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  • 1.
    Kazemi, Kamran
    et al.
    Shiraz University of Technology.
    Ghadimi, Sona
    K.N. Toosi University of Technology.
    Lyaghat, Alireza
    Shiraz University of Technology.
    Tarighati, Alla
    Shiraz University of Technology.
    Golshaeyan, Narjes
    Shiraz University of Technology.
    Abrishami-Moghaddam, Hamid
    K.N. Toosi University of Technology.
    Grebe, Reinhard
    GRAMFC EA 4293, Faculté de Médecine, Université de Picardie Jules Verne.
    Gondary-Jouet, Catherine
    Department of Neuroradiology, Centre Hospitalier Universitaire d'Amiens.
    Wallois, Fabrice
    GRAMFC, EFSN Pediatrique, Centre Hospitalier Universitaire d'Amiens.
    Automatic Fontanel Extraction from Newborns' CT Images Using Variational Level set2009In: COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS / [ed] Jiang, X, Petkov, N, Springer Berlin/Heidelberg, 2009, p. 639-646Conference paper (Refereed)
    Abstract [en]

    A realistic head model is needed for source localization methods used for the study of epilepsy in neonates applying Electroencephalographic (EEG) measurements from the scalp. The earliest models consider the head as a series of concentric spheres, each layer corresponding to a different tissue whose conductivity is assumed to be homogeneous. The results of the source reconstruction depend highly on the electric conductivities of the tissues forming the head.The most used model is constituted of three layers (scalp, skull, and intracranial). Most of the major bones of the neonates’ skull are ossified at birth but can slightly move relative to each other. This is due to the sutures, fibrous membranes that at this stage of development connect the already ossified flat bones of the neurocranium. These weak parts of the neurocranium are called fontanels. Thus it is important to enter the exact geometry of fontaneles and flat bone in a source reconstruction because they show pronounced in conductivity. Computer Tomography (CT) imaging provides an excellent tool for non-invasive investigation of the skull which expresses itself in high contrast to all other tissues while the fontanels only can be identified as absence of bone, gaps in the skull formed by flat bone. Therefore, the aim of this paper is to extract the fontanels from CT images applying a variational level set method. We applied the proposed method to CT-images of five different subjects. The automatically extracted fontanels show good agreement with the manually extracted ones.

  • 2.
    Tarighati, Alla
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Decentralized Hypothesis Testing in Sensor Networks2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Wireless sensor networks (WSNs) play an important role in the future ofInternet of Things IoT systems, in which an entire physical infrastructurewill be coupled with communication and information technologies. Smartgrids, smart homes, and intelligent transportation systems are examples ofinfrastructure that will be connected with sensors for intelligent monitoringand management. Thus, sensing, information gathering, and efficientprocessing at the sensors are essential.

    An important problem in wireless sensor networks is that of decentralizeddetection. In a decentralized detection network, spatially separatedsensors make observations on the same phenomenon and send informationabout the state of the phenomenon towards a central processor. The centralprocessor (or the fusion center, FC) makes a decision about the state of thephenomenon, base on the aggregate received messages from the sensors. Inthe context of decentralized detection, the object is often to make the bestdecision at the FC. Since this decision is made based on the received messagesfrom the sensors, it is of interest to optimally design decision rules atthe remote sensors.

    This dissertation deals mainly with the problem of designing decisionrules at the remote sensors and at the FC, while the network is subjectto some limitation on the communication between nodes (sensors and theFC). The contributions of this dissertation can be divided into three (overlapping)parts. First, we consider the case where the network is subjectto communication rate constraint on the links connecting different nodes.Concretely, we propose an algorithm for the design of decision rules at thesensors and the FC in an arbitrary network in a person-by-person (PBP)methodology. We first introduce a network of two sensors, labeled as therestricted model. We then prove that the design of sensors’ decision rules,in the PBP methodology, is in an arbitrary network equivalent to designingthe sensors’ decision rules in the corresponding restricted model. We alsopropose an efficient algorithm for the design of the sensors’ decision rules inthe restricted model.

    Second, we consider the case where remote sensors share a commonmultiple access channel (MAC) to send their messages towards the FC, andwhere the MAC channel is subject to a sum rate constraint. In this situation,ithe sensors compete for communication rate to send their messages. Wefind sufficient conditions under which allocating equal rate to the sensors,so called rate balancing, is an optimal strategy. We study the structure ofthe optimal rate allocation in terms of the Chernoff information and theBhattacharyya distance.

    Third, we consider a decentralized detection network where not onlyare the links between nodes subject to some communication constraints,but the sensors are also subject to some energy constraints. In particular,we study the network under the assumption that the sensors are energyharvesting devices that acquire all the energy they need to transmit theirmessages from their surrounding environment. We formulate a decentralizeddetection problem with system costs due to the random behavior of theenergy available at the sensors in terms of the Bhattacharyya distance.

  • 3.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Farhadi, Hamed
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Lahouti, Farshad
    School of Electrical and Computer Engineering, University of Tehran.
    Performance Analysis of Noisy Message-Passing Decoding of Low-Density Parity-Check Codes2010In: 6th International Symposium on Turbo Codes & Iterative Informayion Processing, 2010Conference paper (Refereed)
    Abstract [en]

    A noisy message-passing decoding scheme isconsidered for low-density parity-check (LDPC) codes overadditive white Gaussian noise (AWGN) channels. The internaldecoder noise is motivated by the quantization noise in digitalimplementations or the intrinsic noise of analog LDPC decoders.We modelled the decoder noise as AWGN on the exchangedmessages in the iterative LDPC decoder. This is shown to renderthe message densities in the noisy LDPC decoder inconsistent.We then invoke Gaussian approximation and formulate a twodimensionaldensity evolution analysis for the noisy LDPCdecoder. This allows for not only tracking the mean, but also thevariance of the message densities, and hence, quantifying thethreshold of the LDPC code. According to the results, a decodernoise of unit variance, increases the threshold for a regular (3,6) code by 1.672dB.

  • 4.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Gross, James
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Distributed detection in energy harvesting wireless sensor networks2016In: European Signal Process. Conf. (EUSIPCO), Aug. 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
    Abstract [en]

    We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors arearranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy-unconstrained case.

  • 5.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Gross, James
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Decentralized Detection in Energy Harvesting Wireless Sensor Networks2016In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE conference proceedings, 2016, p. 567-571Conference paper (Refereed)
    Abstract [en]

    We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors are arranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy-unconstrained case.

  • 6.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    A General Method for the Design of Tree Networks Under Communication Constraints2014In: Information Fusion (FUSION), 2014 17th International Conference on, IEEE conference proceedings, 2014, , p. 7p. -7Conference paper (Refereed)
    Abstract [en]

    We consider a distributed detection system with communication constraints, where several nodes are arranged in an arbitrary tree topology, under the assumption of conditionally independent observations. We propose a cyclic design procedure using the minimum expected error probability as a design criterion while adopting a person-by-person methodology. We design each node jointly together with the fusion center, while other nodes are kept fixed, and show that the design of each node using the person-by-person methodology is analogous to the design of a network with two nodes, a network which we refer to as the restricted model. We further show how the parameters in the restricted model for the design of a node in the tree network can be found in a computationally efficient manner. The proposed numerical methodology can be applied for the design of nodes arranged in arbitrary tree topologies with arbitrary channel rates for the links between nodes and for a general M-ary hypothesis testing problem.

  • 7.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Bayesian Design of Decentralized Hypothesis Testing Under Communication Constraints2014In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, IEEE Signal Processing Society, 2014, p. -7628Conference paper (Refereed)
    Abstract [en]

    We consider a distributed detection system under communication constraints, where several peripheral nodes observe a common phenomenon and send their observations to a fusion center via error-free but rate-constrained channels. Using the minimum expected error probability as a design criterion, we propose a cyclic procedure for the design of the peripheral nodes using the person-by-person methodology. It is shown that a fine-grained binning idea together with a method for updating the conditional probabilities of the joint index space at the fusion center, decrease the complexity of the algorithm and make it tractable. Also, unlike previous methods which use dissimilarity measures (e.g., the Bhattacharyya distance), a-prior hypothesis probabilities are allowed to contribute to the design in the proposed method. The performance of the proposed method is comparedto a method due to Longo et al.’s and it is shown that the new method can significantly outperform the previous one at a comparable complexity.

  • 8.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Bayesian Design of Tandem Networks for Distributed Detection With Multi-bit Sensor Decisions2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 7, p. 1821-1831Article in journal (Refereed)
    Abstract [en]

    We consider the problem of decentralized hypothesis testing under communication constraints in a topology where several peripheral nodes are arranged in tandem. Each node receives an observation and transmits a message to its successor, and the last node then decides which hypothesis is true. We assume that the observations at different nodes are, conditioned on the true hypothesis, independent and the channel between any two successive nodes is considered error-free but rate-constrained. We propose a cyclic numerical design algorithm for the design of nodes using a person-by-person methodology with the minimum expected error probability as a design criterion, where the number of communicated messages is not necessarily equal to the number of hypotheses. The number of peripheral nodes in the proposed method is in principle arbitrary and the information rate constraints are satisfied by quantizing the input of each node. The performance of the proposed method for different information rate constraints, in a binary hypothesis test, is compared to the optimum rate-one solution due to Swaszek and a method proposed by Cover, and it is shown numerically that increasing the channel rate can significantly enhance the performance of the tandem network. Simulation results for $M$-ary hypothesis tests also show that by increasing the channel rates the performance of the tandem network significantly improves.

  • 9.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Optimality of Rate Balancing in Wireless Sensor Networks2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 14Article in journal (Refereed)
    Abstract [en]

    We consider the problem of distributed binary hypothesis testing in a parallel network topology where sensors independently observe some phenomenon and send a finite rate summary of their observations to a fusion center for the final decision. We explicitly consider a scenario under which (integer) rate messages are sent over an error free multiple access channel, modeled by a sum rate constraint at the fusion center. This problem was previously studied by Chamberland and Veeravalli, who provided sufficient conditions for the optimality of one bit sensor messages. Their result is however crucially dependent on the feasibility of having as many one bit sensors as the (integer) sum rate constraint of the multiple access channel, an assumption that can often not be satisfied in practice. This prompts us to consider the case of an a-priori limited number of sensors and we provide sufficient condition under which having no two sensors with rate difference more than one bit, so called rate balancing, is an optimal strategy with respect to the Bhattacharyya distance between the hypotheses at the input to the fusion center. We further discuss explicit observation models under which these sufficient conditions are satisfied.

  • 10.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rate Allocation for Decentralized Detection in Wireless Sensor Networks2015In: 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Stockholm, June 28 - July 1, 2015, IEEE conference proceedings, 2015, p. 341-345Conference paper (Refereed)
    Abstract [en]

    We consider the problem of decentralized detection where peripheral nodes make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center over a sum-rate constrained multiple access channel. The fusion center then makes a decision about the state of the phenomenon based on the aggregate received data. Using the Chernoff information as a performance metric, Chamberland and Veeravalli previously studied the structure of optimal rate allocation strategies for this scenario under the assumption of an unlimited number of sensors. Our key contribution is to extend these result to the case where there is a constraint on the maximum number of active sensors. In particular, we find sufficient conditions under which the uniform rate allocation is an optimal strategy, and then numerically verify that these conditions are satisfied for some relevant sensor design rules under a Gaussian observation model.

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