A Closed-Form Expression for Estimating Radiated Emissions from the Power Planes in a Populated Printed Circuit Board

An expression for the maximum intensity of radiated emissions from a rectangular power bus structure has been derived based on an analytical cavity-resonator model. The effect of components mounted on the board is modeled by modifying the propagation constant of the waves within the power bus structure. The radiated field intensity is calculated using the equivalent magnetic current around the edges of the power bus structure together with the modified propagation constant. Measurements of a populated test board show that the derived closed-form expression estimates the level of the maximum radiation intensity with reasonable accuracy

20-H Rule Modeling and Measurements

The 20-H rule is a printed circuit board layout guideline. On boards with power and ground planes, the fringing field at the edges of the board is contained by backing the edge of the power plane away from the edge of the board by a distance equal to 20 times the separation distance between the planes. In this study, test boards were built and measured with and without implementing the 20-H rule. The measured results are compared to numerical models. The results of this study show that, although the near fields are more contained, the radiation from a board implementing the 20-H rule is actually slightly higher than the radiation from boards with a traditional desig

Derivation of a Closed-Form Approximate Expression for the Self-Capacitance of a Printed Circuit Board Trace

The electric fields that couple traces on printed circuit boards to attached cables can generate common-mode currents that result in significant radiated emissions. Previous work has shown that these radiated emissions can be estimated based on the self-capacitances of the microstrip structures on a board . In general, the determination of these self-capacitances must be done numerically using three-dimensional static modeling software. In this paper, an approximate closed-form expression for the self-capacitance of microstrip traces is derived. This expression can be used to estimate the voltage-driven common-mode emissions from boards with various microstrip trace geometries. The expression also provides insight relative to the microstrip parameters that have the greatest effect on radiated emissions

Model for Estimating Radiated Emissions from a Printed Circuit Board with Attached Cables Due to Voltage-Driven Sources

Common-mode currents induced on cables attached to printed circuit boards (PCBs) can be a significant source of unintentional radiated emissions. This paper develops a model for estimating the amount of common-mode cable current that can be induced by the signal voltage on microstrip trace structures or heatsinks on a PCB. The model employs static electric field solvers or closed-form expressions to estimate the effective self-capacitances of the board, trace, and/or heatsink. These capacitances are then used to determine the amplitude of an equivalent common-mode voltage source that drives the attached cables. The model shows that these voltage-driven common-mode cable currents are relatively independent of the cable parameters and the trace or heatsink location when the PCB is small relative to the cable length and to a wavelength

Decoupling Strategies for Printed Circuit Boards Without Power Planes

Traditional decoupling capacitors connected between V/CC/ and GND traces can be relatively ineffective at frequencies above their self-resonant frequency. This paper evaluates decoupling capacitor mounting strategies on boards without power planes. Techniques for minimizing mutual inductance and improving decoupling at frequencies above resonance are investigated

Expert System Algorithms for Identifying Radiated Emission Problems in Printed Circuit Boards

Radiated emission algorithms for a printed circuit board EMC expert system are described. The expert system mimics the thinking processes that human EMC engineers would use to analyze circuit boards and make design recommendations. Working with limited information about the enclosure, cables or the exact nature of the signals, the expert system evaluates different structures on the printed circuit board looking for potentially strong radiated emission sources. Results obtained from the analysis of a sample printed circuit board are provided to demonstrate how the expert system quickly identifies problems that would otherwise be difficult to locate

Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Organic light emitting board for dynamic interactive display

Interactive displays involve the interfacing of a stimuli-responsive sensor with a visual human-readable response. Here, we describe a polymeric electroluminescence-based stimuli-responsive display method that simultaneously detects external stimuli and visualizes the stimulant object. This organic light-emitting board is capable of both sensing and direct visualization of a variety of conductive information. Simultaneous sensing and visualization of the conductive substance is achieved when the conductive object is coupled with the light emissive material layer on application of alternating current. A variety of conductive materials can be detected regardless of their work functions, and thus information written by a conductive pen is clearly visualized, as is a human fingerprint with natural conductivity. Furthermore, we demonstrate that integration of the organic light-emitting board with a fluidic channel readily allows for dynamic monitoring of metallic liquid flow through the channel, which may be suitable for biological detection and imaging applications.