nils-herrmann/clean_scirepeval huggingface dataset:
A novel blocking map guided adaptive fuzzy deblocking filter

This paper proposes a novel blocking map guided fuzzy filter to remove blocking artifacts in compressed images. The fuzzy filter is controlled adaptively by a blocking artifact map. The blocking artifact map is built on local characteristics of the image content. The masking effect of human visual system (HVS) is also considered to estimate the blocking artifact level more accurately. Experimental results show that blocking artifact map fits well to the visual system. The proposed deblocking method has better detail preservation and lower computational costs than other methods. It achieves better performance in both reducing noise and maintaining better texture detail between block borders than the other algorithms.
Deep generic features and SVM for facial expression recognition

Motivated by the newly recent trend in pattern recognition - convolutional neural network (CNN), we introduce a new fusion method based on CNN and support vector machines (SVM) for facial expression recognition problem. Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only. We investigate our proposed method on Cohn-Kanade dataset and achieve 96.04% in accuracy rate which is better than other state-of-the-art methods.
A proposal of novel q-DWT for blind and robust image watermarking

In this paper, in order to control the quality of the embedded content, we propose a new domain, called q-DWT (discrete wavelet transform) for novel and robust image watermarking. We embed the watermark in the low-frequency of q-DWT domain in order to achieve the robustness of watermark. We employ the quantization index modulation (QIM) technique for embedding due to its high robustness and blindness. In our proposed method, the tradeoff of robustness and quality can be controlled by parameter Q of QIM and parameter q of the logarithm function.
A real-time localization system for an endoscopic capsule

This paper presents a real-time magnetic tracking system for an endoscopic capsule intended to deliver biomarkers around specific locations of the gastrointestinal (GI) tract. Unlike radio frequency (RF) and ultrasonic signals, static magnetic signals suffer negligible attenuation within the human body. The magnetic tracking systems proposed to date use a large number of sensors, making them too complex to apply in practice. In this paper, a new magnetic tracking system is proposed with much reduced complexity. In this system, the capsule only needs a magnet for localization, and does not require any electronics or battery. Mathematical model and a tracking algorithm were developed to find the location of a magnetic marker in the gut. Laboratory test results and in vivo animal trials show that the system is capable of tracking a magnetic marker with expected accuracy.
Analyzing impacts of physical interference on a transmission in IEEE 802.11 mesh networks

Interference is an inherent property of wireless multi-hop networks. In 802.11 wireless multi-hop networks, the impacting of physical interference on a transmission is complexity phenomenon that is the cause of degraded network performance. Several previous studies use analytical model to clarify the issue as a common approach. However, the models did not take fully considerations of physical interference on a transmission such as back-off freezing phenomenon and inter-flow interference. Hence, this paper proposes a novel analytical model based on Markov chain to model the 802.11 MAC based multi-hop mesh networks under fully physical interference. The model is developed to overcome these existed challenges of previous studies and validated by numerical results.
Application of bidirectional power converters to overcome some disadvantages of a SVC substation in Thainguyen, Vietnam

The analysis results on the operation of reactive power compensators in the Thainguyen power substation in Vietnam show their positive effect on the power system. They have a part in maintaining the voltage at the connection points (220/110/23 kV substation) that equals to the setting value U∗ according to the requirements of the Vietnamese power system dispath center. However, there are still some disadvantages of this substation that need to be overcome: the large number of main devices, much harmonics generated, and very high active power loss. Thus, the author has an idea to solve these problems by application of a bidirectional power converter (BDPC) using a decoupling controller in order to compensate the reactive power corresponding to the setting voltage. The benefit of this new solution is to eliminate the three above fundamental disadvantages. The research results have been verified by simulations in Matlab/Simulink software.
A New Artificial Neural Network - Direct Torque Control for Matrix Converter Fed Three-Phase Induction Motor

This paper presents a new complete artificial neural network (ANN) of direct-torque control (DTC) for the system matrix converter (MC)- induction motor (IM) to decrease the time of calculation of the conventional control system. To avoid the difficult calculation of DTC, the design uses the individual training strategy with the fixed weight and the supervised models. A computer simulation program is developed using Matlab/Simulink together with the neural network toolbox. The simulated results demonstrate the good quality and the robustness of the proposed ANN-DTC for MC-IM
Building an earthquake evacuation ontology from twitter

During the massive Tohoku earthquake, while landlines and mobile phone lines got stuck, Twitter were used to exchange information about evacuation. On 11 March, the number of tweets from Japan dramatically increased to about 33 million, 1.8 times higher than the average figure. However, since texts on Twitter are unstructured data, are more complex than other text media, it is difficult to find a suitable evacuation center. Not only information in Twitter, people but also need to combine other information such as map information, shop information to find the suitable evacuation center. Since it takes much time to do these tasks, these manual processing are not suitable in the emergency status. Therefore, we need an approach to help computers to understand the meaning of evacuation, and to provide the most suitable evacuation center based on earthquake victims' behaviors in real-time. In this paper, we firstly design an earthquake evacuation ontology. Secondly, we indicate that by using this ontology, computers can provide the most suitable evacuation center based on earthquake victims' behaviors in real-time.
Contamination assessment of inductive couple plasma etching chamber under mixture of recipes using statistical method

Inductive Couple Plasma (ICP) etching tool has been commonly used for higher throughput and better width control in semiconductor processing. However, this process is often contaminated by particles, and Particle per Wafer Pass (PWP) test must be carried out to monitor the contamination. Unfortunately, in actual manufacturing, the gaseous recipes used during etching vary on the etched materials, which lead to unexpected and unpredictable byproducts and particle counts in a given production run, rendering the particle count from PWP highly stochastic which may result in missing of the time for necessary wet cleaning of the chamber. In this work, we analyze the daily PWP results from an inductively coupled plasma etching (ICP) chamber for an eight-month period. The behavior of the particle count can be modeled as a stochastic function of the accumulated gaseous recipes flowing though the chamber. The particle count is found to follow a Negative Binomial (NB) distribution with varied parameters. The model is useful in determining the optimal time for wet clean
A Processing Method for Pitch Smoothing Based on Autocorrelation and Cepstral F0 Detection Approaches

Chinese is known as a syllabic and tonal language and tone recognition plays an important role and provides very strong discriminative information for Chinese speech recognition . Usually, the tone classification is based on the F0 (fundamental frequency) contours . It is possible to infer a speaker's gender, age and emotion from his/her pitch, regardless of what is said. Meanwhile, the same sequence of words can convey very different meanings with variations in intonation. However, accurate pitch detection is difficult partly because tracked pitch contours are not ideal smooth curves. In this paper, we present a new smoothing algorithm for detected pitch contours. The effectiveness of the proposed method was shown using the HUB4-NE [3] natural speech corpus.
2 $\times$ 80 Gbit/s DWDM Bidirectional Wavelength Reuse Optical Wireless Transmission

We demonstrate a practical 2×80 Gbit/s dense wavelength division multiplexing (DWDM) bidirectional short-range optical wireless link. The measured power penalties for the bidirectional transmission are less than 0.8 and 0.2 dB compared to the back-to-back link and the unidirectional transmission system, respectively. We also practically evaluate the impact of the offset angles between the optical transmission path and the building windows. The result shows that the power penalty is as small as 0.3 dB. This technology offers a great potential for applications in building-to-building inter-transmission as part of local/wide area networks.
A solid state modulator for driving SLAC 5045 klystrons

A test is ongoing at the Stanford Linear Accelerator Center (SLAC) where a solid state induction modulator is driving a SLAC 5045 klystron. The modulator generates 22 kV, 6 kA pulses that are stepped up by a 15:1 transformer that is a part of the klystron's pulse tank. The modulator's pulse duration is adjustable up to the volt-second limit of its cores, and it is capable of a pulse repetition frequency up to 120 Hz. The modulator's design, construction, and experimental results are the focus of this paper.
Bidirectional gigabit ethernet optical wireless communications system for home access networks

The design, simulation and practical implementation for a bidirectional 1.25 Gbit/s indoor optical wireless communications system has been presented. It is a part of the European community funded hOME Gigabit Access project (OMEGA). The line-of-sight (LOS) system uses angle-diversity transceivers enabling discrete beam steering. Each transceiver uses three transmitting and receiving elements giving an overall field of view of ~25 × 8° and a transmission range of 3 m. Measurement shows that the system can operate at a bit error rate below 10 -9 without channel coding and the handover between cells is <;400<;ns. Detail of a demonstrator of high-definition (HD) video embedded in gigabit ethernet stream using this system is also reported.
A novel genetic algorithm approach for simultaneous feature and classifier selection in multi classifier system

In this paper we introduce a novel approach for classifier and feature selection in a multi-classifier system using Genetic Algorithm (GA). Specifically, we propose a 2-part structure for each chromosome in which the first part is encoding for classifier and the second part is encoding for feature. Our structure is simple in the implementation of the crossover as well as the mutation stage of GA. We also study 8 different fitness functions for our GA based algorithm to explore the optimal fitness functions for our model. Experiments are conducted on both 14 UCI Machine Learning Repository and CLEF2009 medical image database to demonstrate the benefit of our model on reducing classification error rate.
DeepSense-Inception: Gait Identification from Inertial Sensors with Inception-like Architecture and Recurrent Network

Gait recognition has been considered as a new promising approach for biometric-based authentication. Gait signals are commonly obtained by collecting motion data from inertial sensors (accelerometers, gyroscopes) integrated in mobile and wearable devices. Motion data is subsequently transformed to a feature space for recognition procedure. One fashionable, effective way to extract features automatically is using conventional Convolutional Neural Networks (CNN) as feature extractors. In this paper, we propose DeepSense-Inception (DSI), a new method inspired from DeepSense, to recognize users from their gait features using Inception-like modules for better feature extraction than conventional CNN. Experiments for user identification on UCI Human Activity Recognition dataset demonstrate that our method not only achieves an accuracy of 99.9%, higher than that of DeepSense (99.7%), but also uses only 149K parameters, less than one third of the parameters in DeepSense (529K parameters). Thus, our method can be implemented more efficiently in limited resource systems.
Classification of VHR remote sensing images using local feature-based attribute profiles

The present paper introduces an extension of attribute profiles (APs) by extracting their local features. The so-called local feature-based attribute profiles (LFAPs) are expected to provide a better characterization of each APs' filtered pixel (i.e. APs' sample) within its neighborhood, hence better deal with local texture information from the image's content. In this work, LFAP is constructed by extracting some simple first-order statistical features of the local patch around each APs' sample such as mean, standard deviation, range, etc. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In order to evaluate the effectiveness of the proposed technique, supervised classification using Random Forest classifier is performed on the VHR panchromatic Reykjavik image. Experimental results show that LFAPs can considerably improve the classification accuracy of the standard APs and the recently proposed histogram-based APs.
A low-profile compact multi-resonant antenna for wideband and multi-band personal wireless applications

This paper presents a new low-profile compact antenna which was developed for wideband and multiband wireless applications. The uniqueness of the proposed antenna (patent pending) is the ease of configuring it for various wireless commercial needs by adjusting its structure parameters. Indeed, the antenna can be used for applications that require bandwidth from GPS to Bluetooth bands. It can also be configured for dual-band systems requiring the 1800-2500 MHz and the 5-GHz UNII bands, or just the two narrower unlicensed bands (2.45 and 5.25 GHz) for WLAN.
A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover With Applications to Image Fusion, Pixel Clustering, and Anomaly Detection

The Mars Science Laboratory is a robotic rover mission to Mars launched by NASA on November 26, 2011, which successfully landed the Curiosity rover in Gale Crater on August 6, 2012. The Curiosity rover has two mast cameras (Mastcams) that acquire stereo images at a number of different wavelengths. Each camera has nine bands of which six bands are overlapped in the two cameras. These acquired stereo band images at different wavelengths can be fused into a 12-band multispectral image cube, which could be helpful to guide the rover to interesting locations. Since the two Mastcams’ fields of view are three times different from each other, in order to fuse the left- and right-camera band images to form a multispectral image cube, there is a need for a precise image alignment of the stereo images with registration errors at the subpixel level. A two-step image alignment approach with a novel utilization of existing image registration algorithms is introduced in this paper and is applied to a set of Mastcam stereo images. The effect of the two-step alignment approach using more than 100 pairs of Mastcam images, selected from over 500000 images in NASA's Planetary Data System database, clearly demonstrated that the fused images can improve pixel clustering and anomaly detection performance. In particular, registration errors in the subpixel level are observed with the applied alignment approach. Moreover, the pixel clustering and anomaly detection performance have been observed to be better when using fused images.
Design of a Common Observer for Two Linear Systems with Unknown Inputs

This paper considers the design of a common linear functional observer for two linear time-invariant systems with unknown inputs. A structure for a common observer which only uses the available output information is proposed. Here, for the proposed structure, we show that the simultaneous functional observation problem of two plants is reduced to a problem of designing two observers: the first is a full-order unknown input observer of one of the two systems; the second observer is a common unknown input observer of a system comprises two-connected systems. In general, the existence conditions for the second observer are very difficult to satisfy. This paper thus concludes that it is indeed very difficult to find a common observer for two linear systems with unknown inputs
A novel and automatic character extraction and recognition for Sudoku puzzle solving

This paper proposes a novel algorithm to automatically solve a Sudoku puzzle on images taken from magazines or computer game software. The process involves preprocessing the image, extracting characters, recognizing characters and solving puzzle, which all are specifically designed for Sudoku. The algorithm effectiveness is verified with images taken from cameras. In this paper, a pixel to pixel comparison or a three layer Neural Network is used to recognize digits from 1 to 9 in Sudoku puzzles.
Adjacent channel interference cancellation for robust spectrum sharing in satellite communications systems

In this work, we consider adjacent channel interference (ACI) cancellation in the dual satellite communication system which supports simultaneous uplink and downlink communications between an airplane and two satellites over adjacent frequency bands. The transmitter of the uplink communication toward one satellite creates strong ACI to the receiver on the downlink from the second satellite to the airplane. Most ACI cancellation techniques in the literature consider the scenario where both interfering and interfered signals have identical bandwidth (and same symbol rate) where our design is motivated by the uplink/downlink communications between an airplane and Iridium/Inmarsat satellites which happen over frequency bands of different sizes (hence, different symbol rates) for which the conventional ACI cancellation techniques are not applicable. Toward this end, we present the signal and system models for simultaneous communications on adjacent bands with different bandwidths and propose a constant-norm Least Square (CN-LS) based interference cancellation strategy. Our design also addresses the nonlinearity of the High Power Amplifier (HPA) where the first and third order interference terms are identified and canceled out by using the Volterra filter. We demonstrate through numerical studies that our design is robust against the frequency spacing and significantly outperforms the conventional detection scheme without ACI cancellation.
A study on modeling of a hybrid wind wave energy converter system

A model of a hybrid wind wave energy converter (HWWEC) system is proposed in this paper. The HWWEC includes two wave buoys and a vertical axis wind turbine (VAWT) system, which co-works to drive a generator. Wave buoys are arranged in an arc pattern to capture efficiently wave energy from many directions. The hydrodynamic forces are calculated by the Wave Analysis at Massachusetts Institute of Technology WAMIT software. In order to bring the system into resonance with the incident wave frequencies, a variable inertia hydraulic flywheel is employed so that the power output is maximized. Specifications and working principle of the whole system are presented and described. WEC unit model and hybrid mechanism are also presented. Simulations are carried out to evaluate the performance the HWWEC with the given specifications. Then, simulation results under some phase differences are taken to investigate the influence on the overall efficiency.
Automatic Labeling Streaming Data for Event Detection from Heterogeneous Sensors

In this paper, we introduce a new method for detecting events by automatically labeling streaming data from heterogeneous sensors. The proposed method argues that by finding spatio-temporal-theme correlations between physical and social sensory data, events can be detected precisely and explained understandably in real-time. Here, a training stage is designed as a non-stop process with labels assigned automatically to feature vectors in order to build a set of positive and negative samples. Hence, an event model generated by using supervised learning approaches can steadily increase its accuracy. The problem of environmental impacts on asthma attacks is taken into account for evaluating the proposed method. The experimental results show that the proposed method can probably detect the prevalence of asthma risks in a specific spatio-temporal context.
A reduced-order observer-based control system designed in the frequency domain for dual-active-bridge converter

A new strategy for controlling single-phase dual-active-bridge converter is proposed in this paper. It is realized by employing dynamic control of the direct and quadrature current components, detached from the transferred current. The direct component Id is determined approximately from the terminal current, whereas, the quadrature one, Iq, is estimated by a reduced-order observer. All the models and equations are derived based on the frequency-domain analysis. Switching frequency is then variable to manipulate Id, while the phase shift between the two bridges is used to handle Iq and the power flow. Since Id and Iq are controlled, active and reactive power in the circuit are completely handled. A 500 W experiment system is built to verify the proposed control algorithm.
An Efficient Cache Invalidation Strategy in Mobile Information Systems

The development of mobile information systems has brought more advantages for mobile users, enabling them to use mobile devices to get information everywhere and at anytime. However, the application development has limitations due to wireless communications and mobile devices. Caching method needs to overcome some of these difficulties. There are many issues related to caching method, among which is how to guarantee the consistency of the data cached at the clients. In this paper, we propose an efficient strategy for consistent cache at client-side upon data updating at server-side. We carried out experiments on simulation to evaluate the effectiveness of our proposed approach.
An Approach for Wind Power Integration Using Demand Side Resources

Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.