Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate modeling of microwave devices using kriging‐corrected space mapping surrogates

We present an accurate modeling technique that exploits standard space mapping (SM) as a trend function and the correction layer implemented with kriging. The kriging process allows us to efficiently utilize all available fine model data (not possible in the standard SM approach) and to obtain accuracy better than both the SM and recently published combinations of SM and radial basis function interpolation and fuzzy systems. We present application examples of design optimization and statistical analysis of several microwave filters. Copyright © 2011 John Wiley & Sons, Ltd.
Statistical multiplexing of random processes in packet switching networks

The authors present an analytical model for the closed form evaluation of relevant parameters characterising the process at the output of a statistical multiplexer. Compared with previous approaches, the proposed method is able to additionally describe transient conditions and, through the autocorrelation function and the mean length of adjacent packets sequences, to evaluate the extent of the correlation in the output process as a function of that in the input traffic, also taking into account the effect due to a finite buffer size. The reliability of the model is tested, under different operation conditions, by comparison with simulated results, showing everywhere an excellent agreement.
Knowledge Pushing Technology Based on the Knowledge Context Node Similarity

In order to meet the users'demand of knowledge and use knowledge efficiently,this paper adopts modeling methods of multilayer and multidimensional context tree to complete the establishment of the knowledge context tree, build the algorithm of the context entity similarity and realize the knowledge push, improve the similarity of the user with the necessary knowledge, so that a kind of very valuable method for pushing knowledge has also been put forward.
An LVQ based reference model for speaker-adaptive speech recognition

A novel type of hierarchical phoneme model for speaker adaptation, based on both hidden Markov models (HMM) and learned vector quantization (LVQ) networks is presented. Low-level tied LVQ phoneme models are trained speaker-dependently and independently, yielding a pool of speaker-biased phoneme models which can be mixed into high-level speaker-adaptive phoneme models. Rapid speaker adaptation is performed by finding an optimal mixture for these models at recognition time, given only a small amount of speech data; subsequently, the models are fine-tuned to the new speaker's voice by further parameter reestimation. In preliminary experiments with a continuous speech task using 40 context-free phoneme models at task perplexity 111, the authors achieved 82% word accuracy for speaker-dependent recognition and 73% in the speaker-adaptive mode. <<ETX>>
Systolic Array Processor Implementation

A combination of systolic array processing techniques and VLSI fabrication promises to increase signal-processing capabilities by a factor of 100 or more. To achieve a timely marriage of algorithms and hardware, both must be developed concurrently. This article describes the hardware for a programmable, reconfigurable systolic array testbed, implemented with presently available integrated circuits and capable of 32-bit floating-point arithmetic. While this hardware presently requires a small printed circuit board for each processing element, in a few years one or two custom VLSI chips could be used instead, yielding a smaller, faster systolic array processor. This testbed will aid in the evaluation of the many parameters which will have to be optimized in order to design these custom chips.
Research on the Supporting Degree of Multilayer Correlative Rules and Its Minimal Thresholds

Supporting degree and its minimal threshold have important impacts on the designment of quick and effective algorithm of multilayer correlative rules. This paper introduces interzone supporting degree for computing supporting degree of item sets of intersectional layer rationally which avoids unmeaningful intersectional layer rules. Further, this paper comes up with a method of adjusting minimal supporting degree thresholds of different layers by the threshold of each abstract layer from experts and the recording of user’s feedback. This method overcomes the subjective impact of fixed threshold on the extraction of interesting correlative mode in some degree, making the correlative rules promoted to the user are more closer to the user’s interest, shows the character of personalized promotion. Keywords-component; data mining; multilayer correlative rule; supporting degree
An Experimental Study Investigating the Retrieval Effectiveness of a Video Retrieval System Using Tag Query Expansion

This study designed a pilot system in which queries can be expanded through a tag ontology where equivalent, synonymous, or related tags are bound together, in order to improve the retrieval effectiveness of videos. We evaluated the proposed pilot system by comparing it to a tag-based system without tag control, in terms of recall and precision rates. Our study results showed that the mean recall rate in the structured folksonomy-based system was statistically higher than that in the tag-based system. On the other hand, the mean precision rate in the structured folksonomy-based system was not statistically higher than that in the tag-based system. The result of this study can be utilized as a guide on how to effectively use tags as social metadata of digital video libraries.
Tumor classification based on gene microarray data and hybrid learning method

Gene expression microarray data can be used to classify tumor types. We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA/WV (multi-objective evolutionary algorithm/weighted voting). MOEA is used to search for a relatively few subsets of informative genes from the high-dimensional gene space, and WV is used as a classification tool. This new method has been applied to predicate the subtypes of Iymphoma and outcomes of medulloblastoma. The results are relatively accurate and meaningful compared with those from other methods.
A semiautomatic forest management approach using remote sensing techniques

Remote sensing satellite images are effectively used as a tool for decision making in various fields, especially in forest management and related analyses. Different geospatial parameters are required for effective decision making and the possibility of an integrated framework for automation of various analyses has been investigated. Advanced web mining and intelligent techniques have been adopted for the development of a comprehensive open-source framework for this purpose. The effectiveness of the developed methodology has been discussed and illustrated with reference to study areas using various statistical parameters. Adoption of a cellular neural network (CNN) for feature modeling and open-source data for automatic mining seemed to be effective. The developed methodologies were found to outperform existing ones with regard to accuracy and complexity. Investigations revealed that use of CNN is very effective in shape modeling, and improves accuracy of detection.
A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.
People detection method using graphics processing units for a mobile robot with an omnidirectional camera

This paper presents a novel vision system for people detection using an omnidirectional camera mounted on a mobile robot. In order to determine regions of interest (ROI), we compute a dense optical flow map using graphics processing units, which enable us to examine compliance with the ego-motion of the robot in a dynamic environment. Shape-based classification algorithms are employed to sort ROIs into human beings and nonhumans. The experimental results show that the proposed system detects people more precisely than previous methods.
HOW TO GET AN INTONATION CONTOUR FOR A SENTENCE OF ARBITRARY SYNTACTIC COMPLEXITY

Those who study the prosodic aspect of a sentence usually presume that it is sufficient to describe main functional "blocks" of intonation tone units — which characterize relatively simple and short sentences: the problem of synthesis of these blocks into sequences which really occur in linguistic activity when the speakers generate utterances of considerable complexity is left aside. Meanwhile the utterance in a natural language can have theoretically infinite length, and cooccurrence of tone units in grammatically correct sentences is governed by sufficiently sophisticated rules. The problem arises how to delimintate these rules.
Efficient window query processing method using area-maximising points

The use of area-maximising points (AMPs) is proposed as a method for improving the performance of window query processing. The AMP method is a progressive approximation method and is used to identify hits from the candidates. It involves the use of four sampled points of an object and shows a higher filter ratio and shorter processing time when compared with established approximation methods.
Expanding the Boundaries of Your Research Using Social Media: Stand-Up and Be Counted

When Archimedes faced the task of finding if a fraudulent goldsmith mixed silver in the golden crown made for the king, he designed a simple experiment. He immersed the crown in water and measured the volume of water displaced. He then obtained the density of the crown metal as a ratio of mass of the crown and the volume of the water displaced. He proved that if the goldsmith used any metal other than pure gold for making the crown, this ratio would be different. The story goes that overwhelmed by this realization, a completely naked Archimedes ran into the street to announce his scientific discovery shouting “Eureka! ” This unconfirmed anecdote tells us how scientists in medieval times could have drawn the attention of the public to their discoveries.
Analysis and Implementation of the Game OnTop

In the field of artificial intelligence (AI) solving games and creating challenging computer implementations is of high significance. Probably the most famous AI, Deep Blue, was developed for chess and beat world-class players like Kasparov. This thesis focuses on the analysis and implementation of AI for the modern board game OnTop. OnTop contains perfect information and chance events and is categorised as a non-deterministic game. Chance events occur when drawing a random tile. Due to the fact that the individual tile types have different numbers, the chance events are not uniformly distributed. OnTop can be played with up to 4 players. This thesis focuses on the 2-player variation. The thesis starts with an introduction to the game OnTop and follows with the game-tree and state-space complexity. After that the two search techniques Expectimax and Monte-Carlo search are described in detail. In addition, some improvements and enhancements are presented for both techniques. With respect to Expectimax these are Star1, Star2 and Star2.5 of the *-Minimax algorithm family. Concerning Monte-Carlo Search we have investigated Monte-Carlo Tree Search with Upper Confidence bounds applied to Trees and some improved techniques such as parallelisation. The results show that for the *-Minimax algorithms Star2 performs best. Star2 is able to reduce the number of searched nodes for a certain search depth up to 93% compared to Expectimax. The experiments also revealed that move ordering influences the number of nodes serached. Moreover, it was not possible for Star2.5 to perform better than Star2. For Monte Carlo the investigated improvement Monte-Carlo Tree Search (MCTS) reaches the best results. The strength of MCTS was furthermore improved by root parallelisation. The parallelisation approach increased the number of simulated games at least up to 55%. The games between Star2 and MCTS show that MCTS performs much better. This experiment additionally shows that having the upper hand in the beginning of the game is no guarantee of winning the game in the end. So MCTS wins 80% of the simulated games.
Enhanced RLME protocol for Layered Video Multicast in Diffserv Networks

A layered video multicast framework for differentiated service (DS) networks, which provides various levels of QoS guarantee for heterogeneous users with improved performance in network congestion adaptation, is examined in this research. The proposed system consists of three key components: extended active queue management, hierarchical priority marking, and receiver-driven layered multicast with ECN (RLME). Particularly, we introduce RLME protocol that effectively utilizes advanced features of active queues such as random early drop (RED) and early congestion notification (ECN) in DS networks. The RLME protocol quantitatively estimates the network congestion level via ECN and packet loss to improve adaptation capability to network congestion, and utilizes the priority service from DS networks to minimize the packet loss effect on reconstructed video quality. The simulation shows that the proposed system successfully achieves stable and controllable video QoS guarantee for heterogeneous video clients over DS networks.
58.2: A Novel Linear Digital‐to‐Analog Converter using Capacitor Coupled Adder for LCD Driver ICs

We have developed a novel linear digital‐to‐analog converter using a capacitor coupled adder for LCD driver ICs. The proposed 12bit linear DAC can output 4096 voltage levels from only 128 reference voltages. The die size of the proposed 12bit linear DAC is quite smaller than the conventional non‐linear 10bit Resistor‐Resistor‐String DAC (RR‐DAC).
Targeting extracellular glycans: tuning multimeric boronic acids for pathogen-selective killing of Mycobacterium tuberculosis† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc00415g

Innovative chemotherapeutic agents that are active against Mycobacterium tuberculosis (Mtb) are urgently required to control the tuberculosis (TB) epidemic. The Mtb cell envelope has distinct (lipo)polysaccharides and glycolipids that play a critical role in Mtb survival and pathogenesis and disruption of pathways involved in the assembly of the Mtb cell envelope are the primary target of anti-tubercular agents. Here we introduce a previously unexplored approach whereby chemical agents directly target the extracellular glycans within the unique Mtb cell envelope, rather than the intracellular biosynthetic machinery. We designed and synthesised multimeric boronic acids that are selectivity lethal to Mtb and function by targeting these structurally unique and essential Mtb cell envelope glycans. By tuning the number of, and distance between, boronic acid units high selectivity to Mtb, low cytotoxicity against mammalian cells and no observable resistance was achieved. This non-conventional approach may prevent the development of drug-resistance and will act as a platform for the design of improved, pathogen-specific, next generation antibiotics.
Scalable multicast data distribution for different transport service classes

Not all of the problems of large scale multicast can be addressed by automated repeat request (ARQ) based multicast protocols. Simulation studies have shown that independent packet loss among receivers is a worst case situation, where ARQ based protocols scale poorly and react with throughput degradation. Software based forward error correction (FEC) techniques are therefore a good solution and can be used to enhance the throughput efficiency by controlling the packet loss rate. A new method for implementing FEC at the transport layer is presented and performance measurements are shown. The difference between the restricted reliable multicast protocol (RRMP) and existing ARQ/FEC based multicast protocols is the flexibility and adaptability the RRMP provides by offering different transport service classes for different application needs. An application is able to choose the transport service, which best meets the requirements for efficiency, latency, delay, scalability etc. The design and implementation decisions for the FEC and ARQ based error recovery are outlined. Performance measurements demonstrate the suitability of FEC for real-time data transmissions.
Evidence and non-repudiation

The ultimate purpose of a non-repudiation service is to resolve disputes about the occurrence or non-occurrence of a claimed event or action. Dispute resolution relies on the evidence held by the participants. This paper discusses types of non-repudiation evidence, elements of non-repudiation evidence and validity of non-repudiation evidence. We also investigate and compare a number of protocols aiming at fair exchange of non-repudiation evidence.
Spectral norm of random Toeplitz matrices

In this work, we consider symmetric random Toeplitz matrices $T_n$ generated by i.i. d. zero mean random variables ${X_k}$ satisfying the moment conditions: $E|X_k|^2=1$ and $\E|X_1|^n \le n^{\sqrt{n}}$ for all $n\ge 3$. We prove that the largest eigenvalue of $T_n$ scaled by $\sqrt{n log(n)}$ converges almost surely to $1$.
A Python Based Enhanced Secret Sharing Scheme to Secure Information using Cryptography Techniques

There are lots of algorithms and concepts that are employed to secure intellectual property of the user. In this paper a scheme known as secret sharing is used to securely distribute user’s work among different parties. Objective behind the paper is to provide such a protocol that enhance and achieve privacy and other expectations of the owner of the information. The scheme states that, the information is accessible only when at least k shareholders from n must get together. User’s shares are constructing through quadratic polynomial. To make such a scheme more relevant to its users, the concept of cryptography is applied. Here the shares are encrypted so that dishonest dealer and malicious participants are not able to view or alter the data. The shares are enciphered by using different encryption algorithms to make a comparative study and to know most secure and powerful among them. “PyCrypto” a python based cryptography tool is used for this purpose.
Binaural Speaker Recognition for humanoid robots

This paper deals with Automatic Speaker Recognition in a binaural context. Such a problematic, not so widely dealt with within the speech processing community, can have potential applications in humanoid robots where speech can be used as the most natural interface between humans and robots. The proposed recognition system is based on parallel Predictive Neural Networks exploiting MFCCs (Mel Frequency Cepstral Coefficients) to discriminate multiple talkers. Because of the binaural nature of the system, the sensitivity of the proposed algorithm to the speaker spatial position during the learning step is carefully studied. The influence of noise and reverberation on the recognition rate is also reviewed. Finally, preliminary experimental results based on the recorded signals from a binaural dummy head are presented.
Relief reconstruction of rough-textured surface through image analysis

In this study, we propose an original method for a 3D reconstruction of the relief of a textured rough surfaces. This 3D reconstruction is obtained through the elaboration of a photometric model, which takes into account camera and light source positions according to the plan of the rough surface. The proposed model expresses the gray level on the image according to the local relief variations. Three images of the same relief obtained under different angles of lighting are used to reconstruct the altitude map of the rough surface. The effectiveness of this method was checked by comparing the extracted relief to its corresponding relief obtained from a mechanical device method using autofocus laser sensor. This photometric model display good results in simulation experience and will be applied on real photographic images of road covering surface in order to study its wear level and its adherence.
Synthesis and characterization of erbium trioxide nanoparticles as photocatalyzers for degradation of methyl orange dye

Abstract. The present work focuses on the photocatalytic degradation of methyl orange (MO) on erbium trioxide nanoparticles (Er2O3 NPs). In this study, Er2O3 nanoparticles were synthesized and fully characterized via various techniques, including X-ray diffraction, UV–visible spectroscopy and scanning electron microscopy techniques. The results revealed that the photocatalytic activity of the prepared Er2O3 NPs was manifested in MO photodegradation. The optimum efficiency obtained was 16 %.
Are We There Yet ? Finite Curves and E-leisure

Abstract This paper introduces the six papers in the special issue on e-leisure. It begins with a précis of Dennis Gabor’s thoughts in 1963 regarding technological growth and the Age of Leisure. Building from those thoughts, this introduction briefly describes each of the papers in this issue. The paper then concludes with observations about Gabor’s thoughts in relation to the papers in the special issue, as well as the general topic of technology and e-leisure.
Multi-parametric model predictive control-based regulation of blood glucose in type 1 diabetics under unmeasured meal disturbances: a simulation study

Closed-loop control of Blood Glucose Concentration (BGC) is performed using a model differencing-based multi-parametric model predictive controller. An empirical model derived from a virtual nominal patient is used in the design of the controller. A time varying hard constraint is imposed on the control action based on insulin onboard criterion. By varying the model parameters used in the Sorensen’s patient model, 25 virtual patients are created. Varying meal disturbances over a period of five days, the Mean Square Error (MSE) of BGC’s deviation from reference (100 mg/dL) is used to tune the controller. In-silico trials with simulated sensor noise (± 20 mg/dL range) show that the multi-parametric model predictive controller performs well under model uncertainties and also regulates the BGC within the safe limits (above 60 mg/dL) during multiple meal disturbances.
Modeling and Analysis of Container Terminal Logistics System by Extended Generalized Stochastic Petri Nets

In this paper, an extended Generalized Stochastic Petri Net (e-GSPN) was proposed for effective modeling of complex container terminal logistics systems (CTLS). To cope with their complexity and implement rule based dynamic scheduling of CTLS, a special type of place called rule place and a special type of transition called decision transition are integrated in GSPN using object oriented approaches. Guided by this e-GSPN method, a simulation model was built as an example to illustrate the modeling approaches. According to actual operations in one container terminal of Shanghai port in China, we give a case study to analyze the relationships between decision rules and collisions.