Jointly optimal power control and routing for a single cell, dense, ad hoc wireless network

We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Theta(opt) bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form d(opt)((P) over bar (t)) x Theta(opt) with d(opt) scaling as (P) over bar (1/eta)(t), where (P) over bar (t) is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then provide a simple characterisation of the optimal operating point.

Kernels for large margin time-series classification

In this paper we propose a novel family of kernels for multivariate time-series classification problems. Each time-series is approximated by a linear combination of piecewise polynomial functions in a Reproducing Kernel Hilbert Space by a novel kernel interpolation technique. Using the associated kernel function a large margin classification formulation is proposed which can discriminate between two classes. The formulation leads to kernels, between two multivariate time-series, which can be efficiently computed. The kernels have been successfully applied to writer independent handwritten character recognition.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

Energy efficient change detection over a MAC using physical layer fusion

We propose a simple and energy efficient distributed change detection scheme for sensor networks based on Page's parametric CUSUM algorithm. The sensor observations are IID over time and across the sensors conditioned on the change variable. Each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a multiple access channel (MAC) corrupted with IID noise. The fusion center which is the global decision maker, performs another CUSUM to detect the change. We provide the analysis and simulation results for our scheme and compare the performance with an existing scheme which ensures energy efficiency via optimal power selection.

Estimation of remaining life of power transformers

Inadvertent failure of power transformers has serious consequences on the power system reliability, economics and the revenue accrual. Insulation is the weakest link in the power transformer prompting periodic inspection of the status of insulation at different points in time. A close Monitoring of the electrical, chemical and such other properties on insulation as are sensitive to the amount of time-dependent degradation becomes mandatory to judge the status of the equipment. Data-driven Diagnostic Testing and Condition Monitoring (DTCM) specific to power transformer is the aspect in focus. Authors develop a Monte Carlo approach for augmenting the rather scanty experimental data normally acquired using Proto-types of power transformers. Also described is a validation procedure for estimating the accuracy of the Model so developed.

Long-term Accelerated Multistress Aging of Composite Outdoor Polymeric Insulators

Polymeric outdoor insulators are being increasingly used for electrical power transmission and distribution in the recent years. One of the current topics of interest for the power transmission community is the aging of such outdoor polymeric insulators. A few research groups are carrying out aging studies at room temperature with wet period as an integral part of multistress aging cycle as specified by IEC standards. However, aging effect due to dry conditions alone at elevated temperatures and electric stress in the presence of radiation environment has probably not been explored. It is interesting to study and understand the insulator performance under dry conditions where wet periods are either rare or absent and to estimate the extent of aging caused by multiple stresses. This paper deals with the long-term accelerated multistress aging on full-scale 11 kV distribution class composite silicone rubber insulators. In order to assess the long-term synergistic effect of electric stress, temperature and UV radiation on insulators, they are subjected to accelerated aging in a specially designed multistress-aging chamber for 3800 hours. All the stresses are applied at an accelerated level. Using a data acquisition system developed for the work, leakage current has been monitored in LabVIEW environment. Chemical changes due to degradations have been studied using Energy Dispersive X-Ray analysis, Scanning Electron Microscope and Fourier transform Infrared Spectroscopy. Periodically different parameters like low molecular weight (LMW) molecular content, hydrophobicity, leakage current and surface morphology were monitored. The aging study is under progress and only intermediate results are presented in this paper.

Low power pipelined TCAM employing mismatch dependent power allocation technique

This paper presents design of a Low power 256x72 bit TCAM in 0.13um CMOS technology. In contrast to conventional Match line (ML) sensing scheme in which equal power is consumed irrespective of match or mismatch, the ML scheme employed in this design allocates less power to match decisions involving a large number of mismatched bits. Typically, the probability of mismatch is high so this scheme results in significant CAM power reduction. We propose to use this technique along with pipelining of search operation in which the MLs are broken into several segments. Since most words fail to match in first segment, the search operation for subsequent segments is discontinued, resulting in further reduction in power consumption. The above architecture provides 70% power reduction while performing search in 3ns.

Link route pricing for enhanced QoS

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes under multiple service classes. Our work draws upon [1] and [2] in various ways. We use the Tirupati pricing scheme in conjunction with the stochastic approximation based adaptive pricing methodology for queue control (proposed in [1]) for minimizing network congestion. However, unlike the methodology of [1] where pricing for entire routes is directly considered, we consider prices for individual link-service grade tuples. Further, we adapt the methodology proposed in [21 for a single-node scenario to the case of a network of nodes, for evaluating performance in terms of price, revenue rate and disutility. We obtain considerable performance improvements using our approach over that in [1]. In particular, our approach exhibits a throughput improvement in the range of 54 to 80 percent in all cases studied (over all routes) while exhibiting a lower packet delay in the range of 26 to 38 percent over the scheme in [1].

Low PAPR STBCs from Complex Partial-Orthogonal Designs (CPODs)

Space-time codes from complex orthogonal designs (CODs) with no zero entries offer low Peak to Average power ratio (PAPR) and avoid the problem of turning off antennas. But CODs for 2(a) antennas with a + 1 complex variables, with no zero entries are not known in the literature for a >= 4. In this paper, a method of obtaining no zero entry (NZE) codes, called Complex Partial-Orthogonal Designs (CPODs), for 2(a+1) antennas whenever a certain type of NZE code exists for 2(a) antennas is presented. This is achieved with slight increase in the ML decoding complexity for regular QAM constellations and no increase for other complex constellations. Since NZE CODs have been constructed recently for 8 antennas our method leads to NZE CPODs for 16 antennas. Moreover, starting from certain NZE CPODs for n antennas, a construction procedure is given to obtain NZE CPODs for 2n antennas. The class of CPODs do not offer full-diversity for all complex constellations. For the NZE CPODs presented in the paper, conditions on the signal sets which will guarantee full-diversity are identified. Simulations results show that bit error performance of our codes under average power constraint is same as that of the CODs and superior to CODs under peak power constraint.

Low correlation sequences over AM-PSK and QAM alphabets

A construction for a family of sequences over the 8-ary AM-PSK constellation that has maximum nontrivial correlation magnitude bounded as theta(max) less than or similar to root N is presented here. The famfly is asymptotically optimal with respect to the Welch bound on maximum magnitude of correlation. The 8-ary AM-PSK constellation is a subset of the 16-QAM constellation. We also construct two families of sequences over 16-QAM with theta(max) less than or similar to root 2 root N. These families are constructed by interleaving sets of sequences. A construction for a famBy of low-correlation sequences over QAM alphabet of size 2(2m) is presented with maximum nontrivial normalized correlation parameter bounded above by less than or similar to a root N, where N is the period of the sequences in the family and where a ranges from 1.61 in the case of 16-QAM modulation to 2.76 for large m. When used in a CDMA setting, the family will permit each user to modulate the code sequence with 2m bits of data. Interestingly, the construction permits users on the reverse link of the CDMA channel to communicate using varying data rates by switching between sequence famflies; associated to different values of the parameter m. Other features of the sequence families are improved Euclidean distance between different data symbols in comparison with PSK signaling and compatibility of the QAM sequence families with sequences belonging to the large quaternary sequence families {S(p)}.

Localization of handwritten text in documents using moment invariants and Delaunay triangulation

This paper describes an approach based on Zernike moments and Delaunay triangulation for localization of hand-written text in machine printed text documents. The Zernike moments of the image are first evaluated and we classify the text as hand-written using the nearest neighbor classifier. These features are independent of size, slant, orientation, translation and other variations in handwritten text. We then use Delaunay triangulation to reclassify the misclassified text regions. When imposing Delaunay triangulation on the centroid points of the connected components, we extract features based on the triangles and reclassify the text. We remove the noise components in the document as part of the preprocessing step so this method works well on noisy documents. The success rate of the method is found to be 86%. Also for specific hand-written elements such as signatures or similar text the accuracy is found to be even higher at 93%.

Line removal and restoration of handwritten strokes

In document images, we often find printed lines over-lapping with hand written elements especially in case of signatures. Typical examples of such images are bank cheques and payment slips. Although the detection and removal of the horizontal lines has been addressed, the restoration of the handwritten area after removal of lines, persists to be a problem of interest. lit this paper, we propose a method for line removal and restoration of the erased areas of the handwritten elements. Subjective evaluation of the results have been conducted to analyze the effectiveness of the proposed method. The results are promising with an accuracy of 86.33%. The entire Process takes less than half a second for completion on a 2.4 GHz 512 MB RAM Pentium IV PC for a document image.

Enhancing sensor network lifetime using interactive communication

We are concerned with maximizing the lifetime of a data-gathering wireless sensor network consisting of set of nodes directly communicating with a base-station. We model this scenario as the m-message interactive communication between multiple correlated informants (sensor nodes) and a recipient (base-station). With this framework, we show that m-message interactive communication can indeed enhance network lifetime. Both worst-case and average-case performances are considered.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Decentralized sequential change detection using physical layer fusion

We study the problem of decentralized sequential change detection with conditionally independent observations. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. Simulations demonstrate that the proposed techniques have lower detection delays when compared with existing schemes. Moreover we demonstrate that the energy-constrained formulation enables better use of the total available energy than a power-constrained formulation.