Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing.

Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD).

A RPCA-Based ISAR Imaging Method for Micromotion Targets.

Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed.

Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System.

A frequency-hopping (FH)-based dual-function multiple-input multiple-output (MIMO) radar communications system enables implementation of a primary radar operation and a secondary communication function simultaneously. The set of transmit waveforms employed to perform the MIMO radar task is generated using FH codes. For each transmit antenna, the communication operation can be realized by embedding one phase symbol during each FH interval.

Adaptive chaotic MIMO radar based on DPMM clustering and Kalman filtering technique.

A novel scheme to optimize the adaptive transmit waveform of chaotic multiple-input multiple-output (MIMO) radar is developed. The main objective of this work is to achieve high ability in target discrimination by using a Dirichlet process mixture model (DPMM)-based clustering method based on nonparametric Bayesian theory and to improve the capability of target detection by minimizing the mean square error of radar channel response via Kalman filtering (KF) technique. The two stages are the discrimination of multiple range-extended targets and the optimization of the adaptive chaos-based waveform for transmission.

Doppler Data Association Scheme for Multi-Target Tracking in an Active Sonar System.

In many wireless sensors, the target kinematic states include location and Doppler information that can be observed from a time series of range and velocity measurements. In this work, we present a tracking strategy for comprising target velocity components as part of the measurement supplement procedure and evaluate the advantages of the proposed scheme. Data association capability can be considered as the key performance for multi-target tracking in an active sonar system.

Multiple Targets Tracking with Big Data-Based Measurement for Extended Binary Phase Shift Keying Transceiver.

Extended binary phase shift keying (EBPSK) transmit-receive system is considered as a high-resolution radar tracking system. The target kinematic states can be estimated from a time series of target range and velocity measurements. The measurements usually have a huge amount of data.

Cognitive Radar Waveform Optimization Based on Mutual Information and Kalman filtering.

A new strategy to optimizing the waveforms of cognitive radar under transmitted power constraint is presented. Our scheme is to enhance the performance of target estimation by minimizing the MSE (mean-square error) of the estimates of target scattering coefficients (TSC) based on Kalman filtering and then minimizing mutual information (MI) between the radar target echoes at successive time instants. The two steps are the optimal design of transmission waveform and the selection of a reasonable waveform from the ensemble for emission, respectively.

Adaptive Waveform Design for MIMO Radar-Communication Transceiver.

The system architecture for an adaptive multiple input multiple output (MIMO) radar-communication transceiver is proposed. A waveform design approach for communication data embedding into MIMO radar pulse using M-ary position phase shift keying (MPPSK) waveforms is introduced. A waveform optimization algorithm for the adaptive system is presented.

Waveform Optimization for Target Estimation by Cognitive Radar with Multiple Antennas.

A new scheme based on Kalman filtering to optimize the waveforms of an adaptive multi-antenna radar system for target impulse response (TIR) estimation is presented. This work aims to improve the performance of TIR estimation by making use of the temporal correlation between successive received signals, and minimize the mean square error (MSE) of TIR estimation. The waveform design approach is based upon constant learning from the target feature at the receiver.

Performance analysis of single-frequency MPPSK integrated system for ranging applications.

The dual-frequency MPPSK-MODEM is a flexible platform. When ranging accuracy request is low or platform is particularly limited by power, the platform would perform both data transmission and range measurement with single-frequency modes. In this paper, the ranging resolution of MPPSK pulse waveforms with the match filter and impacting filter processing are discussed, respectively.

The dual carrier ABSK system based on a FIR bandpass filter.

The special impacting filter (SIF) with IIR structure has been used to demodulate ABSK signals. The key points of SIF, including the resonance circuit's high Q value and the "slope-phase discrimination" character of the filter sideband, are demonstrated in the paper. The FIR narrow-band bandpass filtering system, which can also provide the impact-filtering effect, is proposed.

Nonlinear detection for a high rate extended binary phase shift keying system.

The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency.

Nonlinear demodulation and channel coding in EBPSK scheme.

The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) performance. However, the conventional threshold decision cannot achieve the optimum performance, and the SIF brings more difficulty in obtaining the posterior probability for LDPC decoding. In this paper, we concentrate not only on reducing the BER of demodulation, but also on providing accurate posterior probability estimates (PPEs).

Classification of fruits using computer vision and a multiclass support vector machine.

Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.

Mobile location with NLOS identification and mitigation based on modified Kalman filtering.

In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed.

Crop classification by forward neural network with adaptive chaotic particle swarm optimization.

This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA).

On BER performance of EBPSK-MODEM in AWGN channel.

In order to satisfy the higher and higher demand for communication systems, an Extended Binary Phase Shift Keying (EBPSK) system with very high spectra efficiency has been proposed. During the research, a special kind of filters was found, which can amplify the signal characteristics and remove utmost noise, i.e.

Novel anti co-channel interference scheme for sensor networks.

With improvement of the automation level, wireless sensors are widely used, but various kinds of interference lead to problems in the application. In order to deal with co-channel interference, a throughput efficient scheme based on Extended Binary Phase Shift Keying (EBPSK) modulation is introduced in physical layer. On this basis, the corresponding transmission scheme and the important impacting filter are presented.

Remote-sensing image classification based on an improved probabilistic neural network.

This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance.

Pattern Recognition via PCNN and Tsallis Entropy.

In this paper a novel feature extraction method for image processing via PCNN and Tsallis entropy is presented. We describe the mathematical model of the PCNN and the basic concept of Tsallis entropy in order to find a recognition method for isolated objects. Experiments show that the novel feature is translation and scale independent, while rotation independence is a bit weak at diagonal angles of 45° and 135°.

Comments on "a separable low complexity 2D HMM with application to face recognition'.

The separable low complexity 2D HMM proposed in [1] builds on the assumption of conditional independence in the relationship between adjacent blocks. The authors view this assumption as the key assumption to reduce the complexity. In this communication, we show that this key assumption is entirely unnecessary.