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Molecular logic gates are information processing devices that can respond to environmental signals and produce a readable output in response through Boolean logic operations. Molecules with these properties have been used to build smart sensors and therapeutic agents. In this work, dual enzyme-responsive molecular AND logic gate is developed with the intention to discriminate various combinations of enzyme level and/or activity. A resorufin-based sensor is substituted with self-immolative tyrosinase recognition site, 3-hydroxy benzyl group. The Hydroxyl group is protected with acetyl moiety which decreases the affinity of the enzyme. When both tyrosinase and esterase are present in the solution, the acetyl group is removed by the latter enzyme, allowing the former to recognise the ligand. Oxidation of the ligand by tyrosinase triggers self-immolative cleavage of the substitution, leading to almost 70 fold enhancement in fluorescence. When single enzyme is applied, there is no significant change in the emission intensity overall, an AND logic gate is constructed. Selectivity and Michaelis-Menten kinetics of the sensor is analysed. Smart molecular probes can contribute to the research on the development of biosensors that can discriminate diseases having characteristic combinations of enzyme activities. [ABSTRACT FROM AUTHOR]

Motion sensors available on smart phones make it possible to recognize human activities. Accelerometer, gyroscope, magnetometer, and their various combinations are used to classify, particularly, locomotion activities, ranging from walking to biking. In most of the studies, the focus is on the collection of data and on the analysis of the impact of different parameters on the recognition performance. The parameter space includes the types of sensors used, features, classification algorithms, and position/orientation of the mobile device. In most of the studies, the impact of some of these parameters is partially analyzed; however, in this work, we investigate the parameter space in detail with a global focus. Particularly, we investigate the impact of using different feature-sets, the impact of using different sensors individually and in combination, the impact of different classifiers, and the impact of phone position. Using an ANOVA analysis, we investigate the importance of various parameters on the recognition performance. We show that these parameters are ranked according to their impact on the recognition performance in the following order: sensor, position, classifier, feature. We believe that such an analysis is important since we can statistically show how much a parameter is affecting the recognition performance. Our observations can be used in future studies by only focusing on the important parameters. We present our findings as a discussion to guide the further studies in this domain. [ABSTRACT FROM AUTHOR]

Fires generally occur due to human carelessness and the change in environmental conditions. The uncontrolled fire results in death incidents of humans and animals as well as severe threats to the ecosystem. The preservation of the natural environment is important. The wireless sensor networks, widely used in different monitoring applications, is used in this work. For fire detection, we use flame, smoke, temperature, humidity, and light intensity sensors in our proposed network node which is low-cost, reduced-size, and power-efficient. The experiments are performed in a well-controlled real-time environment. The proposed node transmits the sensed data to the central node. The central node then transfers the data gathered from all the nodes to the control station using an air interface. To decide whether there is an incident of fire or not, and to have an idea on fire intensity, we combine multiple attributes sensed from a single node using Bayesian approach due to its simplicity and resemblance with human reasoning. In the experimental setup, the conditions for fire with different intensity are generated and the results confirm the validity of the proposed approach in terms of accuracy and less false alarms. [ABSTRACT FROM AUTHOR]

With the scaling of transistor feature size and increased integration density, power density has increased, resulting in high chip temperature, thus degrading the performance and lifetime of the chip. In this work, an ultralow power smart temperature sensor employing a frequency locked loop technique is developed in 65 nm standard CMOS process. The circuit works on the principle of voltage to frequency conversion, exploiting the thermal dependency of threshold voltage (V th) of MOS transistors in the subthreshold region. The sensor along with signal conditioning subcircuits has a low power consumption of 1.92 μ W, while operating over -55 °C to +125 °C with a conversion rate of 10K samples/s. The output frequency corresponding to temperature has its temperature coefficient insensitive to process variation and therefore requires one-point calibration. Due to the simple signal conditioning circuitry, the sensor consumes an estimated area of 0.003 mm2. Since the sensor has all its subcircuits working in the subthreshold region, it is suitable for low power applications. [ABSTRACT FROM AUTHOR]