Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic intraneural electrical stimulation for prosthetic sensory feedback

The functionality of prosthetic limbs is restricted by the limited availability of sensory feedback. The goal of the present research is the development of a multichannel microelectrode array for the presentation of sensory information directly to the sensory afferent neurons of the transected peripheral nerve of an amputee. Intraneural electrode arrays were developed and implanted in the proximal stump of the transected sciatic nerve in 18 New Zealand White rabbits and monitored for chronic functionality. Several novel nerve regeneration implant designs employing axially oriented Platinum/Iridium wire arrays and regeneration tubes were explored. Somatosensory evoked potentials were monitored via chronically implanted epidural electrodes. A behavioral technique was used to monitor stimulus perception; an electrical stimulus was delivered to the implanted nerve followed by an airpuff to the eye with monitoring of the resultant eye-blink reflex. In this classical conditioning technique, the rabbits associate the two stimuli such that electrical stimulation of the sciatic nerve without an airpuff elicits an eye-blink. Neurophysiological and behavioral tests have demonstrated chronic single channel electrode functionality for greater than 2 years in implanted animals. This research supports the possible viability of an implanted neuroelectric interface in providing chronic sensory feedback in humans.
Detecting cell cycle-regulated genes using Self-Organizing Maps with statistical Phase Synchronization (SOMPS) algorithm

Developing computational methods for identifying cell cycle-regulated genes has been one of important topics in systems biology. Most of previous methods consider the periodic characteristics of expression signals to identify the cell cycle-regulated genes. However, we assume that cell cycle-regulated genes are relatively active having relatively many interactions with each other based on the underlying cellular network. Thus, we are motivated to apply the theory of multivariate phase synchronization to the cell cycle expression analysis. In this study, we apply the method known as "Self-Organizing Maps with statistical Phase Synchronization (SOMPS)", which is the combination of self-organizing map and multivariate phase synchronization, producing several subsets of genes that are expected to have interactions with each other in their subset (Kim, 2008). Our evaluation experiments show that the SOMPS algorithm is able to detect cell cycle-regulated genes as much as one of recently reported method that performs better than most existing methods.
An Algorithm for Task Scheduling in Heterogeneous Computing Environment

Heterogeneous computing systems are of considerable interest because a single parallel architecture may not be adequate for exploiting all the parallelism available in the program. In some cases, heterogeneous systems perform better than a single large machine and that too at a lower cost. In heterogeneous systems, task scheduling is done with a view to minimize the overall completion time of a parallel application by properly allocating the tasks to the processors. In this paper, a task-scheduling algorithm called Alternate Heterogeneous Earliest Finish Time (AHEFT) is proposed and its performance is analyzed with respect to Heterogeneous Earliest Finish Time (HEFT) algorithm. A simulation environment has been developed in C++ and both the algorithms were implemented. Different weighted Directed Acyclic Graphs (DAG's) were used to compare the two algorithms. A network of fully connected heterogeneous processors was assumed for task scheduling. The performance indices used for comparison of algorithms are Schedule Length, Speedup Factor and Efficiency. The results substantiate the fact that for higher values of CCR (Communication to Computation Ratio), AHEFT algorithm performs better than HEFT. Moreover, as the number of tasks in the DAG increase, the performance improvement of AHEFT algorithms is more as compared to HEFT.
Workflow Scheduling Algorithm Based on Reliance Group in Cloud Environments

Cloud computing has attracted more people's attention and been extensive research. In order to provide satisfying service for users during the implement process of data-intensive workflow application in cloud environments, an improved algorithm is proposed which based on reliance group with high degree of dependence. By this measure, users can obtain the ideal acceleration radio of cost and service performance. Furthermore, more comprehensive resource service is provided by service providers.
Synthesis of Bis(indolyl)methanes Catalyzed by Acidic Ionic Liquid Immobilized on Silica (ILIS)

A new organocatalytic procedure for the synthesis of bis(indolyl)methanes with acidic ionic liquid immobilized on silica gel (ILIS-SO 2 Cl) has been developed. The reaction condition is -applicable to a variety of aryl and aliphatic aldehydes and indoles. The mild nature of the reaction conditions showed that the acetoxy or TBDMS group in the aldehyde was maintained intact. The catalyst was reused four times in 93% average yield.
Broadcasting topology information in the Survivable Signaling Network

The authors describe the broadcast algorithm used in the Survivable Signaling Network (SSN). SSN provides the Nationwide Emergency Telecommunications System (NETS) a highly survivable and robust network for national security emergency preparedness calls being set up over AT&T Switched Network. SSN achieves this high survivability and robustness through the application of broadcast algorithms to disseminate network status and to ultimately determine the routing of signaling traffic. The SSN broadcast algorithm is based on the Arpanet broadcast algorithm. However, the long node and link recovery times inherent in the Arpanet algorithm are intolerable in telephone signaling. The SSN algorithm extends the Arpanet algorithm to provide a rapid recovery from node and link crashes. It accomplishes this in a simple fashion consistent with the Arpanet algorithm. <<ETX>>
Analyzing the power of classes in an object-oriented software system

Power of a class reflects the relative importance of the class in an object-oriented software system, and has many implications for resource allocating. And how to measure it is still a problem we face. They paper presents to measure it based the connection structure of a network constructed from the software. To support this, we first use a network model to describe the connection structure. Then some centrality metrics used in social network analysis are introduced to quantify the power of classes. The commons and differences among these centrality metrics are revealed. We think these metrics can be used together to have a more systematic view of the importance of each class.
The indoor radio propagation channel

In this tutorial survey the principles of radio propagation in indoor environments are reviewed. The channel is modeled as a linear time-varying filter at each location in the three-dimensional space, and the properties of the filter's impulse response are described. Theoretical distributions of the sequences of arrival times, amplitudes and phases are presented. Other relevant concepts such as spatial and temporal variations of the channel, large-scale path losses, mean excess delay and RMS delay spread are explored. Propagation characteristics of the indoor and outdoor channels are compared and their major differences are outlined. Previous measurement and modeling efforts are surveyed, and areas for future research are suggested. >
Classification of Hyperspectral Data Based on Semi-Supervised Tri-Training Learning Framework

A semi-supervised learning framework based on the tri-training scheme is proposed for the classification of hyper spectral data. The framework involves two stages: multiple classifier learning by the improved tri-training and integrating the outputs of classifiers to the final hypothesis by decision fusion. To settle the ill-posed classification problem, in the stage of classifier learning, the label confidence of each learner is measured by the improved estimation of classifier error, and self-training is introduced to expand the labeled set using unlabeled samples with confident labels assigned by classifiers. Hyper spectral data classification experiments show the effectiveness of the proposed framework.
A 0.24-μm 2.0-V 1T1MTJ 16-kb nonvolatile magnetoresistance RAM with self-reference sensing scheme

A nonvolatile 16-kb one-transistor one-magnetic-tunnel-junction (1T1MTJ) magnetoresistance random access memory with 0.24-/spl mu/m design rules was developed by using a self-reference sensing scheme for reliable sensing margin. This self-reference sensing scheme was achieved by first storing a voltage of the magnetic tunnel junction (MTJ), and then after a time interval storing a reference voltage of the same MTJ (self-reference). The effects of variation in tunneling oxide thickness can be eliminated by this self-reference sensing scheme. As a result, reliable sensing of MRAM devices with MTJ resistance of 2.5-11 k/spl Omega/ was achieved.
Distribution circuit state estimation using a probabilistic approach

Past work on distribution circuit state estimation has focused on the adoption of a transmission state estimator approach, without necessarily accounting for the specific requirement of a distribution circuit-based analysis. On distribution circuits, typically, there are very few available real-time measurements, and thus, researchers have treated customer load demand estimates as pseudo-measurements in a weighted-least-squares formulation. This can lead to convergence problems and also, the approach effectively assumes that all bus load demands are normally distributed (Gaussian) which may not be valid on distribution circuits. This paper presents an alternative approach to distribution circuit state estimation using a probabilistic extension of the radial load flow algorithm while accounting for real-time measurements as solution constraint. The algorithm which takes advantage of the radial nature of distribution circuits also accounts for other issues specific to distribution circuits. Namely, the algorithm accounts for nonnormally distributed loads, incorporates the concept of load diversity (load correlation) and can interact with a load allocation routine. The effectiveness of the algorithm is illustrated through comparisons made with Monte Carlo simulations.
Advancing satellite operations with intelligent graphical monitoring systems

For nearly twenty-five years, spacecraft missions have been operated in essentially the same manner: human operators monitor displays filled with alphanumeric text watching for limit violations or other indicators that signal a problem. The task is performed predominately by humans. Only in recent years have graphical user interfaces and expert systems been accepted within the control center environment to help reduce operator workloads. Unfortunately, the development of these systems is often time consuming and costly. At the NASA Goddard Space Flight Center (GSFC), a new domain specific expert system development tool called the Generic Spacecraft Analyst Assistant (GenSAA) has been developed. Through the use of a highly graphical user interface and point-and-click operation, GenSAA facilitates the rapid, 'programming-free' construction of intelligent graphical monitoring systems to serve as real-time, fault-isolation assistants for spacecraft analysts. Although specifically developed to support real-time satellite monitoring, GenSAA can support the development of intelligent graphical monitoring systems in a variety of space and commercial applications.
General and Partial Shape Matching Approaches on Feature-Based CAD Models to Support Efficient Part Retrieval

Presently, mechanical design activities are almost fully digitalized and a large percentage of CAD models that are digitally created can be reused to facilitate new designs. Conventional alphanumeric-based part retrieval methods are error-prone. In order to support users to efficiently locate desirable parts to reuse, two novel knowledge-based approaches for feature-based model retrieval are proposed in the paper, namely general shape matching and partial shape matching, to assess CAD models by measuring their overall similarity without details and local similarity of sub-parts, respectively. The first approach simplifies feature-based CAD models from fully detailed to less detailed, and progressively simplified shapes are characterized by general shape descriptors. When a 3D query model that represents the general shape of a desired part is sketched, the approach compares the query model against pre-generated general shape descriptors so that all parts similar to the 3D query in overall shapes are retrieved; therefore the general shape matching is achieved. The second approach extracts reusable sub-parts from feature-based CAD models in an unsupervised way and indexes them using partial shape descriptors. Based on the partial shape descriptors indexed, designers could retrieve all existing parts sharing a desirable sub-part by sketching the shape of the sub-part; the partial shape matching is thereby realized to facilitate the part retrieval on local similarity. The approaches proposed in this paper are implemented on a prototype system and tested by hundreds of mechanical parts. The preliminary results show that the proposed approaches are feasible and promising.Copyright © 2008 by ASME
HERMES travels by CAN bus

The new HERMES spectrograph represents the first foray by AAO into the use of commercial off-the-shelf industrial field bus technology for instrument control, and we regard the final system, with its relatively simple wiring requirements, as a great success. However, both software and hardware teams had to work together to solve a number of problems integrating the chosen CANopen/CAN bus system into our normal observing systems. A Linux system running in an industrial PC chassis ran the HERMES control software, using a PCI CAN bus interface connected to a number of distributed CANopen/CAN bus I/O devices and servo amplifiers. In the main, the servo amplifiers performed impressively, although some experimentation with homing algorithms was required, and we hit a significant hurdle when we discovered that we needed to disable some of the encoders used during observations; we learned a lot about how servo amplifiers respond when their encoders are turned off, and about how encoders react to losing power. The software was based around a commercial CANopen library from Copley Controls. Early worries about how this heavily multithreaded library would work with our standard data acquisition system led to the development of a very low-level CANopen software simulator to verify the design. This also enabled the software group to develop and test almost all the control software well in advance of the construction of the hardware. In the end, the instrument went from initial installation at the telescope to successful commissioning remarkably smoothly.
Classification of novel events for structural health monitoring systems

This article reports on results obtained when applying neural networks to the problem of vehicle classification from SHM measurement data. It builds upon previous work which addressed the issue of reducing vast amounts of data collected during an SHM process by storing only those events regarded as being "interesting," thus decreasing the stored data to a manageable size. This capability is extended here by providing a means to group and classify these novel events using artificial neural network (ANN) techniques. Two types of neural systems are investigated, the first one consists of two neural layers employing both supervised and unsupervised learning. The second, which is an extension of the first, has a data pre-processing stage. In this later system, input data presented to the system is first pre-scaled before being presented to the first network layer. The scaling value is retained and later passed to the second layer as an extra input. The results obtained for vehicle classification using these two methods showed a success rate of 60% and 90% for the first and second ANN systems respectively.
SEEKER: Keyword-Based Information Retrieval over Relational Databases

Traditionally, SQL is the main interface to access data from relational databases. However, it is difficult for inexperienced end users to learn the complicated syntax of SQL. Enabling keyword-based information retrieval over relational databases will allow users to acquire information from databases without any knowledge of SQL and the underlying database schema, just like the way of common search engines. This paper describes the design and implementation of SEEKER, a system supporting keyword-based information retrieval over relational databases. While there have been some existing systems that support searching text attributes in relational databases, SEEKER can also search database metadata and numeric attributes. Moreover, SEEKER employs an improved ranking function and supports Top-k queries. Experimental results show that SEEKER can achieve good retrieval performance.
A Supervised and Fuzzy-based Approach to Determine Optimal Multi-resolution Image Segmentation Parameters

Image segmentation is important for object-based classification. One of the most advanced image segmentation techniques is multi-resolution segmentation implemented by eCognition (R). Multi-resolution segmentation requires users to determine a set of proper segmentation parameters through a trial-and-error process. To achieve accurate segmentations of objects of different sizes, several sets of segmentation parameters are required: one for each level. However, the trial-and-error process is time consuming and operator dependent. To overcome these problems, this paper introduces a supervised and fuzzy-based approach to determine optimal segmentation parameters for eCognition (R). This approach is referred to as the Fuzzy-based Segmentation Parameter optimizer (FBSP, optimizer) in this paper. It is based on the idea of discrepancy evaluation to control the merging of sub-segments to reach a target segment. Experiments demonstrate that the approach improves the segmentation accuracy by more than 16 percent, reduces the operation time from two hours to one-half hour, and is operator independent.
Sentences clustering based automatic summarization

There are two ways by which the research on automatic summarization is carried out. One is based on statistics, and the other is based on message understanding. The former has nothing to do with domain, but its accuracy is lower. On the contrary, the latter depends on domain, but its accuracy is higher. In this paper, an algorithm, which summarizes a document by extracting subtopics from the sentences, is based on statistics and partially understanding message, in order to get better summarization and get rid of the dependence on domain. Besides, since it is difficult to determine the length of a summary manually, the algorithm also strives to obtain a better summary with proper length. To this end, a new module of mutual dependence is put forward too and applied to segmentation, which can select accuracy features for the summarizing algorithm. And then new rules are brought forward to evaluate sentences for the summarizing algorithm. Furthermore, a new task based algorithm to evaluating summarization is impersonally offered.
Communication Technology and Application of Seismic Precursor Network Instrument

In this paper, the communication technology of seismic precursor network instrument is introduced, including instruction format and returned information format of instrument login, status information acquisition, and current measured data acquisition. The remote monitoring alarm software is based on this technology, and also introduced that the structure of monitoring information table, abnormal alarm index, and monitoring strategy. The application of the software raises instrument running rate and observation data quality.
Trellis coding with diversity for DS-SS CDMA communication over indoor radio channels

Direct sequence spread spectrum (DS-SS) is an efficient method to combat multipath fading and interference, especially when combined with mitigation techniques like channel coding and diversity. The authors evaluate bounds to the performance of trellis codes in combination with conventional diversity for a DS-SS code division multiple access (CDMA) system in the indoor environment, modeled by a slowly varying, Rayleigh fading, discrete multipath channel. A Gaussian approximation for the multiuser interference, a star network configuration, and average power control are assumed. The index of performance is the average bit error probability. It is shown, for a given average error probability, that there is considerable improvement in the number of users supported by the subject system when compared with systems with either coding or diversity alone. <<ETX>>
A brain-state-in-a-box network for narrative comprehension and recall

Understanding a discourse is considered to involve a series of specific processing phases which final result is a complete semantic, mental representation. This result is not only a representation of the text, but rather of what the text is about. Argument overlapping, connections between text constituents, and situational continuity, connections between discourse segments due to causality, spatiality and temporality, are the two main factors which potentially influence the extent to which readers form such a coherent mental model. This article explores the cognitive representation of narrative prose and the relations among the ideas in text. In our simulations, sentences in narratives are represented as nodes in a brain-state-in-a-box (BSB) network. Competing coalitions of the nodes drive the network into a stable equilibration. Quasi mental clusters (QMCs) are then extracted from narratives. Results shown attest the psychological validity of the model. The high correlation of the QMCs to psychological models may suggest the formation of mental models in text comprehension.
Text Hiding in Mobile Phone Simple Message Service Using Fonts

Problem statement: Hiding secret information in an ordinary mobile phone Simple Message Service (SMS). Approach: In mobile phones, there are two default types of fonts, System and Proportional fonts, which have similar figures to human vision and cannot recognized by human eye. The suggested method hides the information (0,1) in cover SMS message by changing the fonts of each character by one of those two fonts (0 represented by System font and 1 represented by Proportional fonts). After embedding secret information in cover message, the Stego message will look like an ordinary message but each character draw in one of these similarity fonts. Finally, at extract site, it must analyze each character font to retrieve secret information. This study has been implemented by J2ME (Java 2 Micro Edition) programming language to works in mobile (cellular) phones. Results: By using two similarity fonts, we can hide one bit in one character of mobile phone message. Conclusion: Good steganography depends on the human behavior and way of thinking. The most successful hiding method is the uncommon one. Although the proposed method is simple but it is unthinkable one and needs knowledge and experience to be discovered.