Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of tactile testing machine on tact switch

Abstract The main purpose of this paper is to create a tactile testing mechanism for a tact switch. In this paper, the fundamental principle of a tact switch will be discussed. Moreover, the tactile testing machine composed of a sensor, a controller, an actuator, and a mechanism has been introduced. According to the special characteristics of a tact switch, a testing method for the tactile test has been developed and programmed by using a visual basic language. In this tactile testing machine, four kinds of components have been integrated together so that the tactile testing for the tact switch can be performed automatically. Before the tactile testing machine is operated, a complete calibration procedure for the system is performed so as to assure a high precision system. Moreover, to verify the reliability of the tactile testing system, three kinds of products are individually tested 100 times. The testing results reveal that the error range of both the maximal acting force and elasticity ratio in the tactile test system is extremely low with experimental repeatability remaining in tact. Therefore, the tactile testing system is accurate and stable. Consequently, the tactile testing system will automatically provide a quick and precise tactile inspection for the automation machine.
Mitigating Control Channel Saturation in the Dynamic Channel Assignment Protocol

Multi-channel MAC protocols that rely on a dedicated control channel (CC) for data channel reservation face the problem of control channel saturation. When the control channel becomes the bottleneck, data channels are not fully utilized and the spectrum allocated for the network is not used efficiently. For a popular dedicated control channel based multi-channel MAC protocol, the dynamic channel assignment protocol (DCA), we propose and compare two methods for mitigating control channel saturation. The first method is based on the ability of modern wireless cards to use different channel bandwidths. Increasing the bandwidth of the CC allows higher transmission rates and thereby relieves the saturation on the CC. The second method involves TXOP (Transmission Opportunity)-like burst-transmissions, where several date packets are transmitted using one reservation operation on the control channel. Network simulations and analysis show that, in an 802.11 based network, the bandwidth adaptation method yields a throughput improvement of around 30%. The TXOP mechanism performs much better and increases the throughput by a factor of 2 to 10, depending on the length of the data packets.
Introduction to the recognition of patterns in compressed data: 1. image template operations over block-, transform-, runlength-encoded, and vector-quantized data

The processing of compressed or encrypted imagery is a vital new area of research that can achieve computational efficiency and data security by processing fewer data, which may be obscurely encoded. In particular, we have derived numerous image processing algorithms that achieve computational speedups which approach the compression ratio (CR). In this paper, we extend our previous work in computation and pattern recognition over one-dimensional compressed data to include operations over multidimensional imagery. We discuss the processing of transform, block, and runlength encoded imagery, as well as the special case of vector-quantized (VQ) imagery. We note that certain cases of template matching over the range space of the block-encoding or VQ transform can yield a computational speedup that approaches the domain compression ratio (CRd). Defined as the ratio of the number of source data to the number of compressed data, CRd generally exceeds the customary compression ratio. Analyses emphasize computational complexity, information loss, and implementational feasibility.
Evaluation of two-part algorithms for objects' depth estimation

During the last decade, a wealth of research was devoted to building integrated vision systems capable of both recognising objects and providing their spatial information. Object recognition and pose estimation are among the most popular and challenging tasks in computer vision. Towards this end, in this work the authors propose a novel algorithm for objects' depth estimation. Moreover, they comparatively study two common two-part approaches, namely the scale invariant feature transform SIFT and the speeded-up robust features algorithm, in the particular application of location assignment of an object in a scene relatively to the camera, based on the proposed algorithm. Experimental results prove the authors' claim that an accurate estimation of objects' depth in a scene can be obtained by taking into account extracted features' distribution over the target's surface.
Advances in infrared sensor technology and systems

The imaging technology available for medical research and diagnosis has progressed from early systems using single detector scanners to full two-dimensional arrays. The initial emphasis on visible and low light level arrays has expanded dramatically to include the long wave infrared with spectral cut-off as long as twenty-five microns, the short-wave infrared and multispectral arrays. Image enhancement features, originally controlled manually at each channel, are now integral to the sensor. Advanced signal processing, both at the output of the array and at the pixel level, enhances image contrast, automatically recognizes features and controls gain globally and in local areas. Advances in detector and packaging technology have significantly reduced sensor size and weight, opening new applications for imaging technology previously restricted to large platforms. In addition, the innovations in detector and electronics technology have had a large impact on cost reduction, making high performance imaging systems available to a wide variety of users. These significant advances in imaging technology can lead the way to a new era in affordable medical imaging systems, widely available in both the laboratory and physician's office.
Multicore-based parallelized differential evolution for medical image registration

With the popularization of multicore CPU, it will be the trend of CPU and be used in lots of applications. This paper proposed a multicore-based parallelized registration algorithm, which is derived from the image registration based on different evolution. This proposed algorithm takes full advantage of the parallel ability of multicore CPU, thus it accomplishes the parallel evolution algorithm on graphic workstation, which traditionally works on supercomputer, and realizes the multicore-based parallelized fast medical image registration. The experimental results show that, the proposed algorithm has no difference in precision and stability with traditional image registration based on differential evolution, and exhibits fast convergence and a speedup almost growing linearly with the number of kernels.
The Multi-Dimensional Nature of Encoding Tactile and Haptic Interactions: From Psychophysics to Design Guidelines

Designers are very well aware of the multi-dimensionality of visual stimuli and extensively use dimensions such as color, form and contrast to encode information. An analogous multi-dimensionality applies to haptic and tactile stimuli. Based on the fact that the human visual system only has two receptor types (cones and rods) while the sense of touch has closer to ten, one might argue that the design space is even larger for tactile and haptic interactions than for visual. The approach taken in this paper is to take the different sensory systems and their (perceptual) characteristics as a starting point. Bridging the gap between the neurophysiological and psychophysical data and the design guidelines can give this field a head start. For example, data on just noticeable differences of frequency, amplitude, force, and indentation can be an important driver in choosing the encoding dimension in the multidimensional haptic/tactile design space.
Two-dimensional common-centroid stack generation algorithms for analog VLSI

In analog VLSI design, two-dimensional common-centroid stacks, in which devices are two-dimensional symmetry and have a common-centroid, are critical for mismatch minimization and parasitic control. However it is difficult to construct due to several constrains of layout. In this paper, algorithms for generating analog VLSI two-dimensional common-centroid stack are described. We get several theory results by studying symmetric Eulerian graph and symmetric Eulerian trail. Based on those, an O(n) algorithm for dummy transistor insertion, symmetric Eulerian trail construction and two-dimensional common-centroid stack construction are developed. The generated stacks are two-dimensional symmetric and common-centroid. Several stacks with different ratio are generated for one group of devices and they could be chosen during placement according to the performance and area consideration. Experimental results show effectiveness of our algorithms.
Software for predicting the 3D structure of RNA molecules

RNA is not regarded anymore as a simple transfer molecule between DNA and proteins. Indeed, over the past decades a plethora of new functional roles have been assigned to RNA molecules. Such functions are carried out either by RNA molecules alone or through interactions with DNA, other RNA molecules, or proteins. In all cases, the structure that the RNA molecule adopts will impact its function, as it happens with proteins. Therefore, to fully characterize the function of an RNA molecule, its structure needs to be either determined by experiments or predicted by computation. Unfortunately, our knowledge of the atomic mechanism by which RNA molecules adopt their biological active structures is still limited. Such hurdle is now being addressed by the development of new computational methods for RNA structure prediction, which complement experimental methods such as X‐ray crystallography, nuclear magnetic resonance, small‐angle X‐ray scattering, and cryo‐electron microscopy. This software focus is not dedicated to a single computational method but aims at outlining the most adopted methods for computational RNA structure prediction. WIREs Comput Mol Sci 2015, 5:56–61. doi: 10.1002/wcms.1198
The Study of Combination Filtering for Removing Mixed Noise of Remote Sensing Image

The traditional combination filter method in the practical process is often two layers of composite filtering de-noising for the image containing the gaussian mixture noise and pulse noise. This paper proposes a three filter combination algorithm based on the two layers filters, To the Gaussian and pulse mixed noise of the remote sensing image, we use wavelet threshold de-nosing and adaptive median filter de-nosing, and then using the third layer adaptive Wiener filtering de-noising to remove the residual noise. Through theoretical analysis and practical application, the de-nosing results of this method is obvious in processing mixed noise of remote sensing image, this is a practical method of combination filtering de-noising, it can be widely used in the field of image processing.
An Efficient Algorithm for Extracting High Utility Itemsets from Weblog Data

ABSTRACT High utility itemset refers to those set of items which has high utility such as profit in a database. High utility of itemset plays a crucial role in real life. In recent years, various algorithms have been proposed for finding high utility itemset but unfortunately they are not completely relevant at the time and space point of view. In the data mining field, high utility itemset can be found in different categories of data like time series, categorical, etc. Log data is useful for finding behaviour of the user in different aspects. In this paper, we have proposed an algorithm named HUIM (High Utility Itemsets Mining) and construct HUI-FP (High Utility Itemsets-Frequent Pattern) Tree for efficiently mining high utility itemsets from log database. The behaviour of the user can be predicted through the high utility of every visited page. We have also proposed pattern generation technique based on cosine similarities among itemsets. These techniques generate strong patterns, and customized users profile according to that pattern. The proposed algorithm is better than the previous state of the art algorithm for high utility itemset generation.
On the implementation of a real-time information security architecture in frequency domain

This paper presents the real-time implementation of a watermarking-based information security architecture in frequency domain. The scheme emphasises on the human visual system (HVS)-supported watermarking approach using wavelet-lifting technique. In addition to HVS, image registration algorithm is also introduced in order to increase the resiliency as well as the security of the estimated recovered watermark image. The algorithmic steps with optimisation considerations about the real-time implementation on TMS320CDSK6416/6713 fixed/floating point digital signal processor are also projected.
Real-time implementation of second generation of audio multilevel information coding

This paper describes real-time implementation of a novel wavelet- based audio compression method. This method is based on the discrete wavelet (DWT) representation of signals. A bit allocation procedure is used to allocate bits to the transform coefficients in an adaptive fashion. The bit allocation procedure has been designed to take advantage of the masking effect in human hearing. The procedure minimizes the number of bits required to represent each frame of audio signals at a fixed distortion level. The real-time implementation provides almost transparent compression of monophonic CD quality audio signals (samples at 44.1 KHz and quantized using 16 bits/sample) at bit rates of 64-78 Kbits/sec. Our implementation uses two ASPI Elf boards, each of which is built around a TI TMS230C31 DSP chip. The time required for encoding of a mono CD signal is about 92 percent of real time and that for decoding about 61 percent.
Temporally consistent segmentation of point clouds

We consider the problem of generating temporally consistent point cloud segmentations from streaming RGB-D data, where every incoming frame extends existing labels to new points or contributes new labels while maintaining the labels for pre-existing segments. Our approach generates an over-segmentation based on voxel cloud connectivity, where a modified k-means algorithm selects supervoxel seeds and associates similar neighboring voxels to form segments. Given the data stream from a potentially mobile sensor, we solve for the camera transformation between consecutive frames using a joint optimization over point correspondences and image appearance. The aligned point cloud may then be integrated into a consistent model coordinate frame. Previously labeled points are used to mask incoming points from the new frame, while new and previous boundary points extend the existing segmentation. We evaluate the algorithm on newly-generated RGB-D datasets.
Security Analysis of a Secure Dynamic ID based Remote User Authentication Scheme for Multi-server Environment

Recently, user authentication schemes using smart cards for multi-server environment have been proposed for practical applications. In 2009, Liao-Wang proposed a secure dynamic ID based remote user authentication scheme for multi-server environment that can withstand the various possible attacks and provide user anonymity. In this paper, we analyze the security of Liao-Wang`s scheme, and we show that Liao-Wang`s scheme is still insecure against the forgery attack, the password guessing attack, the session key attack, and the insider attack. In addition, Liao-Wang`s scheme does not provide user anonymity between the user and the server.
Artificial neural classification of clustered microcalcifications on digitized mammograms

Investigates the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammograms. Multi-layer neural networks with a backpropagation algorithm were trained for differentiating malignant from benign microcalcification clusters on the basis of both the clinical parameter "age" and radiological features extracted from digitized mammographic images. Radiological features associated with clustered microcalcifications were extracted from digitized images using mathematical morphology and image analysis. These parameters were analyzed using statistical analysis. The most relevant shape parameters were then given as input to the artificial neural network. Successful experimental results obtained by a three-layer neural network indicate that such an approach may provide a potentially useful tool to radiologists in the clustered microcalcification classification task.
Risk-Averse Time-Dependent Route Guidance by Constrained Dynamic A* Search in Decentralized System Architecture

Both the risk of concentration phenomenon for the navigation system and the risk of encountering unacceptable delay are reduced by guiding risk-averse drivers along reliable paths while avoiding high-risk links as far as possible. A constrained dynamic A* algorithm is proposed to search for a reliable path subject to a trip duration constraint in a time-dependent road network. The efficiency of the dynamic A* search is increased by taking advantage of information computed off-line. The algorithms proposed are implemented, and their computational performance is analyzed in simulation.
Tasks mapping in multi-core based system: hybrid ACO&GA approach

In the recent years, multi-core based distributed systems are rapidly gaining its popularity among consumer electronics, telecommunication, military electronics etc. System level synthesis on such systems is totally NP-complete/NP-hard, and traditional approaches always result in low efficiency or local optimality. In this paper, novel memory models, which emphasize the local memory capacity constraint, memory occupation and variable lifetime, are presented; moreover, we present a novel mapping method. For a given architecture and a set of task graphs, tasks are assigned to different PEs using genetic algorithm (GA), and then these partitioned tasks sets are transformed to a special graph, so that tasks scheduling is solved by a new meta-heuristic namely Cooperative Ant Colony Optimization.
3D site surveyor for 360° wireless motion tracking

We developed a lightweight, wireless and most affordable three-dimensional tracking system, called 3D site surveyor, which allows versatile, hands-free and easy-to-use human computer interaction, combining off-the-shelf hardware with custom-made software. We based this system on the Nintendo Wii remote controller hardware. 3D site surveyor is able to detect movement of a user in three dimensions (x, y and z-axis), including rotation in the xy-plane (yaw). We will argue that, within the limits of the system, it is theoretically possible to also measure pitch and roll, making the system capable of detecting 6DoF. The system can be applied to, for example, gaming, virtual reality, ambient-assisted living and academic research (e.g., human motion, perception and attention).
Depth perception enhancement based on chromostereopsis in a 3D display

This study was conducted to enhance the cubic effect by representing an image with a sense of three-dimensional (3D) depth, using chromostereopsis, among the characteristics of human visual perception. An algorithm that enhances the cubic effect, based on the theory that the cubic effect of the chromostereoptic effect and the chromostereoptic reversal effect depends on the lightness of the background, classifies the layers of the 3D image input into the foreground, middle, and background layers according to the depth of the image input. It suits the characteristics of human visual perception because it controls the color factor that was adaptively detected through experiments on each layer; and it can achieve an enhanced cubic effect that is suitable for the characteristics of the image input.
An Improved Mean Shift Algorithm for Vehicle Tracking

Classical mean shift tracking algorithm doesnt show good performance when the tracked objects move fast, change in size or pose. This paper proposes an improved mean shift method used for vehicle tracking. Firstly, a position prediction model based on second order auto-regression process is used to find the initial position of mean shift iteration, reduce times of iteration and enhance the tracking accuracy. Secondly, we employ a position search method based on the weight image to improve the tracking result when the result of basic mean shift tracking is not good. The proposed algorithm is tested in a real traffic video to track a vehicle changing in size and pose with more accurate result than basic mean shift tracking algorithm.
Cross-layer optimization for multiuser video streaming over wireless networks

Video communication over wireless networks is still a considerably challenging task due to the burst of the video stream and the time-varying of the wireless channel. In this paper, we propose a cross-layer optimization approach for multiuser video streaming over wireless networks. We focus on the optimization among application layer, MAC layer and physical layer. Unlike recent schemes which analyze the video stream in the pixel domain, we define a frame relative importance index (FRII) using the compression domain information to express the video characteristics. No need to fully decompress the video stream, it can reduce the computational complexity substantially. Simulation results show that, the proposed framework outperforms the recent scheme with no cross-layer optimization by 1.6 dB in terms of the average PSNR of all users.
The application of VR in the simulation of water gas production

This thesis gives a brief introduction of the background knowledge of producing water gas. Then it proves into the VR theory and the way of realizing it, and introduces how to switch the attribute value into color one. At last, it introduces the modeling and design of complicated imitated system by using the technology of Object-Oriented.
Cellular Automata and Geographic Information Systems

Contemporary geographic information systems (GIS) suffer from a variety of problems, These include poor performance for many operators, poor ability to handle dynamic spatial models, and poor handling of the temporal dimension. Cellular automata (CA) have much in common with raster GIS and also excel in many of the areas in which GIS are deficient. Specifically, CA provide explicit handling of dynamic spatial models and time. In addition, if special hardware—cellular automata machines—are used, the potential for considerable performance benefits exists. Many spatial analytical operators behave, in effect, as CA, with the specific GIS functions representing the CA transition rules. Examples of such operations include filtering and diffusion. If the spatial operators are considered to be CA, an improved ability to characterize the operators mathematically is achieved, resulting in an improved dynamic spatial modeling ability. In this research the similarities between the two models (GIS and CA) are examined and the ability to implement each in the other is demonstrated. In addition, the advantages of integration of the two systems, by means of a cellular automata machine as the analytical engine for GIS, are discussed.
Multi-loop control system design tools

In this paper we describe a menu-driven tool to construct multi-loop controllers that are then automatically translated into real-time code. A standardized configuration is used for each controller and a Simulink model of the controller and display are automatically generated. It is envisaged that the display and command facilities will be on a separate machine from the real time control machine, so these are considered as a separate block.