Automatic configuration tuning helps users who intend to improve software performance. However, the auto-tuners are limited by the huge configuration search space. More importantly, they focus only on performance improvement while being unaware of other important user intentions (e.g., reliability, security). To reduce the search space, researchers mainly focus on pre-selecting performance-related parameters which requires a heavy stage of dynamically running under different configurations to build performance models. Given that other important user intentions are not paid attention to, we focus on guiding users in pre-selecting performance-related parameters in general while warning about side-effects on non-performance intentions. We find that the configuration document often, if it does not always, contains rich information about the parameters' relationship with diverse user intentions, but documents might also be long and domain-specific.

The runtime environment and workload of software are constantly changing, requiring users to make appropriate adjustments to accommodate these changes. The runtime configuration, however, as the interface for users to manipulate software behavior often requires domain-specific knowledge to understand. This usually results in users spending a considerable amount of time wading through document and user manuals trying to understand the runtime configuration. In this paper, we study the possibility of understanding the intention of runtime configuration options through their documents, even sometimes it is difficult for users to understand. Based on these studies, we classify the runtime configuration option’s intention into six categories. Accordingly, we design runtime Configuration Intention Classifier (CIC), a supervised approach based on CNN to classify the runtime configuration option’s intention according to its document. CIC integrates the features of runtime configuration names and descriptions according to different levels of granularity and predicts the intention of runtime configuration options accordingly. Extensive experiments show that our approach can achieve an accuracy of 85.6% and outperform nine comparative approaches by up to 16.6% over the dataset we customized.

This article presents a robust hand-gesture recognition method via multidimensional feature representation and learning specifically designed for commercial frequency-modulated continuous wave (FMCW) multi-input multi-output (MIMO) millimeter-wave radar. First, the optimal configuration of the radar system parameters for the hand-gesture recognition scenario is investigated and a standard procedure to determine the system configuration is given. Then a moving scattering center model is proposed to represent the 3-D point cloud in the range-Doppler (RD)-angular multidimensional feature space. A scattering point detection and tracking algorithm is presented based on a set of motion constraints in terms of position, velocity, and acceleration. It is derived from the space-time continuity of a nonrigid target. Finally, a lightweight multichannel convolutional neural network (CNN) is designed to learn and classify multidimensional gesture features including radial RD and tangential azimuth-elevation. Extensive experiments are carried out with the developed system and a large data set is obtained to train and test the classifier. The results show that the proposed gesture recognition method can effectively distinguish gestures that are easily confused in the RD domain and achieve robust performances under various conditions.

In this paper, we develop a novel serial concatenated trellis-coded differential chaotic modulation (SCTC-DCM) system, which combines serial concatenated trellis-coded modulation (SCTCM) with M-ary differential chaotic shift keying (M-ary DCSK). The proposed system has lower error floor than turbo trellis-coded differential chaotic modulation (TTC-DCM). At the same time, when the signal to noise ratio (SNR) is relatively high, SCTC-DCM scheme achieves better bit error rate (BER) performance. Moreover, simulation results show that the proposed scheme can obtain considerable coding gain compared with uncoded M-ary differential chaotic shift keying over AWGN channel. Furthermore, SCTC-DCM system inherits the advantages of chaos-based modulation system and does not require channel state information (CSI) at the receiver. Therefore, the proposed system can be applied to band-limited communication scenes with poor channel condition.

The turbo trellis-coded differential chaotic modulation (TTC-DCM) is a newly proposed scheme, which is very suitable for bandlimited communication applications with poor channel conditions due to the merits of high bandwidth efficiency, robustness to multipath fading and easy-to-implement property. In this paper, in-phase and quadrature-phase (IQ) interleaved turbo trellis-coded differential chaotic modulation (IQ-TTC-DCM) scheme is proposed, which is capable of increasing the diversity order of conventional TTC-DCM. The IQ-TTC-DCM can obtain significant gains while communicating over fading environment, compared with TTC-DCM scheme. Besides, a code search algorithm utilizing the Extrinsic Information Transfer (EXIT) charts is proposed, aiming to find the component code with the best convergence behavior. The searched codes for different channels are presented for 8-state 8-DCSK TTC-DCM scheme and simulations are carried out to testify their superiorities.