Memory Space Complexity Research Articles

Hybrid Beamformer Exploiting Multistream per User Transmission for Millimeter-Wave NOMA Communications

This treatise investigates multistream hybrid beamforming (HBF) for a millimeter-wave non-orthogonal multiple access (NOMA) system deployed in a downlink of an urban microcell environment. Maximization problem is developed to optimize sum-rate. For the sake of ensuring high correlation of users’ channels, user’s clustering and ordering are based on the angle of arrivals and users’ channel-weights, respectively. An optimized analog combiner of each user is obtained from the first <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N_{s}$ </tex-math></inline-formula> column vectors of the left unitary matrix derived from the singular value decomposition of the channel of each user. Analog beamformer is matched to the phase of the strong users ’ composite intermediate analog channel to maximize the beamforming gain. Both the analog combiner and precoder for sub-connected structure (SCS) are formulated via a novel dominant sub-array matrix elements’ extractor. Conventional zero-forcing processing is contemplated for digital precoding. Memory space complexity is evaluated to corroborate the simplicity of the proposed schemes’ computational complexity. Results obtained from the simulation exhibit that HBF-NOMA attain superior sum-rate than HBF-orthogonal multiple access and conventional multiuser schemes in line of sight link. Lastly, the proposed SCS-HBF-NOMA precoding scheme performs higher than fully connected counterpart in terms of energy efficiency.

Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gas.

Spectral clustering (SC) is one of the most popular clustering methods and often outperforms traditional clustering methods. SC uses the eigenvectors of a Laplacian matrix calculated from a similarity matrix of a dataset. SC has serious drawbacks: the significant increases in the time complexity derived from the computation of eigenvectors and the memory space complexity to store the similarity matrix. To address the issues, I develop a new approximate spectral clustering using the network generated by growing neural gas (GNG), called ASC with GNG in this study. ASC with GNG uses not only reference vectors for vector quantization but also the topology of the network for extraction of the topological relationship between data points in a dataset. ASC with GNG calculates the similarity matrix from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, ASC with GNG achieves to reduce the computational and space complexities and improve clustering quality. In this study, I demonstrate that ASC with GNG effectively reduces the computational time. Moreover, this study shows that ASC with GNG provides equal to or better clustering performance than SC.

Fault-Detection Tactics for Optimized Embedded Systems Efficiency

Embedded systems operational environment poses tightened and usually conflicted design requirements. Software architects aim at introducing effective tradeoff methods to select the most appropriate design solutions to comply with the software specifications of an embedded system. When defining the software architecture for critical embedded systems it is mandatory to balance often conflicting goals to meet the different requirements in terms of resource consumption, schedulability, dependability, and security, among others. This is an engineering problem that can be addressed by employing Multiple-Criteria Decision-Making (MCDM) methods from the operational research domain. This paper combines two of these methods, Analytical Hierarchy Process (AHP) and Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS), to determine the most efficient fault detection design decisions according to relevant metrics. The paper employs two algorithm efficiency metrics: run-time complexity and memory-space complexity. Furthermore, the fault-detection strategy design decisions, Ping/Echo and Heartbeat, were the subjects of this study.

Secured E-Commerce System Using ECC and Multimodal Biometrics

This paper focuses on the need to win electronic commerce customers trust by developing a secured electronic commerce system using elliptic curve cryptography (ECC) integrated with multimodal biometrics (iris and voice) as a methodology. Unlike the use of Rivest, Shamir and Adleman (RSA) algorithm that is commonly used in many computing devices to enforce the confidentiality of information, this research uses ECC, thereby reducing memory space complexity and computation power. Also, freed-up memory space is used to add other security measures in the form of iris and voice biometric to effect authentication. The mathematical expression of ECC was modified to reflect the encryption and decryption of image (iris) and audio (voice) data types integrated in the modeled secured electronic commerce system. Performance evaluation were carried out after implementation and results show a system that is promising by completing the processing of new user registration and logging in within 90 seconds. Although, the use of only RSA was discovered to occupy less space when the bit strings are less than 30 kilobyte, the methods-the integration of iris and voice biometrics into ECC (Sum iv) as used in this research consumes lesser memory, the moment the key-size is greater than 30 kilobyte, and increase the difficulty in eavesdropping through information traffic analysis and impersonating a customer with second and third line authentication measures.

A Mixed-Critical Consistent Update Algorithm in Software Defined Time-Triggered Ethernet Using Time Window

Software Defined Networking (SDN) presents a tremendous opportunity for developing abstract management of network updates. However, network updates introduce challenges in terms of consistency. Many consistent update algorithms are proposed for Ethernet, but there is seldom update algorithm for distributed time-triggered system to satisfy requirements of strict real-time and mixed-critical. The contribution of this paper is to present a distributed update mechanism and a mixed-critical update algorithm for the time-triggered (TT) and rate-constrained (RC) traffics to reduce the memory space complexity, computational time complexity, update time, and keep per-packet consistent strictly. More specifically, consistent update is defined and some basic update algorithms are described. And then a packet-based network model is built to describe network resources. Subsequently, the update mechanism is changed for the TT and RC traffics. Meanwhile, a mixed-critical update algorithm, which contains a time window-based update and a rate-constrained update for TT and RC traffics respectively, is proposed. Whether the mixed-critical update algorithm is consistent updates or not is proved logically. Finally, experiments analyzed the performance of the mixed-critical update algorithm, compared with four conventional updates. The results show that there is no black hole, loop and inconsistent path for the mixed-critical update algorithm. The most important thing is that the memory space complexity is reduced by 19.93% at least, compared with two-phase update. Meanwhile, the computational time complexity and update time are reduced by 17.14% and 7.96% respectively, compared with time-based update. The mixed-critical update algorithm provides a method to optimize policy update tasks for distributed time-triggered system, so as to improve the ability of system reconfiguration.

Probabilistic Neural Network With Complex Exponential Activation Functions in Image Recognition.

If the training data set in image recognition task is not very large, the feature extraction with a convolutional neural network is usually applied. Here, we focus on the nonparametric classification of extracted feature vectors using the probabilistic neural network (PNN). The latter is characterized by the high runtime and memory space complexity. We propose to overcome these drawbacks by replacing the exponential activation function in the Gaussian kernel to the complex exponential functions. Such complex nonlinearities make it possible to accurately approximate the unknown density function using the network with the number of neurons proportional to only cubic root of the database size. As a result, the proposed approach decreases the runtime and memory complexities of the PNN without losing its main advantages, namely, fast training and convergence to the Bayesian decision. In the experimental study, we describe a protocol for comparing recognition methods using the well-known visual object category data sets in the context of the small sample size problem. It has been experimentally shown that our approach rapidly obtains accurate decisions when compared to the known classifiers including the baseline PNN.

A fast learning method for streaming and randomly ordered multi-class data chunks by using one-pass-throw-away class-wise learning concept

Presently, the amount of data occurring in several business and academic areas such as ATM transactions, web searches, and sensor data are tremendously and continuously increased. Classifying as well as recognizing patterns among these data in a limited memory space complexity are very challenging. Various incremental learning methods have proposed to achieve highly accurate results but both already learned data and new incoming data must be retained throughout the learning process, causing high space and time complexities. In this paper, a new neural learning method based on radial-shaped function and discard-after-learn concept in the data streaming environment was proposed to reduce the space and time complexities. The experimental results showed that the proposed method used 1 to 95 times fewer neurons and 1.2 to 2,700 times faster than the results produced by MLP, RBF, SVM, VEBF, ILVQ, ASC, and other incremental learning methods. It is also robust to the incoming order of data chunks.

A memory efficient semi-Naive Bayes classifier with grouping of cases

Previous analysis of learning data can help us to discover hidden relations among features. We can use this knowledge to select the most suitable learning methods and to achieve further improvements in the performance of classification systems. For the known Naive Bayes classifier, several studies have been conducted in an attempt to reconstruct the set of attributes in order to remove or debilitate dependence relations, which can reduce the accuracy of this classifier. These methods are included in the ones known as semi-naive Bayes classifiers. In the present research, we present a semi-Naive Bayes classifier that searches for dependent attributes using a filter approach. In order to prevent the number of cases of the compound attributes from being excessively high, a grouping procedure is always applied after the merging of two variables. This method attempts to group two or more cases of the new variable into a single one, in order to reduce the cardinality of the compound variables. As a result, the model presented is a competitive classifier with respect to the state of the art of semi-Naive Bayes classifiers, particularly in terms of quality of class probability estimates, but with a much lower memory space complexity.

An improved direct labeling method for the max–flow min–cut computation in large hypergraphs and applications

Algorithms described so far to solve the maximum flow problem on hypergraphs first necessitate the transformation of these hypergraphs into directed graphs. The resulting maximum flow problem is then solved by standard algorithms. This paper describes a new method that solves the maximum flow problem directly on hypergraphs, leading to both reduced run time and lower memory requirements. We compare our approach with a state–of–the–art algorithm that uses a transformation of the hypergraph into a directed graph and an augmenting path algorithm to compute the maximum flow on this directed graph: the run–time complexity as well as the memory space complexity are reduced by a constant factor. Experimental results on large hypergraphs from VLSI applications show that the run time is reduced, on average, by a factor approximately 2, while memory occupation is reduced, on average, by a factor of 10. This improvement is particularly interesting for very large instances, to be solved in practical applications.