waashk/dblp huggingface dataset:
joint synchronization using cyclic property. it is well known that the cyclic property of transmitted signal can be used to achieve joint frame and carrier frequency synchronization, while the uncompensated sampling frequency offset (sfo) would still be a problem. based on the observation that the impact of sfo on the received cyclic block and its replica is equivalent to a relative time shift, a joint synchronization scheme solely based on cyclic property is proposed to achieve frame, sampling frequency and carrier frequency synchronization simultaneously. a two-dimensional sliding-window auto-correlator (2d-swac) is presented to implement the joint synchronization with low complexity. the proposed scheme is suitable for any transmission system with similar cyclic property. meanwhile, it is robust to large sampling and carrier frequency offset and strong channel frequency selectivity, as demonstrated by the analysis and simulations.
estimating statistical eigen-beamforming gains using spatial channel correlation. statistical eigen-beamforming (ebf) is a widely used multiple antenna transmission technique in which one antenna weight is used over potentially a large bandwidth (e.g., all subcarriers in ofdm). with n transmit antennas, the maximum gain for ebf at a receiver is 20 log n decibels over a single antenna transmission (assuming a power unfair comparison where each transmit antenna has a fixed power so the addition of new antennas brings a beamforming gain as well as a total transmit power increase). based on statistical analysis of field data and observations, this gain is nearly achievable with line-of-sight transmissions. while useful for characterizing the benefits of ebf, the statistical analysis does not indicate the realizable gain for a particular location. this paper presents a relationship between the ebf gain, spatial correlation of the channel, and the number of transmit antennas. the conjectured gain is compared to the expected gain based on an analysis of field data. the results of this comparison show that the spatial correlation as defined in this paper can be used to predict the ebf gain accurately for a variety of antenna spacings and polarizations.
efficient detection of bots in subscribers' computers. we investigate how an isp can efficiently detect bots in its subscribers' computers, possibly as a value-added service or to prevent collateral damage to its infrastructure. by causing an isp's email servers and network links to get clogged or blacklisted, bots reduce the quality of service the isp provides to its subscribers. we describe dns flagger, a novel device for isp bot detection, and evaluate its efficiency. dns flagger matches subscribers' dns traffic against ip and dns signatures. in real-time experiments, we found that, on average, major antivirus programs (avs) detected only 59% of freshly caught bots, while dns flagger detected 73.1% or 91% of those bots, respectively on hosts that do not or do also have a major av. there were no false alarms. because its processing involves only a small fraction of all network traffic and can be performed at very high speed, a single dns flagger can handle hundreds of thousands of subscribers.
a performance comparison of recent network simulators. a widespread methodology for performance analysis in the field of communication systems engineering is network simulation. while ns-2 has established itself as virtually the standard network simulation tool, other network simulators have gained more and more attention during the last years. in this paper, we briefly survey new developments in the field of network simulation and conduct a performance comparison study by implementing an identical simulation set-up in five simulators, namely ns-2, omnet++, ns-3, simpy and jist/swans. our results reveal large differences according to both run-time performance and memory usage.
sensitive data requests: do sites ask correctly? to ensure the security of sensitive web content, an organization must use tls and do so correctly. however, little is known about how tls is actually used on the web. in this work, we perform large-scale internet-wide measurements to determine if web sites use tls when needed and when they do, if they use it correctly. we find hundreds of thousands of pages where tls is either not used when it should be or is used improperly, putting sensitive data at risk.
non-regenerative multicarrier mimo relay communications based on minimization of mean-squared error. in this paper we propose non-regenerative multi-carrier multiple-input multiple-output (mimo) relay techniques that minimize the mean-squared error (mse) of the signal wave-form estimation. we establish the closed-form optimal precoding matrices at the source and relay nodes in the absence of the direct source-destination link. interestingly, we show that the proposed precoding matrices jointly convert the multicarrier mimo relay channel into parallel single-input single-output (siso) relay channels. in order to reduce the computational complexity of the optimal algorithm, a suboptimal precoding approach based on an upper-bound of the mse expression is developed. numerical examples illustrate a significant performance improvement of the proposed algorithms over the existing techniques.
clack: a network covert channel based on partial acknowledgment encoding. the ability of setting up a covert channel, which allows any two nodes with internet connections to engage in secretive communication, clearly causes a very serious security concern. a number of recent studies have indeed shown that setting up such covert channels is possible by exploiting the protocol fields in the ip, tcp, or application layer. however, the quality of these covert channels is susceptible to unpredictable network condition and active wardens. in this paper, we propose clack, a new covert channel which encodes covert messages into the tcp acknowledgments (acks). since the message encoding is performed in a tcp data channel, clack is reliable and resilience to adverse network conditions. moreover, clack is very difficult to detect in practice, because the tck acks encoded by clack cannot be easily distinguished from the normal acks. we have implemented and tested clack in a test-bed to validate its correctness.
dynamic channel feedback control for limited-feedback multi-user mimo systems. efficient channel feedback methods are becoming more important in limited-feedback multi-user multiple input multiple output (mu-mimo) systems. we propose a new dynamic channel feedback control (defcon) scheme, which efficiently allocates the limited system resources for channel feedback to appropriate users. the main features of the proposed defcon scheme are as follows. firstly, the channel feedback procedure is composed of two stages: rough channel feedback of all users and additional precise channel feedback of the selected users only. secondly, each selected user is allocated with different amount of resources for additional channel feedback according to its channel status. we mathematically solve the resource allocation problem by proposing a suboptimal method, which utilizes convex optimization technique combined with iterative steepest decent direction search algorithm. numerical results show that zero-forcing beamforming (zfbf) with the proposed defcon scheme considerably outperforms previous limited-feedback mu-mimo schemes such as zfbf with conventional static feedback method and per user unitary beamforming rate control (pu2rc).
low-complexity multisampling multiuser detector for time-hopping uwb systems. multiple access interference (mai) in time hopping (th) ultra-wideband (uwb) systems is known to be non-gaussian. much previous research in th-uwb receiver design has attempted to propose better single-user uwb detectors by introducing more accurate models for the distribution of the mai. recently, it was shown that some of the single-user receivers track closely the optimum achievable single-user performance. although, these receivers are simple, all suffer from error rate floors, and hence limited user capacity. multiuser detection (mud) is considered for offering high performance at the cost of complexity that grows exponentially with the number of users. thought to be too complex for low-cost uwb receivers, mud applied in th-uwb systems benefits from the low duty cycle implying that the number of effective interfering users is small compared to the number of active users. a novel low-complexity multisampling multiuser detector inspired by the inferiority of single-user receivers and the small number of effective interfering users in th-uwb systems is proposed. simulation results show that this detector achieves the performance of the conventional matched filter receiver operating in a single-user system.
new constructions of low-complexity convolutional codes. in this paper new constructions of low trellis complexity convolutional codes are presented. new codes are found by searching into a specific class of time varying convolutional codes, which is shaped by some basic properties and search restrictions. an efficient technique for obtaining minimal trellis modules for the proposed codes is provided. finally, new low complexity convolutional codes of various code rates and memory sizes are tabulated.
prmac: pipelined routing enhanced mac protocol for wireless sensor networks. we describe a novel pipelined routing enhanced mac protocol (prmac) for wireless sensor networks that employs cross layer optimization to realize efficient channel access over multiple hops. the main goal of the protocol is to schedule multi hop transmissions of data packets in a single cycle to reduce end-to-end delay. when nodes are awake, a multi-hop flow is constructed using the information from the routing layer, and data packet transmission is scheduled in the subsequent sleep period. when compared with existing solutions such as rmac, our ns-2 based simulation results show an average 62.2% reduction in end to end packet delivery time for unicast traffic on random networks and up to 200% improvement in delivery ratio on multi-hop chains.
on increasing information availability in gnutella-like peer-to-peer networks. in this paper, we address some of the problems such as dead searches, complexity in the study of network topology and network overloading that are associated with gnutella and gnutella-like peer-to-peer (p2p) networks. we use advanced heuristic parameters with information shuffling as a solution for them. we propose an advancement of gnutella using the above-mentioned schemes. at a panoramic level, our work is founded on the following concepts: (a) crawling the p2p networks to shuffle information, so that the knowledge is distributed over the whole network, and (b) bringing the information within searchable hops of each network. these have been verified on a self-built p2p simulator, named peerns, which works on actual p2p network statistics and is, hence, very close to the actual scenario. the results obtained through simulation affirm that the nodes with extremely large number of dead searches benefit the most and are observed to have a sharp decrease in their dead search count after crawling a small part of the overall network.
on the convergence of ica algorithms with symmetric orthogonalization. independent component analysis (ica) problem is often posed as the maximization/minimization of an objective/ cost function under a unitary constraint, which presumes the prewhitening of the observed mixtures. the parallel adaptive algorithms corresponding to this optimization setting, where all the separators are jointly trained, are typically implemented by a gradient-based update of the separation matrix followed by the so-called symmetrical orthogonalization procedure to impose the unitary constraint. this article addresses the convergence analysis of such algorithms, which has been considered as a difficult task due to the complication caused by the minimum-(frobenius or induced 2-norm) distance mapping step. we first provide a general characterization of the stationary points corresponding to these algorithms. furthermore, we show that fixed point algorithms employing symmetrical orthogonalization are monotonically convergent for convex objective functions. we later generalize this convergence result for nonconvex objective functions. at the last part of the article, we concentrate on the kurtosis objective function as a special case. we provide a new set of critical points based on householder reflection and we also provide the analysis for the minima/maxima/saddle-point classification of these critical points.
nested support vector machines. one-class and cost-sensitive support vector machines (svms) are state-of-the-art machine learning methods for estimating density level sets and solving weighted classification problems, respectively. however, the solutions of these svms do not necessarily produce set estimates that are nested as the parameters controlling the density level or cost-asymmetry are continuously varied. such nesting not only reflects the true sets being estimated, but is also desirable for applications requiring the simultaneous estimation of multiple sets, including clustering, anomaly detection, and ranking. we propose new quadratic programs whose solutions give rise to nested versions of one-class and cost-sensitive svms. furthermore, like conventional svms, the solution paths in our construction are piecewise linear in the control parameters, although here the number of breakpoints is directly controlled by the user. we also describe decomposition algorithms to solve the quadratic programs. these methods are compared to conventional (non-nested) svms on synthetic and benchmark data sets, and are shown to exhibit more stable rankings and decreased sensitivity to parameter settings.
sparse reconstruction by separable approximation. finding sparse approximate solutions to large under-determined linear systems of equations is a common problem in signal/image processing and statistics. basis pursuit, the least absolute shrinkage and selection operator (lasso), wavelet-based deconvolution and reconstruction, and compressed sensing (cs) are a few well-known areas in which problems of this type appear. one standard approach is to minimize an objective function that includes a quadratic (l2) error term added to a sparsity-inducing (usually l1) regularizater. we present an algorithmic framework for the more general problem of minimizing the sum of a smooth convex function and a nonsmooth, possibly nonconvex regularizer. we propose iterative methods in which each step is obtained by solving an optimization subproblem involving a quadratic term with diagonal hessian (i.e., separable in the unknowns) plus the original sparsity-inducing regularizer; our approach is suitable for cases in which this subproblem can be solved much more rapidly than the original problem. under mild conditions (namely convexity of the regularizer), we prove convergence of the proposed iterative algorithm to a minimum of the objective function. in addition to solving the standard l2 -- l1 case, our framework yields efficient solution techniques for other regularizers, such as an l∞ norm and group-separable regularizers. it also generalizes immediately to the case in which the data is complex rather than real. experiments with cs problems show that our approach is competitive with the fastest known methods for the standard l2 -- l1 problem, as well as being efficient on problems with other separable regularization terms.
distributed detection in uwb wireless sensor networks. in this paper, we study distributed detection in ultra-wideband (uwb) wireless sensor networks (wsns) over frequency selective channels. several schemes with different requirements on channel state information (csi) are proposed, analyzed, and compared. we study the error exponent and asymptotic optimality for these schemes under different energy, and time-bandwidth product requirements. our study shows that to achieve a nonzero error exponent, the no-feedback scheme requires large energy and time-bandwidth product but has advantages of no synchronization requirement and no feedback overhead. on the other hand, schemes requiring feedback are suitable for energy and bandwidth limited settings but require quasi-synchronization.
a low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network. we propose a scheme for rate-constrained distributed nonparametric regression using a wireless sensor network. the scheme is universal across a wide range of sensor noise models, including unbounded and nonadditive noise; it has low complexity, requiring simple operations such as uniform scalar quantization with dither and message passing between neighboring nodes in the network, and attains minimax optimality for regression functions in common smoothness classes. we present theoretical results on the tradeoff between the compression rate, communication complexity of encoding, and the mse and demonstrate empirical performance of the scheme using simulations.
efficient computation of the binary vector that maximizes a rank-deficient quadratic form. the maximization of a full-rank quadratic form over the binary alphabet can be performed through exponential-complexity exhaustive search. however, if the rank of the form is not a function of the problem size, then it can be maximized in polynomial time. by introducing auxiliary spherical coordinates, we show that the rank-deficient quadratic-form maximization problem is converted into a double maximization of a linear form over a multidimensional continuous set, the multidimensional set is partitioned into a polynomial-size set of regions which are associated with distinct candidate binary vectors, and the optimal binary vector belongs to the polynomial-size set of candidate vectors. thus, the size of the candidate set is reduced from exponential to polynomial. we also develop an algorithm that constructs the polynomial-size candidate set in polynomial time and show that it is fully parallelizable and rank-scalable. finally, we demonstrate the efficiency of the proposed algorithm in the context of adaptive spreading code design.
a sub goal seeking approach for reactive navigation in complex unknown environments. reactive-based approaches are widely used in autonomous navigation. however, in complex unknown environments, pure reactive-based navigation still poses a few challenges since it can be easily trapped by a local minimum and may produce some extra manoeuvres. this paper presents the design of a reactive-based approach for navigation in complex and unknown environments called sub goal seeking, in which depth point maps of the environment are analysed to extract free spaces around the robot. these spaces are then evaluated the one that is most likely to lead to the final goal is chosen as a sub goal. the robot then drives towards these sub goals, instead of the final goal until it is visible. by analysing the environmental structure, dead-ends within robot sensory range are able to be detected thus reducing the chance of being trapped and also reducing unnecessary manoeuvres. this paper also evaluates the performance of the sub goal seeking approach using three criteria, goal achievable ability, safety and maneuvering through extensive simulation and real mobile robot experiments.
collision free path-planning for cable-driven parallel robots. in this study, a path-planning method that has been developed for serial manipulators is adapted to cable-driven robots. the proposed method has two modes. the first one is active when the robot is far from an obstacle. in this mode, the robot moves toward the goal on a straight line. the second mode is active when the robot is near an obstacle. during this mode, the robot finds the best way to avoid the obstacle. moreover, an algorithm is presented to detect the collision between the robot and the obstacle. a similar algorithm is also presented to avoid the collision of the cables with an obstacle. some simulation results are shown, which are then validated experimentally using a built 4-cable-driven parallel manipulator. although the path obtained between the initial and final poses may not be the shortest possible one, it guarantees finding a path, when it exists, no matter how cluttered the environment is.
covariance recovery from a square root information matrix for data association. data association is one of the core problems of simultaneous localization and mapping (slam), and it requires knowledge about the uncertainties of the estimation problem in the form of marginal covariances. however, it is often difficult to access these quantities without calculating the full and dense covariance matrix, which is prohibitively expensive. we present a dynamic programming algorithm for efficient recovery of the marginal covariances needed for data association. as input we use a square root information matrix as maintained by our incremental smoothing and mapping (isam) algorithm. the contributions beyond our previous work are an improved algorithm for recovering the marginal covariances and a more thorough treatment of data association, now including the joint compatibility branch and bound (jcbb) algorithm. we further show how to make information theoretic decisions about measurements before actually taking the measurement, therefore allowing a reduction in estimation complexity by omitting uninformative measurements. we evaluate our work on simulated and real-world data.
consistent triangulation for mobile robot localization using discontinuous angular measurements. localization is a fundamental operation for the navigation of mobile robots. the standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. in this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. the presented method is based on an extended kalman filter (ekf) with a state-vector composed of the external angular measurements. this algorithm keeps track of the angles between actual measurements from robot odometric information. this continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. the article reports experimental results that show the localization accuracy obtained by means of the presented approach. these results are compared to the ones obtained applying the ekf algorithm with the standard pose state-vector. for the experiments, an omnidirectional robotic platform with omnidirectional wheels is used.
efference copies in neural control of dynamic biped walking. in the early 1950s, von holst and mittelstaedt proposed that motor commands copied within the central nervous system (efference copy) help to distinguish 'reafference' activity (afference activity due to self-generated motion) from 'exafference' activity (afference activity due to external stimulus). in addition, an efference copy can be also used to compare it with the actual sensory feedback in order to suppress self-generated sensations. based on these biological findings, we conduct here two experimental studies on our biped ''runbot'' where such principles together with neural forward models are applied to runbot's dynamic locomotion control. the main purpose of this article is to present the modular design of runbot's control architecture and discuss how the inherent dynamic properties of the different modules lead to the required signal processing. we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.