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This paper aims to propose a new fin field-effect transistor (FinFET)-based domino technique low-power series connected foot-driven transistors logic in 32 nm technology and examine its performance parameters by performing transient analysis.

In the proposed technique, the leakage current is reduced at footer node by a division of current to improve the performance of the circuit in terms of average power consumption, propagation delay and noise margin. Simulation of existing and proposed techniques are carried out in FinFET and complementary metal-oxide semiconductor technology at FinFET 32 nm technology for 2-, 4-, 8- and 16-input domino OR gates on a supply voltage of 0.9 V using HSPICE.

The proposed technique shows maximum power reduction of 77.74% in FinFET short gate (SG) mode in comparison with current-mirror-based process variation tolerant (CPVT) technique and maximum delay reduction of 51.34% in low power (LP) mode in comparison with CPVT technique at a frequency of 100 MHz. The unity noise gain of the proposed circuit is 1.10× to 1.54× higher in comparison with different existing techniques in FinFET SG mode and 1.11× to 1.71× higher in FinFET LP mode. The figure of merit of the proposed circuit is up to 15.77× higher in comparison with existing domino techniques.

The research proposes a new FinFET-based domino technique and shows improvement in power, delay, area and noise performance. The proposed design can be used for implementing high-speed digital circuits such as microprocessors and memories.

Full adder cells are building blocks of arithmetic circuits and affect the performance of the entire digital system. The purpose of this study is to provide a low-power and high-performance full adder cell.

Approximate computing is a novel paradigm that is used to design low-power and high-performance circuits. In this paper, a novel 1-bit approximate full adder cell is presented using the combination of complementary metal-oxide-semiconductor, transmission gate and pass transistor logic styles.

Simulation results confirm the superiority of the proposed design in terms of power consumption and power–delay product (PDP) criteria compared to state-of-the-art circuits. Also, the proposed full adder cell is applied in an 8-bit ripple carry adder to accomplish image processing applications including image blending, motion detection and edge detection. The results confirm that the proposed cell has premier compromise and outperforms its counterparts.

The proposed cell consists of only 11 transistors and decreases the switching activity remarkably. Therefore, it is a low-power and low-PDP cell.

Presents an inductive machine learning algorithm called CLIP3 (Cover learning using integer programming). CLIP3 is an extension of the CLILP2 algorithm. CLIP3 generates multiple rules for a given concept from two sets of discrete attribute data. It combines the best concepts of tree‐based and rule‐based algorithms to produce a highly reliable machine‐learning algorithm. The algorithm is run on the benchmark “MONK′s data sets”. Compares the results of standard machine learning algorithms such as the ID and AQ families of algorithms. The algorithm is also run on the breast cancer data set and the results are compared with C4.5 algorithm results.

The purpose of this paper is to present an intelligent data-driven framework which provides an effective group-buying aggregation service and thus offers a new opportunity for personalized services in recommendation and advertisement.

The work presented in the paper analyzes the aggregated group-buying data and creates a compact view of the data which eliminates the potential redundancy and noise. In doing this, the dependencies are discovered from the data in a reverse engineering way. A noise-tolerant method is appreciated, as noise and exception is inevitable in massive data.

The paper finds that, through the implementation of the intelligent framework, the aggregator will provide a compact view of the group-buying data to customers. According to the empirical study, a 38 percent average decrease of redundancy and noise in the searching results is achieved through the newly built views and corresponding data.

The paper presents the innovative process of discovering the dependencies and creating views in a data-driven and noise-tolerant way. The proposed intelligent framework improves the aggregation performance and forms the basis of personalized services.

This study aims to present a problem of noise compensation related to the localization of single-family houses in the restricted use areas (RUA) created around airports. The authors presented different methodological approaches to the valuation of such amends and characterized distinct solutions applied in that area in chosen countries and cities.

To estimate the level of those indemnities, linear models including spatial and generalized linear models, were applied. The set of explanatory variables contains quantitative and qualitative attributes of real estates. The influence of outliers indicated by means of cluster analysis on the received results were also considered.

The results show that after accounting for noise level and house characteristics, houses in noisier areas were sold for less than houses subjected to less noise. Unit prices of houses located outside the RUA were on average 17.05 per cent higher than the prices observed in zone with noise level for the daytime of 60 dB and 8.95 per cent in zone characterized by noise level for the daytime of 55 dB.

Received results can be compared with results obtained by other authors, but its most important application is possibility of use the proposed methodology by judicial appraisers to assess the proper level of noise compensation for home owners or tenants.

An identification of real estate market heterogeneity and its considering in estimation of compensation related to airport noise evidence some novelty of the research.

The purpose of this paper is to propose a method that calibrates the hand-eye relationship for eye-to-hand configuration and afterwards a rectification to improve the accuracy of general calibration.

The hand-eye calibration of eye-to-hand configuration is summarized as a equation AX = XB which is the same as in eye-in-hand calibration. A closed-form solution is derived. To abate the impact of noise, a rectification is conducted after the general calibration.

Simulation and actual experiments confirm that the accuracy of calibration is obviously improved.

Only a calibration plane is required for the hand-eye calibration. Taking the impact of noise into account, a rectification is carried out after the general calibration and, as a result, that the accuracy is obviously improved. The method can be applied in many actual applications.

The purpose of this study is to construct imaging pixels using novel bioactive films. Despite the notable progress in electronic imaging devices, these sensors still cannot compete with biological vision counterparts such as the human eye. Light sensitive biolayers and pigments in living organisms show superior performance in terms of low noise operation and speed. Although photoactive biolayers have been used to construct electronic imaging devices, they are usually hard to develop, and the organisms that produce these active layers have low growth rates.

Among 40 pigment producing prokaryotic marine bacteria, four strains which show faster growth rates in the presence of light are screened and characterized by Fourier transform infrared spectroscopy and visible absorption. Subsequently, they are used as active layers in light sensitive sensors. The performance of the obtained cells is eventually evaluated by time domain photoresponse measurements.

It is shown that while the obtained strains have high growth rates and their mass volume reproduction is relatively simple, they provide many interesting characteristics such as high speed and low noise operation when incorporated as photosensitive layers.

Because the mass reproduction of the obtained cultures is simple, they are an appropriate choice for use in planner and flexible document imaging devices and DNA microarray sensors.

As an extended work of the previous paper (Sul, 2010), this paper provides a guideline information for an anonymous garment pattern in sewing process. The purpose of this paper is to first, provide garment pattern database. By simply taking pictures of garment patterns, the shape database is constructed. Once the shape database is prepared, data retrieval can be done by image indexing, i.e., simply inserting garment pattern boundary shape again to the database. Using shock graph methodology, the pattern sets used for database preparation can be exactly retrieved. Second, to find the nearest shape of a given input pattern shape in the database. If the input garment pattern shape does not exist in the database, the shape matching algorithm provides the next similar pattern data. The user, who is assumed to be non-expert in garment sewing process, can easily predict the position and combination information of various patterns.

Image processing is used to construct the garment pattern shape database. The boundary shapes are extracted from the photographs of garment patterns and their shape recognition information, especially shock graph, is also recorded for later pattern data retrieval.

Using the image processing technique, garment patterns can be converted to electronic format easily. Also the prepared pattern database can be used for finding the nearest shape of an additional given input garment pattern. Patterns with irregular shapes were retrieved easily, while those with a simple shape, such as rectangle, showed a little erroneous result.

Shape recognition has been adopted in various industrial areas, except for garment sewing process. Using the provided methodology, garment pattern shapes can be easily saved and retrieved only by taking pictures of them.