Hybrid Encoding Research Articles

Contaminant source identification in water distribution network based on hybrid encoding

Contaminant source identification in water distribution network based on hybrid encoding

A New Approach of Takagi–Sugeno Fuzzy Modeling Using an Improved Genetic Algorithm Optimization for Oxygen Content in a Coke Furnace

The oxygen content modeling of the coke furnace is important for advanced control design but not an easy job because of various disturbances and nonlinearity. A novel approach is proposed by using an improved genetic algorithm (IGA) combined with the dynamic autoregressive with exogenous input (ARX) Takagi–Sugeno (T-S) fuzzy model. The IGA algorithm automatically generates the input variable, the appropriate fuzzy if–then rules, and the ARX structure to characterize the dynamic nonlinear feature of the oxygen content by processing the operation data from the industrial coke furnace. And a more comprehensive objective function is constructed considering both the modeling precision and structure simplicity. Hybrid encoding, modified genetic operators, particularly the maintain operator, are designed to obtain the satisfactory optimization performance. The modeling accuracy and system structure of the T-S fuzzy model are compared with a benchmark Box–Jenkins gas furnace and the complex industrial coke furnac...

Plane-wave least-squares reverse time migration for rugged topography

We present a method based on least-squares reverse time migration with plane-wave encoding (P-LSRTM) for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O (input/output) cost, the dynamic encoding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate amplitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack PLSRTM is more robust than conventional RTM in the presence of migration velocity errors.

Quantum information transfer with hybrid NV center-photon qubit encoding

We propose a scheme to perform quantum information transfer based on hybrid spin-photon qubit encoding in superconducting quantum circuits. The hybrid qubit consists of a nitrogen-vacancy center spin ensemble coherently coupled to a microwave photon confined in a frequency tunable superconducting co-planar waveguide cavity. The spin ensemble-cavity hybrid system forms quantum nodes capable of sending, receiving, storing, and releasing photonic quantum information by the hybrid encoding. Quantum information transfer between distinct nodes is achieved by the coherent exchange of a single photon. We show the faithful transfer of an arbitrary superposition hybrid quantum state between two separate identical nodes via coherent control on the system’s dynamics. The protocol may have interesting applications in quantum networking with this hybrid spin-photon structure.

VEHICLE ROUTING PROBLEM WITH TIME WINDOWS USING HYBRID ENCODING GENETIC ALGORITHM


 
 The vehicle routing problem is to determine K vehicle routes, where a route is a tour that begins at the depot, traverses a subset of the customers in a specified sequence and returns to the depot. Each customer must be assigned to exactly one of the K vehicle routes and total size of deliveries for customers assigned to each vehicle must not exceed the vehicle capacity. The routes should be chosen to minimize total travel cost. Thispapergivesasolutiontofindanoptimumrouteforvehicle routingproblem using Hybrid Encoding GeneticAlgorithm (HEGA)technique tested on c++ programming.The objective is to find routes for the vehicles to service all the customers at a minimal cost and time without violating the capacity, travel time constraints and time window constraints

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

A New Optimized GA-RBF Neural Network Algorithm

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

A Hybrid Amplitude/Time Encoding Scheme for Enhancing Coding Efficiency and Dynamic Range in Digitally Modulated Power Amplifiers

Digitally modulated power amplifiers (DMPA) are a promising architecture for practical and highly efficient software-defined radio transmitters. Signal encoding schemes for DMPAs have primarily focused either on amplitude or time domain encoding, neither of which can simultaneously provide high coding efficiency, high dynamic range and low design complexity. In this paper, we propose, analyze and evaluate a new hybrid amplitude/time signal encoding scheme that significantly improves the coding efficiency and dynamic range of DMPAs without significantly increasing design complexity. The proposed hybrid amplitude/time encoding scheme combines both the amplitude domain and the time domain to optimally encode information. Evaluation results show that hybrid amplitude/time encoding yields a 35% increase in the average coding efficiency with respect to conventional time encoding, and is only 6.7% lower than peak efficiency when applied to a Wireless Local Area Network signal with a peak to average power ratio equal to 9.9 dB. A new DMPA architecture, based on the proposed hybrid encoding, is also proposed.

Double image encryption based on phase-amplitude hybrid encoding and iterative phase encoding in fractional Fourier transform domains

A new method for double image encryption is proposed that is based on amplitude-phase hybrid encoding and iterative random phase encoding in fractional Fourier transform (FrFT) domains. In the iterative random phase encoding operation, a binary random matrix is defined to encode two original images to a single complex-valued image, which is then converted into a stationary white noise image by the iterative phase encoding with FrFTs. Compared with the previous schemes that uses fully phase encoding, the proposed method reduces the difference between two original images in key space and sensitivity to the FrFT orders. The primitive images can be retrieved exactly by applying correct keys with initial conditions of chaotic system, the pixel scrambling operation and the FrFT orders. Computer simulations demonstrate that the encryption method has impressively high security level and certain robustness against data loss and noise interference.

Crypt analysis of two time pads in case of compressed speech

Keystream reuse, also known as the two time pad problem, is a well known weakness in stream ciphers. The implementers of the cryptographic algorithms are still underestimating this threat. The keystream reuse exploitation techniques presented so far assume the underlying plaintext to be textual data and all the heuristics presented previously are based on the language characteristics of the underlying text based data, which fail when compression is applied on the plaintext before encryption. This paper presents exploitation techniques for two time pads in case of stream ciphered digitized and compressed speech signals. In this paper we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding/compression scheme details of the underlying speech signals are known. Our technique of cryptanalysis is based on the modeling of the speech parameters by exploiting the inter frame correlations between each components of the speech vectors in different frames and then using these models to decode the two speech signals in the keystream reuse scenario. The technique is flexible enough to incorporate all modern speech coding schemes based on parameter or hybrid encoding and compression techniques. The simulation experiments have showed promising results for most of the present day speech digitization and compression techniques.

Application of Hybrid Encoding Genetic Algorithm on Pickup and Delivery Traveling Salesman Problem with Traffic Conditions

Problem statement: This study deals with the pickup and delivery traveling salesman problem with traffic conditions (PDTSPTW), an extension of the pickup and delivery traveling salesman problem (PDTSP) where each customer to be served is associated with two quantities of product to be collected and delivered. Almost PDTSP problems uses distance between each point of customers as Euclidean Distance and are not concerned with other parameters to find minimal cost. Approach: The PDTSPTC concerns more parameters, such as street network and vehicle speed, which results it closer to the real world condition. The study also proposes the developed genetic algorithm called Hybrid Encoding Genetic Algorithm (HEGA). The concept is to combine binary encoding and integer encoding together, causing the in complexity of the algorithm structure and the ease of implementation. Results: the HEGA can save the travel cost about 26-57%. It is obviously seen that HEGA in the PDTSPTC test problems result the fast convergence that is about 12-43% and in all, the computational time is under 6 seconds. Conclusion: The HEGA performs quite well when testing a test problem. Computation times are small in all case. The PDTSPTC is closer to real-world conditions than PDTSP. This problem can be applied in logistics immediately if the distance of streets, traffic conditions (average vehicle speed in each street) and vehicle conditions (fuel consumption rate and vehicle capacity) are known.

A Binary Differential Evolution Algorithm with Hybrid Encoding

A Binary Differential Evolution Algorithm with Hybrid Encoding

A Hybrid‐Ncoding Genetic Algorithm for Inversion of Potential‐Field Data

AbstractThe genetic algorithm is of advantages to solve an inversion of complex non‐linear geophysical equations. Its multi‐point searching is able to find the globally optimal solution and avoid falling into a local extremum. The searching efficiency of the genetic algorithm is a key to successfully resolve a geophysical inversion problem in a huge model space with multi‐parameters. Encoding mechanism impacts mostly in the searching stage of the genetic algorithm. It sometimes is difficult for a standard genetic algorithm (SGA) to make searching successfully, because the crossover and mutation do not receive most effectively searching in mechanisms with only the binary or decimal encoding. For the binary encoding mechanism the operation of the crossover may produce more new individuals. The decimal encoding mechanism, on the other hand, makes the operation of the mutation searching in a larger range. This paper discusses searching potentials between operators in the binary and decimal encoding and presents a hybrid‐encoding genetic algorithm (HEGA) mechanism. The method is based on a hybrid encoding in genetic procedure. The mutation operation is executed with the decimal code and other operations with the binary code. The HEGA guarantees the mutation processing with a high probability. HEGA is beneficial to solving the inversion of complex non‐linear geophysical equations. Synthetic and real data examples demonstrate advantages of using HEGA in inversion of potential‐field data.

Multilevel Wavelet Feature Statistics for Efficient Retrieval, Transmission, and Display of Medical Images by Hybrid Encoding

Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.

A Hybrid Encoding of Howe's Method for Establishing Congruence of Bisimilarity

We give a short description of Hybrid, a new tool for interactive theorem proving. It provides a form of Higher Order Abstract Syntax (HOAS) combined consistently with induction and coinduction. We present a case study illustrating the use of Hybrid for reasoning about the lazy lambda-calculus. In particular, we prove that the standard notion of simulation is a precongruence. Although such a proof is not new, the development is non-trivial, and we attempt to illustrate the advantages of using Hybrid, as well as some issues which are being addressed as further work.

2D SENSE for faster 3D MRI.

Sensitivity encoding in two spatial dimensions (2D SENSE) with a receiver coil array is discussed as a means of improving the encoding efficiency of three-dimensional (3D) Fourier MRI. It is shown that in Fourier imaging with two phase encoding directions, 2D SENSE has key advantages over one-dimensional parallel imaging approaches. By exploiting two dimensions for hybrid encoding, the conditioning of the reconstruction problem can be considerably improved, resulting in superior signal-to-noise behavior. As a consequence, 2D SENSE permits greater scan time reduction, which particularly benefits the inherently time-consuming 3D techniques.Along with the principles of 2D SENSE imaging, the properties of the technique are discussed and investigated by means of simulations. Special attention is given to the role of the coil configuration, yielding practical setups with four and six coils. The in vivo feasibility of the two-dimensional approach is demonstrated for 3D head imaging, permitting four-fold scan time reduction.