Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.     

Dependability evaluation of Altera FPGA-based embedded systems subjected to SEUs

Dependability evaluation of embedded systems due to the integration of hardware and software parts is difficult to analyze. In this paper, we have proposed an experimental method to determine sensitivity to soft errors in an embedded system exploiting Altera SRAM-based FPGAs. The evaluation is performed using both the hardware and software parts of the embedded system in a single framework. To do this, the HDL hardware model of the target system as well as the C-written software codes of the target system, are required. Both permanent and transient faults are injected into the partially- or fully-synthesizable hardware of the target system and this can be performed during the design cycle of the system. The fault injection is composed of injecting SEUs into user design memory, and used configuration memory of the exploited FPGA. Using the experimental results, the sensitivity of Altera FPGAs to SEU faults are analyzed and derived. The analytical results reveal that the configuration memory is more significant than design memory to the SEUs due to the relative number of SRAM bits. Moreover, in this framework, in the case of injecting SEUs into user memory, the fault injection experiments are accelerated by the cooperation between a simulator and the FPGA.     

Fuzzy Models for Link Prediction in Social Networks

Predicting missing links and links that may occur in the future in social networks is an attention grabbing topic amid the social network analysts. Owing to the relationship between human‐based system and social sciences in this field, granular computing can help us to model the systems more effectively. The present study aims to propose two new similarity indices, based on granular computing approach and fuzzy logic. It also presents a new hybrid model for creating synergy between various link prediction models. Results show that fuzzy system analysis, in comparison with the crisp approach, can make more effective predictions through better expression of network characteristics. The indices are tested on collaboration networks. It is found that the accuracy of predictions is significantly higher than the crisp approach. It can modify the models for computing the strength of the links and/or predicting the evolutions of the social networks.     

A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images

ABSTRACT

Lung cancer is one of the deadliest cancers in both men and women. Nowadays, several methods are used to cure this cancer including surgery and radiotherapy. These methods require prior knowledge about the shape of tumours. This type of knowledge may also help physicians to determine the cancer type. In this paper we propose a novel approach for 3D reconstruction of tumour geometry from a sequence of 2D images. The proposed approach consists of two phases: tumour segmentation from computed tomography (CT) images and 3D shape reconstruction. Segmentation is conducted using snake optimisation and Gustafson–Kessel clustering. For 3D reconstruction, first, we propose a new approach to interpolate some intermediate slices between original slices. Then, the well-known marching cubes algorithm is used for surface reconstruction. Eventually, we smoothen the surface using an explicit fairing algorithm. Experiments show that our new approach can highly improve the quality and the accuracy of the reconstructed tumour shape.     

A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia

This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis.     

A type-2 fuzzy expert system based on a hybrid inference method for steel industry

In this paper, a novel type-2 fuzzy expert system for prediction the amount of reagents in desulfurization process of a steel industry in Canada is developed. In this model, the new interval type-2 fuzzy c-regression clustering algorithm for structure identification phase of Takagi–Sugeno (T–S) systems is presented. Gaussian Mixture Model is used to generate partition matrix in clustering algorithm. Then, an interval type-2 hybrid fuzzy system, which is the combination of Mamdani and Sugeno method, is proposed. The new hybrid inference system uses fuzzy disjunctive normal forms and fuzzy conjunctive normal forms for aggregation of antecedents. A statistical test, which uses least square method, is implemented in order to select variables. In order to validate our method, we develop three system modeling techniques and compare the results with our proposed interval type-2 fuzzy hybrid expert system. These techniques are multiple regression, type-1 fuzzy expert system, and interval type-2 fuzzy TSK expert system. For tuning parameters of the system, adaptive-network-based fuzzy inference system is used. Finally, neural network is utilized in order to reduce error of the system. The results show that our proposed method has less error and high accuracy.     

A voltammetric sensor for the detection of 2-aminophenol in the presence of bisphenol A using a carbon paste electrode modified with N-doped reduced graphene oxide/CuO nanocomposite and ionic liquid

Journal of Materials Science: Materials in Electronics - In present work, a novel and facile electrode was constructed using the modification of carbon paste electrode (CPE) with N-doped reduced...     

An efficient inexact Full Adder cell design in CNFET technology with high-PSNR for image processing

The design of inexact circuits at the transistor level remarkably improves figures of merits such as power consumption, delay, energy, and area. Therefore, inexact technique for designing circuits has attracted the attention of researchers worldwide. Designing inexact Full Adder cell as a building block of a variety of arithmetic circuits can affect the entire electronic system’s performance. In this paper, two novel inexact 1-bit Full Adder cells are presented using carbon nanotube field effect transistors (CNFETs). The capacitive threshold logic (CTL) is used to realize the proposed cells. Comprehensive simulations at two levels of abstraction, i.e., application and hardware are carried out to evaluate the efficacy of these circuits. First, the motion detector which is one of the image processing applications is deployed in MATLAB software to measure peak signal-to-noise ratio (PSNR) figure of merit. At hardware level, the HSPICE tool is used to carry out simulations and measure power, delay, power-delay product (PDP), energy-delay product (EDP), power-delay-area product (PDAP) and power-delay-area-PSNR product (PDAPP). Simulation results confirmed the superiority of the proposed Full Adder cells compared to others. For instance, the proposed 6TIFA improves PDAPP metric at least 21% and at most 76% compared to its counterparts at 0.9V power supply.     

A fast response hafnium selective polymeric membrane electrode based on N,N'-bis(alpha-methyl-salicylidene)-dipropylenetriamine as a neutral carrier

In this study a new hafnium selective sensor was fabricated from polyvinylchloride (PVC) matrix membrane containing neutral carrier N,N'-bis(alpha-methyl-salicylidene)-dipropylenetriamine (Mesaldpt) as a new ionophore, sodium tetraphenyl borate (NaTPB) as anionic discriminator and dioctyl phthalate (DOP) as plasticizing solvent mediator in tetrahydrofuran solvent. The electrode exhibits Nernstian response for Hf(4+) (Hafnium(IV)) over a wide concentration range (2.0 x 10(-7) to 1.0 x 10(-1)M) with the determination coefficient of 0.9966 and slope of 15.1+/-0.1 mVdecades(-1). The limit of detection is 1.9 x 10(-7)M. The electrode has a fast response time of 18s and a working pH range of 4-8. The proposed membrane shows excellent discriminating ability towards Hf(4+) ion with regard to several alkali, alkaline earth transition and heavy metal ions. It can be used over a period of 1.5 months with good reproducibility. It is successfully applied for direct determination of Hf(4+) in solutions by standard addition method for real sample analysis.     

A fuzzy reinforcement learning algorithm for inventory control in supply chains

In the real world, applications with very large state and action spaces and unknown state transition probability, classical reinforcement learning algorithms usually show poor performance. One way to address the performance problem is to approximate the policy or value function. Fuzzy rule-based systems are amongst the well-known function approximators. This paper presents a Flexible Fuzzy Reinforcement Learning algorithm, in which value function is approximated by a fuzzy rule-based system. The proposed algorithm has a separate module for tuning the structure of fuzzy rules. Moreover, the parameters of the system are tuned during the learning phase. Next, the proposed algorithm is applied to the problem of inventory control in supply chains. In this problem, a fuzzy agent (supplier) should determine the amount of orders for each retailer based on their utility for supplier, by considering its limited supply capacity. Finally, a simulation is performed to show the capability of the proposed algorithm.