic multi-charger (PMC) and on-demand multi-charger (OMC). The PMC offers guaranteed and sustainable power supplies via periodic charging schedules, while the OMC derives charging schedules in real time to adapt to the dynamism in energy consumption patterns. Both the algorithms achieve high charging efficiency by simultaneously charging sensors with multiple chargers in each charging round. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches.
In this work, we expand on the XENON1T nuclear recoil searches to study the individual signals of dark matter interactions from operators up to dimension eight in a chiral effective field theory (ChEFT) and a model of inelastic dark matter (iDM). We analyze data from two science runs of the XENON1T detector totaling 1t×yr exposure. For these analyses, we extended the region of interest from [4.9,40.9]keVNR to [4.9,54.4]keVNR to enhance our sensitivity for signals that peak at nonzero energies. We show that the data are consistent with the background-only hypothesis, with a small background overfluctuation observed peaking between 20 and 50keVNR, resulting in a maximum local discovery significance of 1.7σ for the Vector⊗Vectorstrange ChEFT channel for a dark matter particle of 70GeV/c2 and 1.8σ for an iDM particle of 50GeV/c2 with a mass splitting of 100keV/c2. For each model, we report 90% confidence level upper limits. We also report upper limits on three benchmark models of dark matter interaction using ChEFT where we investigate the effect of isospin-breaking interactions. We observe rate-driven cancellations in regions of the isospin-breaking couplings, leading to up to 6 orders of magnitude weaker upper limits with respect to the isospin-conserving case. Published by the American Physical Society 2024
The location algorithm in C-V2X is one of the important technical approaches for the development of autonomous driving services. Currently, it is based on many positioning solutions such as base stations and satellites. In autonomous driving positioning scenario, challenges such as positioning accuracy, processing delay, and deployment cost are often encountered. This paper proposes a new fingerprint location algorithm based on RSU in C-V2X, which solves insufficient signature parameter and road grid problems encountered when traditional fingerprint positioning technology is directly applied. Meanwhile, a new polar coordinate fingerprint library used in location algorithm is constructed to be suitable for C-V2X scenarios. In addition, a new method of sub-fingerprint library is used to improve the positioning accuracy and handover effectiveness of fingerprint positioning which provides an effective positioning method for C-V2X scenarios.
"Blended Teaching" is the main trend of higher education nowadays. With the analysis on the blended teaching, this paper presented a primary research on it based on Rain Classroom in the teaching practice of "Japanese for non-Japanese major students". Rain Classroom is a teaching tool developed by Tsinghua University. The computer and mobile terminals of Rain Classroom directly serve teaching. Computer terminal is mainly used for data collection, storage, analysis and decision-making in teaching, while pre class preview courseware and MOOC video as well as exercises are sent to the mobile terminal, from which students can learn the relevant content. It helps students break through the limitation of time and space in their study and enhance their interest in learning. With the help of Rain Classroom, multiple modes of online teaching, classroom real-time answer, barrage interaction, extracurricular online discussion, as well as learning evaluation grade can be established to provide a feasible solution for blended teaching in the teaching practice of Japanese for non-Japanese major students.
A human-machine cooperative path planning model based on cloud model is proposed in this paper. The system enables
the planner take part in the A* searching process and the cloud model integrates fuzziness with randomness of the
qualitative concept. In the process of human-computer cooperation, the position of the leading field is figured out based
on cloud model; it effectively guides the A* searching process and avoids the drawback of the algorithm. Experiment
results demonstrated the validity and the feasibility of the model. It's much more efficient than either a human or a
computer algorithm in the path planning tasks.
Recently, \textit{passive behavioral biometrics} (e.g., gesture or footstep) have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple users at the same time. To this end, we propose \systemname\ as a passive multi-person identification system leveraging deep learning enabled footstep separation and recognition. \systemname\ passively identifies a user by deciphering the unique "footprints" in its footstep. Different from existing gait-enabled recognition systems incurring a long sensing delay to acquire many footsteps, \systemname\ can recognize a person by as few as only one step, substantially cutting the identification latency. To make \systemname\ adaptive to walking pace variations, environmental dynamics, and even unseen targets, we apply an adversarial learning technique to improve its domain generalisability and identification accuracy. Finally, \systemname\ can defend itself against replay attack, enabled by the richness of footstep and spatial awareness. We implement a \systemname\ prototype using commodity hardware and evaluate it in typical indoor settings. Evaluation results demonstrate a cross-domain identification accuracy of over 90\%.
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