A low-power,micromachined, double spiral hotplate for MEMS gas sensors

This paper presents the design, fabrication and reliability testing of a double spiral platinum-based MEMS hotplate for gas sensing applications. The structure of MEMS hotplate consists of a 0.7 µm-thick thermally grown SiO₂ membrane of size 120 µm × 120 µm over which a double spiral platinum resistor is laid out. The hotplate membrane is supported by its four arms connected to the Si-substrate. The design and simulation of the hotplate structure was carried out using MEMS-CAD Tool COVENTORWARE. Based on the design, a double spiral platinum resistor of 103 Ω is fabricated on SiO₂ membrane using lift-off technique. The platinum deposition is carried out using DC sputtering technique. Bulk micromachining of Si is done from front side of the structure to create the suspended SiO₂ membrane. The temperature coefficient of resistance (TCR) of platinum is measured and found to be 2.19 × 10^?3/ °C. The TCR value is used for temperature estimation of the hotplate. The test results show that the microhotplate consumes only 20 mW power when heated up to 500 °C. For reliability testing of fabricated structure, the hotplate is continuously operated at 300 °C for 1.8 h. Also, it can sustain at least 61 cycles pulse-mode operation at 530 °C with ultra-low resistance and temperature drifts. The structure can sustain a maximum temperature and current of 611 °C and 11.55 mA respectively without any damage.     

Generalized differentiability and integrability for fuzzy set-valued functions on time scales

This paper deals with the fuzzy set-valued functions of real variables on time scale whose values are normal, convex, upper semicontinuous and compactly supported fuzzy sets in \(\mathbb {R}^{n}\). We introduce and study the fundamental properties of new class of derivative called generalized delta derivative (\(\Delta _{g}\)-derivative) and generalized delta integral (\(\Delta _{g}\)-integral) for such fuzzy functions.     

Normality and Finite-State Dimension of Liouville Numbers

Liouville numbers were the first class of real numbers which were proven to be transcendental. It is easy to construct non-normal Liouville numbers. Kano (1993) and Bugeaud (2002) have proved, using analytic techniques, that there are normal Liouville numbers. Here, for a given base k ≥ 2, we give a new construction of a Liouville number which is normal to the base k. This construction is combinatorial, and is based on de Bruijn sequences.     

On the applicability of diploid genetic algorithms in dynamic environments

Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other.     

Improving the performance of speaker and language identification tasks using unique characteristics of a class

In classification tasks, the error rate is proportional to the commonality among classes. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. This paper proposes to use unique characteristics of a class at the feature-level and at the phoneme-level, separately, to improve the classification accuracy. At the feature-level, the performance of a classifier has been analyzed by capturing the unique features while modeling, and removing common feature vectors during classification. Experiments were conducted on speaker identification task, using speech data of 40 female speakers from NTIMIT corpus, and on a language identification task, using speech data of two languages (English and French) from OGI_MLTS corpus. At the phoneme-level, performance of a classifier has been analyzed by identifying a subset of phonemes, which are unique to a speaker with respect to his/her closely resembling speaker, in the acoustic sense, on a speaker identification task. In both the cases (feature-level and phoneme-level) considerable improvement in classification accuracy is observed over conventional GMM-based classifiers in the above mentioned tasks. Among the three experimental setup, speaker identification task using unique phonemes shows as high as 9.56 % performance improvement over conventional GMM-based classifier.     

Using non-ideal gates to implement universal quantum computing between uncoupled qubits

In many physical systems, when implementing quantum gate operations unavoidable global and relative phases occur as by-products due to the internal structure of the governing Hamiltonian. To correct, additional phase rotation gates are used, which increases the computational overhead. Here, we show how these phase by-products can actually be used to our advantage by using them to implement universal quantum computing between qubits not directly coupled to each other. The gate operations, CNOT, Toffoli, and swap gates, require much less computational overhead than present schemes, and are achieved with fidelity greater than 99%. We then present a linear nearest-neighbor architecture that takes full advantage of the phase by-products, and we show how to implement gates from a universal set efficiently in this layout. In this scheme gate operations are realized by only varying a single control parameter per data qubit, and the ability to tune couplings is not required.