SUMMARY: Reconstructing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is fundamental for understanding cellular dynamics at the molecular level but requires sophisticated workflows. Here, we introduce CellCraft, a web-based application designed to streamline GRN inference. CellCraft integrates multiple GRN reconstruction tools, including TENET, within a unified web application featuring an intuitive graphical user interface. Notably, CellCraft provides a visual programming interface that simplifies the design and execution of complex multistep analyses, thereby enhancing accessibility and facilitating the visualization and interpretation of computational experiments. Furthermore, its modular plugin architecture ensures extensibility, enabling the incorporation of newly developed single-cell analysis algorithms. Consequently, CellCraft provides a user-friendly and extensible application for integrative GRN analysis of scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: CellCraft is available on GitHub at https://github.com/cxinsys/cellcraft. The source code has been archived on Zenodo at 10.5281/zenodo.17865848.

SUMMARY: SCODE reconstructs gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data using an ordinary differential equation (ODE) model, and has been successfully applied to a wide range of scRNA-seq datasets, including mouse, human, and plant cells. However, its computational performance is limited when processing large datasets due to its sequential execution flow and repeated optimization loops. To overcome this limitation, we have developed FastSCODE, a batch computing version of the SCODE algorithm optimized for acceleration on manycore processors such as GPUs. FastSCODE performs batch computation on multiple gene expression profiles and optimizes the parameters of a linear ODE model using manycore computing. Compared to the original implementation, FastSCODE achieves up to 6000× improvement in performance (from about one month to 10 min) on the CeNGEN scRNA-seq dataset when using multiple GPUs. AVAILABILITY AND IMPLEMENTATION: FastSCODE is publicly available on GitHub at https://github.com/cxinsys/fastscode.

Frost accumulation on refrigerator evaporator coils is a significant source of wasted energy. While automatic de-frosting is a standard feature on modern refrigerators, current commercial solutions use heuristics to determine the frequency of heating cycles, leading to a sub-optimal defrosting routine. The majority of previous defrosting research incorporates cameras or microwave technology to better inform defrost algorithms of frost accumulation, however, these methods are both financially and computationally expensive. In this paper, we propose a low-cost frost detection system using ultrasonic resonance of piezoelectric sensors. We addressed the financial and computational cost challenges by using low-cost sensors and basic circuit components to replace software complexity. Our frost detection system was evaluated extensively in a Samsung refrigerator, resulting in a frost detection accuracy of 99.7%. We believe our solution can be further used for downstream refrigerator control cycle optimizations to achieve improved energy efficiency.

SUMMARY: TENET reconstructs gene regulatory networks from single-cell RNA sequencing (scRNAseq) data using the transfer entropy (TE), and works successfully on a variety of scRNAseq data. However, TENET is limited by its long computation time for large datasets. To address this limitation, we propose FastTENET, an array-computing version of TENET algorithm optimized for acceleration on manycore processors such as GPUs. FastTENET counts the unique patterns of joint events to compute the TE based on array computing. Compared to TENET, FastTENET achieves up to 973× performance improvement. AVAILABILITY AND IMPLEMENTATION: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet.

Robot end effectors with proximity detection and contact sensing capabilities can reactively position the gripper to align objects and ensure successful grasps. In this paper, we introduce SonicFinger, an acoustic aura based sensing system capable of full-surface pre-touch and contact sensing. A single piezoelectric transducer embedded within a novel 3D printed finger is excited using a monotone to create an acoustic aura encompassing the finger; this enables pre-touch sensing and gripper alignment, while changes in finger-transducer acoustic coupling indicate contact. SonicFinger is low-cost, compact, and easy to manufacture and assemble. Sensing capabilities are evaluated using a set of objects with various physical properties such as optical reflectivity, dielectric constants, mechanical properties, and acoustic absorption. A dataset with over 8,000 proximity and contact events is collected. Our system shows a pre-touch detection true positive rate (TPR) of 92.4% and a true negative rate (TNR) of 95.3%. Contact detection experiments show a TPR of 93.7% and a TNR of 98.7%. Furthermore, pretouch detection information from Sonic Finger is used to adjust the robot grippers pose to align a target object at the center of both fingers.

Contact sensing and localization capabilities that mimic human skin are highly desirable for robots. In this paper, we introduce AcouSkin, an acoustic wave based full surface contact localization system. Acoustic waves produced by piezoelectric transceivers using a monotone are coupled to surfaces turning them into an active sensor. Our system leverages information from four piezoelectric transceivers mounted on the surface of an acrylic sheet and vacuum cleaner robot bumper to localize contacts to 18 unique segments. We first characterize acoustic wave propagation based on signal and material properties and then propose hardware and software methods to realize full surface contact localization. Our results show that AcouSkin can reliably localize contact on a flat acrylic sheet with 18 uniformly spaced locations across a 54cm length with mean absolute error (MAE) of ≤ 1 locations using maximum likelihood estimator (MLE) and multilayer perceptron (MLP) models. On the vacuum cleaner robot bumper AcouSkin shows a zero MAE. Further, the system is also able to localize contacts made using forces as low as 2N (Newtons) and as high as 20N. Overall, AcouSkin provides full surface contact localization while requiring minimal instrumentation with easy deployment on real-world robots.

In this paper, we present AmbiSense, an acoustic field based sensing system that performs proximity detection and bearing estimation for safer physical human-robot interactions. A single low cost piezoelectric transducer is used to setup this novel acoustic sensing modality to create a blindspot-free sound field engulfing a robot arm. Two detection algorithms leveraging spectral information from reflected audio waves of objects entering the acoustic field are proposed to infer object presence and bearing. We also present a new receiver structure which improves signal to noise ratio (SNR). AmbiSense is paired with a collision avoidance inverse kinematic solver for real world deployment on a Kinova Gen3 robot. Validation is performed using ten test objects generating 2000 proximity and bearing estimation events in real world settings, we show that AmbiSense detects proximity with 93.8% sensitivity and 96.6 % specificity. It estimates bearing and maps it to three zones on a robot link with 100% sensitivity and specificity, while using fewer sensors than state of the art methods for similar coverage.

SUMMARY: Biological pattern formation is one of the complex system phenomena in nature, requiring theoretical analysis based on mathematical modeling and computer simulations for in-depth understanding. We propose a Python framework named LPF to systematically explore the highly diverse wing color patterns of ladybirds using reaction-diffusion models. LPF supports GPU-accelerated array computing for numerical analysis of partial differential equation models, concise visualization of ladybird morphs, and evolutionary algorithms for searching mathematical models with deep learning models for computer vision. AVAILABILITY AND IMPLEMENTATION: LPF is available on GitHub at https://github.com/cxinsys/lpf.

Energy consumption is a critical concern for the 5G mobile operators. 5G NR network may have increased energy consumption compared to its predecessors as it aims to support a plethora of services demanding diverse QoS constraints and requiring deployment of radios at different frequency layers with potentially wider bandwidth and more antennas. Moreover, it is important for the operators to keep the operational expenses and carbon emissions as low as possible as well. At 3GPP, UE power saving has mainly been the focus from Rel 15 till Rel 17 and in Rel 18, a new study item has been introduced to investigate different network power saving techniques. In this paper, we first discuss why 5G NR poses unique challenges for energy consumption with respect to its predecessors. We aim to explore different potential techniques for network power savings via resource adaptation in time, frequency, spatial, and power domain. We also investigate power consumption based on energy consumption model adopted by 3GPP and provide energy efficiency - throughput trade-off analysis to identify which domain has the highest potential for power savings.

Millimeter wave (mmWave) systems beyond 52.6 GHz carrier frequency are considered as one of the ways to notably improve the data rate of fifth generation New Radio (5G NR) systems. A large amount of spectrum available in such frequency bands can address the capacity demands of the current and future cellular systems. However, the performance of the practical devices operating in the mmWave bands is degraded by phase noise (PN) due to the impairments of the transceiver local oscillators. To address this issue, phase tracking reference signals (PT-RS) are introduced in 5G NR system to mitigate the PN impact at the receiver side. In this paper, we analyze the performance of the conventional PN tracking algorithms with discrete-Fourier-transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) waveform and demonstrate significant performance losses of 5G NR uplink transmission in the presence of timing synchronization errors and PN. A novel PN compensation algorithm and a new PT-RS structure robust to timing errors is proposed. The PN tracking technique and PT-RS enhancements presented in the paper can be efficiently realized in 5G NR system.

Humans have the ability to pour various media, both liquid and granular, to desired ends in various containers. We do this by using multiple senses simultaneously in a constant feedback loop to complete a pouring task. Combining multiple sensing modalities, similar to humans, could aid in robotic pouring control outside of a structured or industrial setting. We present a multi-sensory pouring dataset consisting of human pouring demonstrations of various granular media, coupled with two multi-sensory networks that estimate pouring rate and pouring average height. For both pouring metrics, a combined input of audio and visual data provides a lower median error than either the audio network or visual network. The multi-sensory network achieves a median error of 6.4 mm for average height estimation and 0.06 N/s for pouring rate estimation.

As service robots begin to be deployed to assist humans, it is important for them to be able to perform a skill as ubiquitous as pouring. Specifically, we focus on the task of pouring an exact amount of water without any environmental instrumentation, that is, using only the robot's own sensors to perform this task in a general way robustly. In our approach we use a simple PID controller which uses the measured change in weight of the held container to supervise the pour. Unlike previous methods which use specialized force-torque sensors at the robot wrist, we use our robot joint torque sensors and investigate the added benefit of tactile sensors at the fingertips. We train three estimators from data which regress the poured weight out of the source container and show that we can accurately pour within 10 ml of the target on average while being robust enough to pour at novel locations and with different grasps on the source container.

SUMMARY: High-quality visualization of biological networks often requires both manual curation for proper alignment and programming to map external data to the graphical components. Nezzle is a network visualization software written in Python, which provides programmable and interactive interfaces for facilitating both manual and automatic curation of the graphical components of networks to create high-quality figures. AVAILABILITY AND IMPLEMENTATION: Nezzle is an open-source project under MIT license and is available from https://github.com/dwgoon/nezzle. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this work, we focus on mobile robot navigation in indoor environments where occlusions and field-of-view limitations hinder onboard sensing capabilities. We show that the footprint of a camera mounted on a robot can be drastically improved using learning-based approaches. Specifically, we consider the task of building an occupancy map for autonomous navigation of a robot equipped with a depth camera. In our approach, a local occupancy map is first computed using measurements from the camera directly. Afterwards, an inpainting network adds further information, the occupancy probabilities of unseen grid cells, to the map. A novel aspect of our approach is that rather than direct supervision from ground truth, we combine the information from a second camera with a better field-of-view for supervision. The training focuses on predicting extensions of the sensed data. To test the effectiveness of our approach, we use a robot setup with a single camera placed at 0.5m above the ground. We compare the navigation performance using raw maps from only this camera’s input (baseline) versus using inpainted maps augmented with our network. Our method outperforms the baseline approach even in completely new environments not included in the training set and can yield 21% shorter paths than the baseline approach. A real-time implementation of our method on a mobile robot is also tested in home and office environments.

Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

We consider the problem of planning views for a robot to acquire images of an object for visual inspection and reconstruction. In contrast to offline methods which require a 3D model of the object as input or online methods which rely on only local measurements, our method uses a neural network which encodes shape information for a large number of objects. We build on recent deep learning methods capable of generating a complete 3D reconstruction of an object from a single image. Specifically, in this work, we extend a recent method which uses Higher Order Functions (HOF) to represent the shape of the object. We present a new generalization of this method to incorporate multiple images as input and establish a connection between visibility and reconstruction quality. This relationship forms the foundation of our view planning method where we compute viewpoints to visually cover the output of the multiview HOF network with as few images as possible. Experiments indicate that our method provides a good compromise between online and offline methods: Similar to online methods, our method does not require the true object model as input. In terms of number of views, it is much more efficient. In most cases, its performance is comparable to the optimal offline case even on object classes the network has not been trained on.

Digital audio, as well as image, is one of the most popular media for information hiding. However, even the state-of-the-art deep learning model still has a limitation for detecting basic LSB steganography algorithms that hide secret messages in time domain of WAV audio. To advance audio steganalysis based on deep learning, deep audio steganalysis, in time domain of lossless audio format, we have developed a convolutional neural network that incorporates bit-plane separation, weight-standardized convolution, and channel attention. Training through payload curriculum learning and testing for six steganography methods demonstrated that our proposed model is superior to the other two deep learning models, achieving state-of-the-art performance. We expect our approach will provide insights to create a breakthrough for deep audio steganalysis.

Collision detection is critical for safe robot operation in the presence of humans. Acoustic information originating from collisions between robots and objects provides opportunities for fast collision detection and localization; however, audio information from microphones on robot manipulators needs to be robustly differentiated from motors and external noise sources. In this paper, we present Panotti, the first system to efficiently detect and localize on-robot collisions using low-cost microphones. We present a novel algorithm that can localize the source of a collision with centimeter level accuracy and is also able to reject false detections using a robust spectral filtering scheme. Our method is scalable, easy to deploy, and enables safe and efficient control for robot manipulator applications. We implement and demonstrate a prototype that consists of 8 miniature microphones on a 7 degree of freedom (DOF) manipulator to validate our design. Extensive experiments show that Panotti realizes near perfect on-robot true positive collision detection rate with almost zero false detections even in high noise environments. In terms of accuracy, it achieves an average localization error of less than 3.8 cm under various experimental settings.

We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate “expert” training trajectories from a small amount of human-labeled data. In contrast to the traditional sense-plan-act cycle, we propose a deep learning architecture and training regimen called PtPNet that can estimate effective end-effector trajectories for manipulation directly from a single RGB-D image of an object. Additionally, we present a data collection and augmentation pipeline that enables the automatic generation of large numbers (millions) of training image and trajectory examples with almost no human labeling effort.We demonstrate our approach in a non-prehensile tool-based manipulation task, specifically picking up shoes with a hook. In hardware experiments, PtPNet generates motion plans (open-loop trajectories) that reliably (89% success over 189 trials) pick up four very different shoes from a range of positions and orientations, and reliably picks up a shoe it has never seen before. Compared with a traditional sense-plan-act paradigm, our system has the advantages of operating on sparse information (single RGB-D frame), producing high-quality trajectories much faster than the expert planner (300ms versus several seconds), and generalizing effectively to previously unseen shoes. Video available at https://youtu.be/voIkyiBtwn4.

We propose a transductive Gaussian process (TGP) regression method with regularized Laplacian kernels. Transductive learning exploits not only the labeled data but also the unlabeled test instances for learning. GPs are Bayesian probabilistic regressors which use only labeled data. To use unlabeled data in GPs, regularized Laplacian kernels are used. Similar to the case of a supervised GP regression, the proposed method provides not only the predicted target values but also their error bars. It also provides a hyperparameter selection method based on a Bayesian model selection scheme. We applied the proposed TGP method to the object pose estimation data sets as well as artificial data sets and compared the existing methods. Experimental results show that the proposed method has some advantages over the existing methods.

Multi-cell coordinated beamforming (CB) can mitigate inter-cell interference. However, previous study on CB focuses on systems with only one receive antenna. This paper considers CB for systems with multiple receive antennas. To take fairness among scheduled users into account, CB is designed to maximize the harmonic sum of signal-to-interference-plus-noise ratio (SINR). We develop an iterative algorithm that can guarantee convergence. Simulation shows that the proposed algorithm have 70% and 47% throughput gains over single cell beamforming for 10th percentile user throughput and median user throughput, respectively.

In LTE uplink systems, single-carrier frequency-division multiple access (SC-FDMA) has been employed. In SC-FDMA, orthogonal frequency resources are assigned to different users to avoid intra-cell interference. However, inter-cell interference (ICI) caused by the users in neighboring cells significantly deteriorates the performance. Cooperation among base stations must be used to deal with ICI for multi-cell systems. In this paper, we investigate multi-cell scheduling in SC-FDMA for LTE uplink. We propose a novel cooperative scheduling algorithm that takes inter-cluster and intra-cluster interference into account. We first perform coordinated scheduling and then link adaptation to select modulation and coding scheme (MCS). Simulation results show that the proposed algorithm has significant gains over the single-cell proportional fair (PF) scheduling algorithm on both cell-edge and average throughput. It also outperforms the existing cooperative algorithm under full path loss compensation and fractional open-loop power control (OLPC).

MOTIVATION: Computational multiscale models help cancer biologists to study the spatiotemporal dynamics of complex biological systems and to reveal the underlying mechanism of emergent properties. RESULTS: To facilitate the construction of such models, we have developed a next generation modelling platform for cancer systems biology, termed 'ELECANS' (electronic cancer system). It is equipped with a graphical user interface-based development environment for multiscale modelling along with a software development kit such that hierarchically complex biological systems can be conveniently modelled and simulated by using the graphical user interface/software development kit combination. Associated software accessories can also help users to perform post-processing of the simulation data for visualization and further analysis. In summary, ELECANS is a new modelling platform for cancer systems biology and provides a convenient and flexible modelling and simulation environment that is particularly useful for those without an intensive programming background. AVAILABILITY AND IMPLEMENTATION: ELECANS, its associated software accessories, demo examples, documentation and issues database are freely available at http://sbie.kaist.ac.kr/sub_0204.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Scheduling can obtain multi-user diversity if channel state information (CSI) is known, such as for low-mobility users and can exploit frequency diversity if CSI is not available at the transmitter, such as for high-mobility users. In this paper, we investigate resource allocation exploiting frequency and multiuser diversity for LTE downlink systems with users of different mobilities. To facilitate resource allocation, we first develop a user classification algorithm to identify high- and low-mobility users. Based on user mobility classification, we then propose a scheduling algorithm to simultaneously obtain multi-user diversity for those low-mobility users and frequency diversity for those high-mobility users. It is demonstrated by computer simulation that the performance of the proposed scheduling algorithm provides 6% and 23% gain of overall cell throughput, and 5.6% and 18% gain of 10th percentile throughput over proportional fairness based frequency-selective and frequency-diversity scheduling algorithms, respectively. Furthermore, the proposed scheduling algorithm has the same order of computational complexity as the frequency-selective scheduling algorithm.

In LTE systems with heterogeneous user mobilities, low-mobility users favor frequency selective scheduling while high-mobility users benefit from frequency diversity scheduling. To benefit both low- and high-mobility users simultaneously, scheduling exploiting frequency selectivity and diversity is desired. To enable the scheduling, low-complexity user mobility classification to distinguish these two types of users is required. In this paper, we first propose a user mobility classification algorithm, which is robust to different channel delay profiles (CDPs), for single-transmit-antenna systems. Then, we extend it to multiple-input multiple-output (MIMO) systems. A low-complexity scheduling algorithm, exploiting both frequency-selectivity and diversity for low- and high-mobility users simultaneously, is also developed. As demonstrated by the simulation results, the proposed user classification algorithm is robust to different CDPs and the proposed scheduling algorithm is effective.

The wide spread usage of mobile smart phones has put an emphasis on high efficiency of wireless networks. Inter-cell interference coordination (ICIC) techniques not only help improve cellular data coverage but also allow more efficient use of the valuable wireless spectrum. This paper discusses soft frequency reuse (SFR), a form of ICIC, for LTE systems. We develop a SFR approach that takes both throughput and fairness among multi-users into consideration. Computer simulation demonstrates that the cell average throughput can be increased as large as 17% while maintaining the same cell edge user throughput or the cell edge throughput can be increased by 11% while maintaining the same cell average throughput compared to traditional non-ICIC wireless networks.

In this paper we describe a vision-based algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform. Specifically, we use image-based visual servoing (IBVS) to track the platform in two-dimensional image space and generate a velocity reference command used as the input to an adaptive sliding mode controller. Compared with other vision-based control algorithms that reconstruct a full three-dimensional representation of the target, which requires precise depth estimation, IBVS is computationally cheaper since it is less sensitive to the depth estimation allowing for a faster method to obtain this estimate. To enhance velocity tracking of the sliding mode controller, an adaptive rule is described to account for the ground effect experienced during the maneuver. Finally, the IBVS algorithm integrated with the adaptive sliding mode controller for tracking and landing is validated in an experimental setup using a quadrotor.

In this paper, we develop a scheduling algorithm to obtain multi-user diversity for those low-mobility users and frequency diversity for those high-mobility users. Computer simulation demonstrates that the proposed scheduling algorithm provides 10% and 16% overall cell throughput gain over proportional fairness based frequency-selective and frequency-diversity scheduling algorithm, respectively. In addition, the proposed scheduling algorithm is shown to have the same order of computational complexity as the frequency-selective scheduling algorithm, and can be easily implemented in the LTE downlink systems.

Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.

Multi-modal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and exploits correlations between neighboring pixels in the reference and the floating image. Empirical evaluation on CT-MR/PET-MR rigid registration tasks demonstrates that our approach yields robust performance and outperforms the state of the art methods for multi-modal medical image registration.

In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.

A novel learning algorithm for semisupervised classification is proposed. The proposed method first constructs a support function that estimates a support of a data distribution using both labeled and unlabeled data. Then, it partitions a whole data space into a small number of disjoint regions with the aid of a dynamical system. Finally, it labels the decomposed regions utilizing the labeled data and the cluster structure described by the constructed support function. Simulation results show the effectiveness of the proposed method to label out-of-sample unlabeled test data as well as in-sample unlabeled data.

A new learning algorithm for semi-supervised learning is proposed. The proposed method utilizes a support vector machine to describe domains and a dynamical system to decompose the data space into several labelled disjoint regions. It can classify unlabelled data and predict new unknown data. Effectiveness of the method is veri£ed through simulation results.

A novel active learning method for security assessment of nonlinear systems is presented. The proposed method first extracts a dataset near the stability region boundary by using the direct method, and then learns a SVM model from the data. The constructed SVM classifier is shown to dramatically reduce the conservativness of the estimated stability region and also to make a fast security assessment.

To support very high data rate services that require higher system capacity, the high speed downlink packet access (HSDPA) was proposed in the UMTS standard. One of key techniques supporting the HSDPA services is the adaptive modulation and coding (AMC) in which the modulation scheme and the coding rate are adaptively changed to match the current channel condition reported by the user equipment (UE). Therefore, the mapping between the channel quality indicator (CQI) and signal to interference ratio (SIR) is closely related to the accuracy of AMC and the performance of HSDPA. This paper proposes a novel SIR to CQI mapping method that satisfies the 3GPP requirements. In order to verify the performance of the proposed mapping method, we implemented the link-level simulator which is composed of all the physical layer blocks depicted in the 3GPP standard. With the proposed mapping method, we show that UE can report the exact channel condition and the system can yield performance exceeding the requirements in the 3GPP technical specification.

A topological and dynamical characterization of the cluster structures described by the support vector clustering is developed. It is shown that each cluster can be decomposed into its constituent basin level cells and can be naturally extended to an enlarged clustered domain, which serves as a basis for inductive clustering. A simplified weighted graph preserving the topological structure of the clusters is also constructed and is employed to develop a robust and inductive clustering algorithm. Simulation results are given to illustrate the robustness and effectiveness of the proposed method.

The support vector clustering (SVC) algorithm is a recently emerged unsupervised learning method inspired by support vector machines. One key step involved in the SVC algorithm is the cluster assignment of each data point. A new cluster labeling method for SVC is developed based on some invariant topological properties of a trained kernel radius function. Benchmark results show that the proposed method outperforms previously reported labeling techniques.