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Shooter Localization Based on TDOA and N-Shape Length Measurements of Distributed Microphones
No full textThis paper deals with shooter localization based on measurements of a sensor network with spatially distributed, non-synchronized single microphones. The acoustic events generated during gunfire - shock wave and muzzle blast - provide information about shooter position and firing direction. A new approach is presented that takes into account the length of the N-shape of the shock wave in addition to the typically used measurement of the time difference of arrival (TDOA) between shock wave and muzzle blast. The accuracy of the new approach is evaluated using Cramér-Rao bounds, Monte Carlo simulations, and measurement experiments. The results are particularly promising in cases where no other approach achieves high accuracy
Cramer-Rao Lower Bound of Localization of a Moving Target by a Dynamic Multistatic Radar
No full text518521This paper investigates the estimation performance of the position and velocity of a single moving target by a multistatic radar. Unlike in previous studies where either the transmitter(s), the target, or the receiver(s) is/are stationary, in this research both the transmitter and the receivers are moving and performing localization of a moving target. While the transmitter is not synchronized with the receivers and its position and velocity is unknown, the target location and velocity are jointly estimated using both the back-scattered and direct-path measurements. It has been shown that the movement of the receivers improve the estimation performance in terms of the Cramer-Rao Lower Bound. However, the improvement highly depends on the geometry of the dynamic network
Modeling quantum volume using randomized benchmarking of Room-Temperature NV center quantum registers
No full textAccurately estimating the performance of quantum hardware is crucial for comparing different platforms and predicting the performance and feasibility of quantum algorithms and applications. In this paper, we tackle the problem of benchmarking a quantum register based on the NV center in diamond operating at room temperature. We define the connectivity map as well as single-qubit performance. Thanks to an all-to-all connectivity, the 2 and 3-qubit gates performance is promising and competitive among other platforms. We experimentally calibrate the error model for the register and use it to estimate the quantum volume, a metric used for quantifying the quantum computational capabilities of the register, of 8. Our results pave the way towards the unification of different architectures of quantum hardware and the evaluation of the joint metrics.12
STUDY OF THE NONLINEAR SQUEEZE FILM DAMPING EFFECTS ON A LUMPED PARAMETER MODEL OF A MEMS MICROSPEAKER
No full textMiniaturized micro-electromechanical (MEMS) speakers for in-ear audio applications are a current development trend. If a transducer shrinks to a size that fits on a microelectronic chip, its physics differs in part from the macroscopic world and some of the common assumptions can be violated. In the case of MEMS micro-speakers, one of these effects is the nonlinear squeeze film damping. Understanding this effect is crucial for audio applications as there are strict limits on the allowable harmonic distortion caused by nonlinear forces in loudspeakers. In this work, we discuss the respective nonlinear effects of the squeeze film damping within a lumped parameter model
Evaluation of patient-reported outcome measures for on-demand treatment of hereditary angioedema attacks and design of KONFIDENT, a phase 3 trial of sebetralstat
No full textBackground: Hereditary angioedema (HAE) with C1-inhibitor deficiency (HAE-C1-INH) is characterized by recurrent, debilitating episodes of swelling. Sebetralstat, an investigational oral plasma kallikrein inhibitor, demonstrated promising efficacy for on-demand treatment of HAE-C1-INH in a phase 2 trial. We describe the multipronged approach informing the design of KONFIDENT, a phase 3 randomized, placebo-controlled, three-way crossover trial evaluating the efficacy and safety of sebetralstat in patients aged ≥12 years with HAE-C1-INH. Methods: To determine an optimal endpoint to measure the beginning of symptom relief in KONFIDENT, we engaged patients with HAE on clinical outcome measures and subsequently conducted analyses of phase 2 outcomes. Sample size was determined via a simulation-based approach using phase 2 data. Results: Patient interviews revealed a strong preference (71%) for the Patient Global Impression of Change (PGI-C) over other measures and indicated a rating of “A Little Better” as a clinically meaningful milestone. In phase 2, a rating of “A Little Better” demonstrated agreement with attack severity improvement and resolution on the Patient Global Impression of Severity and had better sensitivity than “Better.” Simulations indicated that 84 patients completing treatment would ensure at least 90% power for assessing the primary endpoint of time to beginning of symptom relief defined as a PGI-C rating of at least “A Little Better” for two time points in a row. Conclusions: Patient feedback and phase 2 data support PGI-C as the primary outcome measure in the phase 3 KONFIDENT trial evaluating sebetralstat, which has the potential to be the first oral on-demand treatment for HAE-C1-INH attacks.13
Detection of pyrrolizidine alkaloid containing herbs using hyperspectral imaging in the short-wave infrared
No full text4555Plants containing pyrrolizidine alkaloids (PA) are unwanted contaminants in consumer products such as herbal tea due to their toxicity to humans. The detection of these plants or their components using hyperspectral imaging was investigated, with focus on application in sensor-based sorting. For this, 431 hyperspectral images of leafs from three common herbs (pepper-mint, lemon balm, stinging nettle) and the poisonous common groundsel were acquired. By using a convolutional neural network, a mean F1 score of 0.89 was obtained for the classification of all four plant products based on the individual spectra. To validate the neural network, significant wavelengths were determined and visualized in an attribution map
Critical and relevant raw materials for aviation with the focus on GWP for hybrid electric aircraft applications
No full textA Lightweight Neural TTS System for High-quality German Speech Synthesis
No full text3943This paper describes a lightweight neural text-to-speech system for the German language. The system is composed of a non-autoregressive spectrogram predictor, followed by a recently proposed neural vocoder called StyleMelGAN. Our complete system has a very tiny footprint of 61 MB and is able to synthesize high-quality speech output faster than real-time both on CPU (2.55x) and GPU (50.29x). We additionally propose a modified version of the vocoder called Multi-band StyleMelGAN, which offers a significant improvement in inference speed with a small tradeoff in speech quality. In a perceptual listening test with the complete TTS pipeline, the best configuration achieves a mean opinion score of 3.84 using StyleMelGAN, compared to 4.23 for professional speech recordings
Efficient Linearization of Explicit Multilinear Systems using Normalized Decomposed Tensors
No full text73127317Multilinear systems allow multiplications of states, inputs, and states with inputs, in all possible combinations. Recently, a new normalized decomposed tensor format of explicit multilinear models was introduced. This paper presents a linearization method for the normalized canonical polyadic decomposed tensor format of explicit multilinear models. The proposed method computes the Jacobian matrix to obtain the linear system evaluated at the equilibrium point. An adaption for large-scale sparse systems is outlined. Performance and computational time are evaluated for different number of states and sparsity structures. The results suggest computational advantages of the explicit multilinear format compared to the non-normalized one. The adaptation to large-scale sparse systems shows clear computational advantage
Phenoliner 2.0: RGB and near-infrared (NIR) image acquisition for an efficient phenotyping in grapevine research
No full text5767In grapevine research, phenotyping needs to be done for different traits such as abiotic and biotic stress. This phenotypic data acquisition is very time-consuming and subjective due to the limitation of manual visual estimation. Sensor-based approaches showed an improvement in objectivity and throughput in the past. For example, the ‘Phenoliner’ a phenotyping platform, based on a modified grape harvester, is equipped with two different sensor systems to acquire images in the field. It has so far been used in grapevine research for different research questions to test and apply different sensor systems. However, the driving speed for data acquisition has been limited to 0.5-1 km/h due to capacity of image acquisition frequency and storage. Therefore, a faster automatic data acquisition with high objectivity and precision is desirable to increase the phenotyping efficiency. To this aim, in the present study a prism-based simultaneous multispectral camera system was installed in the tunnel of the ‘Phenoliner’ with an artificial broadband light source for image acquisition. It consists of a visible color channel from 400 to 670 nm, a near infrared (NIR) channel from 700 to 800 nm, and a second NIR channel from 820 to 1,000 nm. Compared to the existing camera setup, image recording could be improved to at least 10 images per second and a driving speed of up to 6 km/h. Each image is geo-referenced using a real-time-kinematic (RTK)-GPS system. The setup of the sensor system was tested on seven varieties (Riesling, Pinot Noir, Chardonnay, Dornfelder, Dapako, Pinot Gris, and Phoenix) with and without symptoms of biotic stress in the vineyards of Geilweilerhof, Germany. Image analysis aims to segment images into four categories: trunk, cane, leaf, and fruit cluster to further detect the biotic stress status in these categories. Therefore, images have been annotated accordingly and first results will be shown