• Distributed Kalman filtering and control through embedded average consensus information fusion 

      Talebi, Sayed Pouria; Werner, Stefan (Journal article; Peer reviewed, 2019)
      This paper presents a unified framework for distributed filtering and control of state-space processes. To this end, a distributed Kalman filtering algorithm is developed via decomposition of the optimal centralized Kalman ...
    • Distributed Kalman filtering in presence of unknown outer network actuations 

      Talebi, Sayed Pouria; Werner, Stefan (Journal article; Peer reviewed, 2019)
      This letter presents a fully distributed approach for tracking state vector sequences over sensor networks in presence of unknown actuations. The problem arises in large-scale systems where modeling the full dynamics becomes ...
    • Distributed Kalman Filtering with Privacy against Honest-but-Curious Adversaries 

      Moradi, Ashkan; Dasanadoddi Venkategowda, Naveen Kumar; Talebi, Sayedpouria; Werner, Stefan (Chapter, 2021)
      This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the private information of individual network agents from being acquired by honest-but-curious (HBC) adversaries. The proposed approach ...
    • Distributed Kalman filtering: Consensus, diffusion, and mixed 

      Talebi, Sayed Pouria; Werner, Stefan (Chapter, 2018)
      A distributed Kalman filtering technique is developed for tracking state-space processes via sensor networks. Considering the optimal solution to multi-agent sequential filtering of linear Gaussian state-space processes, ...
    • Distributed Learning and Estimation with Enhanced Privacy and Security 

      Moradi, Ashkan (Doctoral theses at NTNU;2023:59, Doctoral thesis, 2023)
      This thesis focuses on threat analysis and management in distributed learning scenarios intending to develop algorithms to mitigate the impact of adversaries in the network. The thesis begins with a threat analysis that ...
    • Distributed Learning over Networks with Non-Smooth Regularizers and Feature Partitioning 

      Gratton, Cristiano; Kumar Dasanadoddi Venkategowda, Naveen; Arablouei, Reza; Werner, Stefan (Chapter, 2021)
      We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the ...
    • Distributed Learning with Non-Smooth Objective Functions 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2020)
      We develop a new distributed algorithm to solve a learning problem with non-smooth objective functions when data are distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented ...
    • Distributed quantile regression with non-convex sparse penalties 

      Mirzaeifard, Reza; Gogineni, Vinay Chakravarthi; Kumar Dasanadoddi Venkategowda, Naveen; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of ...
    • Distributed Ridge Regression with Feature Partitioning 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2019)
      We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of ...
    • Distributed source coding in sensor networks: A practical implementation 

      Petersen, Sigmund Seehuus (Master thesis, 2007)
      In this thesis we take a closer look at wireless sensor networks and source coding. A necessary condition for this work to have any meaning is that the sensors in the network are spatially co-located and that there is ...
    • Distributed Topology for Improved Thermal Performance in RFPA Design 

      Kristensen, Tobias (Master thesis, 2022)
      Denne oppgaven tar for seg hvordan en distribuert topologi kan benyttes for å redusere den termiske belastningen i effektforsterkere. Dette er en høyaktuell problemstilling i moderne radiosystem med redusert effektivitet ...
    • Distribution Based Spectrum Sensing in Cognitive Radio 

      Christiansen, Jørgen Berle (Master thesis, 2010)
      Blind spectrum sensing in cognitive radio is being addressed in this thesis. Particular emphasis is put on performance in the low signal to noise range. It is shown how methods relying on traditional sample based estimation ...
    • Diving-wave time-lapse delay for CO2 thin layer detection 

      Martinez Guzman, Ricardo Jose; Vinje, Vetle; Stovas, Alexei; Mispel, Joachim; Ringrose, Philip Stefan; Duffaut, Kenneth; Landrø, Martin (Peer reviewed; Journal article, 2024)
      We have derived an analytical approximate expression to estimate the delay in diving seismic waves due to thin layers of CO2. The expression is valid for high frequencies and can be used to estimate the delay in diving ...
    • DML in VIDEO-CONFERENCING APPLICATIONS 

      Giske, Mats Andreas (Master thesis, 2012)
      Today's audio in video-conference rooms do not in general have high quality audio standards.Most of the set-ups are PC-Speakers mounted on the wall, with a microphone on the table. With this, strong room modes are often ...
    • DNA walks in virus genomics 

      Belinsky, Alexandra; Kouzaev, Guennadi (Journal article; Peer reviewed, 2024)
      This paper studies published results in imaging and digital processing of virus RNAs (ribonucleic acid) using DNA (deoxyribonucleic acid) walks. The complicated nature and physicochemical properties of these nucleotide ...
    • A DNN Based Speech Enhancement Approach to Noise Robust Acoustic-to-Articulatory Inversion 

      Sabzi Shahrebabaki, Abdolreza; Siniscalchi, Sabato Marco; Salvi, Giampiero; Svendsen, Torbjørn Karl (Chapter, 2021)
      In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy condition within the deep neural network (DNN) framework. We claim that DNN vector-to-vector regression for ...
    • Do Students Reflect on Sustainability? Student Development of Competencies for Sustainability in Project-Based Learning 

      Bolstad, Torstein; Lundheim, Lars Magne; Orlandic, Milica; Strømberg, Anders; Zimmermann, Pauline Hardeberg (Peer reviewed; Journal article, 2023)
      Higher education plays a crucial role in supporting a society based on sustainable development through the facilitation of students’ acquisition of competencies for sustainable development. A suitable arena in which to ...
    • Documenting skin bruises with smartphone imaging 

      Zimmermann, Pauline Hardeberg (Master thesis, 2021)
      Målet med denne oppgaven er å utvikle og teste en standardisert metode for dokumentasjon av blåmerker. En slik metode kan bidra til å forbedre rettsmedisinsk bevis i rettsaker som omhandler vold og kan bistå helsepersonell ...
    • Doherty effektforsterker for 1,8 GHz 

      Hagen, Morten (Master thesis, 2007)
      Formålet med denne oppgaven er å designe en Dohertyforsterker. En klasse AB og en klasse C effektforsterker til bruk i denne konfigurasjonen skal designes, simuleres og gjøres målinger på hver for seg. Disse skal så å bli ...
    • Dopant incorporation in Al0.9Ga0.1As0.06Sb0.94 grown by molecular beam epitaxy 

      Patra, Saroj Kumar; Tran, Thanh-Nam; Vines, Lasse; Kolevatov, Ilia; Monakhov, Edouard; Fimland, Bjørn-Ove (Journal article, 2017)
      Incorporation of beryllium (Be) and tellurium (Te) dopants in epitaxially grown Al0.9Ga0.1As0.06Sb0.94 layers was investigated. Carrier concentrations and mobilities of the doped layers were obtained from room temperature ...