A Fuzzy Neuron with Binary Input and its Training Algorithm

This paper is concerned with a proposal for a fuzzy artificial neuron with bi-nary input. The fuzzy neuron is based on fuzzy logic in that each component of the input vector is compared to a number which represent the membership value for a 0 in that position. The results of the comparisons are then combined using a generalized mean function to produce a single number which is compared to a threshold as in the case of a perceptron consisting of a linear combiner with hard limiting function. A training algorithm is developed based on an algorithm for linear inequalities described by Ho and Kashyap in a paper titled An Algorithm for Linear Inequalities and its Applications. The results obtained by simulation look promising.     

Extending the rand,adjusted rand and jaccard indices to fuzzy partitions

The first stage of knowledge acquisition and reduction of complexity concerning a group of entities is to partition or divide the entities into groups or clusters based on their attributes or characteristics. Clustering is one of the most basic processes that are performed in simplifying data and expressing knowledge in a scientific endeavor. It is akin to defining classes. Since the output of clustering is a partition of the input data, the quality of the partition must be determined as a way of measuring the quality of the partitioning (clustering) process. The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. This paper looks at some commonly used clustering measures including the rand index (RI), adjusted RI (ARI) and the jaccuard index(JI) that are already defined for crisp clustering and extends them to fuzzy clustering measures giving FRI,FARI and FJI. These new indices give the same values as the original indices do in the special case of crisp clustering. The extension is made by first finding equivalent expressions for the parameters, a, b, c, and d of these indices in the case of crisp clustering. A relationship called bonding that describes the degree to which two cluster members are in the same cluster or class is first defined. Through use in crisp clustering and fuzzy clustering the effectiveness of the indices is demonstrated.     

Development of a set of simple bacterial biosensors for quantitative and rapid measurements of arsenite and arsenate in potable water

Testing for arsenic pollution is commonly performed with chemical test kits of unsatisfying accuracy. Bacterial biosensors are an interesting alternative as they are easily produced, simple, and highly accurate devices. Here, we describe the development of a set of bacterial biosensors based on a nonpathogenic laboratory strain of Escherichia coli, the natural resistance mechanism of E. coli against arsenite and arsenate, and three reporter proteins: bacterial luciferase, beta-galactosidase and Green Fluorescent Protein (GFP). The biosensors were genetically optimized to reduce background expression in the absence of arsenic. In calibration experiments with the biosensors and arsenite-amended potable water, arsenite concentrations at 4 microg of As/L (0.05 microM) were routinely and accurately measured. The currently most quantitative system expressed the bacterial luciferase as reporter protein, responding proportional with a concentration range between 8 and 80 microg of As/L. Sensor cells could be stored as frozen batches, resuspended in plain media, and exposed to the aqueous test sample, and light emission was measured after 30-min incubation. Field testing for arsenite was achieved with a system that contained beta-galactosidase, producing a visible blue color at arsenite concentrations above 8 microg/L. For this sensor, a protocol was developed in which the sensor cells were dried on a paper strip and placed in the aqueous test solution for 30 min after which time color development was allowed to take place. The GFP sensor showed good potential for continuous rather than end point measurements. In all cases, growth of the biosensors and production of the strip test was achieved by very simple means with common growth media, and quality control of the sensors was performed by isolating the respective plasmids with the genetic constructs according to simple standard genetic technologies. Therefore, the biosensor cells and protocols may offer a realistic alternative for measuring arsenic contamination in potable water.     

Analysis and CAD implementation techniques of total harmonic distortion in analog VLSI circuits

This paper presents a new methodlogy to accurately evaluate the total harmonic distortion (THD) behavior of modern integrated circuits. The methodology is general, technology independent and is used to determine large-signal or reactive THD of signal processing circuits operating in the voltage or the current domain. It is based on Fourier series analysis and Parseval's theorem, where numerical integration may be needed to accurately compute THD. For low-frequency THD, the numerical integration can be simplified to a small number of summation without degrading the accuracy. The new methodology is incorporated in a computer-aided environment which accurately estimates THD, and the speed of calculation for many circuit is several orders of magnitude faster than SPICE or other commercial CAD tools. In addition, optimization of transistor sizes to reduce THD can be achieved by incorporating the methodology in an object-oriented CAD tool such as APLAC.Currently on leave as a Fulbright-Hays professor at the Helsinki University of Technology, Finland.     

A method for fuzzy clustering with ordinal attributes

Pattern vectors to be clustered may have attributes of various types including ordinal. The latter type of attribute with values such as “poor,” “very poor,” “good,” and “very good” is neither entirely numerical nor entirely qualitative. This leads to difficulties in clustering because it is meaningless to take differences of values of these ordinal attributes as is required for finding distance between pattern vectors. Representing ordinal values by numbers and then finding differences is incorrect. Rather, the ordinal values themselves may be considered as linguistic values of linguistic variables corresponding to fuzzy sets. This article discusses a method of fuzzy c-means clustering that uses fuzzy sets to represent ordinal values. Both the ratio-scaled and ordinal-scaled values can be treated in the same way by treating the ratio-scaled values as singletons. The same results are then obtained for the ratio-scaled attributes as in the traditional method. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 599–620, 2007.     

Bacterial bioassay for rapid and accurate analysis of arsenic in highly variable groundwater samples

In this study, we report the first ever large-scale environmental validation of a microbial reporter-based test to measure arsenic concentrations in natural water resources. A bioluminescence-producing arsenic-inducible bacterium based on Escherichia coli was used as the reporter organism. Specific protocols were developed with the goal to avoid the negative influence of iron in groundwater on arsenic availability to the bioreporter cells. A total of 194 groundwater samples were collected in the Red River and Mekong River Delta regions of Vietnam and were analyzed both by atomic absorption spectroscopy (AAS) and by the arsenic bioreporter protocol. The bacterial cells performed well at and above arsenic concentrations in groundwater of 7 microg/L, with an almost linearly proportional increase of the bioluminescence signal between 10 and 100 microg As/L (r2 = 0.997). Comparisons between AAS and arsenic bioreporter determinations gave an overall average of 8.0% false negative and 2.4% false positive identifications for the bioreporter prediction at the WHO recommended acceptable arsenic concentration of 10 microg/L, which is far betterthan the performance of chemical field test kits. Because of the ease of the measurement protocol and the low application cost, the microbiological arsenic test has a great potential in large screening campaigns in Asia and in other areas suffering from arsenic pollution in groundwater resources.     

Crystallization‐Induced Phase Separation in Solution‐Processed Small Molecule Bulk Heterojunction Organic Solar Cells

The driving forces and processes associated with the development of phase separation upon thermal annealing are investigated in solution‐processed small molecule bulk heterojunction (BHJ) organic solar cells utilizing a diketopyrrolopyrrole‐based donor molecule and a fullerene acceptor (PCBM). In‐situ thermal annealing X‐ray scattering is used to monitor the development of thin film crystallization and phase separation and reveals that the development of blend phase separation strongly correlates with the nucleation of donor crystallites. Additionally, these morphological changes lead to dramatic increases in blend electron mobility and solar cell figures of merit. These results indicate that donor crystallization is the driving force for blend phase separation. It is hypothesized that donor crystallization from an as‐cast homogeneous donor:acceptor blend simultaneously produces donor‐rich domains, consisting largely of donor crystallites, and acceptor‐rich domains, formed from previously mixed regions of the film that have been enriched with acceptor during donor crystallization. Control of donor crystallization in solution‐processed small molecule BHJ solar cells employing PCBM is thus emphasized as an important strategy for the engineering of the nanoscale phase separated, bicontinuous morphology necessary for the fabrication of efficient BHJ photovoltaic devices.