The nonlinearity induced by light-emitting diodes in visible light communication (VLC) systems presents a challenge to the parametrization of orthogonal frequency division multiplexing (OFDM). The goal of the multi-objective optimization problem presented in this study is to maximize the transmitted power (superimposed LED bias-current and signal amplification) for both conventional and constant envelope (CE) OFDM while also maximizing spectral efficiency. The bit error rate (BER) metric is used to evaluate the optimization using the non-dominated sorting genetic algorithm II. Simulation results show that for a BER of 1×10 −3 , the signal-to-noise ratio (SNR) required decreases with the guard band due to intermodulation distortions. In contrast to SNR values of approximately 13 and 25 dB achieved by traditional OFDM-based systems, the VLC system with CE signals achieves a guard band of 6% of the signal bandwidth with required SNR values of approximately 10.8 and 24 dB for 4-quadrature amplitude modulation (QAM) and 16-QAM modulation orders, respectively.
Author supplied from the article: Abstract A temperature compensated hydrogen sensor was designed and made capable of detecting H2 within a broad range of 100–10.000 ppm while compensating instantaneously for large (±25 °C) temperature variations. Two related operational constraints have been simultaneously addressed: (1) Selective, and sensitive detection under large temperature changes, (2) Fast warning at low and increasing H2 levels. Accurate measurements of hydrogen concentrations were enabled by matching relevant time-constants. This was achieved with a microchip having two temperature coupled palladium nanowires. One of the H2 sensitive Pd nanowires was directly exposed to hydrogen, whilst the other nanowire was used as a temperature sensor and as a reference. A drop forging technique was used to passivate the second Pd wire against H2 sensing. Temperature effects could be substantially reduced with a digital signal processing algorithm. Measurements were done in a test chamber, enabling the hydrogen concentration to be controlled over short and long periods. An early response for H2 sensing is attainable in the order of 600 milliseconds and an accurate value for the absolute hydrogen concentration can be obtained within 15 s.
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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