On data recovery with restraints on the spectrum range and the process range

The paper considers recovery of signals from incomplete observations and a problem of determination of the allowed quantity of missed observations, i.e. the problem of determination of the size of the uniqueness sets for a given data recovery procedures. The paper suggests a way to bypass solution of this uniqueness problem via imposing restrictions investigates possibility of data recovery for classes of finite sequences under a special discretization of the process range. It is shown that these sequences can be dense in the space of all sequences and that the uniqueness sets for them can be singletons. Some robustness with respect to rounding of input data can be achieved via including additional observations

Methods to reduce perturbation effects in compressive sampling

With compressive sampling (CS), few measurements or samples will be enough for signal reconstruction as long as the signal can be represented in a basis domain and the coefficients are sparse. Fortunately, many signals in nature can be expressed with sparse bases. However, there arise the CS problems of perturbations which can be broadly classified into additive and multiplicative. The additive perturbation such as additive white Gaussian noise (AWGN) inevitably incurs recovery noise in general, but can be more serious if CS is used. Signal power should be sufficiently large compared to the amount of the additive perturbation to apply CS. Simply increasing the signal power, however, may incur additional interference noise if there are multiple signal sources. Furthermore, another serious problems may arise when there exist multiplicative perturbations. Multiplicative perturbation may cause a mismatch between the assumed signal basis and that in the measurements, and as a result, signal-dependent noise is generated. Therefore, boosting of signal power will also increase the noise from the multiplicative perturbation. To use CS, the adverse effects from additive and multiplicative perturbations should be reduced. In this thesis, methods to alleviate the adverse effects from these perturbations are suggested. Firstly, diversified-CS (dCS) method is introduced as a remedy against the additive perturbation of CS. This method will cut down the noise of the recovered signal by extracting diversity gain from given measurements with virtual multiple branches of recovery. Diversity technique is commonly used in a wireless receiver to reduce noise by combining signals from multi-sensors. However, dCS method uses only a single sensor to extract the diversity gain by building virtual branches. This technique is also applied to the applications of spectrum sensing and spherical harmonics reconstruction to demonstrate the noise reduction. Furthermore, simulation results verify dCS method is effective in reducing the recovery noise. Secondly, an iterative basis refinement method is suggested for the reduction of the adverse effects from multiplicative perturbation. This method determines active bases (initially blindly), estimates the mismatch in the identified active bases, and adjusts the bases according to the perturbation. It is applied to the application of CS wireless receiver for the sparse signal acquisition and reconstruction, where the source of the multiplicative perturbation is the Doppler frequency offset introduced by a wireless fading channel. Simulation results corroborate the effectiveness of this algorithm in suppressing the adverse effects of multiplicative perturbations on signal recovery. Although the proposed methods in this thesis are mainly introduced to wireless signal applications, it has a potential to be used in other CS applications that suffer from additive and multiplicative perturbations

희소인지를 이용한 전송기술 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 심병효.The new wave of the technology revolution, named the fifth wireless systems, is changing our daily life dramatically. These days, unprecedented services and applications such as driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants are emerging. Communication mechanisms associated with these new applications and services are way different from traditional communications in terms of latency, energy efficiency, reliability, flexibility, and connection density. Since the current radio access mechanism cannot support these diverse services and applications, a new approach to deal with these relentless changes should be introduced. This compressed sensing (CS) paradigm is very attractive alternative to the conventional information processing operations including sampling, sensing, compression, estimation, and detection. To apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. In the last decade, CS techniques have spread rapidly in many applications such as medical imaging, machine learning, radar detection, seismology, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been studied. Notable examples include channel estimation, interference cancellation, angle estimation, spectrum sensing, and symbol detection. The distinct feature of this work, in contrast to the conventional approaches exploiting naturally acquired sparsity, is to exploit intentionally designed sparsity to improve the quality of the communication systems. In the first part of the dissertation, we study the mapping data information into the sparse signal in downlink systems. We propose an approach, called sparse vector coding (SVC), suited for the short packet transmission. In SVC, since the data information is mapped to the position of sparse vector, whole data packet can be decoded by idenitifying nonzero positions of the sparse vector. From our simulations, we show that the packet error rate of SVC outperforms the conventional channel coding schemes at the URLLC regime. Moreover, we discuss the SVC transmission for the massive MTC access by overlapping multiple SVC-based packets into the same resources. Using the spare vector overlapping and multiuser CS decoding scheme, SVC-based transmission provides robustness against the co-channel interference and also provide comparable performance than other non-orthogonal multiple access (NOMA) schemes. By using the fact that SVC only identifies the support of sparse vector, we extend the SVC transmission without pilot transmission, called pilot-less SVC. Instead of using the support, we further exploit the magnitude of sparse vector for delivering additional information. This scheme is referred to as enhanced SVC. The key idea behind the proposed E-SVC transmission scheme is to transform the small information into a sparse vector and map the side-information into a magnitude of the sparse vector. Metaphorically, E-SVC can be thought as a standing a few poles to the empty table. As long as the number of poles is small enough and the measurements contains enough information to find out the marked cell positions, accurate recovery of E-SVC packet can be guaranteed. In the second part of this dissertation, we turn our attention to make sparsification of the non-sparse signal, especially for the pilot transmission and channel estimation. Unlike the conventional scheme where the pilot signal is transmitted without modification, the pilot signals are sent after the beamforming in the proposed technique. This work is motivated by the observation that the pilot overhead must scale linearly with the number of taps in CIR vector and the number of transmit antennas so that the conventional pilot transmission is not an appropriate option for the IoT devices. Primary goal of the proposed scheme is to minimize the nonzero entries of a time-domain channel vector by the help of multiple antennas at the basestation. To do so, we apply the time-domain sparse precoding, where each precoded channel propagates via fewer tap than the original channel vector. The received channel vector of beamformed pilots can be jointly estimated by the sparse recovery algorithm.5세대 무선통신 시스템의 새로운 기술 혁신은 무인 차량 및 항공기, 스마트 도시 및 공장, 원격 의료 진단 및 수술, 인공 지능 기반 맟춤형 지원과 같은 전례 없는 서비스 및 응용프로그램으로 부상하고 있다. 이러한 새로운 애플리케이션 및 서비스와 관련된 통신 방식은 대기 시간, 에너지 효율성, 신뢰성, 유연성 및 연결 밀도 측면에서 기존 통신과 매우 다르다. 현재의 무선 액세스 방식을 비롯한 종래의 접근법은 이러한 요구 사항을 만족할 수 없기 때문에 최근에 sparse processing과 같은 새로운 접근 방법이 연구되고 있다. 이 새로운 접근 방법은 표본 추출, 감지, 압축, 평가 및 탐지를 포함한 기존의 정보 처리에 대한 효율적인 대체기술로 활용되고 있다. 지난 10년 동안 compressed sensing (CS)기법은 의료영상, 기계학습, 탐지, 컴퓨터 과학, 통계 및 기타 여러 분야에서 빠르게 확산되었다. 또한, 신호의 희소성(sparsity)를 이용하는 CS 기법은 다양한 무선 통신이 연구되었다. 주목할만한 예로는 채널 추정, 간섭 제거, 각도 추정, 및 스펙트럼 감지가 있으며 현재까지 연구는 주어진 신호가 가지고 있는 본래의 희소성에 주목하였으나 본 논문에서는 기존의 접근 방법과 달리 인위적으로 설계된 희소성을 이용하여 통신 시스템의 성능을 향상시키는 방법을 제안한다. 우선 본 논문은 다운링크 전송에서 희소 신호 매핑을 통한 데이터 전송 방법을 제안하며 짧은 패킷 (short packet) 전송에 적합한 CS 접근법을 활용하는 기술을 제안한다. 제안하는 기술인 희소벡터코딩 (sparse vector coding, SVC)은 데이터 정보가 인공적인 희소벡터의 nonzero element의 위치에 매핑하여 전송된 데이터 패킷은 희소벡터의 0이 아닌 위치를 식별함으로 원신호 복원이 가능하다. 분석과 시뮬레이션을 통해 제안하는 SVC 기법의 패킷 오류률은 ultra-reliable and low latency communications (URLLC) 서비스를 지원을 위해 사용되는 채널코딩방식보다 우수한 성능을 보여준다. 또한, 본 논문은 SVC기술을 다음의 세가지 영역으로 확장하였다. 첫째로, 여러 개의 SVC 기반 패킷을 동일한 자원에 겹치게 전송함으로 상향링크에서 대규모 전송을 지원하는 방법을 제안한다. 중첩된 희소벡터를 다중사용자 CS 디코딩 방식을 사용하여 채널 간섭에 강인한 성능을 제공하고 비직교 다중 접속 (NOMA) 방식과 유사한 성능을 제공한다. 둘째로, SVC 기술이 희소 벡터의 support만을 식별한다는 사실을 이용하여 파일럿 전송이 필요없는 pilotless-SVC 전송 방법을 제안한다. 채널 정보가 없는 경우에도 희소 벡터의 support의 크기는 채널의 크기에 비례하기 때문에 pilot없이 복원이 가능하다. 셋째로, 희소벡터의 support의 크기에 추가 정보를 전송함으로 복원 성능을 향상 시키는 enhanced SVC (E-SVC)를 제안한다. 제안된 E-SVC 전송 방식의 핵심 아디디어는 짧은 패킷을 전송되는 정보를 희소 벡터로 변환하고 정보 복원을 보조하는 추가 정보를 희소 벡터의 크기 (magnitude)로 매핑하는 것이다. 마지막으로, SVC 기술을 파일럿 전송에 활용하는 방법을 제안한다. 특히, 채널 추정을 위해 채널 임펄스 응답의 신호를 희소화하는 프리코딩 기법을 제안한다. 파일럿 신호을 프로코딩 없이 전송되는 기존의 방식과 달리, 제안된 기술에서는 파일럿 신호를 빔포밍하여 전송한다. 제안된 기법은 기지국에서 다중 안테나를 활용하여 채널 응답의 0이 아닌 요소를 최소화하는 시간 영역 희소 프리코딩을 적용하였다. 이를 통해 더 적확한 채널 추정을 가능하며 더 적은 파일럿 오버헤드로 채널 추정이 가능하다.Abstract i Contents iv List of Tables viii List of Figures ix 1 INTRODUCTION 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Three Key Services in 5G systems . . . . . . . . . . . . . . . 2 1.1.2 Sparse Processing in Wireless Communications . . . . . . . . 4 1.2 Contributions and Organization . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Sparse Vector Coding for Downlink Ultra-reliable and Low Latency Communications 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 URLLC Service Requirements . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Ultra-High Reliability . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 URLLC Physical Layer in 5G NR . . . . . . . . . . . . . . . . . . . 18 2.3.1 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Frame Structure and Latency-sensitive Scheduling Schemes . 20 2.3.3 Solutions to the Coexistence Problem . . . . . . . . . . . . . 22 2.4 Short-sized Packet in LTE-Advanced Downlink . . . . . . . . . . . . 24 2.5 Sparse Vector Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.1 SVC Encoding and Transmission . . . . . . . . . . . . . . . 25 2.5.2 SVC Decoding . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.3 Identification of False Alarm . . . . . . . . . . . . . . . . . . 33 2.6 SVC Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 36 2.7 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.1 Codebook Design . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.2 High-order Modulation . . . . . . . . . . . . . . . . . . . . . 49 2.7.3 Diversity Transmission . . . . . . . . . . . . . . . . . . . . . 50 2.7.4 SVC without Pilot . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.5 Threshold to Prevent False Alarm Event . . . . . . . . . . . . 51 2.8 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 52 2.8.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 52 2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3 Sparse Vector Coding for Uplink Massive Machine-type Communications 59 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2 Uplink NOMA transmission for mMTC . . . . . . . . . . . . . . . . 61 3.3 Sparse Vector Coding based NOMA for mMTC . . . . . . . . . . . . 63 3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.2 Joint Multiuser Decoding . . . . . . . . . . . . . . . . . . . . 66 3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 68 3.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 69 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4 Pilot-less Sparse Vector Coding for Short Packet Transmission 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Pilot-less Sparse Vector Coding Processing . . . . . . . . . . . . . . 75 4.2.1 SVC Processing with Pilot Symbols . . . . . . . . . . . . . . 75 4.2.2 Pilot-less SVC . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.2.3 PL-SVC Decoding in Multiple Basestation Antennas . . . . . 78 4.3 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 80 4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 81 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 Joint Analog and Quantized Feedback via Sparse Vector Coding 84 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 System Model for Joint Spase Vector Coding . . . . . . . . . . . . . 86 5.3 Sparse Recovery Algorithm and Performance Analysis . . . . . . . . 90 5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.4.1 Linear Interpolation of Sensing Information . . . . . . . . . . 96 5.4.2 Linear Combined Feedback . . . . . . . . . . . . . . . . . . 96 5.4.3 One-shot Packet Transmission . . . . . . . . . . . . . . . . . 96 5.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.2 Results and Discussions . . . . . . . . . . . . . . . . . . . . 98 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 6 Sparse Beamforming for Enhanced Mobile Broadband Communications 101 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.1.1 Increase the number of transmit antennas . . . . . . . . . . . 102 6.1.2 2D active antenna system (AAS) . . . . . . . . . . . . . . . . 103 6.1.3 3D channel environment . . . . . . . . . . . . . . . . . . . . 104 6.1.4 RS transmission for CSI acquisition . . . . . . . . . . . . . . 106 6.2 System Design and Standardization of FD-MIMO Systems . . . . . . 107 6.2.1 Deployment scenarios . . . . . . . . . . . . . . . . . . . . . 108 6.2.2 Antenna configurations . . . . . . . . . . . . . . . . . . . . . 108 6.2.3 TXRU architectures . . . . . . . . . . . . . . . . . . . . . . 109 6.2.4 New CSI-RS transmission strategy . . . . . . . . . . . . . . . 112 6.2.5 CSI feedback mechanisms for FD-MIMO systems . . . . . . 114 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.3.1 Basic System Model . . . . . . . . . . . . . . . . . . . . . . 116 6.3.2 Beamformed Pilot Transmission . . . . . . . . . . . . . . . . 117 6.4 Sparsification of Pilot Beamforming . . . . . . . . . . . . . . . . . . 118 6.4.1 Time-domain System Model without Pilot Beamforming . . . 119 6.4.2 Pilot Beamforming . . . . . . . . . . . . . . . . . . . . . . . 120 6.5 Channel Estimation of Beamformed Pilots . . . . . . . . . . . . . . . 124 6.5.1 Recovery using Multiple Measurement Vector . . . . . . . . . 124 6.5.2 MSE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 128 6.6 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 130 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7 Conclusion 136 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 139 Abstract (In Korean) 152Docto

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

OFDM Coupled Compressive Sensing Algorithm for Stepped Frequency Ground Penetrating Radar

Dating back to as far as 1940, the US road and bridge infrastructure system has garnered quite the status for strategically connecting together half a continent. As monumental as the infrastructure\u27s status, is its rate of deterioration, with the average bridge age coming at a disconcerting 50 years. Aside from visual inspection, a battery of non-destructive tests were developed to conduct structural fault assessment and detect laminations, in order to preemptively take preventive measures. The mainstream commercially favored test is the impulse time domain ground penetrating radar (GPR). An extremely short, high voltage pulse is used to visualize cross-sections of the bridge decks. While effective and it does not disturb traffic flow, impulse radar suffers from major drawbacks. The drawbacks are namely, its limited dynamic range and high cost of system manufacturing. A less prominent yet highly effective system, stepped frequency continuous wave (SFCW) GPR, was developed to address the aforementioned drawbacks. Mostly developed for research centers and academia, SFCW boasts a high dynamic range and low cost of system manufacturing, while producing comparable if not identical results to the impulse counterpart. However, data procurement speed is an inherent problem in SFCW GPR, which seems to keep impulse radar in the lead for production and development. I am proposing a novel approach to elevate SFCW\u27s data acquisition speed and its scanning efficiency altogether. This approach combines an encoding method called orthogonal frequency division multiplexing (OFDM) and an emerging paradigm called compressive sensing (CS). In OFDM, a digital data stream, the transmit signal, is encoded on multiple carrier frequencies. These frequencies are combined in such a way to achieve orthogonality between the carrier frequencies, while mitigating any interference between said frequencies. In CS, a signal can be potentially reconstructed from a few samples below the standardized Nyquist rate. A novel design of the SFCW GPR architecture coupled with the OFDM-CS algorithm is proposed and evaluated using ideal channels and realistically modelled bridge decks

Nowe metody przetwarzania losowo próbkowanych wielowymiarowych eksperymentów NMR

The topic of this dissertation is a new algorithm for processing of sparsely sampled data sets from multidimensional nuclear magnetic resonance (NMR) experiments. NMR remains one of the major experimental technique for studying biological macromolecules. However, increasing size of investigated objects poses a challenge for NMR due to rapidly decreasing sensitivity and increasing signal crowding. The first chapter focuses on recent advances in sensitivity enhancements and summarises a few solutions for resolution of spectral overlap. Subsequently, one describes the crucial and limiting problem of signal sampling in multidimensional NMR, which, up to recently, has impeded the widespread use of high-dimensional NMR methods. Major fast acquisition and non-uniform sampling (NUS) approaches are presented. The particular emphasis was put on detailed discussion of competetive approaches to processing of data from NUS experiments. In chapter 3 the new iterative algorithm is proposed for artefact suppression in high-resolution NMR spectra. The detailed description of its design and implementation is given, and followed by comparison with selected processing methods. The efficacy of the algorithm is demonstrated on model synthetic and experimental data. The last chapter of the thesis shows various applications of the proposed method to existing and new four- and five-dimensional NMR experiments. The algorithm is proven most beneficial in challenging applications including spectra for assignment of sidechain resonances in protein and nucleic acids, NOESY spectra for structural analysis, and cross-correlated relaxation measurements for proteins. // Niniejsza praca jest poświecona nowej metodzie przetwarzania danych pochodzących z oszczędnie próbkowanych wielowymiarowych eksperymentów jądrowego rezonansu magnetycznego (ang. Nuclear Magnetic Resonance, NMR). Technika ta jest, obok krystalografii rentgenowskiej, główną eksperymentalną metodą badawczą pozwalającą na określenie struktury i dynamiki makromolekuł o znaczeniu biologicznym. Jednakże NMR napotyka dwie istotne przeszkody w odniesieniu do dużych biomolekuł, a mianowicie gwałtownie pogarszającą się czułość oraz krytyczne zatłoczenie sygnałów w widmach. W rozdziale pierwszym przedstawiono ostatnie osiagnięcia w poprawie czułości technik NMR oraz rozwiązania służące podniesieniu rozdzielczości widm. Następnie opisano kluczowy problem próbkowania wielowymiarowych sygnałów NMR, który do niedawna uniemożliwiał wykorzystanie pełnego potencjału tych technik do rozdzielenia sygnałów. Omówiono pokrótce współczesne podejścia do szybkiej akwizycji i oszczędnego próbkowania sygnałów NMR (ang. non-uniform sampling, NUS). Szczególny nacisk położono na porównanie i dyskusje wad i zalet stosowanych obecnie metod przetwarzania sygnałów niejednorodnie próbkowanych. W rozdziale 3-cim opisano nowy iteracyjny algorytm oparty o transformacje Fouriera, usuwający artefakty oszczędnego próbkowania w wysokorozdzielczych widmach NMR. Szczegółowo omówiono schemat algorytmu oraz jego programową implementację. Rozdział uzupełnia porównanie wyników algorytmu oraz wybranych metod przetwarzania na wysymulowanych oraz modelowych danych eksperymentalnych. W ostatnim rozdziale pracy zademonstrowano użyteczność nowej metody do literaturowych oraz nowych cztero- i pieciowymiarowych eksperymentów NMR. Wśród proponowanych zastosowań wymienić można widma do przypisania sygnałów w łańcuchach bocznych aminokwasów (w białkach) i pierścieniach rybozy (w kwasach rybonukleinowych), widma NOESY służące określeniu struktury trójwymiarowej biomolekuł, oraz pomiary szybkości relaksacji skorelowanej w łańcuchach głównych białek