Search

With the development of synchrophasor measurement techniques, ambient signal-based load modeling is recently proposed to track the time-varying characteristics of power loads. On this basis, the hardware realization of this approach, which includes the local load modeling devices and the data center, is proposed and tested with the field measurement data from China Southern Power Grid. In this way, practical load model parameters can be identified from field measurement data both online and offline by the hardware system. To begin with, the hardware system, which is the first realization of an ambient signal-based load modeling algorithm, offers sufficient field measurements for load modeling. Then, the identification results of the field measurements verify the effectiveness of the ambient signal-based load modeling algorithm. Besides, the identified load model parameters for different substations during different times demonstrate the time-varying and the distributed characteristics of power loads. [ABSTRACT FROM AUTHOR]

A novel constant power charging strategy is proposed for LCC resonant capacitor charging power supply (CCPS) in this article, which combines the advantages of discontinuous current mode (DCM) and continuous current mode (CCM) to increase the charging speed of capacitor and the utilization of input grid capacity. In order to implement the multimode constant power control (CPC) strategy, a steady-state model of LCC series-parallel resonant converter is built by state plane analysis, and the analytical expressions for output voltage, average output current, and switching frequency are derived. By the analytical expressions, this control strategy directly determines the relationship of the output voltage and the switch frequency, so the frequency control curve of the pulse frequency modulation could be realized easily. Accordingly, the converter operating along the control curve can take advantage of high average and low peak current of CCM, small switching loss of DCM. Finally, the accuracy of LCC state plane model and the effectiveness of multimode CPC are verified by the simulation and a 2-kW CCPS experiment. [ABSTRACT FROM AUTHOR]

The block relocation problem (BRP) is a fundamental operational issue in modern warehouse and yard management, which, however, is very challenging to solve. In this article, to advance our understanding of this problem and to provide substantial assistance to practice, we adopt the following: 1) introduce a classification scheme and present a rather comprehensive review on all 16 BRP variants; 2) develop a general framework to derive lower bounds on the number of necessary relocations and demonstrate its connection to existing lower bounds on the unrestricted BRP variants; 3) propose and employ a couple of new critical substructure concepts to analyze the BRP and obtain a lower bound that dominates all existing ones; 4) build a new and strong mixed integer programming (MIP) formulation that is adaptable to compute eight BRP variants, and design a novel MIP-formulation-based iterative procedure to compute exact BRP solutions; and 5) extend the MIP formulation to address four typical industrial considerations. Computational results on standard and practical test instances show that the new lower bound is significantly stronger, and our new MIP computational methods have superior performances over the state-of-the-art formulation and a heuristic adopted in a steel plant. Note to Practitioners—This article was motivated by a practical operational problem which occurs in container yards, steel slab yards, and many other warehouses. In a container yard, containers are stored vertically in stacks. Cranes are employed to move containers and can access only the topmost ones. If the container to be retrieved is not at the top of a stack, containers above it have to be relocated to other stacks. As relocations are very time- and energy-consuming, minimizing the number of relocations involved in a retrieval process is a serious concern. Similar situations can also be found in steel slab yards. In this article, in addition to theoretical derivations, we propose a strong MIP formulation and an MIP-formulation-based iterative procedure to compute optimal relocation plans with the least number of relocations. Both methods have superior performances over state-of-the-art formulation. Moreover, we demonstrate that the MIP formulation can be flexibly customized to address additional industrial considerations, for example, stacking restrictions and the energy cost. Hence, we believe that the presented results are powerful management tools and can provide substantial decision-making support to practitioners. [ABSTRACT FROM AUTHOR]

The growing demand for high-performance ultra-high-definition video coding leads to H.265/high-efficiency video coding (HEVC), where the increased computational complexity and data/timing dependence hinder its coding throughput. To address these challenges, this paper presents four algorithm adaptations and a fully parallel hardware architecture for an H.265/HEVC intra encoder. To the best of our knowledge, this is the first fully parallel H.265/HEVC intra encoder. This design supports 35 prediction modes and all coding tree unit partitions. All PUs are independently processed in four prediction engines for high parallelism. An appropriate set of intra prediction modes, RDO candidates, and CABAC rate estimate instances is assigned to each prediction engine, where internal computational tasks are pipelined and scheduled to maximize the processing throughput. Compared with the HM-15.0 software, the proposed algorithm adaptations lead to a reduction of 27% in computational workload, while the average BD-rate and BD-PSNR are 4.39% and −0.21 dB, respectively. This BD-rate is lower than the existing designs with the same video resolution. FPGA implementation of the proposed design shows that it operates at 120 MHz and supports 45 fps of 1080P video sequences using 201-K logic elements and 120-KB on-chip SRAM. ASIC implementation of the proposed design in TSMC 90-nm technology shows that its clock frequency reaches 320 MHz with a hardware gate count of 2288 K, and that it supports real-time encoding of 30 fps of 4-K video sequences. Compared with the state-of-the-art designs, our proposed design demonstrates advantages in computational complexity, bit rate, video quality, throughput, reliability, and flexibility. [ABSTRACT FROM AUTHOR]

Considering the difficulty and high cost of collecting sufficient data in the real world, driving simulators are used in many studies as an alternative data source, which can provide a much easier and safer way to collect driving data. However, because of the inherent differences between the virtual and real world, the recognition model for driving behavior trained using simulation-based data cannot fit the real driving scenes well. To fill the gap between simulation and real data, a knowledge transfer framework is proposed in this paper. Two transfer learning (TL) methods namely semi-supervised manifold alignment (SMA) and kernel manifold alignment (KEMA) are used in the proposed framework to map the data collected from the virtual and real world to one latent common space. A typical lane-changing scenario is selected for a case study. Three classifiers are trained in the latent space and used to do the lane-changing behavior recognition in the real world. In this way, sufficient simulation data are transferred to supplement the training set with few labeled real data, and thus improve the performance of behavior recognition in the real world. Compared with the traditional methods without knowledge transfer, classifiers combined with TL can reduce the error rate of recognition from around 30% (when only the real data are used) or higher than 50% (when only the simulation data are used) to as low as 11%. [ABSTRACT FROM AUTHOR]

High computational complexity and heavy data/timing dependency in high efficiency video coding (HEVC) hinder the throughput of video encoding. In order to address both design challenges, we present high-performance algorithm adaptations and high-throughput hardware architecture for the HEVC intra encoders. Particularly, the chroma intra prediction includes the derived luma mode, which greatly improves the worst-case video compression performance. Compared with the HM-15.0 reference software, the proposed algorithms reduce the computational workload by 22%, while the average BD-Rate and BD-PSNR for classes A-F and 4K are 2.79% and −0.13 dB, respectively. This BD-Rate is lower than existing designs in the literature with the same video resolution. The field-programmable gate array (FPGA) implementation of proposed design shows it operates at 120 MHz and supports 45 f/s of 1080P video sequences using 196K logic elements and 127-KB on-chip SRAM. The application-specified integrated circuit (ASIC) implementation of the proposed design in TSMC 90 nm technology shows that its clock frequency reaches 320 MHz with a hardware gate count of 2186K and power consumption of 290 mW, and it supports real-time encoding of 4K videos at 30 f/s. [ABSTRACT FROM AUTHOR]

Learning-based methods have gained increasing attention in the intelligent vehicle community for developing highly autonomous vehicles and advanced driving assistance systems (ADAS). However, traditional offline learning methods lack the ability to adapt to individual driving behavior. To overcome this limitation, a combined learning framework (CLF) based on the Natural Actor Critic (NAC) learning and general regression neural network (GRNN) is developed in this paper. GRNN can be trained offline based on the historical data, while NAC is carried out online. In this way, the general behavior learned by the offline module can be reused and adjusted by the online module to capture the driver-specific behavior. Driving data collected from human drivers through a driving simulator are used to test the proposed learning framework. The complex overtaking behavior is selected to formulate the learning problem and test scenarios. Experimental results show that the proposed system performs well on learning driver-specific behavior for overtaking, and compared with the Gaussian mixture model-maximum-a-posterior method, CLF shows a more flexible performance when newly-involved drivers are considered. [ABSTRACT FROM AUTHOR]

For the in-wheel motor (IWM)-driven electric vehicle, the drive motor is directly integrated in the wheel. The magnet gap deformation of the motor can be generated due to road surface roughness (RSR) excitation, uneven load, and other reasons. The magnet gap deformation will lead to unbalanced magnetic force, which is a critical vibration source to the vehicle dynamics. Focusing on this problem, an IWM-driven electric vehicle without a speed reducer is considered as the research subject, and an 11-degree-of-freedom dynamics model is developed and verified to study the magnetic force influence on vehicle vertical and lateral coupling dynamics. The effect of magnetic force on the vehicle dynamics is analyzed under two operation conditions first. The results show that the magnetic force makes all dynamic response variables deteriorate in different degrees, regardless of the operation conditions, which indicates that the magnetic force has some negative influence on both the vertical and lateral vehicle dynamics. The results also show that even if the hub bearing stiffness is 5 MN/m, the maximum magnet gap deformation of the IWMs can still reach a big value. Therefore, the hub bearing stiffness should be designed to be stiff enough to conquer the magnet gap deformation under permitting conditions. In addition, extended study is performed with different motor speeds to further investigate the vehicle coupling dynamics. The results show that all the root-mean square (RMS) values of the evaluation indexes increase with the increase in motor speed. This result contributes to both RSR and magnetic force, since the motor speed is not only related to road excitation but also concerns magnetic force. For electric vehicles driven by IWMs, magnetic force must be considered as one of the important factors in the system design. This study can provide some theoretical basis for the design, optimization, and coordinated control of the IWM-driven electric vehicles. [ABSTRACT FROM PUBLISHER]

In this paper, the feasibility of a low-temperature polysilicon (LTPS) microscale energy harvester for a wireless sensor node is investigated. For that purpose, two device-level models for the LTPS solar cell and thin-film transistors are proposed and employed in system-level evaluation of an energy harvesting system. The results of our analysis indicate that: 1) the maximum power operating point for the solar cell is different when connected to a lossy power converter; 2) increasing the average grain size of the LTPS film can reduce the circuit area by 20 times, while increasing the output power by 6%; and 3) the proposed bottom–up approach enables the designers to identify system bottlenecks and improve the performance accordingly. [ABSTRACT FROM AUTHOR]

This paper describes an online short-term voltage stability assessment scheme from an overall view of the load area in the power system. In this scheme, data acquisitions are completed by post-contingency phasor measurements and a time series shapelet classification method is employed for classification learning. Combined with decision trees, this novel approach can not only hold a high performance of classification but also offer an acceptable interpretation of classification results. An improved algorithm to speed up shapelet searching is proposed, which makes it more practical. Semi-supervised cluster learning is also adopted in this scheme to mitigate the unreliability of the previous practical criteria. The test results on the Nordic test system demonstrate the effectiveness and reliability of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Dynamic modeling of a power system is fundamental to its safe operation and control. With the development of wide-area measurement system (WAMS) and low level probing tests, it is feasible to estimate a multiple-input multiple-output (MIMO) model of a power system without large disturbances. In practice, outputs during the low level probing tests consist of measurement noise, system noise and probing responses. Before applying a field experiment of MIMO modeling, noise influence to the system modeling as well as applicability of existed identification methods should be examined. In this paper, four kinds of subspace methods are carefully compared for their applicability in power systems' MIMO modeling with low level probing test. Results with different noise conditions explore the noise influence and the suitable methods for field experiment. Monte Carlo method is used for statistic analysis in a small system and conclusions are validated in a large complex system, China Southern Grid. [ABSTRACT FROM PUBLISHER]

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician. [ABSTRACT FROM AUTHOR]

Accurately recognizing braking intensity levels (BIL) of drivers is important for guaranteeing the safety and avoiding traffic accidents in intelligent transportation systems. In this article, an instance-level transfer learning framework is proposed to recognize BIL for a new driver with insufficient driving data by combining the Gaussian mixture model (GMM) and the importance-weighted least-squares probabilistic classifier (IWLSPC). By considering the statistic distribution, GMM is applied to cluster the data of braking behaviors into three levels with different intensities. With the density ratio calculated by unconstrained least-squares importance fitting, the least-squares probabilistic classifier is modified as IWLSPC to transfer the knowledge from one driver to another and recognize BIL for a new driver with insufficient driving data. Comparative experiments with nontransfer methods indicate that the proposed framework obtains a higher accuracy in recognizing BIL in the car-following scenario, especially when sufficient data are not available. [ABSTRACT FROM AUTHOR]

Reports on the development of a time-domain large-signal model to investigate nonlinear interactions between picosecond optical pulses and semiconductor active waveguide devices, including distributed-feedback and Fabry-Perot lasers as well as optical amplifiers. Influence of electrical carrier dynamics and feedback from a Bragg grating and/or reflecting facets on output pulses; Relation of time delay of reflected and transmitted pulses to Bragg grating parameters.

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

The approach of ambient signals-based load modeling (ASLM) was recently proposed to better track the time-varying changes of load models. To improve computation efficiency and model structure complexity, a hierarchical framework for ASLM is proposed in this paper. Through this framework, the hidden quasi-convexity of the load modeling problem is explored for the first time. This allows more complicated static load model structures and gradient or Hessian-based optimization algorithms to be used. In the upper level, the identification of dynamic load parameters is regarded as an optimization problem. In the lower level, the optimal static load parameters are obtained through linear regression for a given group of dynamic load parameters. Afterward, the regression residuals are regarded as the objective function (OF) of the upper level optimization problem. The proposed method is validated by the case study results on the Guangdong Power Grid. The results show that the OF is mostly quasi-convex after the transformation of the induction motor model, which provides the basis for the application of gradient or Hessian-based optimization algorithms. The case study results also validate that the proposed approach has better computation efficiency and model structure complexity compared with the previous ASLM approaches. [ABSTRACT FROM AUTHOR]

Due to advantages of handling problems with nonlinearity and uncertainty, Gaussian process regression (GPR) has been widely used in the area of driver behaviour modelling. However, traditional GPR lacks the ability of transferring knowledge from one driver to another, which limits the generalisation ability of GPR, especially when sufficient data for driver behaviour modelling are not available. To solve this limitation, in this paper, a novel GPR model, Importance Weighted Gaussian Process Regression (IWGPR) is proposed. The importance weight (IW) represents the probabilistic density ratio between two drivers and the unconstrained least-squares importance fitting (ULSIF) is applied to calculate IW. Meanwhile, an IW-based model selection (IWMS) method is proposed to help the model select optimal parameters. Using IWGPR, sufficient historical data collected from one driver can be used to model another driver with insufficient data, and thus improve the generalisation ability of GPR. To verify the proposed algorithm, a toy regression problem is used to illustrate the working mechanism of IWGPR. With simulated and naturalistic driving data, three experiments for driver behaviour modelling in the lane change scenario, are designed and carried out. Experimental results indicate that IWGPR performs better than GPR when sufficient data are not provided by the new driver, which proves the generalisation ability of IWGPR. Meanwhile, the comparative study between different transferable driver behaviour learning methods is detailed and analysed. [ABSTRACT FROM AUTHOR]

This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators’ fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions. [ABSTRACT FROM AUTHOR]

The papers in this special section examine modeling and simulation methods for the analysis and design of advanced energy conversion systems. Energy conversion lies aEnergyveral fronts including materials, conversion methods, power electronics, and controls have created new opportunities for efficient energy conversion from both the conventional and new sources of energy. Our urgent need to solve many critical problems with regards to the sustainability and security of our energy system as well as the ever-increasing environmental challenges facing humanity have further spurred unprecedented opportunities for creation of innovative solutions to address these challenges. [ABSTRACT FROM AUTHOR]

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian mixture model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on artificial neural networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios. [ABSTRACT FROM AUTHOR]

Usage of wide area PSSs (WAPSS) in actual power systems is limited because of their restricted adaptability to different operating conditions. Numerous adaptive control methods have been proposed to solve this problem, but they are limited due to strict prerequisites, such as the necessity of consistent excitations or large disturbances. This paper, therefore, presents a novel adaptive wide area PSS (AWAPSS), which has the ability of tracking system operating condition without the need for meeting the above prerequisites. The inherent dead-band structure of a WAPSS enables a steady system to perform like in an open loop, thus allowing the parameters of a WAPSS to be tuned adaptively through output-only modal analysis. The effectiveness of the novel AWAPSS has been validated in a two-area four-machine system as well as in a large complex system, China Southern Grid. [ABSTRACT FROM AUTHOR]

Flexible traction power supply system (FTPSS) can eliminate neutral sections of all catenaries and improve the poor power quality of railway substations. The static power converter (SPC) is the key equipment of FTPSS and its internal faults must be quickly identified and cleared with minimal impact on catenaries to ensure the safety and reliability. This article investigates the two-stage fault characteristics in FTPSS. Before blocking, overcurrent and voltage distortion may result in a wide range of abnormal power supply. After blocking, capacitor overvoltage becomes the primary problem threatening the safety of faulty SPC because of the energy feeding from other SPCs. Equivalent circuits are established and applied to various fault conditions. The most serious fault case can be found through the comparison, so the complicated large-scale simulation scanning can be avoided. A T-shaped grounding method is proposed to solve the problem of fault current and capacitor overvoltage. Based on the calculation and simulation results of several typical fault cases, the dimensioning criterion of grounding circuit is provided for reference. Moreover, a comprehensive scheme of grounding protection is designed to detect and isolate serious faults. The theoretical fault analysis and the proposed grounding method are verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

A large high-voltage direct current (HVdc) reactor is required in modular multilevel converter (MMC) projects to realize dc fault current limiting (FCL). This condition may result in adverse effects on MMC control dynamics and oscillation and instability of HVdc systems. In this article, a virtual reactor (VR) control strategy is proposed to reduce a fixed large HVdc reactor. The VR characteristic of MMCs is revealed and analyzed as a controlled reactor, and the inductance of this reactor can be directly controlled by the discharge degree of submodule capacitors. A virtual reactor fault current limiting (VRFCL) control strategy in which VR can effectively limit dc fault current and substitute the fixed HVdc reactor is proposed accordingly. Simulation and experimental results verify the proposed control strategy. The adoption of the proposed strategy can eliminate the undesirable effect caused by an HVdc reactor, and the sub-module (SM) capacitor voltage can be reserved well. In addition, fault clearance time and fault energy dissipation can be reduced. The proposed VRFCL control is beneficial for MMCs to safely ride through dc fault in a continuous operation manner. [ABSTRACT FROM AUTHOR]

Brillouin optical time domain analyzer (BOTDA) fiber sensors have shown strong capability in static long haul distributed temperature/strain sensing. However, in applications such as structural health monitoring and leakage detection, real-time measurement is quite necessary. The measurement time of temperature/strain in a BOTDA system includes data acquisition time and post-processing time. In this article, we propose to use hardware accelerated support vector regression (SVR) for the post-processing of the collected BOTDA data. Ideal Lorentzian curves under different temperatures with different linewidths are used to train the SVR model to determine the linear SVR decision function. The performances of SVR are evaluated under different signal-to-noise ratios (SNRs) experimentally. After the model coefficients are determined, algorithm-specific hardware accelerators based on field-programmable gate arrays (FPGAs) are used to realize SVR decision function. During the implementation, hardware optimization techniques based on loop dependence analysis and batch processing are proposed to reduce the execution latency. Our FPGA implementations can achieve up to $42\times $ speedup compared with software implementation on an i7- $5960x$ computer. The post-processing time for 96 100 Brillouin gain spectrums (BGSs) along with 38.44-km fiber under test (FUT) is only 0.46 s with FPGA board ZCU104, making the post-processing time no longer a limiting factor for dynamic sensing. Moreover, the energy efficiency of our FPGA implementation can reach up to $226.1\times $ higher than the software implementation based on CPU. [ABSTRACT FROM AUTHOR]

In a multithreaded execution environment, race condition leads to computational errors and system hazards. Up to date, a series of task scheduling strategies have been presented in the literature to reduce the risk of race condition. Because of the increasing complexity of this problem in multi-core systems, existing task scheduling approaches are not very efficient. To deal with this challenge, in this work, we develop a race-condition-aware and hardware-oriented task partitioning and scheduling algorithm using entropy maximization model. We model uncertainty as a probabilistic occurrence within a time interval, and hence the characteristics of event ordering are analyzed through an uncertainty matrix. Next, a metric is developed to measure the uncertainty of task execution in various execution environments. Finally, a maximum entropy model is generated to ensure the lowest probability of race condition during task execution. The smallest one among maximum entropy values is chosen and used in our proposed task scheduling algorithm. Experimental results show that the proposed task scheduling strategy based on our maximum entropy model outperforms existing state-of-the-art approaches. For example, in a 128-core computing system, the task execution time, CPU utilization ratio, and throughput of our proposed task scheduling is improved by [ABSTRACT FROM AUTHOR]

The use of advanced modulation formats helps to reduce the number of components required for short reach optical communication modules at 100 Gb/s and beyond. Unlike long haul optical communication systems where externally modulated high order modulation formats with coherent detection receivers are used, direct modulated lasers with direct detection are likely to be employed for short reach system implementation. Here we discuss the performance of pulse amplitude modulation, carrierless amplitude/phase modulation, and direct detection orthogonal frequency- division multiplexing for short reach optical communication systems. Their possible use for future high-capacity short reach optical communication systems is explored. [ABSTRACT FROM PUBLISHER]