nils-herrmann/clean_scirepeval huggingface dataset:
Congestion relief in wireless LANs

The rapid proliferation of WiFi enabled devices is generating wireless traffic management challenges particularly at certain popular spaces ("hot-spots") within the network. We address two subsets of traffic engineering, namely load balancing and explicit channel planning amongst a group of access points (APs). The goal is to accommodate more users by optimizing capacity allocation, guarantee a minimum level of user experience and maximize resource utilization in the face of dynamic traffic patterns that change with location and time. We propose a procedure that minimizes an approximation to the weighted mean end-to-end delay experienced by wireless users that is attributable to the wireless portion of the network. We derive an expression for the cost function to be optimized and develop a two-step approach. In step 1, we nearly optimally assign stations to APs. In step 2, we optimally allocate frequencies to APs by formulating an optimal integer linear programming (ILP) problem. We show that step 1 in our algorithm improves average end-to-end delays in several two APs scenarios by 20% to 35%. It also supports over 50% more stations for the same mean end-to-end delay. The second step offers additional improvements of 40% in the overall mean end-to-end delay across multiple APs, as compared to other techniques that have been proposed or implemented.
Enhanced wavelet based audio coder

Several enhancements on the wavelet based audio coder proposed by Sinha and Tewfik (see.  1993) are discussed. These enhancements include use of temporal information in masking for audio segments with sudden change of amplitudes in them, efficient quantization of the variances and run-length coding for the zeros. Experiments suggest that these enhancements give almost transparent compression of monophonic CD quality audio signals (sampled at 44.1 kHz) at bit rates of 55-63 kbits/sec. We also discuss a real time implementation of a simplified version of the wavelet based audio coder at somewhat higher bit rates (64-78 kbits/sec).<<ETX>>
Fast multiscale statistical signal processing algorithms

It is shown that a large set of (non necessarily stationary) correlation matrices may be transformed into a matrix that consists of essentially banded subblocks. The transformation is accompanied by pre- and postmultiplication with an orthogonal matrix whose elements are derived from the impulse responses of a suitably designed cascade of alias free multirate analysis filter banks. It is further proved that the Cholesky factor of the transformed matrix also consists of essentially banded subblocks. These two observations are combined to show that the linear positive definite systems of equations that arise in statistical signal processing can be solved in O(max(N log /sup 2/(N), N/sup 2/)) operations while matrix-vector multiplication steps may be implemented in O(n log (N)) operations. An error analysis of the proposed linear positive definite system solver is also provided.<<ETX>>
LMS-based attack on watermark public detectors

We describe a generalized attack on image watermarking schemes when the decoder is publicly available. The attack applies to most common watermarking schemes. In particular, we describe in detail the implementation of the attack against correlator-based watermark detectors using the least mean square (LMS) algorithm. We establish the effectiveness of the attack in removing the watermark with least distortion. Also, we describe a counterattack to avoid this problem by using nonparametric decision boundaries at the detector.
DNA Copy Number Detection and Sigma Filter

DNA copy number aberrations are characteristic of many genomic diseases including cancer. Microrray-based comparative genomic hybridization (aCGH) is a recently developed high-throughput technique to map and detect DNA copy number (DCN) aberrations. Unfortunately, the observed copy number changes are corrupted by noise, making aberration boundaries hard to detect. They may have false positive or may miss true positive breakpoints. As a result, many approaches proposed to eliminate fluctuations on the DCN data within each aberrant interval and to preserve edges across them. In this paper, we propose a Sigma filter algorithm, because it has superior performance for denoising such data and low computational complexity. We present a comparison study between our approach and other smoothing and statistical approaches, the wavelet-based, LookAhead, CGH segmentation and HMM. Finally, we provide examples using real data sets to illustrate the performance of the algorithms.
Biclustering of Gene with Coherent Evolutions

Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. This can be done by applying a biclustering procedure to gene expression data. Given a microarray data set with M genes and TV conditions, we define a bicluster with coherent evolution as a subset of genes with expression levels that are non-decreasing as a function of a particular ordered subset of conditions. We propose a new biclustering procedure that identifies all biclusters with a specified number of A conditions in parallel with O(MK) complexity. Unlike almost all prior biclustering techniques, the proposed approach is guaranteed to find all biclusters with a specified minimum numbers of genes and conditions in the data set. All of the biclusters it identifies have no imperfection, i.e., the evolutions of the genes in each bicluster will be coherent across all conditions in the bicluster. Furthermore, the complexity of the proposed approach is lower than that of prior approaches
A nonparametric sequential kolmogorov-smirnov test for transmit opportunity detection at the MAC layer

Efficient and reliable detection of transmission opportunity is the key enabler of cognitive radio networks. This paperintroduces a novel transmission opportunity sensing paradigm which operates at the MAC layer of the primary network in contrast to most previous work. We propose a non-orthogonal overlay architecture based on detecting changes in the probability density distribution of primary network packet statistics. Specifically, we develop a novel sequential version of the Kolmogorv-Smirnov goodness-of-fit test which allows the secondary network to operate subject to an interference constraint that ensures a given QOS in the primary network nodes. We provide an efficient implementation of the test on an experimental testbed and demonstrate its utility and viability under field testing on an IEEE 802.11 WLAN. Compared with results reported so far in the literature, our experiments achieve a significantly improved 50%–75% utilization of hitherto unused transmit opportunities while limiting the probability of interference to ≪ 0.02. The average detection delay is 140 ms to 290 ms and compares favorably with the traditional physical layer based approaches.
Progressive resolution motion indexing of video object

We present a novel motion-based video indexing scheme for fast content-based browsing and retrieval in a video database. The proposed technique constructs a dictionary of prototype objects to support query by motion. The first step in our approach extracts moving objects by analyzing layered images constructed from the coarse data in a 3-D wavelet decomposition of the video sequence. These images capture motion information only. Moving objects are modeled as collections of interconnected rigid polygonal shapes in the motion sequences that we derive from the wavelet representation. The motion signatures of the object are computed from the rotational and translational motions associated to the elemental polygons that form the objects. These signatures are finally stored as potential query terms.
Local error detection in sparse magnetic resonance imaging

Due to the physical and the physiological constraints, the sparse magnetic resonance imaging (MRI) techniques operate in a regime where the theoretical guarantees of compressed sensing for recovery with high fidelity are improbable. Thus, the effective signal encoding in sparse MRI techniques is lossy, even at low acceleration factors. For widespread clinical use of sparse MRI, following two problems are proposed: 1) detection of errors in sparse recovered images and, 2) localized and highly fast acquisition of lossless image information in regions with high errors. This paper focuses on the former problem of detecting erroneously recovered image regions and proposes a solution based on joint statistics of wavelet coefficients across multiple subbands. The proposed technique uses multivariate generalized Gaussian distributions to jointly model the wavelet coefficients for all local regions conforming to a unique boundary signature in the image. Detection of local errors is formulated as measuring the degree of variation in the joint statistical model for boundary signatures between the recovered image and a training image. The training image can be a single Nyquist sampled image acquired prior to or during the sparse MRI volumetric scan. The preliminary experimental results show good conformance of the proposed method in detecting local error regions. The high error regions are detected with an accuracy of (91.8±1.6)% at (29.2±4.7)% false detection rate for acceleration factors up to 4.
A real-time cardiac surface tracking system using Subspace Clustering

Catheter based radio frequency ablation of atrial fibrillation requires real-time 3D tracking of cardiac surfaces with sub-millimeter accuracy. To best of our knowledge, there are no commercial or non-commercial systems capable to do so. In this paper, a system for high-accuracy 3D tracking of cardiac surfaces in real-time is proposed and results applied to a real patient dataset are presented. Proposed system uses Subspace Clustering algorithm to identify the potential deformation subspaces for cardiac surfaces during the training phase from pre-operative MRI scan based training set. In Tracking phase, using low-density outer cardiac surface samples, active deformation subspace is identified and complete inner & outer cardiac surfaces are reconstructed in real-time under a least squares formulation.
Replica placement with update latency guarantees

Content delivery networks (CDNs) are a promising new paradigm for delivering content to a pervasive client population. In such distributed networks, finding an optimal replication scheme that minimizes the amount of network traffic, given read and write frequencies for various objects, is NP-hard in general. This work aims at addressing this problem by proposing a new static centralized algorithm for replicating objects that can keep a balanced load on sites. To better meet the requirements of the dynamic nature of the CDNs and the rapid change in the access pattern of the objects, we also investigate a dynamic distributed algorithm, in the context of read queries only, where each site relies on some collected information to decide on where to replicate and migrate objects to achieve good performance and fault-tolerance levels.
Reduced complexity sphere decoding and application to interfering IEEE 802.15.3a piconets

The sphere decoding (SD) algorithm has been widely recognized as an important algorithm to solve the maximum likelihood detection (MLD) problem, given that symbols can only be selected from a set with a finite alphabet. The complexity of the sphere decoding algorithm is much lower than the directly implemented MLD method, which needs to search through all possible candidates before making a decision. However, in high-dimensional and low signal-to-noise ratio (SNR) cases, the complexity of sphere decoding is still prohibitively high for practical applications. In this paper, a simplified SD algorithm, which combines the K-best algorithm and SD algorithm, is proposed. With carefully selected parameters, the new SD algorithm, called SD-KB algorithm, can achieve very low complexity with acceptable performance degradation compared with the traditional SD algorithm. The low complexity of the new SD-KB algorithm makes it applicable to the simultaneously operating piconets (SOP) problem of the multi-band orthogonal frequency division multiplex (MB-OFDM) scheme for the high- speed wireless personal area network (WPAN). We show in particular that the proposed algorithm provides over 4 dB gain in bit error rate (BER) performance over the baseline MB-OFDM scheme when several piconets interfere with each other. The SD-KB algorithm can provide pseudo-MLD solutions, which have significant performance gain over the baseline method, especially when the signal-to-interference ratio (SIR) is low. The cost of performance improvement is higher complexity. However, the new SD algorithm has predictable computation complexity even in the worst scenario.
Multi-rate 802.11 WLANs

In IEEE 802.11 WLANs, stations can operate at different data rates. For example, in 802.11b, wireless stations transmit at four date rates 11, 5.5, 2 and 1 Mbps. A station changes its modulation type and transmits at a lower rate when it faces signal fading and interference. In this paper we introduce a model to capture the capability of the standard to operate at different data rates under both, finite load and saturation conditions. Many improvements are also made in order to make the model more consistent with the standard than existing models. The proposed analysis applies to both the basic access and the RTS/CTS access mechanisms. The results we obtained from the model also show the performance anomaly of the standard when it operates at different data rates that were previously observed in the literature, our experiments and simulation. We use the model to study a scheme we proposed earlier to avoid the degradation in the performance by changing the frame sizes of the stations according to their speeds. Throughput performance evaluation is provided and an analytical expression for average service time is given. Experiments were performed to validate the model
Discrete orthogonal M-band wavelet decompositions

The authors generalize the discrete orthogonal two-band (or dyadic) wavelet decomposition to the M-band case. Specifically, it is shown that any finite energy signal can be explained in terms of the dilates and translates of M-1 M-band wavelets. The advantage of such decompositions is that they are much more compact than two-band wavelet decompositions. This compactness is important both in coding applications and for the development of fast signal processing algorithms. A complete characterization of discrete orthogonal M-band wavelets is given, including a recipe for constructing such wavelets.<<ETX>>
Integration of Wearable Wireless Sensors and Non-Intrusive Wireless in-Home Monitoring System to Collect and Label the Data from Activities of Daily Living

We describe an inexpensive in-home monitoring system designed to assist patients with traumatic brain injuries plan and execute daily activities. The system consists of fixed and wearable wireless sensors, including motion, pressure, door, flow, accelerometer, magnetometer, temperature, light and sound sensors. The sensors provide information that can be used to localize the patient, detect the activity they are engaged in and interruptions that may prevent them from completing the activity in a timely manner. During the system design and training phase, we augment the system with time stamped video and audio collection to provide a ground truth and label training data.
Classification of continuously executed early morning activities using wearable wireless sensors

In this paper, we study the personal monitoring system that classifies the continuously executed early morning activities of daily living. The system is intended to assist those with cognitive impairments due to traumatic brain injuries. The system can be used to help therapists in hospitals or could be deployed in one's home to track and monitor the activities executed by the recovering patients. We begin by briefly describing the infrastructure of our cost-effective system which uses fixed and wearable wireless sensors and show results related to the detection of activities continuously executed in the morning. Both frequency and time domain features from an accelerometer attached to the right wrist were extracted and used for classification using Gaussian mixture models, followed by a finite state machine. We show promising classification results obtained from 5 subjects. Overall classification rate is 88.3 % for 4 activities of interests.
Data subset selection for efficient SVM training

Training a support vector machine (SVM) on large data sets is a computationally intensive task. In this paper, we study the problem of selecting a subset of data for training the SVM classifier under requirement that the loss of performance due to training data reduction is low. A function quantifying suitability of a selected subset is proposed, and a greedy algorithm for solving the subset selection problem is introduced. The algorithm is evaluated on hand digit recognition and other binary classification tasks, and its performance is compared to stratified sampling methods.
Primary Traffic Characterization and Secondary Transmissions

Channel idle time distribution based secondary transmission strategies have been studied intensively in the literature. Under various performance metrics, the ultimate performance of secondary devices are eventually dictated by the presumed channel idle time distribution. Such distributions can take any arbitrary form in practice. In this work, we study idle time distributions in wireless local area networks (WLAN) using large amount of the channel idle time data collected in real-world WLAN networks. We demonstrate with experimental data that the channel idle time distribution can be closely modeled by hyper-exponential distribution. Furthermore, one can treat the primary packet arrival process as a semi-Markov modulated Poisson process. Several secondary transmission strategies are discussed under this model. It is shown that using only one hyper-exponential distribution, the secondary user can achieve a desirable performance when the primary packet arrival process is stationary. However, experimental data suggests that in practice, this process is not stationary and the secondary user can experience a large performance loss with stationary transmission strategy. We propose a novel transmission strategy that achieves suboptimal secondary user performance when the idle time distribution is not stationary. The performances of secondary transmission strategies are demonstrated using experimental data.
Edge detection using spectral estimation techniques

It has been shown that the problem of detecting edges in a digital image is equivalent to the problem of estimating the wave number vectors of complex exponentials in the spatial frequency domain. This observation has been used to show that most of the known non-model-based edge detection algorithms can be interpreted as variations of the periodogram method of spectral estimation. The above observation has also been used to derive three edge detection algorithms. The first algorithm is based on the fact that complex exponentials are the homogeneous solution of a difference equation with proper initial conditions. It derives estimates of the edge locations by performing a singular-value decomposition of a Hankel matrix formed from the fast Fourier transform of the underlying image. The second and third approaches use the maximum-likelihood spectral estimation method and various maximum-entropy spectral estimation technique on the fast Fourier transform of the underlying image to estimate the edge locations. The main advantage of the three approaches is that they do not involve the use of a smoothing filter or gradient operations.<<ETX>>
Analysis of order preserving genes biclusters

In this paper, we describe an approach for finding all order preserving genes biclusters from a set of DNA microarray experimental data that combines the algorithm that finds biclusters with constant values on columns developed in one of our previous study with an adaptive gene expression level quantization procedure. All the biclusters discovered with the proposed methodology have no imperfections and, the complexity of the algorithm is shown to be lower than that of previous approaches. Application of the method to ovarian cancer data seems to reveal significant local pattern.
Low power detection on capacitive touch screens

This paper presents a novel, power efficient and low-latency method for detecting touch locations on capacitive touch screens. In capacitive touch screens, only a few touch sensors experience detectable change corresponding to the touch locations. We show that power consumption can be reduced by monitoring fewer number of samples than the standard method to sense changes in capacitances and their locations. The proposed method uses group testing for touch sensing and location detection. We also propose a decoding method based on Tikhonov regularization, and provide a closed form of the numerical solution. To verify the proposed method, we compare detection performance and power consumption with those of traditional approach. The results show that the proposed alternative leads to equivalent detection performance with significant reduction in power consumption and sensing latency.
All-digital receiver structures for MC-UWB systems

We introduce all-digital receiver structures for ultra wideband orthogonal frequency division multiplexing (UWB-OFDM) systems. These all-digital receiver structures are digital equivalents or extensions of equivalent analog structures that have previously been proposed. The structures use one bit K-tone sigma-delta analog-to-digital converters. They deal with multipath, observation and quantization noise simultaneously. The digital implementation of different receivers, such as maximum likelihood multi-carrier (ML-MC) receiver, single-carrier RAKE receiver and sub-carrier pilot assisted receiver, are studied.
High bit rate ultra-wideband OFDM

We describe a candidate high-bit rate communication system for short range networking in high performance computing clusters. The system uses a hybrid ultra-wideband orthogonal frequency division-multiplexing scheme. The transmitted signals are sparse pulse trains modulated by a frequency selected from a properly designed set of frequencies. The train itself consists of frequency modulated ultra-wide pulses. The system achieves good detection by integrating several pulses, and high throughput by transmitting frequencies in parallel. Unlike traditional orthogonal frequency division-multiplexing systems, a given tone is transmitted only during parts of the transmission interval. We provide an analysis of the system in a multipath environment and illustrate its performance in a realistic setting.
An experimental assessment of transmit opportunities in packet based networks

In this paper we propose a novel suite of transmit opportunity detection methods that effectively exploit the gray space present in high traffic packet networks. Our method adopts a holistic view of the primary network and proactively decides the maximum safe transmit power that does not affect any of the nodes in the primary network. This is achieved via an implicit feedback mechanism from the Primary system to the Secondary system. This is the first time that such a PU-SU feedback link has been shown to naturally arise in the framework of Dynamic Spectrum Access. A novel use of primary packet interarrival duration is developed to rapidly and robustly perform change detection in the primary network's Quality of Service. We develop and evaluate the performance of a battery of tests for such a network state change detection and provide guidelines for transitioning from one test to another in a sequential manner. We thoroughly validate the efficacy of the methods through exhaustive field testing on real world large scale IEEE 802.11 WLAN deployments.