nils-herrmann/clean_scirepeval huggingface dataset:
Super-resolution from unregistered aliased images with unknown scalings and shifts

We consider the problem of super-resolution from unregistered aliased images with unknown spatial scaling factors and shifts. Due to the limitation of pixel size in the image sensor, the sampling rate for each image is lower than the Nyquist rate of the scene. Thus, we have aliasing in captured images, which makes it hard to register the low-resolution images and then generate a high-resolution image. To work out this problem, we formulate it as a multichannel sampling and reconstruction problem with unknown parameters, spatial scaling factors and shifts. We can estimate the unknown parameters and then reconstruct the high-resolution image by solving a nonlinear least square problem using the variable projection method. Experiments with synthesized 1-D signals and 2-D images show the effectiveness of the proposed algorithm.
Lower bounds on the MSE in N/sup th/ order multi-bit multi-loop /spl Sigma//spl Delta/ modulation with DC inputs

In /spl Sigma//spl Delta/ modulation, optimal decoding is defined from the intrinsic behavior of the encoder which is to induce a partition of the space of discrete-time signals. It is shown, in the multi-loop configuration, that this partition has some particular lattice properties which constitute potential tools for the analysis of mean square error (MSE) lower bounds in the general case of time-varying bandlimited signals. Their immediate application in the case of constant inputs is shown. An analysis of the intrinsic behavior of an encoder, based on signal partitioning is presented, thus providing a basic tool for the theoretical analysis of optimal decoding MSE.<<ETX>>
Sensorcam: an energy-efficient smart wireless camera for environmental monitoring

Reducing energy cost is crucial for energy-constrained smart wireless cameras. Existing platforms impose two main chal-lenges: First, most commercial smart phones have a closed platform, which makes it impossible to manage low-level circuits. Since the sampling frequency is moderate in environmental monitoring context, any improper power management in idle period will incur significant energy leak. Secondly, low-end cameras tailored for wireless sensor networks usually have limited processing power or communication range, and thus are not capable of outdoor monitoring task under low data rate. To tackle these issues, we develop Sensorcam, a long-range, smart wireless camera running a Linux-base open system. Through better power management in idle period and the “intelligence” of the camera it-self, we demonstrate an energy-efficient wireless monitoring system in a real deployment.
The Fukushima inverse problem

Knowing what amount of radioactive material was released from Fukushima in March 2011 is crucial to understand the scope of the consequences. Moreover, it could be used in forward simulations to obtain accurate maps of deposition. But these data are often not publicly available, or are of questionable quality. We propose to estimate the emission waveforms by solving an inverse problem. Previous approaches rely on a detailed expert guess of how the releases appeared, and they produce a solution strongly biased by this guess. If we plant a nonexistent peak in the guess, the solution also exhibits a nonexistent peak. We propose a method based on sparse regularization that solves the Fukushima inverse problem blindly. Together with the atmospheric dispersion models and worldwide radioactivity measurements our method correctly reconstructs the times of major events during the accident, and gives plausible estimates of the released quantities of Xenon.
On the interaction of data representation and routing in sensor networks

We consider data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information, collected by the nodes, to the sink node. This problem requires a joint optimization of the data representation at the nodes and of the transmission structure. First, we study the case when the measured data are correlated random variables, both in the lossless scenario with Slepian-Wolf coding, and in the high-resolution lossy scenario with optimal rate-distortion allocation. We show that the optimal transmission structure is the shortest path tree, and we find, in closed-form, the rate and distortion allocation. Second, we study the case when the measured data are deterministic piecewise constant signals, and data is described with adaptive level wavelet-based multiresolution representation. We show experimentally that, when computation is decentralized, there is an optimal network division into node groups of adaptive size. Finally, we also analyze the node positioning problem where, given a correlation structure and an available number of sensors, the goal is to place the nodes optimally in terms of minimizing the transmission cost; our results show that important gains can be obtained compared to a uniformly distributed sensor positioning
Baffled circular loudspeaker array with broadband high directivity

Super-directional loudspeaker arrays can be used to achieve high directivity in a limited low-frequency range. As opposed to microphone arrays, the distance between the loudspeakers has to be relatively large, resulting in aliasing starting at relatively low frequencies. On the other hand, mounting a loudspeaker on a rigid baffle (e.g., a rigid cylinder or sphere) increases its directivity with frequency. Using super-directional array techniques at low frequencies and leveraging loudspeakers' increased directivity at high frequencies enables achieving high directivity both at low and high frequencies. The design of baffled circular loudspeaker arrays and an improved beamforming procedure for achieving high directivity in a broad frequency range is described.
Orthogonal time-varying filter banks and wavelet packets

Considers the construction of orthogonal time-varying filter banks. By examining the time domain description of the two-channel orthogonal filter bank the authors find it possible to construct a set of orthogonal boundary filters, which allows to apply the filter bank to one-sided or finite-length signals, without redundancy or distortion. The method is constructive and complete. There is a whole space of orthogonal boundary solutions, and there is considerable freedom for optimization. This may be used to generate subband tree structures where the tree varies over time, and to change between different filter sets. The authors also show that the iteration of discrete-time time-varying filter banks gives continuous-time bases, just as in the stationary case. This gives rise to wavelet, or wavelet packet, bases for half-line and interval regions.
EigenMaps: Algorithms for optimal thermal maps extraction and sensor placement on multicore processors

Chip designers place on-chip sensors to measure local temperatures, thus preventing thermal runaway situations in multicore processing architectures. However, thermal characterization is directly dependent on the number of placed sensors, which should be minimized, while guaranteeing full detection of all hot-spots and worst case temperature gradient. In this paper, we present EigenMaps: a new set of algorithms to recover precisely the overall thermal map from a minimal number of sensors and a near-optimal sensor allocation algorithm. The proposed methods are stable with respect to possible temperature sensor calibration inaccuracies, and achieve significant improvements compared to the state-of-the-art. In particular, we estimate an entire thermal map for an industrial 8-core industrial design within 1°C of accuracy with just four sensors. Moreover, when the measurements are corrupted by noise (SNR of 15 dB), we can achieve the same precision only with 16 sensors.
Reduction of the MSE in R-times oversampled A/D conversion O(1/R) to O(1/R2)

In oversampled analog-to-digital conversion, the usual reconstruction method using lowpass filtering leads to a mean squared error (MSE) inversely proportional to the oversampling ratio R. The authors prove, under certain assumptions and with periodic analog input signals, that optimal reconstruction achieves an MSE with an oversampling ratio dependence order of at least O(1/R/sup 2/). That is, an MSE slope of /spl minus/6 dB per octave of oversampling is obtained, rather than the conventional /spl minus/3 dB/octave slope of classical schemes.
On the asymptotic capacity of Gaussian relay networks

We determine the asymptotic capacity of a Gaussian multiple-relay channel as the number of relays tends to infinity. The upper bound is an application of the cut-set theorem, and the lower bound follows from an argument involving uncoded transmission. Hence, this paper gives one more example where the cut-set bound is achievable, and one more example where uncoded transmission achieves optimal performance. In the latter sense, the result is an extension of the work of Gastpar, Rimoldi and Vetterli (see  May 2001). The arguments of this paper are also relevant to wireless networks, yielding an asymptotic capacity result.
Optimal time segmentation for signal modeling and compression

The idea of optimal joint time segmentation and resource allocation for signal modeling is explored with respect to arbitrary segmentations and arbitrary representation schemes. When the chosen signal modeling techniques can be quantified in terms of a cost function which is additive over distinct segments, a dynamic programming approach guarantees the global optimality of the scheme while keeping the computational requirements of the algorithm sufficiently low. Two immediate applications of the algorithm to LPC speech coding and to sinusoidal modeling of musical signals are presented.
Wavelets and signal processing

A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes. The discussion includes nonstationary signal analysis, scale versus frequency, wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing. The main definitions and properties of wavelet transforms are covered, and connections among the various fields where results have been developed are shown.<<ETX>>
SensorScope: Out-of-the-Box Environmental Monitoring

Environmental monitoring constitutes an important field of application for wireless sensor networks. Given the severity of potential climate changes, environmental impact on cities, and pollution, it is a domain where sensor networks can have great impact and as such, is getting more and more attention. Current data collection techniques are indeed rather limited and make use of very expensive sensing stations, leading to a lack of appropriate observations. In this paper, we present SensorScope, a collaborative project between environmental and network researchers, that aims at providing an efficient and inexpensive out-of-the-box environmental monitoring system, based on a wireless sensor network. We especially focus on data gathering and present the hardware and network architecture of SensorScope. We also describe a real-world deployment, which took place on a rock glacier in Switzerland, as well as the results we obtained.
Robust diffusion-based unsupervised object labelling in distributed camera networks

Recently, a new ICT paradigm emerged, which considers Multiple Devices that cooperate in Multiple Tasks (MDMT). Under this paradigm, cooperation among the nodes can be beneficial when subsets of the nodes share common interests or observations. For cooperation to be successful, it is thus necessary to account for a decentralized labelling scheme that allows to uniquely identify every object of interest. Such labelling not only ensures proper data exchange among the nodes but also allows the formation of interest-specific clusters and hence, might also be beneficial from a communications cost perspective. The research question addressed in this paper is to develop robust distributed labelling strategies in the context of camera networks where no central unit is available for fusing all the information. Simulation results demonstrate that a high labelling accuracy can be achieved in the considered setup (planar scene) with a correct classification performance close to the centralized solution. The proposed methodology is a promising strategy for distributed clustering in camera networks that can be extended to more complex scenarios.
Multiple description transform coding of images

Generalized multiple description coding (GMDC) is source coding for multiple channels such that a decoder which receives an arbitrary subset of the channels may produce a useful reconstruction. This paper reports on applications of two recently proposed methods for GMDC to image coding. The first produces statistically correlated streams such that lost streams can be estimated from the received data. The second uses quantized frame expansions and hence is conceptually similar to block channel coding, except it is done prior to quantization.
Power, spatio-temporal bandwidth, and distortion in large sensor networks

For a class of sensor networks, the task is to monitor an underlying physical phenomenon over space and time through an imperfect observation process. The sensors can communicate back to a central data collector over a noisy channel. The key parameters in such a setting are the fidelity (or distortion) at which the underlying physical phenomenon can be estimated by the data collector, and the cost of operating the sensor network. This is a network joint source-channel communication problem, involving both compression and communication. It is well known that these two tasks may not be addressed separately without sacrificing optimality, and the optimal performance is generally unknown. This paper presents a lower bound on the best achievable end-to-end distortion as a function of the number of sensors, their total transmit power, the number of degrees of freedom of the underlying source process, and the spatio-temporal communication bandwidth. Particular coding schemes are studied, and it is shown that in some cases, the lower bound is tight in a scaling-law sense. By contrast, it is shown that the standard practice of separating source from channel coding may incur an exponential penalty in terms of communication resources, as a function of the number of sensors. Hence, such code designs effectively prevent scalability. Finally, it is outlined how the results extend to cases involving missing synchronization and channel fading.
Source coding and transmission of signals over time-varying channels with side information

We look at the problem of transmitting information over time-varying channels with side information, where for time-varying channels the statistics of the channel change with time and by channel side information we mean the current state of the channel. We show that when this side information is available at both the transmitter and the receiver, then for the power-constrained channel, the power allocation policy that achieves minimum end-to-end distortion is not necessarily the same as the one required for maximum transmission rate.
A fast Hadamard transform for signals with sub-linear sparsity

A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an N dimensional signal with a K-sparse WHT, where N is a power of two and K = O(Nα), scales sublinearly in N for some 0 <; α <; 1. Assuming a random support model for the nonzero transform domain components, the algorithm reconstructs the WHT of the signal with a sample complexity O(K log2(N/K)), a computational complexity O(K log2(K) log2(N/K)) and with a very high probability asymptotically tending to 1. The approach is based on the subsampling (aliasing) property of the WHT, where by a carefully designed subsampling of the time domain signal, one can induce a suitable aliasing pattern in the transform domain. By treating the aliasing patterns as parity-check constraints and borrowing ideas from erasure correcting sparse-graph codes, the recovery of the nonzero spectral values has been formulated as a belief propagation (BP) algorithm (peeling decoding) over an sparse-graph code for the binary erasure channel (BEC). Tools from coding theory are used to analyze the asymptotic performance of the algorithm in the “very sparse” (α ∈ (0, 1/3]) and the “less sparse” regime (α ∈ (1/3, 1)).
Linear phase wavelets: theory and design

The authors present new theoretical results on FIR (finite impulse response) filter banks based on Diophantine equations and continued fraction expansions and use them to show how general wavelets may be designed. They further show that by considering a noncausal IIR (infinite impulse response) structure it is possible to have a linear phase paraunitary solution. A number of design examples illustrating the advantages of the new results are presented.<<ETX>>
R/D optimal linear prediction

A common technique to extend linear prediction to nonstationary signals is time segmentation: the signal is split into small portions and the modelization is carried out locally. The accuracy of the analysis is, however, dependent on the window size and on the signal characteristics, so that the problem of finding a good segmentation is crucial to the entire modeling scheme. In this paper, we present an algorithm which determines the optimal segmentation with respect to a cost function relating prediction error to modeling cost. The proposed approach casts the problem in a rate/distortion (R/D) framework, whereby the segmentation is implicitly computed while minimizing the modelization distortion for a given modelization cost. The algorithm is implemented by means of dynamic programming and takes the form of a trellis-based Lagrangian minimization. The optimal linear predictor, when applied to speech coding, dramatically reduces the number of bits per second devoted to the modeling parameters in comparison to fixed-window schemes.
On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks

We consider sensor networks that measure spatio-temporal correlated processes. An important task in such settings is the reconstruction at a certain node, called the sink, of the data at all points of the field. We consider scenarios where data is time critical, so delay results in distortion, or suboptimal estimation and control. For the reconstruction, the only data available to the sink are the values measured at the nodes of the sensor network, and knowledge of the correlation structure: this results in spatial distortion of reconstruction. Also, for the sake of power efficiency, sensor nodes need to transmit their data by relaying through the other network nodes: this results in delay, and thus temporal distortion of reconstruction if time critical data is concerned. We study data gathering for the case of Gaussian processes in one- and two-dimensional grid scenarios, where we are able to write explicit expressions for the spatial and time distortion, and combine them into a single total distortion measure. We prove that, for various standard correlation structures, there is an optimal finite density of the sensor network for which the total distortion is minimized. Thus, when power efficiency and delay are both considered in data gathering, it is useless from the point of view of accuracy of the reconstruction to increase the number of sensors above a certain threshold that depends on the correlation structure characteristics.
MFC: A multi-scale fully convolutional approach for visual instance retrieval

Previous work has shown that feature maps of deep convolutional neural networks (CNNs) can be interpreted as feature representation of an image. Image features aggregated from these feature maps have achieved steady progress in terms of performances on visual instance retrieval tasks in recent years. The key to the success of such methods is feature representation. In this paper, we study how to represent an image using discriminative features. We demonstrate first that image size is an important factor which affects the performance of instance retrieval but has not been thoroughly discussed in previous work. Based on experimental evaluations, we propose a multi-scale fully convolutional (MFC) approach to encode the image efficiently and effectively. The proposed method is simple to implement, which does not employ sophisticated post-processing techniques such as query expansion, yet shows promising results on four public datasets.
View-invariant action recognition using cross ratios across frames

We present a new method of computing invariants in videos captured from different views to achieve view-invariant action recognition. To avoid the constraints of collinearity or coplanarity of image points for constructing invariants, we consider several neighboring frames to compute cross ratios, namely cross ratios across frames (CRAF), as our invariant representation of action. For every five points sampled with different intervals from the trajectories of action, we construct a pair of cross ratios (CRs). Afterwards, we transform the CRs to histograms as the feature vectors for classification. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in effectiveness and stability.
Gabor Ordinal Measures for Face Recognition

Great progress has been achieved in face recognition in the last three decades. However, it is still challenging to characterize the identity related features in face images. This paper proposes a novel facial feature extraction method named Gabor ordinal measures (GOM), which integrates the distinctiveness of Gabor features and the robustness of ordinal measures as a promising solution to jointly handle inter-person similarity and intra-person variations in face images. In the proposal, different kinds of ordinal measures are derived from magnitude, phase, real, and imaginary components of Gabor images, respectively, and then are jointly encoded as visual primitives in local regions. The statistical distributions of these visual primitives in face image blocks are concatenated into a feature vector and linear discriminant analysis is further used to obtain a compact and discriminative feature representation. Finally, a two-stage cascade learning method and a greedy block selection method are used to train a strong classifier for face recognition. Extensive experiments on publicly available face image databases, such as FERET, AR, and large scale FRGC v2.0, demonstrate state-of-the-art face recognition performance of GOM.
Abnormal Activity Recognition in Office Based on R Transform

This paper introduces an abnormal activity recognition method based on a new feature descriptor for human silhouette. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The information that the initial silhouette carries is transformed in a compact way preserving important spatial information of the activities. Then a set of HMMs based on the features extracted by our method are trained to recognize abnormal activities. Experiments have proved the accuracy and efficiency of the proposed method, and the comparison with Fourier descriptor illustrates its robustness to disjoint shapes and shapes with holes.