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The sizes of astronomical surveys are increasing rapidly. Hence, the automatic classification of objects is growing more important. This classification is traditionally based, e.g., on point-spread function fitting. Recently several different neural network approaches have been introduced. In this paper we introduce a simple method that is based on fuzzy set reasoning. The analysis presented here concentrates on separating point sources (stars) from extended ones. The tests show that the neural network approach is superior if compared to direct fuzzy classification. The paper shows that the inherent ability of neural networks to process complex nonlinear data justifies the use of them in astronomical classification. However, a combined fuzzy and neural network approach can be useful at least in special cases.

Wearable device technologies for sweat analytics present a versatile application for monitoring physiological state, which can circumvent the requirement for inconvenient and invasive blood sampling. This paper reports a miniature electrochemical sensor platform for non-invasive and wireless real-time monitoring of lactate in exercise-induced human sweat. The conformal and low profile sensor platform is composed of (a) a flexible electronic readout tag with wireless charging and data acquisition, and (b) a disposable enzymatic amperometric biosensor patch with electrodes fabricated using high throughput roll-to-roll processing. Data were generated in real time from sensor response to lactate in exercise-induced sweat from multiple body regions simultaneously. The biosensor demonstrates current response proportional to lactate at physiological concentration range between 5 and 30 mM. This developed platform can be adapted for sensing of other sweat constituents including ions or metabolites, and therefore advances wearable technology for personalized physiological monitoring

The roll-to-roll (R2R) printing process enables large-scale manufacturing of conductive wirings for the system integration of printed and hybrid electronics. As the number of R2R printed units in a run is typically high, a systematic approach is required to verify the outcome of manufacturing process extensively. This study presents a piece of automated R2R testing equipment, which is capable of the comprehensive electrical testing of printed and flexible electronics, and discusses the main findings provided by the test system in a use case of an R2R printed wiring backplane for a large-area display element. The automated R2R tester utilizes a bed-of-nails test method incorporated into the reeling equipment as a stop and go process. In this scheme, a bed-of-nails is a layout-specific test fixture providing physical contacts on the web. The actual measurement operations are performed by general-purpose electrical test equipment through standard industrial buses. Overall, the movement of the web containing printed electronics and the test operations for these are controlled by a test automation software. In this study, the R2R functional tester is utilized to evaluate the outcome of a rotary screen printing process for a large area backplane wiring of s 7 × 20 RGB LED matrix. The study presents the needed test cases for the full test coverage of printed wiring at an area of approximately 190 mm × 405 mm in each backplane unit. As the roll length is over 150 meters, the testing process systematically produces over 100 000 resistance measurement results to evaluate the manufacturing process yield and process variations from an electrical perspective. The paper concludes the main test results, and discusses the usability of the developed testing system and the protocol in this use case application.

The size of survey data is increasing rapidly, and the automatic classification of objects is becoming more important. The classification is traditionally based, e.g., on point-spread function (PSF) fitting. Recently, several different neural network approaches have been introduced for classification. In this paper we use both self-organized map and learning vector quantization based neural networks for star-galaxy separation. Finally, we test a hybrid algorithm using fuzzy classifier and back-propagation neural networks. We show that different methods give relatively similar results. The classification accuracy is good enough for real data analysis, and selection between different methods must be done based on algorithmic complexity and availability of preclassified training sets.

Flux trapping and random flux movement are common problems in superconducting thin-film devices. Ultrasensitive magnetic field sensors based on superconducting quantum interference devices (SQUIDs) coupled to large pickup coils are especially vulnerable to strong external fields. The issue has become particularly relevant with the introduction of SQUID-based ultra-low-field (ULF) nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques. In this paper, we study the constraints of thin-film-based magnetometers and gradiometers as exposed to magnetic field sequences of ULF MRI. In particular, we address issues such as response recovery, transient noise, magnetization and behaviour under shielded room conditions after prepolarization. As a result, we demonstrate sensors that are suitable for a combined multi-channel magnetoencephalography (MEG) and MRI imaging system.

Practical assessment of risks for Electrostatic Discharge (ESD) failures of semiconductor devices, due to charges induced on devices in a manufacturing or repair environment of electronics has been difficult, because easily measurable parameters such as the electrostatic field and the potential of a charged surface do not directly quantify the risk. In this paper a new method of assessing the risks with induction charging of a sensitive device is presented by introducing a well-defined dummy device, which is a simple modification of the probe of DC type non-contacting electrostatic voltmeter. By placing the modified potential probe (mimicking large sensitive device) in front of charged surface, risks of ESD failure for a device due to induction charging can be assessed. The electrostatic response of the probe at different distances between charged surface and the probe has been verified by numerical model calculations. (6 refs.)