nils-herrmann/clean_scirepeval huggingface dataset:
Design of smart video surveillance system for indoor and outdoor scenes

Smart video surveillance of indoor and outdoor scenes is a challenging task for modern surveillance systems. Different imaging conditions like bad illumination, adverse weather, etc., makes the surveillance process difficult. Recently, researchers have proposed smart surveillance systems with additional features for more accurate monitoring of events, but not much attention is paid to improve the system such that the monitoring process consumes as minimum resources as possible. In this paper, we propose a novel surveillance system that enhances visibility in adverse weather conditions and summarizes the captured videos automatically to reduce storage space. As the summarization process is based on the events in a scene, video interpretation becomes fast and easy. We propose perceptual features that can be used for more meaningful and robust summarization of the video than the existing summarization algorithms. We test the system for both indoor and outdoor scenes and show that the system works well even with multiple moving objects and complex motions.
Development of a CAD integrated DFE workbench tool

The development of environmentally superior products and processes (EPS) is becoming an extremely important issue for electronic manufacturers operating in, and servicing European markets. Design for the environment (DFE) represents an effective strategy for developing ESPs and it needs to be implemented as early as possible in the design process. This paper describes a new framework for DFE methodology and tool development. A new CAD integrated DFE tool, called the DFE workbench, which is based on this new framework is also described.
Contrasting Effects of Vocabulary Knowledge on Temporal and Parietal Brain Structure across Lifespan

Using behavioral, structural, and functional imaging techniques, we demonstrate contrasting effects of vocabulary knowledge on temporal and parietal brain structure in 47 healthy volunteers who ranged in age from 7 to 73 years. In the left posterior supramarginal gyrus, vocabulary knowledge was positively correlated with gray matter density in teenagers but not adults. This region was not activated during auditory or visual sentence processing, and activation was unrelated to vocabulary skills. Its gray matter density may reflect the use of an explicit learning strategy that links new words to lexical or conceptual equivalents, as used in formal education and second language acquisition. By contrast, in left posterior temporal regions, gray matter as well as auditory and visual sentence activation correlated with vocabulary knowledge throughout lifespan. We propose that these effects reflect the acquisition of vocabulary through context, when new words are learnt within the context of semantically and syntactically related words.
Emotion recognition from speech VIA boosted Gaussian mixture models

Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. Then, classification is performed to minimize the classification error w.r.t. the estimated class-conditional distributions. We call this method the EM-GMM algorithm. In this paper, we introduce a boosting algorithm for reliably and accurately estimating the class-conditional GMMs. The resulting algorithm is named the Boosted-GMM algorithm. Our speech emotion recognition experiments show that the emotion recognition rates are effectively and significantly ȁboosted” by the Boosted-GMM algorithm as compared to the EM-GMM algorithm. This is due to the fact that the boosting algorithm can lead to more accurate estimates of the class-conditional GMMs, namely the class-conditional distributions of acoustic features.
Computer Vision And Image Compression

Broadcasting television signal, upon digitization, results in approximately 60 Mbits per second, while the capacity at the bottom level of the ISDN hierarchy is 64 Kbits per second. For many applications such as videophone and teleconferencing, one would like very much to squeeze the digitized video signal through the 64 Kbits/sec channel: A compression of about 1OOO:l is needed. Conventional compression techniques, such as the CCITT proposed standard, do not give good reconstructions when the amount of motion in the scene is even moderately high. To achieve good quality at 64 Kbits/sec or even lower rates, radical new approaches are called for. One potential direction is model-based compression. The basic idea is to have a static 3D model of the scene constructed at the receiving end. Then, the transmitting end will extract and transmit only the 3D motion information. The receiver takes this motion information and uses it to drive the 3D scene model; from the dynamic 3D model, computer graphics methods can be used to generate 2D image sequences for viewing. In this talk we shall estimate the bit rates achievable by this model-based approach, give some preliminary results, and discuss some of the difficult issues that need to be resolved in order for the approach to be feasible.
Characteristics of substrate-induced low frequency oscillations in GaAs HBT devices and circuits

Spurious oscillations in the low frequency region (below 100kHz) were investigated in InGaP/GaAs HBT devices and circuits. These low frequency oscillations (LFOs) are predominantly activated by the field across the semi-insulating GaAs substrate located between the subcollector and backside metal (of an emitter-up device). Several experiments were conducted to control and eliminate LFOs by floating the backside potential or applying a voltage to the backside metal. In both cases, the electric field across the GaAs substrate is minimized, thereby reducing the mechanism of the field-enhanced capture of electrons by traps in this region.
Connected vibrations: a modal analysis approach for non-rigid motion tracking

A new framework is presented in this paper for tracking non-rigid motion in image sequences. This method models non-rigid motions as the deformations of connected 2D membrane patches. Local smoothness constraints for each patch are the low-frequency vibration modes obtained from modal analysis. These constraints are incorporated with the template matching algorithm into a local motion estimator. To maintain the global topological structure, different patches are connected together by hinges, which introduce additional constraints in the tracking process. The resulted over-constrained linear system is solved efficiently using a least square estimator. To improve the robustness of the tracker, stochastic filtering technique is employed to take advantage of the temporal continuity of the deformations. Promising results on both natural and synthetic facial motion sequences are demonstrated in this paper.
A framework for grid-based image retrieval

In this paper, we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.
Determination of InP-based HBT device models at end of life

InP-based HBTs have been stressed to approximate degradation from a 15 year system life and device models were generated. The basic characteristics of these models are increases in the emitter resistance (Re) and in the base-collector capacitance (Cjc). The end of life model can be obtained from the standard model by increasing Re from 3.6 to 5.7 /spl Omega/ and Cjc from 20.1 to 23.4 fF. These changes lead to decreases in: transistor gain, Ft, and Fmax, and an increase in the base-emitter voltage (V/sub bel/) at typical operating conditions. The changes in the device parameters generally fall within the normal process variations. No increase in low current base emitter voltage (V/sub be0/) occurs, indicating the lack of Be diffusion into the emitter.
Design of a circular antenna array for MIMO channel sounding application at 2.53 GHz

For future mobile technologies, the characterization of multiple links is becoming increasingly important to enhance the performance of the multiple-input multiple-output (MIMO) techniques in Long Term Evolution (LTE) and 5G networks. These technologies require extensive channel measurements to investigate channel characteristics and develop new channel models. In this paper we present a dual polarized, circularly arranged, low loss switched patch antenna array for wave propagation measurements at 2.53 GHz. The array comprises two rings with eight elements (2×8 SPUCA). All 16 elements are dual polarized and the array is able to handle 40 W CW RF power. The antenna array was optimized for high resolution parameter estimation (HRPE).
A study of non-frontal-view facial expressions recognition

The existing methods of facial expression recognition are typically based on the near-frontal face data. The analysis of non-frontal-view facial expression is a largely unexplored research. The accessibility to a recent 3D facial expression database (BU-3DFE database) motivates us to explore an interesting question: whether non-frontal-view facial expression analysis can achieve the same as or better performance than the existing frontal-view facial expression method. Our extensive recognition experiments on data of 100 subjects with 5 yaw rotation view angles suggests that the non-frontal-view facial expression classification can outperform frontal-view facial expression recognition, given the manually labeled facial key points.
Design and evaluation of a linearized C-band helix TWT for digital communications experiments

Reduction in amplifier distortion is often achieved by backing-off from saturation. As an alternative to aggressively backing-off in output power to meet new demands of linearity, this work explores techniques to optimize the pitch profile of TWT helices to reduce distortion. A C-band helix TWT circuit optimized for linearity was designed at NRL and subsequently built by Northrop Grumman. We describe the circuit design and the design methodology, and compare the measured TWT performance with model predictions.
De-correlation distance of the large scale parameters in an urban macro cell scenario

The investigation of new channel models plays a fundamental role for designing and evaluating wireless communication systems. Therefore an extensive analysis of channel measurement data is indispensable. Besides the knowledge of the parameters and their distribution functions, the distance where the parameters are getting uncorrelated is of great interest and can be exploited for more realistic channel simulations. In this contribution the de-correlation distance of the large scale parameters (LSP) in the power and delay domain is investigated. Different parameter estimation procedures are applied taking the changing automatic gain control (AGC) level within one MIMO snapshot into account. The AGC level changes strongly depending on the channel environment and due to the spatial filtering of the directional antennas. Additionally, different antenna heights are considered.
Controller reconfiguration for nonlinear systems using composite observer

This paper addresses the issues of fault estimation and controller design for nonlinear systems in the event of actuator failures. The reconfigurable controller is designed based on nonlinear output regulation. It is shown how a composite observer, to estimate both the faults and the states, can be elegantly constructed using Lie transforms. Thus, the reconfigured regulator achieves the desired regulation based on the information about the states and failed actuators that is reconstructed by the composite observer.
Development of a Gun-Launched, Instrumented Seabed Penetrator System

The use of instrumented penetrators to obtain in situ measurements of marine-sediment properties has been limited to measurements at depths on the order of 10 m or less. This report describes a first-generation, gun-launched, instrumented seabed penetrator system currently being developed that will provide the capability to obtain in situ measurements at depths on the order of 50 m in soft clayey sediments and that may also be used for emplacement of solidified radioactive wastes in geologically stable marine-sediment formations. Also discussed are the design considerations that led to this first-generation system, which will be tested in the late summer of 1981, and plans for future improvements, including development and testing of a variety of instruments and development of a telemetry system with greater channel capacity.
Distributed secondary control with reduced communication in low-voltage DC microgrid

DC microgrid includes various sources and loads, interconnected to the dc system, using power electronic converters. Low voltage regulation and proportional load sharing are the important control objectives of dc microgrid. Distributed secondary controller with communication among these sources help in achieving the aforementioned control objectives. Most of the existing techniques utilize communication among all the converters. This increases communication infrastructure requirement. In this paper, a distributed control strategy based on cooperative control is proposed to attain low voltage regulation and proportional load sharing with less communication. Each source communicates only to its neighbors, thereby utilizing reduced number of communication links in the dc microgrid. The underlying communication topology is represented by a fixed, directed and balanced graph. Each source uses an average current estimator which estimates the average of per-unit currents of all the sources in the microgrid utilizing localised communication. These estimates are used by each source converter for regulating their respective voltages. The small signal model of the microgrid with the proposed control scheme is derived and discussed in this paper. Simulation results are presented which validate the performance of the microgrid with the proposed control scheme.
Bio-nano composite for remote melting

The fabrication of biocompatible composites, consisting of magnetite nanoparticles embedded into a matrix of meltable dextran ester is presented which can be softened under an induced alternating magnetic field and may allow in that way a magnetically controlled release application. This could be a suitable alternative for swellable hydrogel composites embedded with magnetic particles as remote controlled biomaterial. Furthermore a texturing of particles in the polymer matrix by a static magnetic field was investigated.
Bridges I have crossed

In this paper, types of electrical measurement apparatus based on bridge circuits are discussed. Instruments based on Wheatstone's bridge such as Christie's bridge, Leeds & Northrup Wheatstone bridges, Kelvin bridge and General Radio AC bridges are discussed. These instruments range from the most basic to ones with many enhancements. All of the bridges described and illustrated were donated by various American college and university physics departments. These tools offer a history of the quickly developing field of electrical measurement in the late 19th and early 20th Centuries.
Estimating burn-in fall-out for redundant memory

Integrated circuits can exhibit significant early life or infant mortality failures. Methods to estimate and/or reduce the number of such failures are therefore of great interest to industry. Applications employing multi-chip modules (MCMs), where several die must be independently reliable, are particularly vulnerable to early life failures. Maximizing the reliability of each die is therefore of significant importance. This paper presents an integrated yield-reliability model that allows one to estimate the number of burn-in failures for repairable memory chips, a common component in many MCMs. Since defects in integrated circuits tend to cluster, memory chips that have been repaired have a greater chance of containing a latent defect than chips with no repairs. The result is a higher incidence of infant mortality failure among memory chips that have been repaired.
A DCT-domain blind watermarking system using optimum detection on Laplacian model

This paper presents a digital watermarking system in the DCT-domain. The watermarking system is designed to satisfy strictly the perceptual constraints. Under the constraints, robust and fragile watermarking techniques are designed by varying the watermark strength. The optimum detection structure is constructed based on the Laplacian model, which has the properties that high performance can be achieved for a large variety of statistical models, and the computation is efficient. The detection does not need the original images. It can also detect the tampered regions very accurately. The simulations validate the watermarking system. The system can be used in copyright notification and protection, broadcast monitoring and tracking, and authentication and tamper-proofing.
CortexSuite: A synthetic brain benchmark suite

These days, many traditional end-user applications are said to “run fast enough” on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging. This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six world-class machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in “clean C” so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.
Comparison of flux switching and surface mounted permanent magnet generators for high-speed applications

For embedded power generation within civil gas turbine engines, generators are anticipated to endure high rotor speeds coupled with large rotor diameters. With variable frequency permanent magnet generators finding increased roles because of the perceived higher torque density, the issue of rotor integrity becomes a critical issue. Although banding has been previously used in surface mounted PM (SPM) machines, they inevitably increase the airgap and hence reduce the torque capability of the machine. The flux-switching PM (FSPM) machine has been an intense topic of research for many years. By combining its armature and field sources in the stator, its rotor is a passive single piece, salient steel lamination, making it inherently suitable for high speed applications. This study compares both machines when optimised for high speed applications by designing the rotors of both machines for mechanical integrity and comparing the electromagnetic performance. The electromagnetic trade off in the mechanical optimisation of the FSPM machine is found to be negligible, in contrast to the ~33~ reduction of SPM flux linkage because of the increased airgap length. The performance of FSPM machine is validated on a scaled FSPM prototype.
3D-Antenna Array Model for IST-WINNER Channel Simulations

The IST-WINNER channel model (WIM) offers a complete channel model description in a sense of large-scale as well as small-scale effects in MIMO radio-channel for B3G system designs. This paper proposes the extension of 1D regular array structures being currently supported within WIM (uniform linear array - ULA) to arbitrary 3D antenna arrays. Arbitrary antenna structures are necessary for both modelling and validation purposes since antenna arrays define the spatial dimension of the radio-channel. General 3D antenna array description also embeds polarization concepts that are becoming important for modelling of 3D rotation of antenna array. In this paper two different strategies of using 3D antenna arrays in channel model simulations are proposed and discussed. Both methods are compared in terms of the computational complexity and their suitability for the current WIM channel model implementation.
Adaptive reduction of interfering speaker noise using the least mean squares algorithm

In this paper, the adaptive Least Mean Squares (LMS) algorithm is used to separate speaker-produced "information" from interferer-produced "noise" on the basis of the difference in power levels associated with the two phenomena. This method exploits the property of LMS that it rapidly adapts for the dominant excitation modes while simultaneously adapting very slowly for the weaker modes of excitation. This selective convergence property of LMS is next analyzed using an eigenvalue-eigenvector approach which easily displays the signal separation property. Lastly, computer simulations are presented which verify the analysis above for representative synthetic speech waveforms.
A scalable method for parallelizing sampling-based motion planning algorithms

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine.