TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

In this paper, a novel time of arrival (TOA) estimation method is proposed based on an iterative cleaning process to extract the first path signal. The purpose is to address the challenge in dense multipath indoor environments that the power of the first path component is normally smaller than other multipath components, where the traditional match filtering (MF)-based TOA estimator causes huge errors. Along with parameter estimation, the proposed process is trying to detect and extract the first path component by eliminating the strongest multipath component using a band-elimination filter in fractional Fourier domain at each iterative procedure. To further improve the stability, a slack threshold and a strict threshold are introduced. Six simple and easily calculated termination criteria are proposed to monitor the iterative process. When the iterative 'cleaning' process is done, the outputs include the enhanced first path component and its estimated parameters. Based on these outputs, an optimal reference signal for the MF estimator can be constructed, and a more accurate TOA estimation can be conveniently obtained. The results from numerical simulations and experimental investigations verified that, for acoustic chirp signal TOA estimation, the accuracy of the proposed method is superior to those obtained by the conventional MF estimators

An integrated energy-efficient operation methodology for metro systems based on a real case of Shanghai Metro Line One

Metro systems are one of the most important transportation systems in people's lives. Due to the huge amount of energy it consumes every day, highly-efficient operation of a metro system will lead to significant energy savings. In this paper, a new integrated Energy-efficient Operation Methodology (EOM) for metro systems is proposed and validated. Compared with other energy saving methods, EOM does not incur additional cost. In addition, it provides solutions to the frequent disturbance problems in the metro systems. EOM can be divided into two parts: Timetable Optimization (TO) and Compensational Driving Strategy Algorithm (CDSA). First, to get a basic energy-saving effect, a genetic algorithm is used to modify the dwell time of each stop to obtain the most optimal energy-efficient timetable. Then, in order to save additional energy when disturbances happen, a novel CDSA algorithm is formulated and proposed based on the foregoing method. To validate the correctness and effectiveness of the energy-savings possible with EOM, a real case of Shanghai Metro Line One (SMLO) is studied, where EOM was applied. The result shows that a significant amount of energy can be saved by using EOM

Exponentially weighted particle filter for simultaneous localization and mapping based on magnetic field measurements

This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy

Intrusion detection for IoT based on improved genetic algorithm and deep belief network

With the advent of the Internet of Things, the network security of the transport layer in the Internet of Things is getting more and more attention. Traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of the Internet of Things. Therefore, it is extremely urgent to study the intrusion detection system corresponding to today's Internet of Things security. This paper presents an intrusion detection model based on Genetic Algorithm (GA) and Deep Belief Network (DBN). Through multiple iterations of GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate. Finally, the KDDCUP99 data set was used to simulate and evaluate the model and algorithm. Experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the network

Magnetic-field-induced splitting of Rydberg Electromagnetically Induced Transparency (EIT) and Autler-Townes (AT) spectra in 87^{87}Rb vapor cell

We theoretically and experimentally investigate the Rydberg electromagnetically induced transparency (EIT) and Autler-Townes (AT) splitting of $^{87}$Rb vapor under the combined influence of a magnetic field and a microwave field. In the presence of static magnetic field, the effect of the microwave field leads to the dressing and splitting of each $m_F$ state, resulting in multiple spectral peaks in the EIT-AT spectrum. A simplified analytical formula was developed to explain the EIT-AT spectrum in a static magnetic field, and the calculations are in excellent agreement with experimental results.We further studied the enhancement of the Rydberg atom microwave electric field sensor performance by making use of the splitting interval between the two maximum absolute $m_F$ states under static magnetic field. The traceable measurement limit of weak electric field by EIT-AT splitting method was extended by an order of magnitude, which is promising for precise microwave electric field measurement.Comment: 12 pages, 4 figure

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc

Research on Comprehensive Evaluation and Early Warning of Transmission Lines' Operation Status Based on Dynamic Cloud Computing

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability