nils-herrmann/clean_scirepeval huggingface dataset:
A new distance for scale-invariant 3D shape recognition and registration

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this space—the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.
A Realistic Integrated Model of Parallel System Workloads

Performance evaluation is a significant step in the study of scheduling algorithms in large-scale parallel systems ranging from supercomputers to clusters and grids. One of the key factors that have a strong effect on the evaluation results is the workloads (or traces) used in experiments. In practice, several researchers use unrealistic synthetic workloads in their scheduling evaluations because they lack models that can help generate realistic synthetic workloads. In this paper we propose a full model to capture the following characteristics of real parallel system workloads: 1) long range dependence in the job arrival process, 2) temporal and spatial burstiness, 3) bag-oftasks behaviour, and 4) correlation between the runtime and the number of processors. Validation of our model with real traces shows that our model not only captures the above characteristics but also fits the marginal distributions well. In addition, we also present an approach to quantify burstiness in a job arrival process (temporal) as well as burstiness in the load of a trace (spatial).
Carbon nanotube integrated on silicon tips for electron field emitter: tip apex patterning and emission characterization

In this work, we developed a selective patterning and lift-off technique for synthesis and patterning of carbon nanotube (CNT) at the apex of a silicon tip. Purpose of this work is to improve the throughput and yield rate of forming CNT at the silicon tip as electron field emitters. The sharp silicon micro-tip were formed by an ICP-RIE dry etching and wet etching in a TMAH solution. After thermally oxidation at 950/spl deg/C and selective etching in a buffer-HF (BHF) solution, a sharp silicon tip was formed at the center of a small silicon oxide aperture. CNTs were entirely grown over the tip by a Hot Filament-Chemical Vapor Deposition (HF-CVD) using Fe as a catalyst. After removing of the sacrificial silicon oxide layer in the BHF solution, small size CNTs bundles were remained at the apex of the silicon tip. The emission properties of the grown individual and bundle CNT at the Si tip were measured, and compared with a single silicon tip. The threshold voltage of the individual CNT was found to be 4V//spl mu/m, that is 5 times lower than that of the silicon tip. Emission pattern of the bundle CNT/Si tip was observed using a phosphor anode screen (ZnO, Zn).
A Multi-CAP Visible-Light Communications System With 4.85-b/s/Hz Spectral Efficiency

In this paper, we experimentally demonstrate a multiband carrierless amplitude and phase modulation format for the first time in VLC. We split a conventional carrierless amplitude and phase modulated signal into m subcarriers in order to protect from the attenuation experienced at high frequencies in low-pass VLC systems. We investigate the relationship between throughput/spectral efficiency and m, where m = {10, 8, 6, 4, 2, 1} subcarriers over a fixed total signal bandwidth of 6.5 MHz. We show that transmission speeds (spectral efficiencies) of 31.53 (4.85), 30.88 (4.75), 25.40 (3.90), 23.65 (3.60), 15.78 (2.40), and 9.04 (1.40) Mb/s (b/s/Hz) can be achieved for the listed values of m, respectively.
A high-speed integrated optoelectronic photoreceiver

The authors present a high-speed optoelectronic photoreceiver, specifically designed for use in optical interconnections. This photoreceiver is composed of an MSM (metal-semiconductor-metal) photodetector coupled with a HEMT (high electron mobility transistor) transimpedance amplifier. Through the use of the lattice-mismatched materials system of AlGaAs/InGaAs/GaAs and quarter-micron gate length HEMTs, speeds in excess of 4 GHz as well as transimpedance gains of over 50 dB were achieved, well matching the results of the iSMILE simulator. The high performance of the pseudomorphic HEMTs was reflected in their transit frequency of f/sub 1/=66 GHz. This design also included an MSM photodetector with a measured dark current of less than 10 nA.<<ETX>>
An HMM-based temporal difference learning with model-updating capability for visual tracking of human communicational behaviors

In the development of natural interaction for systems, the trend of using vision toward translating human actions into instruction symbols is arising for the recognition of non-verbal communication channels. We propose an adaptive vision-based attentive tracker (AVAT) to track human intended communicational actions with attentive zooming capability as well as an exploration function for model updating. The AVAT isolates such human intended actions from the ordinary walking behavior based on an algorithm with two subprocesses: one is for modeling the movement of human body parts as the environment using HMMs (Hidden Markov Models), and the other is for learning the model of the tracker's action using a model-based TD (Temporal Difference) algorithm. We describe the integration of the two algorithms and then derive the model updating formulas from the newly optimized TD policies. An experimental result of isolating the human sign action during his natural walking motion is shown for demonstrating the feasibility of our system. Identification of the sign gesture context using a confirmation method using wavelet analysis provides rewards for optimizing the tracker's action models.
Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding

In this paper, we propose a data-driven image reconstruction algorithm that specifically aims to reconstruct undersampled dynamic contrast enhanced (DCE) MRI data. The proposed method is based on the convolutional sparse coding algorithm, which leverages the Fourier convolution theorem to accelerate the process of learning a collections of filters and iteratively refines the reconstruction result using the sparse codes found during the reconstruction process. We introduce a novel energy formation based on the learning over time-varing DCE-MRI images, and propose an extension of Alternating Direction Method of Multiplier (ADMM) method to solve the constrained optimization problem efficiently using the GPU. We assess the performance of the proposed method by comparing with the state-of-the-art dictionary-based compressed sensing (CS) MRI method.
Damage identification for composite structures with a Bayesian network

Recent development on the application of distributed sensor networks, in structural health monitoring (SHM) for large structural areas has resulted in more complicated system identification techniques, particularly for those with multiple information sources. This paper presents an application of Bayesian inference network to detection of hole-type damages on a composite plate using multiple sensing data streams from a distributed sensor network. Representative damage features from 50 damage scenarios were used for the learning process. The Bayesian net is found to be promising when correctly diagnosing the damage's location and size for a validation case.
A Difference of Convex Functions Algorithm for Switched Linear Regression

This technical note deals with switched linear system identification and more particularly aims at solving switched linear regression problems in a large-scale setting with both numerous data and many parameters to learn. We consider the recent minimum-of-error framework with a quadratic loss function, in which an objective function based on a sum of minimum errors with respect to multiple submodels is to be minimized. The technical note proposes a new approach to the optimization of this nonsmooth and nonconvex objective function, which relies on Difference of Convex (DC) functions programming. In particular, we formulate a proper DC decomposition of the objective function, which allows us to derive a computationally efficient DC algorithm. Numerical experiments show that the method can efficiently and accurately learn switching models in large dimensions and from many data points.
Context-Aware Mobile Intelligent Transportation Systems

This paper proposes a practical quantification model for mobile phone based traffic state estimation systems (M-TES). The low penetration rate issue, an inherent issue impeding the realization of a mobile phone based application such as the M-TES, is thoroughly discussed. A notable solution framework, namely the intelligent context-aware velocity-density inference circuit (ICIC), is proposed to effectively resolve the low penetration rate issue. In the ICIC model, velocities and densities calculated directly from the sensed data and inferred by using different inference models such as the Greeshields or the moving average model are appropriately integrated. In addition, appropriate contexts extracted from data reported by mobile devices are utilized to identify the optimal estimation parameters leading to the optimal estimation effectiveness. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.
An efficient approach for phishing detection using single-layer neural network

Phishing is an attempt by an individual or a group of person to steal personal information such as password, banking account and credit card information, etc. Most of these phishing web pages look similar to the real web pages in terms of website interface and uniform resource locator (URL) address. Many techniques have been proposed to detect phishing websites, such as Blacklist-based technique, Heuristic-based technique, etc. However, these techniques are still inefficient. While Blacklist-based techniques cannot detect the phishing sites that are not in the blacklist database, the weights of the heuristic in the heuristic-based approach significantly depend on the statistics from the training dataset. Therefore, a new phishing detection approach in which either the new phishing websites can be detected or the weights of the heuristic are derived objectively is necessary. In this paper, an efficient approach for detecting phishing websites based on the single-layer neural network is proposed. Specifically, the proposed technique calculates the value of heuristics objectively. Then, the weights of heuristic are generated by a single-layer neural network. The proposed technique is evaluated with a dataset of 11,660 phishing sites and 10,000 legitimate sites. The results show that the technique can detect over 98% phishing sites.
A Control Approach for Thrust-Propelled Underactuated Vehicles and its Application to VTOL Drones

A control approach is proposed for a class of underactuated vehicles in order to stabilize reference trajectories either in thrust direction, velocity, or position. The basic modeling assumption is that the vehicle is pro-pulsed via a thrust force along a single body-fixed direction and that it has full torque actuation for attitude control (i.e., a typical actuation structure for aircrafts, vertical take-off and landing (VTOL) vehicles, submarines, etc.). Additional assumptions on the external forces applied to the vehicle are also introduced for the sake of control design and stability analyses. They are best satisfied for vehicles which are subjected to an external force field (e.g., gravity) and whose shape induces lift forces with limited amplitude, unlike airplanes but as in the case of many VTOL drones. The interactions of the vehicle with the surrounding fluid are often difficult to model precisely whereas they may significantly influence and perturb its motion. By using a standard Lyapunov-based approach, novel nonlinear feedback control laws are proposed to compensate for modeling errors and perform robustly against such perturbations. Simulation results illustrating these properties on a realistic model of a VTOL drone subjected to wind gusts are reported.
An early flood detection system using mobile networks

In this paper, an early flood warning system has been studied and developed. The system consists of an early flood warning station powered by solar energy, a monitoring center, and a notification system. The flood warning station is equipped with the water level sensor and the precipitation sensor. The data, which is received from sensors, will be stored in memory card and transmitted to monitoring center via GPRS protocol. The functions of monitoring center are to receive, analyse data from the stations and send results to notification system where warning can be generated to the considered residential areas. The warning levels of flood can be set up easily by using the buttons in the circuit board or by using the SMS message from user's mobile phone. A website was also created to display the river water level, rainfall and the predicted information for the next few hours.
A Novel Multi-ACK Based Data Forwarding Scheme in Wireless Sensor Networks

Data forwarding schemes that use a single ACKbased retransmission mechanism are widely used to increase the packet delivery ratio in wireless sensor networks. However, these schemes may waste a considerable amount of energy for data transmissions, especially when the link quality is low for ACK transmissions. In this paper, we propose a novel multi-ACK-based data forwarding scheme to minimize the energy consumption for unnecessary data retransmissions. The next hop forwarder selection significantly affects the energy efficiency of communications. Hence, we also develop a new next hop forwarder selection metric, called effective energy consumption (EEC), which makes the proposed data forwarding scheme suitable for geographic routing protocols. Mathematical analysis and simulation results demonstrate that the proposed multi-ACK data forwarding scheme can save much energy and reduce the total amount of traffic load in the network. More specifically, the combination use of multi-ACK data forwarding and EEC metric increases the energy efficiency by 47.8% when node density is low, and 75.0% when node density is high, compared to the geographic forwarding protocols which use a well-known metric, PRR £ Distance.
A control channel based MAC protocol for time critical and emergency communications in Industrial Wireless Sensor Networks

For time critical applications in industrial environments, failure in communication or unwanted delay can have devastating effects. It is therefore, important that Industrial Wireless Sensor Networks (IWSNs) offer reliable communication platform for time critical applications without violating the hard deadlines. In this paper, a MAC protocol for time critical and emergency communications is proposed. The proposed protocol, EE-MAC incorporates emergency communication and allows immediate channel access for such data. The paper presents mathematical model of the proposed protocol. For evaluation purposes, the performance of EE-MAC is compared to IEEE 802.15.4e LLDN. The results show that the proposed protocol offers up to 92% reduction in channel access delay of emergency communication at the cost of 5% to 15% increase in delay of non-critical and less time-sensitive data.
3D image presentation: new technique for hidden-surface removal in SIRDS algorithm

Displaying a real 3-dimensional (3D) objects with vivid depth information is always a difficult and cost-consuming process. Representing 3D scenes with the single image random dots stereogram (SIRDS) is another case. With a practiced method of viewing depth dimension can be easily obtained, without requiring any special instrument. Hidden-surface removal is an important phase in the implementation of the algorithm. It makes the autostereogram (product of the algorithm) more "random" but more truly reconstructs the real 3D world. It also decides the speed of the implementation. The paper proposes a new hidden-surface removal technique, which shows its advantage in the two aforementioned aspects compared to the predecessors.
A 450-MHz RISC microprocessor with enhanced instruction set and copper interconnect

This superscalar microprocessor is the first implementation of a 32-bit RISC architecture specification incorporating a single-instruction, multiple-data vector processing engine. Two instructions per cycle plus a branch can be dispatched to two of seven execution units in this microarchitecture designed for high execution performance, high memory bandwidth, and low power for desktop, embedded, and multiprocessing systems. The processor features an enhanced memory subsystem, 128-bit internal data buses for improved bandwidth, and 32-KB eight-way instruction/data caches. The integrated L2 tag and cache controller with a dedicated L2 bus interface supports L2 cache sizes of 512 KB, 1 MB, or 2 MB with two-way set associativity. At 450 MHz, and with a 2-MB L2 cache, this processor is estimated to have a floating-point and integer performance metric of 20 while dissipating only 7 W at 1.8 V. The 10.5 million transistor, 83-mm/sup 2/ die is fabricated in a 1.8-V, 0.20-/spl mu/m CMOS process with six layers of copper interconnect.
An Intelligent System Approach for Probabilistic Volume Rendering Using Hierarchical 3D Convolutional Sparse Coding

In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study.
Adaptive stereo vision system using portable low-cost 3D mini camera lens

This article describes the design and process of a 3D Mini Camera Lens to be utilised in multi-purpose 3D stereo reconstruction on computers, mobile devices and robots. It can also be useful for various tasks including demonstration, knowledge-sharing, online programming, database sharing, results comparison in the stereo vision research community, and recreation used by non-specialists. In contrast with many existing systems that use stereo cameras, our proposed one only use a single camera. However, the acquired image streams are divided into two side by side images by a 3D Mini Camera Lens. The use of the lens has some advantages over the state of the art ones. It is very low-cost, at 5–7 USD; it can be implemented with any small image sensors of web cams, smartphones, tablets. The lens is light weight, simple to assemble and disassemble, convenient and portable; and its two stereo pictures are perfectly synchronised (as the same image sensor produces them). We develop a fully functioning system which horizontally aligns images and calculates the dense distance map of the scene using a guided semi-global stereo matching algorithm. The simple horizontal and vertical image alignment is very lightweight, using adaptive methods, and is carried out during the stereo matching; thus, no separated processes are needed. The semi-global stereo matching uses full or partial profiles generated by local block matching algorithms to achieve an efficient dynamic programming stereo matching and produce higher-quality disparity maps. The system is, therefore, plug-and-play, no special setup or calibration is required. The system gradually estimates the best parameters, including disparity range, to produce the most suitable depth image. The evaluated results have shown that this system was demonstrably accurate, fast, and relatively robust for stereo correspondence. It could be used in many simple stereo vision system such as computer games, gesture control, robotic vision, self-flying drones or -driving cars.
A Leakage Management System Based on Clock Gating Infrastructure for a 65-nm Digital Base-Band Modem Chip

In this paper we present a leakage management system which takes advantage of the existing clock gating infrastructure. This methodology avoids both RTL and software changes, at the block and chip level. We illustrate this approach with a 65-nm digital base band modem while achieving standby leakage in the 100-muA range and overall 1200times leakage reduction including process, circuit and system optimization
A new routing method used for automatic meter reading in buildng

This paper describes a wireless routing method that can be applied in automatic electricity/water meter reading in building. The routing method is based on the geologic location of the sending and receiving devices. In this case, the geologic location is the combination of floor, area and room within a building. The method is built on the application layer of Zigbee protocol  , which is a reliable and energy-saving protocol. The Zigbee stack (z-stack 1.4.3 of Texas Instrument) are applied on kit CC2431DK  of Texas Instrument. In addition, several test cases, which verify the routing method in some special contexts, are also considered.
A novel stable teleoperation with haptic feedback by means of impedance adjustment via arbitrary time delay environment for rehabilitation

In this paper, a teleoperation mechanism capable of providing force feedback by means of adjusting the system's impedance is proposed for a master-slave telerehabilitation robot system. The stability of the teleoperation with haptic feedback via a time-delay communication environment is mathematically proved. By using the proposed teleoperation mechanism, the telerehabilitation system can work theoretically with both passive assisted movement and active assisted movement for the rehabilitation of upper limb function. Experiments with single DOF master-slave robot system show the effectiveness of the proposed mechanism.