nils-herrmann/clean_scirepeval huggingface dataset:
Opportunistic Beamforming with Precoder Diversity in Multi-User MIMO Systems

In this paper, we propose a dynamic multi-user MIMO beamforming and scheduling scheme, where co- scheduled users are served by mutually orthogonal beams. Our scheme differs from the traditional opportunistic beamforming and scheduling scheme in two ways. Firstly, multiple sets of orthogonal beams are employed as candidate beamforming directions (instead of one as in the traditional opportunistic beamforming case) and only users whose preferred beams are within the same set of orthogonal beams can be co-scheduled. This can effectively improve co-scheduled users' SINR. Secondly, the number of co-scheduled users is variable, which will change as the SNR and/or the total number of users in the system change in order to achieve a higher throughput. In addition, some analytical results are provided for the interference limited regime (i.e. at high SNR). In particular, for a reasonably large number of users, the sum-rate of our scheme grows logarithmically as the total number of users increases and by introducing multiple sets of orthogonal beams (or what we call precoder diversity), a gain in sum rate can be achieved.
A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device

Restoring the interaction between disabled people and the 3-D physical world via a brain-computer interface (BCI) is an exciting topic. To this end, we designed a wearable BCI system based on the steady-state visual evoked potential (SSVEP), which enables 3-D navigation of quadcopter flight with immersive first-person visual feedback using a head-mounted device. In addition, to alleviate the user’s operational burden, this paper provides asynchronous switch control for the users. The transitional state due to head movement in an asynchronous BCI was isolated online and translated into hover to eliminate its influence. The experimental results in the physical environment showed that the subjects could accomplish the 3-D flight tasks accurately and smoothly using our system. In particular, in this paper, we proposed an information transfer rate metric that is suitable for the asynchronous task. We demonstrated the feasibility of using the head-mounted device and a proper control strategy to facilitate the portability and practicability of the SSVEP-based BCI system for its navigation utility.
Low-Rank Matrix Recovery From Noisy, Quantized, and Erroneous Measurements

This paper proposes a communication-reduced, cyber-resilient, and information-preserved data collection framework. Random noise and quantization are applied to the measurements before transmission to compress data and enhance data privacy. Leveraging the low-rank property of the data, we develop novel methods to recover the original data from quantized measurements even when partial measurements are corrupted. The data recovery is achieved through solving a constrained maximum likelihood estimation problem. The recovery error is proven to be order-wise optimal and decays in the same order as that of the state-of-the-art method when there is no corruption. The data accuracy is thus maintained while the data privacy is enhanced. A proximal algorithm with convergence guarantee is proposed to solve the nonconvex problem. The analyses are extended to situations when some measurements are lost, or when multiple copies of noisy, quantized measurements are sent for each data point. A new application of this framework for data privacy in power systems is discussed. Experiments on synthetic data and real synchrophasor data in power systems demonstrate the effectiveness of our method.
Quasi-orthogonal Space-Time Block Codes for Four Antenna

This paper analyzes the structure and characteristics of several types of Quasi-orthogonal Space-Time Block Codes (QSTBC), and proposes two novel methods of QSTBC for four antennas to enrich the family of QSTBC. Experiment results indicate that, while the decoding complexity is the same as other quasi-orthogonal codes, the two new QSTBC approaches achieve good performance as TBH and Jafarkhani cases. This paper proposes a Zero Forcing (ZF) decoding algorithm for new QSTBC. Simulation results indicate that ZF algorithm possesses good performance and can reduce the complexity of decoding.
Experimental investigations of surface flashover characteristics of some common used insulation mediums under impulse voltage in vacuum

This paper has investigated the surface flashover characteristics of some common used insulation mediums, such as Nylon and Lucite, under an impulse voltage in vacuum with a set of experimental device. The half-angle of insulation frustum varied from 0 degree to 60 degree The rise-time and fall-time of applied impulse voltage were all 20 ns, and the flat-width of voltage varied from about 30 ns to 160 ns. The phenomena of surface flashover of each specimen in high vacuum have been observed. Based on the results of a series of experiments and computer simulations, the paper obtained the relationships between the thickness of insulator, the half-angle of frustum and the flashover voltage under impulse condition, and discovered the rule between field stress of triple-junction and actual flashover voltage. The results of this paper have been applied in the special insulation structure designing.
VIDEOWHISPER: Towards unsupervised learning of discriminative features of videos with RNN

We present VidedWhisfer, a novel approach for unsupervised video representation learning, in which video sequence is treated as a self-supervision entity based on the observation that the sequence encodes video temporal dynamics (e.g., object movement and event evolution). Specifically, for each video sequence, we use a pre-learned visual dictionary to generate a sequence of high-level semantics, dubbed “whisper”, which encodes both visual contents at the frame level and visual dynamics at the sequence level. VidedWhisfer is driven by a novel “sequence-to-whisper” learning strategy. Naturally, an end-to-end sequence-to-sequence learning model using RNN is modeled and trained to predict the whisper sequence. We propose two ways to generate video representation from the model. Through extensive experiments we demonstrate that video representation learned by VidedWhisfer is effective to boost fundamental video-related applications such as video retrieval and classification.
SPC02-2: Joint Data Detection and Channel Estimation for MIMO-OFDM Systems via EM Algorithm and Sphere Decoding

We consider joint data detection and channel estimation for multiple-input multiple-output orthogonal frequency division multiplexing systems. The expectation maximization algorithm is employed to capture the dynamics of a time-varying fading channel and also to recover the channel input. This is done iteratively: channel estimates are updated in the E-step, whilst the sphere decoding algorithm is used in the M-step to realize data recovery. Initial channel estimates are obtained through the use of a small number of pilot symbols. We show by simulation that the performance of the proposed algorithm can approach coherent detection with reasonable computational complexity.
High voltage gate drive IC with a novel NFFP HVI structure

A novel NFFP HVI structure which implements high breakdown voltage without using additional FFP and process steps is proposed in this paper. An 850 V half bridge gate drive IC with the NFFP HVI structure is experimentally realized by using a thin epitaxial high voltage BCD process. Compared with the conventional MFFP HVI structure, the proposed NFFP HVI structure shows simpler process and lower cost. The experimental high side offset voltage of the half bridge gate drive IC with the NFFP HVI structure is almost as same as that with the self-shielding structure.
Experimental study on electrical evaluation of rotor insulation system of DFIG for wind power generations

The failure rate of doubly-fed induction generators (DFIG) for wind turbine generations was relatively higher than that of direct-driven generators, according to statistical data from some wind power industries. The rotor insulation was a relatively weak part of the insulation system for DFIG because it was normally subjected to a PWM impulse voltage during the long-time operation. In this paper, this research focuses on the electrical evaluation based on accelerated electrical aging tests for rotor insulation of DFIG. The accelerated aging tests under the bipolar repetitive impulse with the voltage of 5.3, 6.4 and 7.3 kV were carried out. Some of non-destructive characteristic parameters, polarization and depolarization current were measured periodically during the each interval of cycled aging periods. In addition, the appearance features of the insulation layers at different distances away from the conductor on the aged or failure specimen were observed by microscope. The feasibility and effectiveness of these non-destructive characteristic parameters on the aging status assessments of the rotor insulation system of DFIG are analyzed. The failure times or lifetime of the electrical aging from some specimens were got. Finally, the appropriate selections of the applied voltage levels for the accelerated aging tests were also discussed.
Distinguishing Patients with Gastritis and Cholecystitis from the Healthy by Analyzing Wrist Radial Arterial Doppler Blood Flow Signals

This paper tries to fill the gap between Traditional Chinese Pulse Diagnosis (TCPD) and Doppler diagnosis by applying digital signal analysis and pattern classification techniques to wrist radial arterial Doppler blood flow signals. Doppler blood flows signals (DBFS) of patients with cholecystitis, gastritis and healthy people are classified by L2-soft margin SVM and 5 linear classifiers using the proposed feature - piecewise axially integrated bispectra (PAIB). A 5-fold cross validation is used for performance evaluation. The classification accuracies between either two groups of subjects are greater than 93%. Gastritis can be recognized with higher accuracy than cholecystitis. Cholecystitis can be recognized with higher accuracy on left hand data than right. The findings in this paper partly conform to the theory of TCPD. Though the sample size is relatively small, we could still argue that the methods proposed here are effective and could serve as an assistive tool for TCPD.
Experimental study on surface flashover behavior in vacuum

The experimental study of surface flashover behavior of solid insulated material in a vacuum environment (4/spl times/10/sup -4/ to 3/spl times/10/sup -1/Pa) was carried out. Tests of two such insulators, nylon and organic glass, were conducted under the DC high voltage and pulsed high voltage which had the pulse width of 90 ns and the rising time of 8 ns. We display experimental data and discuss the influence of the sample construction size, /spl theta/ angle, the vacuity of vacuum discharge chamber, the applied voltage, etc., on flashover voltage or flashover strength.
3D deep shape descriptor

Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category. We have developed a concise deep shape descriptor to address challenging issues from ever-growing 3D datasets in areas as diverse as engineering, medicine, and biology. Specifically, in this paper, we developed novel techniques to extract concise but geometrically informative shape descriptor and new methods of defining Eigen-shape descriptor and Fisher-shape descriptor to guide the training of a deep neural network. Our deep shape descriptor tends to maximize the inter-class margin while minimize the intra-class variance. Our new shape descriptor addresses the challenges posed by the high complexity of 3D model and data representation, and the structural variations and noise present in 3D models. Experimental results on 3D shape retrieval demonstrate the superior performance of deep shape descriptor over other state-of-the-art techniques in handling noise, incompleteness and structural variations.
Location-Aware, Flexible Task Management for Collaborating Unmanned Autonomous Vehicles

Unmanned Autonomous Vehicles (UAVs) are emerging as a breakthrough concept in technology. A main challenge related to UAV control is devising flexible strategies with predictable performance in hard-to-predict conditions. This paper proposes an approach to performance predictive collaborative control of UAVs operating in environments with fixed targets. The paper offers detailed experimental insight on the quality, scalability and computational complexity of the proposed method.
Performance analysis of improved 64-ary triangular quadrature amplitude modulation in AWGN channel

It's well known that the improved 64-ary triangular quadrature amplitude modulation (TQAM) who provides considerable power gain over square quadrature amplitude modulation (SQAM) at the expense of slight increase in detection complexity was proposed. In this paper, we derive the general formula calculating the average energy per symbol and exact analytical expression formula for the SER in additive white Guassian Noise (AWGN) of the improved 64-ary TQAM. We also analyze the symbol error rate (SER) and the bit error rate (BER) of the improved 64-ary TQAM and compare them with the error performance of the SQAM and TQAM by simulation. Theoretically, analysis and simulation results show that the improved 64-ary TQAM can provide advantages of 0.6dB and 0.2dB in signal to noise ratio over the SQAM and the TQAM at a target symbol error rate of 10−6.
Removal of water scattering

In this paper, an efficient and effective method is proposed using dark channel prior to restore the original clarity of the images underwater. Images taken in the underwater environment are subject to water attenuation and particles in water's scattering, a phenomenon similar to the effect of heavy fog in the air. Using dark channel prior, the depth of the turbid water can be estimated by the assumption that most local patches in water-free images contain some pixels which have very low intensities in at least one color channel. In this way, the effect of turbid water can be removed and the original clarity of images can be unveiled. The results processed by this method are presented in the paper.
Identification of Successive “Unobservable” Cyber Data Attacks in Power Systems Through Matrix Decomposition

This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting “unobservable” cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.
The fuzzy neural network control with adaptive algorithm for a PM synchronous motor drive

In this study a fuzzy neural network (FNN) control system with adaptive algorithm is proposed to control permanent magnet synchronous motor (PMSM) drive system. First, the DSP field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, the adaptive FNN control system is proposed to control the rotor of the PMSM servo drive for the tracking of periodic reference inputs. In the adaptive FNN control system, the FNN controller is used to mimic an optimal control law, and the compensated controller with adaptive algorithm is proposed to compensate the difference between the optimal control law and the FNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the learning capability of the FNN. The effectiveness of the proposed control schemes is verified by experimental results.
A novel sliding mode control for series elastic actuator torque tracking with an extended disturbance observer

To realize ideal force/torque source in applications such as monopod hopping robots and assistive robots, force/torque controlled series elastic actuators(SEA) are widely used components. Most of existing SEA force/torque control approaches are based on the simple SEA whose force/torque output is proportional to the position of the motor with respect to the payload, however the structure of SEAs is becoming more and more complex, which leads to a nonlinear trend in SEA models. To overcome the problem, we propose a general control method, which is not restricted by the SEA mechanical structure, to control SEA output torque based on nonlinear disturbance observer(NDOB). Specifically, the presented strategy yields the first control solution, with guaranteed theoretical analysis, to successfully address the nonlinear SEA torque control problem, with simultaneous payload angular velocity identification as an additional benefit. The tracking performance of the designed control system is theoretically ensured by Lyapunov analysis. Furthermore, taking a nonlinear SEA for instance, simulation and experimental results suggest the effectiveness and superior performance of the proposed method by comparing it with existing method.
Video distortion reduction with network coding and device-to-device communications

In this paper, we propose a scheme of wireless multimedia network coded broadcast retransmission based on Device-to-Device (D2D), in which there are network coding conflicts and transmission conflicts between multiple terminal devices. For this reason, a novel instantly decodable network coding (IDNC) graph is constructed, in which all the feasible coding and non-transmission collision cases are shown in the graph. Based on the constructed IDNC graph, we design the optimal broadcast strategy for batch transmission and sliding window transmission, and propose a heuristic maximum weight network coding selection algorithm (MWSA-NC) to reduce the complexity of the optimal search algorithm. The simulation results show that compared with the traditional scheme, the proposed algorithm can greatly improve the throughput of the system, reduce decoding latency and reduce video stream distortion.
Protein Secondary Structure Prediction Based on Statistical Dictionaries

This study first gives the definitions and construction methods of statistical dictionary and similar list, and then proposes two novel algorithms, hybrid windows prediction (HWP) and similar searching prediction (SSP), for protein secondary structure prediction. Our methods not only implement the incremental learning to utilize the increasing protein structure data, but also achieve high prediction accuracies of overall Q 3 and SOV by the blind test on the dataset composed of 2825 protein chains.
Discriminative feature learning-based pixel difference representation for facial expression recognition

Recently, researchers have proposed different feature descriptors to achieve robust performance for facial expression recognition (FER). However, finding a discriminative feature descriptor remains one of the critical tasks. In this paper, we propose a discriminative feature learning scheme to improve the representation power of expressions. First, we obtain a discriminative feature matrix (DFM) based pixel difference representation. Subsequently, all DFMs corresponding to the training samples are used to construct a discriminative feature dictionary (DFD). Next, DFD is projected on a vertical two-dimensional linear discriminant analysis in direction (V-2DLDA) space to compute between and within-class scatter because V-2DLDA works well with the DFD in matrix representation and achieves good efficiency. Finally, nearest neighbor (NN) classifier is used to determine the labels of the query samples. DFD represents the local feature changes that are robust to the expression, illumination et al. Besides, we exploit V-2DLDA to find an optimal projection matrix since it not only protects the discriminative features but reduces the dimensions. The proposed method achieves satisfying recognition results, reaching accuracy rates as high as 91.87% on CK+ database, 82.24% on KDEF database, and 78.94% on CMU Multi-PIE database in the LOSO scenario, which perform better than other comparison methods.
A Nondestructive Method of Extracting the Width and Thickness of Interconnects for a 40-nm Technology

In this paper, a simple and nondestructive method of modeling 40-nm interconnects is proposed. Traditional methods based on charge-based capacitance measurement model the interconnects by fitting the capacitance or resistance curves, first by assuming one constant process parameter, such as metal thickness, and then by extracting the metal width, metal spacing, and interlevel dielectric (ILD) thickness from certain test patterns that may therefore result in model inaccuracy while the transmission and scanning electron microscopy methods are both destructive and time consuming. The proposed new methodology directly extracts the metal width based on the metal resistance test structures, and then the metal thickness, metal spacing, and ILD thickness without any presumption. It is also nondestructive and fast, with a model accuracy higher than 95%. Furthermore, with the ensured accuracy of layout parameter extraction, the necessity of an accurate interconnect model in the 40 nm technology and beyond is emphasized.
EM-Based Receiver Design for Uplink MIMO-OFDMA Systems

In this paper we propose an iterative receiver for uplink MIMO-OFDMA systems based on the expectation maximization (EM) algorithm. Iterating between the E-step and the M-step, the EM-based receiver updates the channel estimates and, refines data detection by increasing the likelihood function. Practical implementation issues are also considered: space-time block-coding (STBC) is incorporated to improve system performance against fading; a reduced-complexity algorithm is proposed, which simplifies the computation whilst not compromising performance.
Joint pushing and caching communication with 2-dimensional queue model: Delay-cost tradeoff (Invited Paper)

Joint pushing and proactive caching holds the promise of significantly increasing the throughput and decreasing the latency of content-centric wireless networks. In this work, we import the pushing and caching mechanism into a traditional packet-oriented network for decreasing the average queueing delay. Since the decreasing relies on extra costs paid for the pushing-caching mechanism, such as the cost for the cache buffer's capacity, extra transmission power for pushing and so on, we mainly derive the average queueing delay under given cost in order to study the relationship between the average queueing delay and the extra cost. In order to do so, a metric named System State is defined to describe the transmitter and user's current buffer states. Based on the System State, a two-dimensional Markov Chain is built to derive the average queueing delay and the average hitting ratio. The results show us how much the average queueing delay can achieve when the cache buffer's capacity which usually has a high cost is given, and then provide us a method to make a tradeoff between the average queueing delay and the cost of the user's buffer size. Simulation results validate our conclusion.