Ali Jalali

Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks

A fuzzy-based protocol is presented for clustered wireless sensor networks.The main objective is to form balanced clusters over the network.A hybrid swarm intelligence algorithm is utilized to optimize fuzzy rule table.Proposed routing protocol is successfully tested on 10 heterogeneous networks.Results show that our methodology outperforms the compared routing protocols. Wireless sensor networks are rapidly evolving technological platforms with tremendous applications in several domains. Since sensor nodes are battery powered and may be used in dangerous or inaccessible environments, it is difficult to replace or recharge their power supplies. Clustering is an effective approach to achieve energy efficiency in wireless sensor networks. In clustering-based routing protocols, cluster heads are selected among all sensor nodes within the network, and then clusters are formed by simply assigning each node to the nearest cluster head. The main drawback is that there is no control on the distribution of cluster heads over the network. In addition to the problem of generating unbalanced clusters, almost all routing protocols are designed for a certain application scope, and could not cover all applications. In this paper, we propose a swarm intelligence based fuzzy routing protocol (named SIF), in order to overcome the mentioned drawbacks. In SIF, fuzzy c-means clustering algorithm is utilized to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Mamdani fuzzy inference system. This strategy not only guarantees to generate balanced clusters over the network, but also has the ability to determine the precise number of clusters. In fuzzy-based routing protocols in literature, the fuzzy rule base table is defined manually, which is not optimal for all applications. Since tuning the fuzzy rules very affects on the performance of the fuzzy system, we utilize a hybrid swarm intelligence algorithm based on firefly algorithm and simulated annealing to optimize the fuzzy rule base table of SIF. The fitness function can be defined according to the application specifications. Unlike other routing protocols which have been designed for a certain application scope, the main objective of our methodology is to prolong the network lifetime based on the application specifications. In other words, SIF not only prolongs the network lifetime, but also is applicable to any kind of application. Obtained simulation results over 10 heterogeneous networks show that SIF outperforms the existing clustering-based protocols in terms of generating balanced clusters and prolonging the network lifetime.

An efficient architecture for designing reverse converters based on a general three-moduli set

In this paper, a high-speed, low-cost and efficient design of reverse converter for the general three-moduli set {2^@a, 2^@b-1, 2^@b+1} where @a<@b is presented. The simple proposed architecture consists of a carry save adder (CSA) and a modulo adder. As a result it can be efficiently implemented in VLSI circuits. The values of @a and @b are set in order to provide the desired dynamic range and also to obtain a balanced moduli set. Based on the above, two new moduli sets {2^n^+^k, 2^2^n-1, 2^2^n+1} and {2^2^n^-^1, 2^2^n^+^1-1, 2^2^n^+^1+1}, which are the special cases of the moduli set {2^@a, 2^@b-1, 2^@b+1} are proposed. The reverse converters for these new moduli sets are derived from the proposed general architecture with better performance compared to the other reverse converters for moduli sets with similar dynamic range.

An evolutionary-based methodology for symbolic simplification of analog circuits using genetic algorithm and simulated annealing

An evolutionary-based multi-objective methodology is proposed for automatic symbolic simplification of analog circuits.The simplified symbolic expressions are generated in MATLAB automatically from the input netlist of the circuit.A hybrid algorithm based on genetic algorithm and simulated annealing is used to test the proposed simplification criterion.The proposed algorithm is successfully tested on three analog circuits, and its results are compared with HSPICE. In this paper, an evolutionary-based multi-objective criterion is introduced for simplified symbolic small-signal analysis of analog circuits containing MOSFETs. After circuit analysis via modified nodal analysis technique, the derived exact symbolic transfer function of the circuit behavior is automatically simplified. In contrast to traditional simplification criteria, the main objective of our criterion is to control the final simplification error rate. The proposed simplification methodology can be performed by such optimization algorithms as local-search algorithms, heuristic algorithms, swarm intelligence algorithms, etc. In this paper, a hybrid algorithm based on genetic algorithm and simulated annealing is applied to validate the proposed methodology. It is remarkable that all steps including netlist text processing, symbolic analysis, post-processing, simplification, and numerical analysis are consecutively derived in an m-file MATLAB program. The proposed methodology was successfully tested on three analog circuits, and the numerical results were compared with HSPICE.

Optimized sugeno fuzzy clustering algorithm for wireless sensor networks

Clustering is the most common approach to achieve energy efficiency in wireless sensor networks. The existing clustering techniques exhibit some drawbacks which limit their usage for practical networks. First, cluster heads are typically selected among all sensor nodes within the network, and consequently, unbalanced clusters may be generated. Second, the controllable parameters are defined manually. Third, the protocol is not adjusted and tuned based on application specifications. In this paper, we propose an adaptive fuzzy clustering protocol (named LEACH-SF), in order to overcome the mentioned drawbacks. In LEACH-SF, fuzzy c-means algorithm is used to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Sugeno fuzzy inference system. The fuzzy inputs of the Sugeno fuzzy inference system include the residual energy, the distance from sink, and the distance from cluster centroid. Unlike the existing fuzzy-based routing protocols in which the fuzzy rule base table is defined manually, we utilize artificial bee colony algorithm to adjust the fuzzy rules of LEACH-SF. The fitness function of the algorithm is defined to prolong the network lifetime, based on the application specifications. In other words, LEACH-SF not only prolongs the lifetime, but also is applicable to any kind of application. Simulations over 10 heterogeneous wireless sensor networks show that LEACH-SF outperforms the existing cluster-based routing protocols. A Sugeno fuzzy clustering algorithm is presented for wireless sensor networks.FCM algorithm is utilized to form balanced clusters over the network.Artificial bee colony algorithm is utilized to optimize the Sugeno fuzzy rules.Proposed Sugeno model can be adaptively tuned via ABC for any application.

High-speed full adder based on minority function and bridge style for nanoscale

In this paper a new high-speed and high-performance Full Adder cell, which is implemented based on CMOS bridge style and minority function, is proposed. Several simulations conducted at nanoscale using different power supplies, load capacitors, frequencies and temperatures demonstrate the superiority of the proposed design in terms of delay and power-delay product (PDP) compared to the other cells. In addition the proposed structure improves the robustness and reduces sensitivity to the process variations of the other Bridge-Cap Full Adder cell already presented in the literature.

High-Performance Mixed-Mode Universal Min-Max Circuits for Nanotechnology

In this paper a low-power, high-speed and high-resolution voltage-mode Min-Max circuit, as well as a new efficient universal structure for determining the minimum and maximum values of the input digital signals, is proposed for nanotechnology. In addition, the proposed designs provide rail-to-rail input and output signals which enhance the performance and the robustness of the circuits. The advantage of the proposed Min-Max circuit is that it is extendable for any arbitrary n-digit and radix-r input numbers. Comprehensive simulation results at CMOS and CNFET technologies demonstrate the low-power and high-performance operation as well as insusceptibility to PVT variations of the proposed structure.

Automatic Simplified Symbolic Analysis of Analog Circuits Using Modified Nodal Analysis and Genetic Algorithm

In this paper, a hybrid methodology based on modified nodal analysis (MNA) and genetic algorithm (GA) is presented for simplified symbolic small-signal analysis of analog circuits containing semiconductor devices like MOSFETs. At first, the circuit is analyzed by the MNA, and the derived exact continuous-time transfer function is automatically simplified via GA. We propose a new multi-objective criterion for symbolic simplification of continuous-time transfer functions, which can be performed by such optimization algorithms as local-search algorithms, heuristic algorithms, swarm-intelligence algorithms, etc. In this paper, GA is used to validate the proposed simplification criterion. All processes including netlist text pre-processing, symbolic analysis via MNA, post-processing, and simplification via GA are consecutively run in an m-file MATLAB program. The comparison of obtained numeric results with HSPICE demonstrates the efficiency of the proposed methodology.

An Improved Exponentiation Algorithm for RSA Cryptosystem

RSA encryption is one of the public-key methods that has been popular in last decade. Considering increment of security requirements, size of the keys has been larger. With key length growing, delay of exponentiation computation has changed into major problem in selecting longer keys. The binary or in other words square-and-multiply method is the classical exponentiation technique that is used in RSA. In this paper a new algorithm of exponentiation in RSA is presented that works in parallel, needs fewer multiplications and so has less delay. Therefore this technique is more useful in larger key computations.

Simplified symbolic transfer function factorization using combined artificial bee colony and simulated annealing

Display Omitted A swarm intelligence based simplified transfer function factorization methodology is proposed.We extend the traditional root splitting technique for factorization of the expanded transfer function.A hybrid global and local search algorithm based on ABC and SA (named GLABCSA) is introduced.Our method guarantees to limit pole/zero displacements in both factorization and simplification phases via user-specified thresholds. Symbolic circuit analysis inherits the exponential growth of transfer function complexity with the circuit size. Therefore, symbolic simplification is an NP-hard problem. Although many simplification techniques have been presented, the simplified transfer functions are not written in a factorized form, and consequently, it is difficult to assess the contribution of poles and zeros on the circuit behavior. In this paper, a swarm intelligence based methodology is presented for the simplified factorized symbolic analysis of analog circuits. In this method, an extension of the root splitting technique is utilized to rewrite the expanded transfer function of the circuit into a factorized form comprising DC-gain, poles, and zeros. Then, the derived factorized transfer function is simplified using a hybrid Global and Local search algorithm based on Artificial Bee Colony and Simulated Annealing (named GLABCSA). The objective function is defined to minimize the complexity of the symbolic factorized transfer function while minimizing the DC-gain error and pole/zero displacements. The presented approach has been successfully developed in MATLAB. The program can derive the simplified factorized symbolic transfer function automatically from the input text netlist of the circuit. Symbolic and numerical results over two analog amplifiers are given to illustrate the efficiency of the presented methodology.

Simplified Symbolic Gain, CMRR and PSRR Analysis of Analog Amplifiers Using Simulated Annealing

Modeling common-mode rejection ratio (CMRR) and power-supply rejection ratio (PSRR) is of utmost importance during the design of analog integrated circuits. Unfortunately, symbolic analysis suffers from the exponential growing of the complexity of expressions with the circuit size, symbolic simplification techniques should be utilized for the analysis of practical circuits. In this paper, we propose a methodology for the automatic simplified symbolic analysis of gain, CMRR and PSRR in analog amplifiers. We introduce a multi-objective simulated annealing for the simplification of derived symbolic expressions. The fitness function is to minimize the number of symbolic terms while satisfying the optimization constraints. In contrast to the classical criteria which simplify different polynomials separately, the main objective of the proposed criterion is to consider the correlation between different polynomials, during the simplification procedure. The method has been successfully coded in MATLAB and simulated over two analog amplifiers. Comparison of the numerical results extracted from the simplified symbolic expressions with HSPICE demonstrates the efficiency of the proposed methodology.