Artificial intelligence in endoscopy: the challenges and future directions

Artificial intelligence based approaches, in particular deep learning, have achieved state-of-the-art performance in medical fields with increasing number of software systems being approved by both Europe and United States. This paper reviews their applications to early detection of oesophageal cancers with a focus on their advantages and pitfalls. The paper concludes with future recommendations towards the development of a real-time, clinical implementable, interpretable and robust diagnosis support systems

Parameter estimation of GOES precipitation index at different calibration timescales

We examined two techniques that adjust the parameters of the GOES Precipitation Index (GPI) by combining the polar microwave and the geosynchronous infrared observations at three frequencies: daily, pentad, and monthly. The first technique is the adjusted GPI (AGPI), and the second is the universally adjusted GPI (UAGPI). The study shows that rainfall estimates can be improved by frequent calibrations providing there is sufficient superior (microwave) rainfall sampling within the calibration time and space domain. For this work, daily and pentad calibrations produce monthly rainfall estimates almost as good as monthly calibration. The daily calibration produced better daily rainfall estimates than pentad and monthly calibration, but it generates similar pentad rainfall estimates to these of the pentad calibration. The monthly calibrated scheme is not suitable for the daily and pentad rainfall estimates. Under the current twice-per-day sampling rate of polar-orbiting microwave observations, the pentad calibration scheme is suggested for the monthly, pentad, and daily rainfall. The potentials of applying the UAGPI and the AGPI techniques for daily rainfall estimation are also investigated. Copyright 2000 by the American Geophysical Union

Jitter Analysis and a Benchmarking Figure-of-Merit for Phase-Locked Loops

This brief analyzes the jitter as well as the power dissipation of phase-locked loops (PLLs). It aims at defining a benchmark figure-of-merit (FOM) that is compatible with the well-known FOM for oscillators but now extended to an entire PLL. The phase noise that is generated by the thermal noise in the oscillator and loop components is calculated. The power dissipation is estimated, focusing on the required dynamic power. The absolute PLL output jitter is calculated, and the optimum PLL bandwidth that gives minimum jitter is derived. It is shown that, with a steep enough input reference clock, this minimum jitter is independent of the reference frequency and output frequency for a given PLL power budget. Based on these insights, a benchmark FOM for PLL designs is proposed

A chaotic approach to rainfall disaggregation

The importance of high-resolution rainfall data to understanding the intricacies of the dynamics of hydrological processes and describing them in a sophisticated and accurate way has been increasingly realized. The last decade has witnessed a number of studies and numerous approaches to the possibility of transformation of rainfall data from one scale to another, nearly unanimously pointing to such a possibility. However, an important limitation of such approaches is that they treat the rainfall process as a realization of a stochastic process, and therefore there seems to be a lack of connection between the structure of the models and the underlying physics of the rainfall process. The present study introduces a new framework based on the notion of deterministic chaos to investigate the behavior of the dynamics of rainfall transformation between different temporal scales aimed toward establishing this connection. Rainfall data of successively doubled resolutions (i.e., 6, 12, 24, 48, 96, and 192 hours) observed at Leaf River basin, in the state of Mississippi, United States of America, are studied. The correlation dimension method is employed to investigate the presence of chaos in the rainfall transformation. The finite and low correlation dimensions obtained for the distributions of weights between rainfall data of different scales indicate the existence of chaos in the rainfall transformation, suggesting the applicability of a chaotic model. The formulation of a simple chaotic disaggregation model and its application to the Leaf River rainfall data provides encouraging results with practical potential. The disaggregation model results themselves indicate the presence of chaos in the dynamics of rainfall transformation, providing support for the results obtained using the correlation dimension method

Compact Circularly Polarized Patch Antenna Using a Composite Right/Left-Handed Transmission Line Unit-Cell

A compact circularly polarized (CP) patch antenna using a composite right/left-handed (CRLH) transmission line (TL) unit-cell is proposed. The CRLH TL unit-cell includes a complementary split ring resonator (CSRR) for shunt inductance and a gap loaded with a circular-shaped slot for series capacitance. The CSRR can decrease the TM10 mode resonance frequency, thus reducing the electrical size of the proposed antenna. In addition, the asymmetry of the CSRR brings about the TM01 mode, which can be combined with the TM10 mode by changing the slot radius. The combination of these two orthogonal modes with 90° phase shift makes the proposed antenna provide a CP property. The experimental results show that the proposed antenna has a wider axial ratio bandwidth and a smaller electrical size than the reported CP antennas. Moreover, the proposed antenna is designed without impedance transformer, 90° phase shift, dual feed and ground via

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling