A Pattern Classification Based approach for Blur Classification

Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach

A MLMVN WITH ARBITRARY COMPLEX-VALUED INPUTS AND A HYBRID TESTABILITY APPROACH FOR THE EXTRACTION OF LUMPED MODELS USING FRA

A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality

Review on Some Methods used in Image Restoration

The restoration image is manner of mending the inventive image by eradicating noise and fuzziness from image. Image fuzziness is troublesome to shun in several things similar shooting, to confiscate motion blur caused by camera stillness, measuring device imaging to eradicate the outcome of image scheme retort, etc. The aim of image restoration is guesstimate the innovative image from surveillance image despoiled by haziness and preservative noise as much as promising. Altered image restoration techniques have urbanized by many researches. In this review I will discuss different images restoration methods

Temperature and Humidity Control System for Pole-Mounted Metering Circuit Breaker with Artificial Neural Network Methods

Pole-mounted Metering Circuit Breaker (PMCB) is a medium voltage protection device. Problems in the PMCB because operating at medium voltage causes insulation problems. The isolation problem that arises is due to partial discharge. Partial discharge can trigger the risk of flashover. In addition, corona discharge causes corrosion of the conductor, the effect is a failure and disconnection of electricity. This control system aims to maintain the temperature and humidity of the PMCB at the nominal values according to the standard. Based on SPLN D3.021-1:2020, it is known that under normal service conditions, the ambient air temperature does not exceed 40°C and the average temperature for 24 hours does not exceed 35°C and the highest relative humidity is 100% RH. The control system uses an AC voltage controller which is used to control the input voltage of the heater and exhaust fan so that the temperature and humidity can reach nominal operating conditions. The control method used is an artificial neural network (ANN) to find the ignition angle of the AC voltage controller as a TRIAC control. The test results using the ANN control method, system simulation produces a temperature error of 1.029% and humidity error of 2.48% and the hardware system produces a temperature error of 2.364% and humidity error of 8.673% compared to the set point temperature of 35°C and humidity of 50% RH. It can be concluded that the ANN control method can maintain the PMCB temperature and humidity according to standard

Fish species classification in unconstrained underwater environments based on deep learning

Underwater video and digital still cameras are rapidly being adopted by marine scientists and managers as a tool for non-destructively quantifying and measuring the relative abundance, cover and size of marine fauna and flora. Imagery recorded of fish can be time consuming and costly to process and analyze manually. For this reason, there is great interest in automatic classification, counting, and measurement of fish. Unconstrained underwater scenes are highly variable due to changes in light intensity, changes in fish orientation due to movement, a variety of background habitats which sometimes also move, and most importantly similarity in shape and patterns among fish of different species. This poses a great challenge for image/video processing techniques to accurately differentiate between classes or species of fish to perform automatic classification. We present a machine learning approach, which is suitable for solving this challenge. We demonstrate the use of a convolution neural network model in a hierarchical feature combination setup to learn species-dependent visual features of fish that are unique, yet abstract and robust against environmental and intra-and inter-species variability. This approach avoids the need for explicitly extracting features from raw images of the fish using several fragmented image processing techniques. As a result, we achieve a single and generic trained architecture with favorable performance even for sample images of fish species that have not been used in training. Using the LifeCLEF14 and LifeCLEF15 benchmark fish datasets, we have demonstrated results with a correct classification rate of more than 90%

Comparison of file sanitization techniques in usb based on average file entropy valves

Nowadays, the technology has become so advanced that many electronic gadgets are in every household today. The fast growth of technology today gives the ability for digital devices like smartphones and laptops to have a huge size of storage which is letting people to keep many of their infonnation like contact lists, photos, videos and even personal infonnation. When these infonnation are not useful anymore, users will delete them. However, the growth of technology also letting people to recover back data that has been deleted. In this case, users do not realise that their deleted data can be recovered and then used by unauthorized user. The data deleted is invisible but not gone. This is where file sanitization plays it role. File sanitization is the process of deleting the memory of the content and over write it with a different characters. In this research, the methods chosen to sanitize file are Write Zero, Write Zero Randomly and Write Zero Alternately. All of the techniques will overwrite data with zero. The best technique is chosen based on the comparison of average entropy value of the files after they have been overwritten. Write Zero is the only technique that is provided by many software like WipeFile and BitKiller. There is no software that provide Write Zero Randomly technique except for sanitizing disk using dd. As for that, Write Zero Randomly and proposed technique, Write Zero Alternately are developed using C programming language in Dev-C++. In this research, sanitization with Write Zero has the lowest average entropy value for text document (TXT), Microsoft Word (DOCX) and image (JPG) with 100% of data in the files undergone this technique have been zero-filled compared to Write Zero Randomly and Write Zero Alternately. Next, Write Zero Alternately is more efficient in tenns of average entropy by 4.64 bpB to its closest competitor which is Write Zero Randomly with 5.02 bpB. This shows that Write Zero is the best sanitization method. These file sanitization techniques are important to keep the confidentiality against unauthorized user

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI