Zuwairie Ibrahim

A Particle Swarm Optimization Approach to Robotic Drill Route Optimization

Most of the operational time of a PCB Robotic Drill is spent on moving the drill bit between the holes. This operational time can be kept at a minimal level by optimizing the route taken by the robot. An optimized route translates to a minimal cost of operating the robot. This paper proposes a new model that implements Particle Swarm Optimization (PSO) in order to find optimized routing path when using the PCB Robotic Drill. The main task of the PCB Robotic Drill is to drill holes at Printed Circuit Board (PCB). This PCB Robotic Drill will route the drill site by moving the drill bit along Cartesian axes from it’s initial position. Then, the drill bit will return back to the initial position. The drill route consists of a number of potential locations where the holes are going to be drilled. As the number of holes required increases so thus does the complexity to find the optimized route. The proposed model can be used to solve this complex problem with minimal computational time. The result of a case study indicates that the proposed model is capable to find the shortest path for the robot to complete its task. Thus concluded the proposed model can be implemented in any drill route problems.

Analysis of the wavelet-based image difference algorithm for PCB inspection

The methodology and results regarding the use of wavelet transform and multiresolution analysis in automated visual printed circuit board (PCB) inspection provide the motivation for this research. In the paper, the wavelet-based image difference algorithm proposed is applied to a sample PCB image. The algorithm is applied by using a Haar wavelet where several different numbers of levels are considered. One conclusion from this paper is that the second level Haar wavelet transform should be selected for the application of visual PCB inspection.

Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Are motorways rational from slime mould's point of view?

We analyse the results of our experimental laboratory approximation of motorway networks with slime mould Physarum polycephalum. Motorway networks of 14 geographical areas are considered: Australia, Africa, Belgium, Brazil, Canada, China, Germany, Iberia, Italy, Malaysia, Mexico, the Netherlands, UK and USA. For each geographical entity, we represented major urban areas by oat flakes and inoculated the slime mould in a capital. After slime mould spanned all urban areas with a network of its protoplasmic tubes, we extracted a generalised Physarum graph from the network and compared the graphs with an abstract motorway graph using most common measures. The measures employed are the number of independent cycles, cohesion, shortest paths lengths, diameter, the Harary index and the Randić index. We obtained a series of intriguing results, and found that the slime mould approximates best of all the motorway graphs of Belgium, Canada and China, and that for all entities studied the best match between Physarum and motorway graphs is detected by the Randić index (molecular branching index).

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

BackgroundGene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.MethodsWe propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.ResultsThe performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.

A Particle Swarm Optimization Approach for Routing in VLSI

The performance of very large scale integration (VLSI) circuits is depends on the interconnected routing in the circuits. In VLSI routing, wire sizing, buffer sizing, and buffer insertion are techniques to improve power dissipation, area usage, noise, crosstalk, and time delay. Without considering buffer insertion, the shortest path in routing is assumed having the minimum delay and better performance. However, the interconnect delay can be further improved if buffers are inserted at proper locations along the routing path. Hence, this paper proposes a heuristic technique to simultaneously find the optimal routing path and buffer location for minimal interconnect delay in VLSI based on particle swarm optimization (PSO). PSO is a robust stochastic optimization technique based on the movement and information sharing of swarms. In this study, location of doglegs is employed to model the particles that represent the routing solutions in VLSI. The proposed approach has a good potential in VLSI routing and can be further extended in futureTo seek for a hyperchaotic attractor with complex topological attractor structure, a new four-dimensional continuous autonomous hyperchaotic system is proposed. Within a wider region of the variation of the control parameter, this system can generate novel hperchaotic and chaotic attractors along with quasi-periodic and periodic orbits. By employing Lyapunov exponent spectrum, bifurcation diagram, Poincaré mapping and phase portrait, etc., the existence of hyperchaotic behaviors of new system is verified and the dynamical routes from period, quasi-period, chaos and hyperchaos are observed. Furthermore, a practical circuit is designed to realize the system, which the experimental results indicate that new four-dimensional hyperchaotic system is a realizable chaotic system with potential values of engineering applications.

A new readout approach in DNA computing based on real-time PCR with taqman probes

A new readout approach for the Hamiltonian Path Problem (HPP) in DNA computing based on the real-time polymerase chain reaction (PCR) is investigated. Several types of fluorescent probes and detection mechanisms are currently employed in real-time PCR, including SYBR Green, molecular beacons, and hybridization probes. In this study, real-time amplification performed using the TaqMan probes is adopted, as the TaqMan detection mechanism can be exploited for the design and development of the proposed readout approach. Double-stranded DNA molecules of length 120 base-pairs are selected as the input molecules, which represent the solving path for an HPP instance. These input molecules are prepared via the self-assembly of 20-mer and 30-mer single-stranded DNAs, by parallel overlap assembly. The proposed readout approach consists of two steps: real-time amplification in vitro using TaqMan-based real-time PCR, followed by information processing in silico to assess the results of real-time amplification, which in turn, enables extraction of the Hamiltonian path. The performance of the proposed approach is compared with that of conventional graduated PCR. Experimental results establish the superior performance of the proposed approach, relative to graduated PCR, in terms of implementation time.

Performance evaluation of wavelet-based PCB defect detection and localization algorithm

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB) manufacturing. Due to the fatigue and speed requirement, manual inspection is ineffective to inspect every printed circuit board. Hence, this paper presents an efficient algorithm for an automated visual PCB inspection system that is able to automatically detect and locate any defect on PCBs. The defect is detected by utilizing wavelet-based image difference algorithm. The coarse resolution defect localization algorithm, is also presented. The coarse resolution defect localization algorithm is applied to the coarse resolution differenced image in order to locate the defective area on the fine resolution tested PCB image. In addition, the performance of the algorithm is evaluated to verify the efficiency of the proposed algorithm in term of computation time. This new method turned out to be computationally less intensive than traditional image difference operation. One conclusion from this paper is that the second level Haar wavelet transform should be chosen for the application of automated visual PCB inspection.

Identifying Gene Knockout Strategies Using a Hybrid of Bees Algorithm and Flux Balance Analysis for in Silico Optimization of Microbial Strains

Genome-scale metabolic networks reconstructions from different organisms have become popular in recent years. Genetic engineering is proven to be able to obtain the desirable phenotypes. Optimization algorithms are implemented in previous works to identify the effects of gene knockout on the results. However, the previous works face the problem of falling into local minima. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. This paper involves two case studies that consider the production of succinate and lactate as targets, by using E.coli as model organism. The results from this experiment are the list of knockout genes and the growth rate after the deletion. BAFBA shows better results compared to the other methods. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems.

A Non-linear Function Approximation from Small Samples Based on Nadaraya-Watson Kernel Regression

Solving function approximation problem is to appropriately find the relationship between dependent variable and independent variable(s). Function approximation algorithms normally require sufficient amount of samples to approximate a function. However, insufficient samples may result in unsatisfactory prediction to any function approximation algorithms. It is due to the failure of the function approximation algorithms to fill the information gap between the available and very limited samples. In this study, a function approximation algorithm which is based on Nadaraya-Watson Kernel Regression (NWKR) is proposed for approximating a non-linear function with small samples. Gaussian function is chosen as a kernel function for this study. The results show that the NWKR is effective in the case where the target function is non-linear and the given training sample is small. The performance of the NWKR is compared with other existing function approximation algorithms, such as artificial neural network.