Energy-Efficient System Design for Future Wireless Communications

The exponential growth of wireless data traffic has caused a significant increase in the power consumption of wireless communications systems due to the higher complexity of the transceiver structures required to establish the communication links. For this reason, in this Thesis we propose and characterize technologies for improving the energy efficiency of multiple-antenna wireless communications. This Thesis firstly focuses on energy-efficient transmission schemes and commences by introducing a scheme for alleviating the power loss experienced by the Tomlinson-Harashima precoder, by aligning the interference of a number of users with the symbols to transmit. Subsequently, a strategy for improving the performance of space shift keying transmission via symbol pre-scaling is presented. This scheme re-formulates complex optimization problems via semidefinite relaxation to yield problem formulations that can be efficiently solved. In a similar line, this Thesis designs a signal detection scheme based on compressive sensing to improve the energy efficiency of spatial modulation systems in multiple access channels. The proposed technique relies on exploiting the particular structure and sparsity that spatial modulation systems inherently possess to enhance performance. This Thesis also presents research carried out with the aim of reducing the hardware complexity and associated power consumption of large scale multiple-antenna base stations. In this context, the employment of incomplete channel state information is proposed to achieve the above-mentioned objective in correlated communication channels. The candidate’s work developed in Bell Labs is also presented, where the feasibility of simplified hardware architectures for massive antenna systems is assessed with real channel measurements. Moreover, a strategy for reducing the hardware complexity of antenna selection schemes by simplifying the design of the switching procedure is also analyzed. Overall, extensive theoretical and simulation results support the improved energy efficiency and complexity of the proposed schemes, towards green wireless communications systems

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Advanced signal processing concepts for multi-dimensional communication systems

Die weit verbreitete Nutzung von mobilem Internet und intelligenten Anwendungen hat zu einem explosionsartigen Anstieg des mobilen Datenverkehrs geführt. Mit dem Aufstieg von intelligenten Häusern, intelligenten Gebäuden und intelligenten Städten wächst diese Nachfrage ständig, da zukünftige Kommunikationssysteme die Integration mehrerer Netzwerke erfordern, die verschiedene Sektoren, Domänen und Anwendungen bedienen, wie Multimedia, virtuelle oder erweiterte Realität, Machine-to-Machine (M2M) -Kommunikation / Internet of Things (IoT), Automobilanwendungen und vieles mehr. Daher werden die Kommunikationssysteme zukünftig nicht nur eine drahtlose Verbindung über Gbps bereitstellen müssen, sondern auch andere Anforderungen erfüllen müssen, wie z. B. eine niedrige Latenzzeit und eine massive Maschinentyp-Konnektivität, während die Dienstqualität sichergestellt wird. Ohne bedeutende technologische Fortschritte zur Erhöhung der Systemkapazität wird die bestehende Telekommunikationsinfrastruktur diese mehrdimensionalen Anforderungen nicht unterstützen können. Dies stellt eine wichtige Forderung nach geeigneten Wellenformen und Signalverarbeitungslösungen mit verbesserten spektralen Eigenschaften und erhöhter Flexibilität dar. Aus der Spektrumsperspektive werden zukünftige drahtlose Netzwerke erforderlich sein, um mehrere Funkbänder auszunutzen, wie zum Beispiel niedrigere Frequenzbänder (typischerweise mit Frequenzen unter 10 GHz), mm-Wellenbänder (einige hundert GHz höchstens) und THz-Bänder. Viele alternative Technologien wie Optical Wireless Communication (OWC), dynamische Funksysteme und zellulares Radar sollten ebenfalls untersucht werden, um ihr wahres Potenzial abzuschätzen. Insbesondere bietet OWC ein großes, aber noch nicht genutztes optisches Band im sichtbaren Spektrum, das Licht als Mittel zur Informationsübertragung nutzt. Daher können zukünftige Kommunikationssysteme als zusammengesetzte Hybridnetzwerke angesehen werden, die aus einer Anzahl von verschiedenen drahtlosen Netzwerken bestehen, die auf Funk und optischem Zugang basieren. Auf der anderen Seite ist es eine große Herausforderung, fortschrittliche Signalverarbeitungslösungen für mehrere Bereiche von Kommunikationssystemen zu entwickeln. Diese Arbeit trägt zu diesem Ziel bei, indem sie Methoden für die Suche nach effizienten algebraischen Lösungen für verschiedene Anwendungen der digitalen Mehrkanal-Signalverarbeitung demonstriert. Insbesondere tragen wir zu drei verschiedenen Anwendungsgebieten bei, d.h. Wellenformen, optischen drahtlosen Systemen und mehrdimensionaler Signalverarbeitung. Gegenwärtig ist das Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) die weit verbreitete Multitragetechnik für die meisten Kommunikationssysteme. Um jedoch die CP-OFDM-Nachteile in Bezug auf eine schlechte spektrale Eingrenzung, Robustheit in hoch asynchronen Umgebungen und Unflexibilität der Parameterwahl zu überwinden, wurden viele alternative Wellenformen vorgeschlagen. Solche Mehrfachträgerwellenformen umfassen einen Filter bank Multicarrier (FBMC), ein Generalized Frequency Division Multiplexing (GFDM), einen Universal Filter Multicarrier (UFMC) und ein Unique Word Orthogonal Orthogonal Frequency Division Multiplexing (UW-OFDM). Diese neuen Luftschnittstellenschemata verwenden verschiedene Ansätze, um einige der inhärenten Mängel bei CP-OFDM zu überwinden. Einige dieser Wellenformen wurden gut untersucht, während andere sich noch in den Kinderschuhen befinden. Insbesondere die Integration von Multiple-Input-Multiple-Output (MIMO) -Konzepten mit UW-OFDM und UFMC befindet sich noch in einem frühen Forschungsstadium. Daher schlagen wir im ersten Teil dieser Arbeit neuartige lineare und sukzessive Interferenzunterdrückungstechniken für MIMO UW-OFDM-Systeme vor. Das Design dieser Techniken zielt darauf ab, Empfänger mit einer geringen Rechenkomplexität zu erhalten. Ein weiterer Schwerpunkt ist die Anwendbarkeit von Space-Time Block Codes (STBCs) auf UW-OFDM und UFMC-Wellenformen. Zu diesem Zweck stellen wir neue Techniken zusammen mit Detektionsverfahren vor. Wir vergleichen auch die Leistung dieser Wellenformen mit unseren vorgeschlagenen Techniken mit den anderen Wellenformen des Standes der Technik, die in der Literatur vorgeschlagen wurden. Wir zeigen, dass raumzeitblockierte UW-OFDM-Systeme mit den vorgeschlagenen Methoden nicht nur andere Wellenformen signifikant übertreffen, sondern auch zu Empfängern mit geringer Rechnerkomplexität führen. Der zweite Anwendungsbereich umfasst optische Systeme im sichtbaren Band (390-700 nm), die in Plastic Optical Fibers (POFs), Multimode-Fasern oder OWC-Systemen wie der Kommunikation über Visible Light Communication (VLC) verwendet werden können. VLC kann Lösungen für eine Reihe von Anwendungen anbieten, einschließlich drahtloser lokaler, persönlicher und Körperbereichsnetzwerke (WLAN, WPAN und WBANs), Innenlokalisierung und -navigation, Fahrzeugnetze, U-Bahn- und Unterwassernetze und bietet eine Reihe von Datenraten von wenigen Mbps zu 10 Gbps. VLC nutzt voll sichtbare Light Emitting Diodes (LEDs) für den doppelten Zweck der Beleuchtung und Datenkommunikation bei sehr hohen Geschwindigkeiten. Daher verwenden solche Systeme Intensitätsmodulation und Direct Detection (IM / DD), wodurch gefordert wird, dass das Sendesignal reellwertig und positiv sein sollte. Dies impliziert auch, dass die herkömmlichen Wellenformen, die für die Radio Frequency (RF) Kommunikation ausgelegt sind, nicht direkt verwendet werden können. Zum Beispiel muss eine hermetische Symmetrie auf das CP-OFDM angewendet werden, um ein reellwertiges Signal zu erhalten (oft als Discrete Multitone Transmission (DMT) bezeichnet), das im Gegenzug die Bandbreiteneffizienz verringert. Darüber hinaus begrenzt die LED / LED-Treiberkombination die elektrische Bandbreite. Alle diese Faktoren erfordern die Verwendung spektral effizienter Übertragungsverfahren zusammen mit robusten Entzerrungsschemata, um hohe Datenraten zu erzielen. Deshalb schlagen wir im zweiten Teil der Arbeit Übertragungsverfahren vor, die für solche optischen Systeme am besten geeignet sind. Insbesondere demonstrieren wir die Leistung der PAM-Blockübertragung mit Frequenzbereichsausgleich. Wir zeigen, dass dieses Schema nicht nur leistungsstärker ist, sondern auch alle modernen Verfahren wie CP-DMT-Schemata übertrifft. Wir schlagen auch neue UW-DMT-Schemata vor, die vom UW-OFDM-Konzept abgeleitet sind. Diese Schemata zeigen auch ein sehr überlegenes Bitfehlerverhältnis (BER) -Performance gegenüber den herkömmlichen CP-DMT-Schemata. Der dritte Anwendungsbereich konzentriert sich auf mehrdimensionale Signalverarbeitungstechniken. Bei der Verwendung von MIMO, STBCs, Mehrbenutzerverarbeitung und Mehrträgerwellenformen bei der drahtlosen Kommunikation ist das empfangene Signal mehrdimensional und kann eine multilineare Struktur aufweisen. In diesem Zusammenhang können Signalverarbeitungstechniken, die auf einem Tensor-Modell basieren, gleichzeitig von mehreren Formen von Diversität profitieren, um Mehrbenutzer-Signaltrennung / -entzerrung und Kanalschätzung durchzuführen. Dieser Vorteil ist eine direkte Konsequenz der Eigenschaft der wesentlichen Eindeutigkeit, die für matrixbasierte Ansätze nicht verfügbar ist. Tensor-Zerlegung wie die Higher Order Singular Value Decomposition (HOSVD) und die Canonical Polyadic Decomposition (CPD) werden weithin zur Durchführung dieser Aufgaben empfohlen. Die Leistung dieser Techniken wird oft mit zeitraubenden Monte-Carlo-Versuchen bewertet. Im letzten Teil der Arbeit führen wir eine Störungsanalyse erster Ordnung dieser Tensor-Zerlegungsmethoden durch. Insbesondere führen wir eine analytische Performanceanalyse des Semi-algebraischen Frameworks für approximative Canonical polyadic decompositions Simultaneous matrix diagonalizations (SECSI) durch. Das SECSI-Framework ist ein effizientes Werkzeug zur Berechnung der CPD eines rauscharmen Tensor mit niedrigem Rang. Darüber hinaus werden die erhaltenen analytischen Ausdrücke in Bezug auf die Momente zweiter Ordnung des Rauschens formuliert, so dass abgesehen von einem Mittelwert von Null keine Annahmen über die Rauschstatistik erforderlich sind. Wir zeigen, dass die abgeleiteten analytischen Ergebnisse eine ausgezeichnete Übereinstimmung mit den Monte-Carlo-Simulationen zeigen.The widespread use of mobile internet and smart applications has led to an explosive growth in mobile data traffic. With the rise of smart homes, smart buildings, and smart cities, this demand is ever growing since future communication systems will require the integration of multiple networks serving diverse sectors, domains and applications, such as multimedia, virtual or augmented reality, machine-to-machine (M2M) communication / the Internet of things (IoT), automotive applications, and many more. Therefore, in the future, the communication systems will not only be required to provide Gbps wireless connectivity but also fulfil other requirements such as low latency and massive machine type connectivity while ensuring the quality of service. Without significant technological advances to increase the system capacity, the existing telecommunications infrastructure will be unable to support these multi-dimensional requirements. This poses an important demand for suitable waveforms with improved spectral characteristics and signal processing solutions with an increased flexibility. Moreover, future wireless networks will be required to exploit several frequency bands, such as lower frequency bands (typically with frequencies below 10 GHz), mm-wave bands (few hundred GHz at the most), and THz bands. Many alternative technologies such as optical wireless communication (OWC), dynamic radio systems, and cellular radar should also be investigated to assess their true potential. Especially, OWC offers large but yet unexploited optical band in the visible spectrum that uses light as a means to carry information. Therefore, future communication systems can be seen as composite hybrid networks that consist of a number of different wireless networks based on radio and optical access. On the other hand, it poses a significant challenge to come up with advanced signal processing solutions in multiple areas of communication systems. This thesis contributes to this goal by demonstrating methods for finding efficient algebraic solutions to various applications of multi-channel digital signal processing. In particular, we contribute to three different scientific fields, i.e., waveforms, optical wireless systems, and multi-dimensional signal processing. Currently, cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) is the widely adopted multicarrier technique for most of the communication systems. However, to overcome the CP-OFDM demerits in terms of poor spectral containment, poor robustness in highly asynchronous environments, and inflexibility of parameter choice, and many alternative waveforms have been proposed. Such multicarrier waveforms include filter bank multicarrier (FBMC), generalized frequency division multiplexing (GFDM), universal filter multicarrier (UFMC), and unique word orthogonal frequency division multiplexing (UW-OFDM). These new air interface schemes take different approaches to overcome some of the inherent deficiencies in CP-OFDM. Some of these waveforms have been well investigated while others are still in its infancy. Specifically, the integration of multiple-input multiple-output (MIMO) concepts with UW-OFDM and UFMC is still at an early stage of research. Therefore, in the first part of this thesis, we propose novel linear and successive interference cancellation techniques for MIMO UW-OFDM systems. The design of these techniques is aimed to result in receivers with a low computational complexity. Another focus area is the applicability of space-time block codes (STBCs) to UW-OFDM and UFMC waveforms. For this purpose, we present novel techniques along with detection procedures. We also compare the performance of these waveforms with our proposed techniques to the other state-of-the-art waveforms that has been proposed in the literature. We demonstrate that space-time block coded UW-OFDM systems with the proposed methods not only outperform other waveforms significantly but also results in receivers with a low computational complexity. The second application area comprises of optical systems in the visible band (390-700 nm) that can be utilized in plastic optical fibers (POFs), multimode fibers or OWC systems such as visible light communication (VLC). VLC can provide solutions for a number of applications including wireless local, personal, and body area networks (WLAN, WPAN, and WBANs), indoor localization and navigation, vehicular networks, underground and underwater networks, offering a range of data rates from a few Mbps to 10 Gbps. VLC takes full advantage of visible light emitting diodes (LEDs) for the dual purpose of illumination and data communications at very high speeds. Because of the incoherent nature of the LED sources, such systems employ intensity modulation and direct detection (IM/DD), thus demanding that the transmit signal should be real-valued and positive. This also implies that the conventional waveforms designed for the radio frequency (RF) communication cannot be directly used. For example, a Hermitian symmetry has to be applied to the CP-OFDM spectrum to obtain a real-valued signal (often referred to as discrete multitone transmission (DMT)) that in return reduces the bandwidth efficiency. Moreover, the LED/LED driver combination limits the electrical bandwidth. All these factors require the use of spectrally efficient transmission schemes along with robust equalization schemes to achieve high data rates. Therefore, in the second part of the thesis, we propose transmission schemes that are best suited for such optical systems. Specifically, we demonstrate the performance of PAM block transmission with frequency domain equalization. We show that this scheme is not only more power efficient but also outperforms all of the state-of-the-art schemes such as CP-DMT schemes. We also propose novel UW-DMT schemes that are derived from the UW-OFDM concept. These schemes also show a much superior bit error ratio (BER) performance over the conventional CP-DMT schemes. The third application area focuses on multi-dimensional signal processing techniques. With the use of MIMO, STBCs, multi-user processing, and multicarrier waveforms in wireless communications, the received signal is multidimensional in nature and may exhibit a multilinear structure. In this context, signal processing techniques based on a tensor model can simultaneously benefit from multiple forms of diversity to perform multi-user signal separation/equalization and channel estimation. This advantage is a direct consequence of the essential uniqueness property that is not available for matrix based approaches. Tensor decompositions such as the higher order singular value decomposition (HOSVD) and the canonical polyadic decomposition (CPD) are widely recommended for performing these tasks. The performance of these techniques is often evaluated using time consuming Monte-Carlo trials. In the last part of the thesis, we perform a first-order perturbation analysis of the truncated HOSVD and the Semi-algebraic framework for approximate Canonical polyadic decompositions via Simultaneous matrix diagonalizations (SECSI). The SECSI framework is an efficient tool for the computation of the approximate CPD of a low-rank noise corrupted tensor. Especially, the SECSI framework shows a much improved performance and comparatively low-complexity as compared to the conventional algorithms such as alternative least squares (ALS). Moreover, it also facilitates the implementation on a parallel hardware architecture. The obtained analytical expressions for both algorithms are formulated in terms of the second-order moments of the noise, such that apart from a zero-mean, no assumptions on the noise statistics are required. We demonstrate that the derived analytical results exhibit an excellent match to the Monte-Carlo simulations

Enabling Technologies for Internet of Things: Licensed and Unlicensed Techniques

The Internet of Things (IoT) is a novel paradigm which is shaping the evolution of the future Internet. According to the vision underlying the IoT, the next step in increasing the ubiquity of the Internet, after connecting people anytime and everywhere, is to connect inanimate objects. By providing objects with embedded communication capabilities and a common addressing scheme, a highly distributed and ubiquitous network of seamlessly connected heterogeneous devices is formed, which can be fully integrated into the current Internet and mobile networks, thus allowing for the development of new intelligent services available anytime, anywhere, by anyone and anything. Such a vision is also becoming known under the name of Machine-to-Machine (M2M), where the absence of human interaction in the system dynamics is further emphasized. A massive number of wireless devices will have the ability to connect to the Internat through the IoT framework. With the accelerating pace of marketing such framework, the new wireless communications standards are studying/proposing solutions to incorporate the services needed for the IoT. However, with an estimate of 30 billion connected devices, a lot of challenges are facing the current wireless technology. In our research, we address a variety of technology candidates for enabling such a massive framework. Mainly, we focus on the nderlay cognitive radio networks as the unlicensed candidate for IoT. On the other hand, we look into the current efforts done by the standardization bodies to accommodate the requirements of the IoT into the current cellular networks. Specifically, we survey the new features and the new user equipment categories added to the physical layer of the LTE-A. In particular, we study the performance of a dual-hop cognitive radio network sharing the spectrum of a primary network in an underlay fashion. In particular, the cognitive network consists of a source, a destination, and multiple nodes employed as amplify-and-forward relays. To improve the spectral efficiency, all relays are allowed to instantaneously transmit to the destination over the same frequency band. We present the optimal power allocation that maximizes the received signal-to-noise ratio (SNR) at the destination while satisfying the interference constrains of the primary network. The optimal power allocation is obtained through an eigen-solution of a channel-dependent matrix, and is shown to transform the transmission over the non-orthogonal relays into parallel channels. Furthermore, while the secondary destination is equipped with multiple antennas, we propose an antenna selection scheme to select the antenna with the highest SNR. To this end, we propose a clustering scheme to subgroup the available relays and use antenna selection at the receiver to extract the same diversity order. We show that random clustering causes the system to lose some of the available degrees of freedom. We provide analytical expression of the outage probability of the system for the random clustering and the proposed maximum-SNR clustering scheme with antenna selection. In addition, we adapt our design to increase the energy-efficiency of the overall network without significant loss in the data rate. In the second part of this thesis, we will look into the current efforts done by the standardization bodies to accommodate the equirements of the IoT into the current cellular networks. Specifically, we present the new features and the new user equipment categories added to the physical layer of the LTE-A. We study some of the challenges facing the LTE-A when dealing with Machine Type communications (MTC). Specifically, the MTC Physical Downlink control channel (MPDCCH) is among the newly introduced features in the LTE-A that carries the downlink control information (DCI) for MTC devices. Correctly decoding the PDCCH, mainly depends on the channel estimation used to compensate for the channel errors during transmission, and the choice of such technique will affect both the complexity and the performance of the user equipment. We propose and assess the performance of a simple channel estimation technique depends in essence on the Least Squares (LS) estimates of the pilots signal and linear interpolations for low-Doppler channels associated with the MTC application

Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin

Job shop scheduling with artificial immune systems

The job shop scheduling is complex due to the dynamic environment. When the information of the jobs and machines are pre-defined and no unexpected events occur, the job shop is static. However, the real scheduling environment is always dynamic due to the constantly changing information and different uncertainties. This study discusses this complex job shop scheduling environment, and applies the AIS theory and switching strategy that changes the sequencing approach to the dispatching approach by taking into account the system status to solve this problem. AIS is a biological inspired computational paradigm that simulates the mechanisms of the biological immune system. Therefore, AIS presents appealing features of immune system that make AIS unique from other evolutionary intelligent algorithm, such as self-learning, long-lasting memory, cross reactive response, discrimination of self from non-self, fault tolerance, and strong adaptability to the environment. These features of AIS are successfully used in this study to solve the job shop scheduling problem. When the job shop environment is static, sequencing approach based on the clonal selection theory and immune network theory of AIS is applied. This approach achieves great performance, especially for small size problems in terms of computation time. The feature of long-lasting memory is demonstrated to be able to accelerate the convergence rate of the algorithm and reduce the computation time. When some unexpected events occasionally arrive at the job shop and disrupt the static environment, an extended deterministic dendritic cell algorithm (DCA) based on the DCA theory of AIS is proposed to arrange the rescheduling process to balance the efficiency and stability of the system. When the disturbances continuously occur, such as the continuous jobs arrival, the sequencing approach is changed to the dispatching approach that involves the priority dispatching rules (PDRs). The immune network theory of AIS is applied to propose an idiotypic network model of PDRs to arrange the application of various dispatching rules. The experiments show that the proposed network model presents strong adaptability to the dynamic job shop scheduling environment.postprin