Information Processing Paradigm Research Articles

Relational granulation method based on Quotient Space Theory for maximum flow problem

Granular computing (GrC) is a problem-solving concept deeply rooted in human thinking. GrC, as a new and rapidly growing paradigm of information processing, has attracted the attention of many researchers and practitioners. GrC is related to granulation, i.e., a process of drawing a set of objects (or points) together based on their indiscernibility, similarity, proximity, or functionality. In general, two types of granulation processes exist: functional granulation and relational granulation. If the process is based entirely on the attributes of the objects, it is known as functional granulation, whereas if the granulation process is based on the relationship between objects, it is known as relational granulation. This paper proposes a novel method, called the maximum flow based on Quotient Space Theory (MF−QST), for solving the maximum flow problems based on Quotient Space Theory for relational granulation. Using the method MF−QST, substructures are first detected, and the community is described as a substructure. Next, the relational granulation criterion is discussed in detail. The substructure that satisfies the relational granulation criterion is regarded as a coarse-grained node. Subsequently, the construction of a quotient network that is coarser than the original network is described. Finally, the maximum flow algorithm is used to compute the maximum flow on the quotient network as the approximated maximum flow on the original network within a much shorter period of time. Experimental results demonstrate that the novel method MF−QST reduces the cumulative running time after simplifying the network structure with a low error rate. The size of the quotient network is significantly reduced, and the node and edge scales are reduced to 20.59% and 21.62% on average, respectively.

Neuromorphic Computing Based on Silicon Photonics and Reservoir Computing

We present our latest progress using new neuromorphic paradigms for optical information processing in silicon photonics. We show how passive reservoir computing chips can be used to perform a variety of tasks (bit level tasks, nonlinear dispersion compensation, etc.) at high speeds and low power consumption. In addition, we present a spatial analog of reservoir computing based on pillar scatterers and a cavity that can be used to speed up classification of biological cells.

Voice controller mobile android application

Mobile devices are becoming an indispensable part of daily lives. This system has been developed interactive android application to assist and provide the support helps people such as blind and other physically limited people who face. They will be done for outgoing calling to his family and also the area around them. Speech recognition is technology that uses desired equipment and a service which can be controlled through voice of the android smart phone. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The device proposed here is an interactive android platform, which is capable of recognizing spoken words. We propose to developed interactive android application and also added some features which are currently available in android based smartphones such as attending for a line calling feature and out-going calling feature, over the voice commands which can run on the tablet or any android based phone. Speech recognition have a data set to translate between users’ descriptions and the labels in the database, which has entries on the speech recognition (sounds and patterns) that make up the sound.

Valleytronics: Opportunities, Challenges, and Paths Forward.

A lack of inversion symmetry coupled with the presence of time-reversal symmetry endows 2D transition metal dichalcogenides with individually addressable valleys in momentum space at the K and K' points in the first Brillouin zone. This valley addressability opens up the possibility of using the momentum state of electrons, holes, or excitons as a completely new paradigm in information processing. The opportunities and challenges associated with manipulation of the valley degree of freedom for practical quantum and classical information processing applications were analyzed during the 2017 Workshop on Valleytronic Materials, Architectures, and Devices; this Review presents the major findings of the workshop.

Artificial intelligence based defect classification for weld joints

This paper mainly deals with the development of a defect classification system that uses Artificial Neural Network (ANN) to classify weld defects based on ultrasonic test data. The system enables real-time identification of weld defects which finds application in testing of critical welding applications and also reduces dependency on skilled workforce for the function. The study mainly consists of three parts- (i) Weld defect detection using Ultrasonic Testing (UT) (ii) Implementation of ANN (iii) Defect classification. An ultrasonic test performed on welded samples shows different results for welds with and without defects and further between defects as well. The ultrasonic test data is fed into the ANN algorithm to train it to identify the various weld defects. An Artificial Neural Network (ANN) is an information processing paradigm that uses a large number of highly interconnected processing elements called neurons, working in unison to solve the specific problems. There are two types of neural network architectures that are used for classification - a back propagation network (BPN) and a probabilistic neural network (PNN). Back propagation network has been used for the purpose of this study. In order to test the performance of the back propagation neural network, four classes of defect namely porosity, lack of side wall fusion, lack of penetration and slag inclusion are considered.

On characterizations of a pair of covering-based approximation operators

Granular computing is an emerging computing paradigm of information processing. Rough set theory is a kind of models of granular computing and is used to deal with the vagueness and granularity in information systems. Covering-based rough set theory is one of the most important extensions of the classical Pawlak rough set theory. In the covering-based rough set theory, the covering lower and upper approximation operators are two basic concepts. The axiomatic characterizations of covering-based approximation operators guarantee the existence of coverings reproducing the operators, so the rough set axiomatic system is the foundation of the covering-based rough set theory. In this paper, we present a new covering upper approximation operator and explore the basic properties of the covering upper approximation operator to find an axiomatic set for characterizing the covering upper approximation operator. We also discuss the relationships between the covering upper approximation operator and three other covering upper approximation operators, compare the covering upper approximation operator with the other covering upper approximation operators by defining precision degrees and rough degrees, and present the necessary and sufficient conditions for the covering upper approximation operators to be identical. The results not only are beneficial to enrich the kinds of the covering rough sets, but also have theoretical and actual significance to covering rough sets.

MENGKAJI PERILAKU HARGA KOMODITI PANGAN DI KOTA PALU MENGGUNAKAN METODE BACKPROPAGATION

Artificial neural network is an information processing paradigm that is inspired by biological neural cell systems, like the brain, that processes information. The purpose of this research is to develop neural networks to predict the price of food commodities using backpropagation method. The research was conducted by using the rate of monthly price of food commodities in Palu from January 2011 - December 2015. The data is used to predict food commodity prices forduring 2016. The backpropagation networks consists of three layers. The first layer of input is constructedin the form of monthly prices of IR 64, ciherang, membramo, cimandi, superwin, sintanur, cisantana, sticky black, sticky white, yellow corn dry, white corn, soybeans, peanuts, green beans, cassava, sweet potato, onion, garlic, red pepper large, red pepper curls, cayenne pepper, cabbage round, potatoes, tomatoes, carrots, cauliflower, beans, onion, avocado, red apples, green apples, oranges, jackfruit, mango, pineapple, papaya, banana, banana horns, rambutan, bark, olive, durian, watermelon, and mangosteen from January – December that consist of 12 variables. One hidden layer consistof five neurons and the other one is the output, that is the food commodity prices. The training process shows that on a maximum iterations on 500, constant learning rate 0,3 and 0,6 momentum, the predictions have 97.92% of level accuracy. The identification resultof food commodity prices behavior in Palu is predicted as follow: IR 64 Rp7.387, ciherang Rp8.182, membramo Rp8.150, cimandi Rp8.131, superwin Rp8.228, sintanur Rp8.660, cisantana Rp8.122, black sticky rice Rp21.383, white sticky rice Rp16.558, dry yellow corn Rp5.983, white corn Rp9.283, soybeans Rp14.600, peanuts Rp20.008, green beans Rp16.375, cassava Rp8.225, sweet potato Rp8. 542, red onion Rp28.550, garlic Rp21.208, red chili Rp27.308, curly red chili Rp23.650, cayenne Rp36.450, round cabbage Rp6.833, Rp12.067 potatoes, tomatoes Rp6.108, carrots 11.000, cauliflower Rp8.625, beans Rp10.333, scallion Rp25.242, avocado 11.000, red apple Rp29.023, green apple Rp31.067, orange Rp6.083, jackfruit Rp23.483, mango Rp11.187, pineapple Rp8.183, papaya Rp10.600, bananas Rp8.481, horn banana Rp2.683, rambutan Rp8.450, barking Rp5.625, tan Rp8.366, durian Rp19.208, watermelon Rp14.528 and mangosteen Rp18.067. It is predicted that the food commodity prices increased monthly.

Designing Plasmonic Eigenstates for Optical Signal Transmission in Planar Channel Devices

On-chip optoelectronic and all-optical information processing paradigms require compact implementation of signal transfer for which nanoscale surface plasmons circuitry offers relevant solutions. This work demonstrates the directional signal transmittance mediated by 2D plasmonic eigenmodes supported by crystalline cavities. Channel devices comprising two mesoscopic triangular input and output ports and sustaining delocalized, higher-order plasmon resonances in the visible to infra-red range are shown to enable the controllable transmittance between two confined entry and exit ports coupled over a distance exceeding 2 $\mu$m. The transmittance is attenuated by > 20dB upon rotating the incident linear polarization, thus offering a convenient switching mechanism. The optimal transmittance for a given operating wavelength depends on the geometrical design of the device that sets the spatial and spectral characteristic of the supporting delocalized mode. Our approach is highly versatile and opens the way to more complex information processing using pure plasmonic or hybrid nanophotonic architectures.

Valleytronics in merging Dirac cones: All-electric-controlled valley filter, valve, and universal reversible logic gate

Despite much anticipation of valleytronics as a candidate to replace the ageing CMOS-based information processing, its progress is severely hindered by the lack of practical ways to manipulate valley polarization all-electrically in an electrostatic setting. Here we propose a class of all-electric-controlled valley filter, valve and logic gate based on the valley-contrasting transport in a merging Dirac cones system. The central mechanism of these devices lies on the pseudospin-assisted quantum tunneling which effectively quenches the transport of one valley when its pseudospin configuration mismatches that of a gate-controlled scattering region. The valley polarization can be abruptly switched into different states and remains stable over semi-infinite gate-voltage windows. Colossal tunneling valley-pseudo-magnetoresistance ratio of over 10,000\% can be achieved in a valley-valve setup. We further propose a valleytronic-based logic gate capable of covering all 16 types of two-input Boolean logics. Remarkably, the valley degree of freedom can be harnessed to resurrect logical-reversibility in two-input universal Boolean gate. The (2+1) polarization states -- two distinct valleys plus a null polarization -- re-establish one-to-one input-to-output mapping, a crucial requirement for logical-reversibility, and significantly reduce the complexity of reversible circuits due to the built-in nature of valley degree of freedom. Our results suggest that the synergy of valleytronics and digital logics may provide new paradigms for valleytronic-based information processing and reversible computing.

Horizontal Extrapolation of Wind Speed Distribution Using Neural Network for Wind Resource Assessment

To evaluate the wind potential on a site for future wind energy project, an accurate representation of the wind speed distribution is required. However, due to the lack of observations, wind engineers are conducted to use some statistical tools to estimate the characteristics of wind by the measurements from a nearby reference or data obtained from a short period. In this work, we aim at applying an information processing paradigm that is inspired by biological neurons, formal neurons, for the assessment of wind speed distribution. Two different learning algorithms are used so as to generate Artificial Neural Network with one hidden layer. Results prove that learning by means of Bayesian regularization, in comparison with Levenberg–Marquardt learning algorithm, gives the best performance. In addition, the proposed network allows significant results in horizontal wind extrapolation.

Dissipative quantum error correction and application to quantum sensing with trapped ions

Quantum-enhanced measurements hold the promise to improve high-precision sensing ranging from the definition of time standards to the determination of fundamental constants of nature. However, quantum sensors lose their sensitivity in the presence of noise. To protect them, the use of quantum error-correcting codes has been proposed. Trapped ions are an excellent technological platform for both quantum sensing and quantum error correction. Here we present a quantum error correction scheme that harnesses dissipation to stabilize a trapped-ion qubit. In our approach, always-on couplings to an engineered environment protect the qubit against spin-flips or phase-flips. Our dissipative error correction scheme operates in a continuous manner without the need to perform measurements or feedback operations. We show that the resulting enhanced coherence time translates into a significantly enhanced precision for quantum measurements. Our work constitutes a stepping stone towards the paradigm of self-correcting quantum information processing.

Structure of Multipartite Entanglement in Random Cluster-Like Photonic Systems

Quantum networks are natural scenarios for the communication of information among distributed parties, and the arena of promising schemes for distributed quantum computation. Measurement-based quantum computing is a prominent example of how quantum networking, embodied by the generation of a special class of multipartite states called cluster states, can be used to achieve a powerful paradigm for quantum information processing. Here we analyze randomly generated cluster states in order to address the emergence of multipartite correlations as a function of the density of edges in a given underlying graph. We find that the most widespread multipartite entanglement does not correspond to the highest amount of edges in the cluster. We extend the analysis to higher dimensions, finding similar results, which suggest the establishment of small world structures in the entanglement sharing of randomised cluster states, which can be exploited in engineering more efficient quantum information carriers.

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

Quantum computer has an amazing potential of fast information processing. However, realisation of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a novel platform, quantum reservoir computing, to solve these issues successfully by exploiting natural quantum dynamics, which is ubiquitous in laboratories nowadays, for machine learning. In this framework, nonlinear dynamics including classical chaos can be universally emulated in quantum systems. A number of numerical experiments show that quantum systems consisting of at most seven qubits possess computational capabilities comparable to conventional recurrent neural networks of 500 nodes. This discovery opens up a new paradigm for information processing with artificial intelligence powered by quantum physics.

ANALYSIS OF THE MEDICINAL LEAVES BY USING IMAGE PROCESSING TECHNIQUES AND ANN

Neural Network has emerged over the years and has made remarkable contribution to the advancement of various fields of endeavor. The purpose of this work is to examine neural networks and their emerging applications in the field of engineering, focusing more on controls. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the token, process information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true for ANNs as well. Neural networks perform a variety tasks, such as prediction and function approximation, pattern classification, they are also capable of complex data and signal classification task and many other using.

A Novel Design and Implementation of New Double Feynman and Six-correction logic (DFSCL) gates in Quantum-dot Cellular Automata (QCA)

In recent years, quantum cellular automata (QCA) have been used widely to digital circuits and systems. QCA technology is a promising alternative to CMOS technology. It is attractive due to its fast speed, small area and low power consumption. The QCA offers a novel electronics paradigm for information processing and communication. It has the potential for attractive features such as faster speed, higher scale integration, higher switching frequency, smaller size and low power consumption than transistor based technology. In this paper, Double Feynman and Six-correction logic gate (DFSCL) is proposed based on QCA logic gates: MV gate and Inverter gate. The proposed circuit is a promising future in constructing of nano-scale low power consumption information processing system and can stimulate higher digital applications in QCA.

A Novel Design and Implementation of 8-3 Encoder Using Quantum-dot Cellular Automata (QCA) Technology

In recent years Quantum-dot Cellular Automata (QCA) has been considered one of the emerging nano-technology for future generation digital circuits and systems. QCA technology is a promising alternative to Complementary Metal Oxide Semiconductor (CMOS) technology. Thus, QCA offers a novel electronics paradigm for information processing and communication system. It has attractive features such as faster speed, higher scale integration, higher switching frequency, smaller size and low power consumption compared to the transistor based technology. It is projected as a promising nanotechnology for future Integrated Circuits (ICs). A quantum dot cellular automaton complex gate is composed from simple 3-input majority gate. In this paper, a 8-3 encoder circuit is proposed based on QCA logic gates: the 4-input Majority Voter (MV) OR gate. This 7-input gate can be configured into many useful gate structures such as a 4-input AND gate, a 4-input OR gate, 2-input AND and 2-input OR gates, 2-input complex gates, multi-input complex gates. The proposed circuit has a promising future in the area of nano-computing information processing system and can be stimulated with higher digital applications in QCA.

Design and Implementation of New Feynman and Toffoli (NFT) Gates in Quantum-dot Cellular Automata (QCA)

In this paper, New Feynman and Toffoli (NFT) gate is proposed based on QCA logic gates. The proposed circuit is a promising future in constructing of nano-scale low power consumption information processing system and can stimulate higher digital applications in QCA. QCA technology is a promising alternative to CMOS technology. It is attractive due to its fast speed, small area and low power consumption. A novel electronics paradigm for information processing and communication by QCA offers technology. QCA technology has the potential for attractive features such as faster speed, higher scale integration, higher switching frequency, smaller size and low power consumption than transistor based technology.

Whither culture? On the predominance of cognitivism in media and communication studies

This article seeks to historically contextualise the various cognitivist models used in communication studies, namely that of media effects, focusing on the external contingencies that have influenced the predominance of the information processing paradigm. Indeed, the heavy reliance on research methods in the field of communication pays tribute to the legacy of cognitivism, because most methods used in cross-cultural and comparative studies tend to confine cultural phenomena to static predefined categories. It is argued that cognitivist communication models cannot ultimately be separated from an ideological view of human agency as prone to passive reception and susceptible to persuasion and manipulation. Understanding these ideological underpinnings implies being attentive to Foucault’s interrogation of transcendental universalism through a Kantian-inspired exploration of the conditions for the possibility of experience and a Nietzschian-inspired genealogy that emphasises knowledge as produced and maintained by technologies of power.

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State.

The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.

Homoepitaxial graphene tunnel barriers for spin transport

Tunnel barriers are key elements for both charge-and spin-based electronics, offering devices with reduced power consumption and new paradigms for information processing. Such devices require mating dissimilar materials, raising issues of heteroepitaxy, interface stability, and electronic states that severely complicate fabrication and compromise performance. Graphene is the perfect tunnel barrier. It is an insulator out-of-plane, possesses a defect-free, linear habit, and is impervious to interdiffusion. Nonetheless, true tunneling between two stacked graphene layers is not possible in environmental conditions usable for electronics applications. However, two stacked graphene layers can be decoupled using chemical functionalization. Here, we demonstrate that hydrogenation or fluorination of graphene can be used to create a tunnel barrier. We demonstrate successful tunneling by measuring non-linear IV curves and a weakly temperature dependent zero-bias resistance. We demonstrate lateral transport of spin currents in non-local spin-valve structures, and determine spin lifetimes with the non-local Hanle effect. We compare the results for hydrogenated and fluorinated tunnel and we discuss the possibility that ferromagnetic moments in the hydrogenated graphene tunnel barrier affect the spin transport of our devices.