In this research, we studied isolation of the cellulolitic fungi from the persimmon tree, grapevine, pomegranate tree and walnut soils. Among of them, persimmon soil was selected because of maximum cellulolitic fungi. Seven fungi werw isolated from persimmon soil, which three of them had suitable cellulose activity and they were identificated by 18S rRNA and named: Aspergillus niger MZM 89-a2, Penicillium decumbens ZHE 89-p3, Penicillium decumbens MMH 89-p1. Cellulase activities of these fungi were respectively (U/d): FPA 3.1671, 3.5740, 3.1812 and Avicelase 1.6605, 0.3869, 1.1451 and CMCase 2.950, 0.2644, 0.4604. Response surface activity (RSM) was studied to evaluate the effects of temperature, moisture content and particle size for FPA by Aspergillus niger MZM 89-a2. The optimum FPA was in temperature, moisture content and particle size respectively: 28.49oC, 66.62٪ and 1.5-2 (mm). The maximum predicted FPA was 4.35 (U/g) and obtained FPA under this condition was 4.42 (U/g), which indicates the efficacy of the model for prediction of FPA activity under different conditions of the medium.

Probabilistic data structures are so popular in membership queries, network applications, and databases and so on. Bloom filter and Cuckoo filter are two popular space efficient models that incorporate in set membership checking part of many important protocols. They are compact representations of data that use hash functions to randomize a set of items. Being able to store more elements while keeping a reasonable false positive probability is a key factor of design. A new algorithm is proposed to improve some of the performance properties of Cuckoo filter such as false positive rate and insertion performance and solve some drawbacks of the Cuckoo algorithm such as endless loop. Main characteristic of the Bloom filter is used to improve Cuckoo filter, so we have a smart Cuckoo filter which is modified by Bloom filter so called as SCFMBF. SCFMBF uses the same table of buckets as Cuckoo filter but instead of storing constant Fingerprints, it stores Bloom filters. Bloom filters can be accumulated in the table’ s buckets which leads to higher insertion feasibility. We also address the endless loop problem of Cuckoo filter that means an inserted item is stuck in an iterative process of finding an empty bucket, so a smart algorithm is designed which not only solves endless loop problems but also prevents insertion failure. Hence our smart algorithm prevents double checking of a bucket and avoids making loops. Consequently, the capacity of SCFMBF is improved significantly. Results of comparison with Cuckoo filter shows that false positive probability of SCFMBF method is enhanced compared to Cuckoo filter.

The probability hypothesis density (PHD) filter sequentially computes the first-order multi-target moment for the full multi-target probability density function and dramatically reduces the computational expense of tracking problem. In this paper، we propose an improved implementation of the PHD using the notion of auxiliary particle filter to enhance the effectiveness of the Sequential Monte Carlo (SMC) implementation of the PHD filter. The proposed method differs from traditional SMC implementations because it demonstrates an ability to simultaneously search in an effective way for persistent and newborn targets where the birth intensity is uniform and noninformative. Simulation results indicate that our novel method dramatically improves the accuracy of PHD approximation when compared to traditional SMC implementation methods for the same number of particles.

The PHD filter recursion is introduced to enable the implementation of expensive computational algorithms of multitarget Bayesian filtering. The goal of this recursion is to update and propagate the posterior intensity of a Random Finite Set during time steps. To that end, Cardinalized PHD is introduced as an extension of PHD filter to overcome the PHD’s weakness in estimating the number of targets. In the CPHD filter, the posterior intensity function and the cardinality distribution are updating at the same time. In this paper, we use auxiliary particle filter to implement the CPHD filter. The benefit of the proposed algorithm is to sample at the higher dimensional space compared to the dimensional of the target space in order to generate approximating samples of the CPHD filter and this will improve the estimation accuracy.To that end, we first reformulize the CPHD recursion in a way which is suitable for auxiliary particle filter. Then, to sample in a higher dimensional space, we first use an auxiliary variable which is the index of previously generated samples and then we apply another auxiliary variable which is the index of current measurements to improve the estimation of the number and position of multiple targets. Comparison between mean and variance of estimated cardinality and error of multitarget position estimation obtained from simulation results indicate the superiority of our proposed algorithm compared to the current implementation method of the CPHD filter by using SIR particle filter.