nils-herrmann/clean_scirepeval huggingface dataset:
Lifetime-Distortion Trade-off in Image Sensor Networks

We examine the trade-off between lifetime and distortion in image sensor networks deployed for gathering visual information over a monitored region. Users navigate over the monitored region by specifying a viewpoint that varies with time, and the network attempts to meet the user requirement by synthesizing the desired view using a selection of cameras. We compare two camera selection methods, the first maximizes PSNR for the user's view without considering the cameras' available energy whereas the second uses knowledge of available energy at the cameras to maximize network lifetime. Our simulation results demonstrate a clear trade-off between the two strategies, with selection based on energy alone performing up to 3 dB worse initially than the PSNR based selection yet providing significantly higher coverage lifetime. In addition, we observe that under a fixed total energy constraint, more cameras with lower energy per node are preferable over fewer cameras with higher energy per node. Our results suggest that a hybrid or adaptive camera selection algorithm may provide the optimal lifetime-distortion trade-off.
Solar/wind hybrid energy harvesting for supercapacitor-based embedded systems

For autonomous medium power (1–10 W) field systems deployed in off-grid applications without established power infrastructure, two system design criteria are crucially important: i) continuous availability of power, ii) robust and low-maintenance operation. In this paper, we provide circuit and system designs for energy harvesters that address both issues by utilizing supercapacitors as their energy buffer and hybrid solar and wind power sources for their power supply. By utilizing the complimentary nature of solar and wind power sources, the necessity for large supercapacitor buffers is eliminated. Although a rich body of solar-only designs exist in the literature, in our knowledge, this is the first paper that demonstrates a hybrid harvester design for the medium power range. We experimentally demonstrate the functionality of our harvester designs.
Collusion-resilient fingerprinting using random prewarping

Fingerprinting of audio-visual content using digital watermarks is an effective means of determining the originators of unauthorized copies and fighting piracy in digital distribution networks. In particular, watermarks embedded within the content help trace the traitor responsible for the piracy. A group of users may, however, collude and collectively escape identification by creating an average of their individually watermarked copies that appears unwatermarked. We propose a novel collusion-resilience mechanism, wherein the host signal is warped randomly prior to watermarking. As each copy undergoes a distinctive warp, collusion through averaging either yields low-quality results or requires substantial computational resources to undo random warps. The proposed method is independent of the watermarking scheme used and does not impose any restrictions on the watermark signal that are required by some collusion resistant watermarking schemes. We demonstrate the effectiveness of this approach on digital images.
The BER analysis of the high altitude platforms assisted long range cooperative communication

In this paper, we propose the long range cooperative communication via high altitude platforms (HAPs). The HAPs are temporary platforms placed in the stratosphere, at the typical heights of 17-25 km above the earth surface, which is considered to be a low air density region. The error performance is analyzed for the proposed source-HAPs-destination cooperative system, where the source and the destination are separated by a very long distance on earth surface. Considering the decode and forward (DF) strategy at the HAPs, we find an accurate closed form expression of average BER for binary phase shift keying (BPSK) constellation.
Selective Duty cycle Algorithm for beacon-enabled Low Rate Wireless Personal Area Networks

Today in various fields, Low-Rate Wireless Personal Area Network (LR-WPAN) with IEEE 802.15.4 can be utilized for various applications such as Home Automation, Factory Automation, Search & Rescue, Environmental Control, Health Monitoring, etc. In some of the applications, there is no issue of energy consumption; however, there are so many in which energy consumption is a major issue. In fact, performance of the standard should be optimized according to the traffic condition and application. In this paper, performance of IEEE 802.15.4 or Low-Rate WPAN is analyzed with the variation of Superframe Order (SO) or Duty Cycle. On the basis of performance analysis, a Selective Duty cycle Algorithm (SDA) is proposed which can be employed to optimize the performance of the standard according to the packet rate. Through simulations, it is shown that SDA is performing well as compared to other duty cycles.
Joint multichannel pansharpening for multispectral imagery

Pan-sharpening is a common post-processing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. Previously proposed pan-sharpening techniques operate on a per-channel basis, sharpening each multispectral band independently based on the panchromatic image, often in an ad hoc manner. In contrast with most prior techniques, we formulate pan-sharpening as the problem of jointly estimating the high resolution multispectral images to minimize the combined squared residual error in physically motivated observation models of the low resolution multispectral and the high resolution panchromatic images. To realize pan-sharpening using our proposed formulation, we develop an iterative algorithm to solve the joint minimization resulting in an overall algorithm with modest computational complexity. We evaluate our proposed algorithm and benchmark it against previously proposed methods using established quantitative measures of SNR, SAM, ERGAS, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods.
Latent Embeddings for Zero-Shot Classification

We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables. Instead of learning a single bilinear map, it learns a collection of maps with the selection, of which map to use, being a latent variable for the current image-class pair. We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image. We empirically demonstrate that our model improves the state-of-the-art for various class embeddings consistently on three challenging publicly available datasets for the zero-shot setting. Moreover, our method leads to visually highly interpretable results with clear clusters of different fine-grained object properties that correspond to different latent variable maps.
A 4X1 High-Definition Transcranial Direct Current Stimulation Device for Targeting Cerebral Micro Vessels and Functionality Using NIRS

The paper presents the development of 4X1 high-definition transcranial direct current stimulation (tDCS). High-Definition Transcranial Direct Current Stimulation (HD-tDCS) has been proposed as a focal, noninvasive brain stimulation technique. In this technique, cerebral micro vessel and primary motor cortex are targeted with weak intensity direct current (approximately 2 mA) through small array of electrodes placed on the scalp. The primary mechanism of this technique is based on polarity-dependent shift of neuronal membrane potentials. These polarity-dependent shifts are used to up and down-regulate cortical activity depending on weak direct current stimulation. The paper focuses on the design of the set up for HD-tDCS and conventional tDCS. The power dissipation for high-definition tDCS has also been compared with conventional tDCS. The performance of the HD-tDCS device has been analyzed and compared with a conventional anodal tDCS device in terms of regulating the cerebral micro vessels functionality, which thereby yields signals with better signal to noise ratio (SNR) using near infrared spectroscopy (NIRS). The average SNR of the NIRS device was found to be 39.1939 dB in the OFF-state. In ON-state it is 40.80 dB and 41.910 dB for conventional and high-definition tDCS respectively.
Kinetics of intermetallic growth between lead-free solders and silver-palladium metallizations

Kinetics of intermetallic growth is studied between two lead free solders 3.5 wt% Ag-96.5 wt% Sn, 4 wt% Ag-95.5 wt% Sn-0.5 wt% Cu and two Dupont metallizations 6138, 7484 and pure palladium. The wt% Ag:Pd ratio in Dupont 6138 and 7484 is 19 and 3 respectively. Diffusion couples are prepared by solder reflow on (1) low temperature cofired ceramic (LTCC) substrates having Ag-Pd metallizations and (2) pure palladium foils. These reflowed samples are then annealed at different temperatures. The Ag-Sn and Ag-Sn-Cu and Du Pont 7484 diffusion couples are annealed at temperatures of 100, 130 and 156 °C. The Ag-Sn and Du Pont 6138 samples are annealed at temperatures of 125, 156 and 176 °C. Ag-Sn/Pd and Ag-Sn-Cu/Pd samples are annealed at 156, 175, 195 and 210 °C. At different time intervals samples are taken out of the furnace and cross section analysis is done to determine the intermetallic thickness. For the times and temperature ranges investigated is this study diffusion controlled intermetallic growth is observed for (1) all the metallization and solder diffusion couples (2) Ag-Sn-Cu/ Pd diffusion couples. For Ag-Sn/Pd sample diffusion controlled growth is observed for temperatures 175, 195 and 210 °C. At 156 °C the rate controlling mechanism changes to reaction control. This variation in rate controlling mechanism is correlated to the change in the intermetallic phase morphology that forms in the Ag-Sn/Pd diffusion couple at 156 °C. Activation energies and reaction rate constants for diffusion controlled intermetallic growth are determined for the different sample schemes.
8-Bit high speed, power efficient SAR ADC designed in 90 nm CMOS technology

In this work a high speed, power efficient successive approximation register (SAR) analog to digital converter (ADC) is designed using 90nm CMOS technology. Double tail dynamic latched comparator is used instead of conventional comparator which shows the significant reduction in power consumption and increases in the device speed. The simulation results indicate the suitability of the proposed novel structure with 3.933Watt power consumption (24.61% Power saving) and 250MHz sampling frequency for replacing the conventional SAR ADC CMOS structure.
Local Image Registration by Adaptive Filtering

We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion
A voltage balanced back-to-back stacked multicell converter

This paper presents a voltage-balancing back-to-back stacked multicell converter (B2B-SMC) topology, which is easy to implement, does not require auxiliary capacitor charging circuit and can be easily extended for the increased number of levels. Unidirectional switches are added in the B2B-SMC to maintain the voltage across the capacitors. The advantages of the B2B-SMC over flying capacitor multilevel converter (FCC) and stacked multicell converter (SMC) are increased number of levels in the output voltage, low voltage total harmonic distortion (THD), lower switching losses, and reduced filter requirement. The B2B-SMC consist of two conventional stacked multicell converter (SMC), which are connected back-to-back using two low-switching frequency switches. Simulation results of a 33-level B2B-SMC are provided to verify the proposed topology for voltage-balancing of capacitors at different modulation indices.
A local-linear-fitting-based matting approach for accurate depth upsampling

We propose an approach for upsampling depth information for RGB color plus depth (RGB-D) images captured with common acquisition systems, where RGB color information is available at all pixel locations whereas depth information is only available at a subset of the pixels. Depth upsampling is formulated as a minimization of an objective function composed of two additive terms: a data fidelity term that penalizes disagreement with the low-resolution observed data and a regularization term that penalizes weighted depth deviations from a local linear model in spatial coordinates, where the weights are determined to ensure consistency between the RGB color image and the estimated depth image. Analogous to techniques used for optimization formulations of image matting, the upsampled depth image is then obtained by solving a large sparse linear system of equations. Visual evaluation of results obtained with the proposed algorithm demonstrate that the method provides high resolution depth maps that are consistent with the color images. Quantitative comparisons demonstrate that the method offers an improvement in accuracy over current state of the art techniques for depth upsampling.
Signal analysis for detecting motor symptoms in Parkinson's and Huntington's disease using multiple body-affixed sensors: A pilot study

We report on a pilot study for signal processing based detection and analysis of motor symptoms associated with Parkinson's and Huntington's diseases. In contrast with prior studies using prototype body-worn sensors, that are typically obtrusive, we use light-weight, low-power sensors that can be affixed to the body like adhesive temporary tattoos, allowing for unobtrusive attachment of multiple sensors for continuous motion measurement over durations of up to 48 hours. Signal analysis of the accelerometer data from the sensors highlights the benefit of the proposed approaches: clear signatures are seen for different clinically observed motor symptoms either in the signals recorded in a specific sensor, or in the interrelations across sensor signals.
Latent max-margin metric learning for comparing video face tubes

Comparing “face tubes” is a key component of modern systems for face biometrics based video analysis and annotation. We present a novel algorithm to learn a distance metric between such spatio-temporal face tubes in videos. The main novelty in the algorithm is based on incorporation of latent variables in a max-margin metric learning framework. The latent formulation allows us to model, and learn metrics to compare faces under different challenging variations in pose, expressions and lighting. We propose a novel dataset named TV Series Face Tubes (TSFT) for evaluating the task. The dataset is collected from 12 different episodes of 8 popular TV series and has 94 subjects with 569 manually annotated face tracks in total. We show quantitatively how incorporating latent variables in max-margin metric learning leads to improvement of current state-of-the-art metric learning methods for the two cases when the testing is done with subjects that were seen during training and when the test subjects were not seen at all during training. We also give results on a challenging benchmark dataset: YouTube faces, and place our algorithm in context w.r.t. existing methods.
Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos

Modeling human behavior and activity patterns for recognition or detection of anomalous events has attracted significant research interest in recent years, particularly among the video surveillance community. An anomalous event might be characterized as an event that deviates from the normal or usual, but not necessarily in an undesirable manner, e.g., An anomalous event might just be different from normal but not a suspicious event from the surveillance stand point. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability. Therefore, most works model the expected patterns on the scene, instead, based on video sequences where anomalous events do not occur. Assuming images captured from a single camera, we propose a novel spatiotemporal feature descriptor, called Histograms of Optical Flow Orientation and Magnitude (HOFM), based on optical flow information to describe the normal patterns on the scene, so that we can employ a simple nearest neighbor search to identify whether a given unknown pattern should be classified as an anomalous event. Our descriptor captures spatiotemporal information from cuboids (regions with spatial and temporal support) and encodes both magnitude and orientation of the optical flow separately into histograms, differently from previous works, which are based only on the orientation. The experimental evaluation demonstrates that our approach is able to detect anomalous events with success, achieving better results than the descriptor based only on optical flow orientation and outperforming several state-of-the-art methods on one scenario (Peds2) of the well-known UCSD anomaly data set, and achieving comparable results in the other scenario (Peds1).
A complementary local feature descriptor for face identification

In many descriptors, spatial intensity transforms are often packed into a histogram or encoded into binary strings to be insensitive to local misalignment and compact. Discriminative information, however, might be lost during the process as a trade-off. To capture the lost pixel-wise local information, we propose a new feature descriptor, Circular Center Symmetric-Pairs of Pixels (CCS-POP). It concatenates the symmetric pixel differences centered at a pixel position along various orientations with various radii; it is a generalized form of Local Binary Patterns, its variants and Pairs-of-Pixels (POP). Combining CCS-POP with existing descriptors achieves better face identification performance on FRGC Ver. 1.0 and FERET datasets compared to state-of-the-art approaches.
Extending Face Identification to Open-Set Face Recognition

Face identification plays an important role in biometrics and surveillance. However, before applying face id1entification methods in real scenarios, we have to determine whether the subject in a test sample is known (enrolled in the face gallery). In this work, we focus on approaches to determine whether a given face sample belongs to a subject enrolled in the face gallery. We show how the approaches can be combined with face identification methods so they can perform open-set face recognition. Among the five approaches described in this work, four are based on responses from the face identification, and one is based on comparisons between known samples and samples from an independent background set. The approaches differ on features explored in the data, scalability and accuracy. We evaluate the proposed approaches in two standard and challenging datasets for face recognition (FRGC and PubFig83). Results considering different number of enrolled subjects show which approach can be considered in scenarios where, for instance, one is interested in recognizing few wanted subjects.
Assigning Relative Importance to Scene Elements

The human brain is able to rapidly understand scenes through the recognition of their composing elements and comprehension of the role that each of them plays. This process, related to human perception, impacts in what people care when they see an image and the priority they give to each element. The idea of priority, also referred as importance, is based on biological features of perception and social aspects that interfere in how people perceive what they see and what is considered relevant. In this context, this paper proposes the Element Importance Relative Assignment (EIRA), an approach that models how humans attribute importance to elements in a scene. This approach is based on perceptual, compositional and contextual features employed to assign importance to elements in a scene. To evaluate the proposed approach, tests are conducted in different image datasets with emphasis on the UIUC Pascal Sentence Dataset, where our approach achieves an average accuracy of 86.89%.
Face Verification Based on Relational Disparity Features and Partial Least Squares Models

Face verification approaches aim at determining whether two given faces are from the same person. This scenario has several applications, such as information security, forensics, surveillance and smart cards. Several works extract features independently from each face image, i.e., any sort of relation between the two faces is not modeled a priori to either training or classification stages. In this work, we propose an approach that compares a pair of faces by extracting relational features, assuming the hypothesis that modeling the relation between two faces can be useful for increasing the robustness and performance of the face verification task. Then, we employ multiple classification models based on Partial Least Squares to verify whether a given pair of images belongs the same subject (genuine) or belongs to different subjects (impostor). We validate our approach on the Labeled Faces in the Wild (LFW) and on the Public Figures (Pubfig) datasets, using only few images for training. According to the experiments, our approach achieves results up to 0.966 of area under the curve (AUC) for the LFW dataset using its unrestricted, labeled outside data protocol and an average equal error (EER) of 13.65% on PubFig dataset.
CBRA: Color-based ranking aggregation for person re-identification

The problem of automatically tracking a pedestrian within camera networks with non-overlapping field-of-view, known as person re-identification, is a challenging task with still suboptimal results. Different features have been proposed in the literature, specially colors which achieved the best results when fused in a unique feature representation. Despite being better than considering individually, the fusion still does not explores all the feature discriminative power. Therefore, we propose the use of rank aggregation to improve the results. In this paper, we address the person re-identification problem using a Color-based Ranking Aggregation (CBRA) method, which explores different feature representations to obtain complementary ranking lists and combine them using the Stuart ranking aggregation method. The obtained experimental results demonstrate a great improvement in state-of-the-art, reaching top-1 rank recognition rates of 50.0% and 56.9% in the ViPER and PRTD450S data sets, respectively.