Portable ultrasound is increasingly used to assess jugular venous pressure (JVP) to approximate volume status in patients with congestive heart failure (CHF). Traditionally, increases in jugular venous pressure height signify increasing circulating blood volume. Emerging evidence, suggests that JVP correlates well with sonographic images of the internal jugular vein (IJV). This paper represents a preliminary investigation on the ability of cross-sectional area (CSA) of the IJV to measure relative changes in circulating blood volume. Fourteen healthy subjects had serial transverse ultrasound videos of their IJV captured while lying at five angles designed to simulate relative changes in blood volume. Ultrasound videos of the IJV were both manually and semi-automatically segmented, the CSA was measured, outliers were detected and removed, and Rotation Forest classifier was used to classify the data. By limiting the number of classes from five to three and removing outliers the accuracies improved from 59.50% to 91.05% and 62.74% to 91.89% for manual and semi-automatic segmentation, respectively. This pilot demonstrated that serial measurement of the CSA of the IJV in combination with machine learning techniques represents a viable opportunity to monitor changes in circulating blood volume in healthy subjects, setting the stage for a trial monitoring of patients with CHF.

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions, including sepsis, trauma, dialysis, and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from the cross-sectional area of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters, such as the image quality, shape, and temporal variation. In this paper, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm is applied to 65 ultrasound videos captured from 13 healthy subjects, with each video containing 450 frames. The results show that spatial and temporal adaptation of the energy function significantly improves segmentation performance when compared with the current state-of-the-art active contour algorithms.

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramér-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough.

In this paper, we present a framework for self-management of mobile clouds, based on self organized distributed coalition formation process for spectrum sharing in interference channel for large scale ad hoc networks. In this approach we use concept of coalition clusters within the network where mutual interdependency between different clusters is characterized by the concept of spatial network correlation. Then by using stochastic models of the process we give up some details characteristic for coalition game theory in order to be able to include some additional parameters for network scaling. Applications of this model are: a) Estimation of average time τ to reach grand coalition and its variance στ2through closed form equations. These parameters are important in designing the process in dynamic environment. b) Managing the dimensioning the coalition cluster within the network c) Modelling the network spatial correlation characterizing mutual visibility of the interfering links. d) Modelling of the effect of the new link activation/inactivation on the coalition forming process. e) Modelling the effect of link mobility on the coalition forming process.

In this paper we present nonlinear 3 player game model for joint routing, network coding, and scheduling problem. To define such a game model, first routing and network coding are modeled by using a new approach based on compressed topology matrix that takes into account the inherent multicast gain of the network. Topology matrix includes the set of all possible paths, including network coded paths, from sources to their corresponding sinks. These paths are identified and compressed, and then by switching between some of them with appropriate usage rates (frequencies), achievable throughput is optimized. The scheduling is optimized by a new approach called network graph soft coloring. Soft graph coloring is designed by switching between different components of a wireless network graph, which we refer to as graph fractals, with appropriate usage rates. Therefore each link can be painted with more than one different colors selected with appropriate probabilities. In the proposed game which is a nonlinear cubic game, the strategy sets of the players are links, path, and network components. The outputs of this game model are mixed strategy vectors of the second and the third players at equilibrium. Strategy vector of the second player specifies optimum multi-path routing and network coding solution while mixed strategy vector of the third players indicates optimum switching rate among different network components or membership probabilities for optimal soft scheduling approach. Optimum throughput is the value of the proposed nonlinear cubic game at equilibrium. The proposed nonlinear cubic game is solved by extending fictitious playing method. Numerical and simulation results prove the superior performance of the proposed techniques compared to other conventional schemes.

In this paper, we present a framework for analysis of self organized distributed coalition formation process for spectrum sharing in interference channel for large scale ad hoc networks. In this approach we define coalition clusters within the network where mutual interdependency between different clusters is characterized by the concept of spatial network correlation. Then by using stochastic models of the process we give up some details which are characteristic for coalition game theory in order to be able to include some additional parameters for network scaling. Applications of this model are: a) Estimation of average time to reach grand coalition and its variance στ2through closed form equations. These parameters are important in designing the process in dynamic environment. b) Dimensioning the coalition cluster within the network c) Modelling the network spatial correlation characterizing mutual visibility of the interfering links. d) Modelling of the effect of the new link activation/inactivation on the coalition forming process. e) Modelling the effect of link mobility on the coalition forming process.

In this paper, we use matrix games framework for joint optimization of routing and network coding under conflict free scheduling for multi-source wireless ad-hoc networks. The impact of multicast diversity on scheduling is controlled by using topology compression concept quantified through compressed multicast topology matrix. To define topology matrix, first a set of all possible paths, including network coded paths, is identified and compressed. Depending on the nature of data selected path can be unicast or multicast. Then by switching between these paths with appropriate rates (frequencies), achievable scaled throughput is maximized. A link conflict free environment is designed by appropriate conflict free network partitioning using network graph coloring algorithm. For each possible coloring scheme and considering priority assigned to each source, link scheduling partitions topology matrix into multiple sub-matrices, one for each partial topologies. Each sub-matrix is used as a payoff matrix for an asymmetrical matrix game where against any single move of the second player, first player has multiple (K) moves, corresponding to different partial topologies or their equivalent colors. The strategy sets for the players are links and paths respectively. Such a game will be formally referred to as Asymmetrical Matrix Game with notation AMG(2,K,1) and the value of this game is inverse of the scaled throughput. In this notation 2 indicates two dimensional games. At the equilibrium, mixed strategy vector of the first player indicates optimum percentage of time or optimum number of time slots dedicated to the selected partial topologies for a given partitioning of the network graph while, mixed strategy vectors of the second player is proportional to optimum usage rates of the paths. Numerical results are presented for a simple butterfly network including 6 nodes and 2 sources. One source transmits multicast and second one unicast data with different priority.

In this paper, we present a novel application of matrix game theory for optimization of link scheduling in wireless ad-hoc networks. Optimum scheduling is achieved by soft coloring of network graphs. Conventional coloring schemes are based on assignment of one color to each region or equivalently each link is member of just one partial topology. These algorithms based on coloring are not optimal when links are not activated with the same rate. Soft coloring, introduced in this paper, solves this problem and provide optimal solution for any requested link usage rate. To define the game model for optimum scheduling, first all possible components of the graph are identified. Components are defined as sets of the wireless links can be activated simultaneously without suffering from mutual interference. Then by switching between components with appropriate frequencies (usage rate) optimum scheduling is achieved. We call this kind of scheduling as soft coloring because any links can be member of more than one partial topology, in different time segments. To simplify this problem, we model relationship between link rates and components selection frequencies by a matrix game which provides a simple and helpful tool to simplify and solve the problem. This proposed game theoretic model is solved by fictitious playing method. Simulation results prove the efficiency of the proposed technique compared to conventional scheduling based on coloring.

In this paper, performance analysis of the decision directed (DD) maximum likelihood (ML) multiple-input multiple-output (MIMO) channel tracking algorithm is presented. The ML channel tracking algorithm presents efficient performance especially in the DD mode of the operation. Because of the nonlinear decision device, analysis of the DD based algorithms is very difficult. In this paper, first the decision error rate is computed for the known channel tracking error and then the channel tracking error is evaluated for the known decision error rate. The presented analysis is compared with the simulation results for different Doppler frequency shifts and SNRs, and it is shown that the analysis and simulation results are in a good match even for high rank MIMO channels and high Doppler shifts.

In this paper, we present new methodology and results for joint routing and link scheduling optimization in multicast wireless ad-hoc networks. The impact of multicast diversity on scheduling is controlled by using topology compression concept quantified through compressed multicast topology matrix. To define topology matrix, first a set of all possible multicast paths, including network coded paths, is identified. A subset of these paths with appropriate rates is chosen to maximize achievable throughput or throughput per sum of transmitting powers on the route, in a conflict free environment created by proper scheduling. Therefore this optimization provides two improvement gains, throughput gain from scheduling on a compressed network topology and the gain from mixed path selection optimizations with a careful compromise between the multicast diversity and network coding. Numerical results, as illustration, are presented for a simple wireless butterfly network. If just throughput regardless of power consumption is maximized, then depending on the system parameters both a network coded structure or a plain routing can be optimum. On the other hand if throughput per power is maximized, a mixed set of plain routing paths is the optimum solution.

In this paper, we investigate the optimum detection of MIMO signals in the presence of channel estimation errors. A maximum likelihood (ML)-based detection algorithm is proposed for optimal MIMO signal detection based on the modified ML criterion which takes into account channel estimation errors. Moreover, we evaluate for both the uncoded and coded case, the bit-error-rate (BER) performance of the hard decoded MIMO systems. The new algorithm, taking into account channel estimation errors, achieves improvement in terms of BER gain over the conventional MIMO detector algorithm ignoring channel estimation errors. Moreover, the optimal designed constellations taking channel estimation errors into account, show an increase in the capacity over conventional schemes.

In this paper, a joint detection algorithm is proposed for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channels. In MIMO OFDM channels, if enough cyclic prefix (CP) is used, intersymbol interference is completely cancelled and consequently spatial multiplexing interference (SMI) is the main limiting factor in performance. The proposed algorithm is based on a new soft parallel interference cancellation (SPIC) technique where, in each iteration, a part of SMI proportional to the probability of correctness of estimation in last iteration is cancelled. This probability is calculated approximately assuming the correct estimation for other sub-streams in last iteration. The proposed algorithm approaches the optimum performance in only a few iterations. The proposed algorithm is simulated for 4 states 1/2 turbo code and 8times12, 12times12 and 4times4 MIMO OFDM channels for different values of the Eb/N0. Bit error rate (BER) and frame error rate (FER) are considered as comparison criteria and it is shown that its performance is good and very close to interference free (lower bound) case.

Communication systems utilizing multiple transmit and receive antennas have attracted much attention in the recent years, because of their significantly increased capacities. Estimation of the multiple-input multiple-output (MIMO) fading channel is a major challenge for multiple antenna systems because the detection of information symbols depends critically on the availability of full or partial channel state information. Recently, there has been increasing interest in iterative channel estimation and data decoding, where data decision obtained from either hard or soft decoding is used as additional information to refine the channel estimates. This work addresses joint channel estimation and data recovery for a general system with multiple transmitting and multiple receiving antennas, impaired by multipath fading. A constant modulus algorithm (CMA) is used to find estimates of the channel and the transmitted data. To the best of our knowledge, this is a novel scheme on joint channel estimation and data detection which profits from a low complexity structure. Computer simulations are presented to confirm the robustness of the analysis and illustrate the acceptable performance of this new approach by considering its bit error rate (BER) and tracking behavior.

In this paper, Bussgang algorithm is developed as a blind equalizer for multi-carrier multi-input multi-output (MIMO) channels to combat both spatial multiplexing interference and channel variations. Also, the Bussgang technique is extended with adapting the one or two parameters in its nonlinear function. The Bussgang algorithm and its two extended version are simulated and compared to the full known channel (ideal) and decision directed cases. Frame error rate (FER) and bit error rate (BER) of both coded and uncoded cases, are considered as comparison criteria and it is shown that the much performance improvement is achieved compared to decision directed algorithm and each of the three proposed schemes presents the best performance in the different criteria and Eb/NO.

In this paper, the tracking performance analysis of the least squares (LS) multiple-input multiple-output (MIMO) channel estimation and tracking algorithm is presented. MIMO channel estimation is a novel application of the LS algorithm that presents near-optimum performance by Karami and Shiva in 2003 and 2006. In this paper, the mean square error (MSE) of tracking of the LS MIMO channel estimator algorithm is derived as a closed-form function of the Doppler shift, forgetting factor, channel rank, and the length of training sequences. In the analysis, all training symbols are considered as randomly generated equal-power vectors on the unit circle, or in other words, phase-shift keying (PSK) signaling. By evaluating this function, some insights into the tracking behavior of the LS MIMO channel estimator are achieved. Then, the calculated tracking error is compared with the tracking error derived from Monte Carlo simulation for quaternary-PSK-based training signals to verify the validation of the presented analysis. Finally, the optimum forgetting factor is derived to minimize the error function, and it is shown that the optimum forgetting factor is highly dependent on the training length, Doppler shift, and Eb/No. Also, it is concluded that in low Eb/Novalues, the number of transmitter antennas has negligible effect on the optimal value of the forgetting factor.

In this paper a new algorithm for detection of DS/CDMA signals is presented that by definition of an annealed probability for users' data estimated in each iteration a fraction of multiple access interference (MAI) proportional to this probability is cancelled from the output of the matched filters. This algorithm is simulated and compared with some of the well-known multi-user detectors such as minimum mean square error (MMSE), decorrelating, maximum likelihood (ML), and single user lower bound for randomly spread signals which is an efficient spreading model for long codes or code-hopping based DS/CDMA systems in under-load, full-load, and over-load conditions. It is shown that the proposed algorithm has optimum performance in under- load conditions and that it has only about 1 dB difference in full-load conditions. In all cases, the proposed algorithm outperforms the other well-known multi-user detectors. The proposed algorithm is simulated for large number of active users in full-load condition and its performance is compared with other suboptimum multi-user detectors, and single user lower bound. It is observed that the proposed algorithm greatly improves the performance and is very close to single user lower bound especially in the high SNR region. Finally, the proposed algorithm is also simulated for combating near-far problem. It is shown that the performance loss of the proposed algorithm as compared with the optimum detector is negligible

In this paper, an efficient method for multiuser detection of CDMA signal is proposed, that provides soft output with remaining noise distribution close to the Gaussian. Therefore the proposed algorithm is efficient for both coded and uncoded signals. This algorithm is simulated for BPSK signals with random spreading. The performance of the receiver is analyzed in terms of BER and spectral efficiency of each user for full load and overload conditions. Simulation results show that performance of the proposed algorithm is very close to the optimum receiver for both coded and uncoded user signals and also that it outperforms the previously proposed algorithms.