Index modulation is a crucial technique for dual functional radar and communication (DFRC) systems to improve the communication data rate. However, in automotive radar-based DFRC system towards Internet of Vehicles (IoV), the introduction of index modulation will compromise the Doppler sensing performance of the radar. To address this issue, a novel deployable two-stage-modulation DFRC system based on index modulation is proposed in the paper. In the design of transmitter, a two-stage signal modulation strategy based on index modulation is devised, including time slot index modulation for data rate improvement and inter-chirp phase modulation for range sensing performance maintenance. In the design of receiver, considering the sparsity of the sensing signal in range-Doppler domain, a 2D sparse signal recovery algorithm, named adaptive iterative soft-threshold algorithm based basis pursuit (AISTA-BP) is derived to reduce the Doppler sidelobes of targets. Specifically, the sparse recovery problem is formulated in range-Doppler domain and relaxed as basis pursuit (BP) problem. Furthermore, adaptive sparsity-limiting threshold is introduced to iterative soft-threshold algorithm (ISTA) for the solution of the BP problem, avoiding volatile performance and excessive iterations of empirical threshold. Simulations show that the proposed system outperforms the state-of-the-art radar-based DFRC systems in terms of communication data rate. Simultaneously, it maintains respectable sequence error rate (SER) performance. Furthermore, real-world experiments validate that the proposed DFRC system can effectively reduce the Doppler sidelobes with the improved data rate in practical point-to-point and multi-vehicle communication scenarios.

Keystroke authentication is essential in daily IoT applications. However, two existing challenges are the vulnerability in dynamic environments and the poor performance with limited data. RaKey, a novel millimeter-wave radar (mmWave radar) keystroke authentication scheme, is proposed as a secondary encryption to address these issues. The mmWave radar, with the advantages of being contactless, privacy-preserving and light-insensitive, can authenticate robustly in dynamic scenarios. First, the mmWave radar signal model of keystroke behavior is presented and analyzed for the first time. Second, the micro-Doppler Maps of Overall keystroke behavior (ODMs) and Finger keystroke rhythm (FDMs) are introduced to enhance the expression of keystroke identity features. And RK-AW, an FDMs extraction method based on wavelet with adaptive selection of decomposition level, is proposed to separate the finger keystroke rhythm from overall behavior. Third, to solve the challenge of authentication performance with limited data, RK-Net, a multimodal few-shot network for radar-based keystroke that incorporates complementation signatures and an MCA-LSTM block to fuse multimodal features and thereby enhance the capability of feature extraction, is proposed. Extensive experiments demonstrate that RaKey can achieve considerable authentication accuracy in verifying identities with limited data, while maintaining robustness against environmental variations and potential attacks.

Although chirp-based frequency-modulated continuous wave (FMCW) modulation is popular in automotive radars, it presents a crucial challenge for the deployment of vehicle-to-vehicle (V2V) communication because the impact of synchronization bias between radars on signal processing is not considered. A novel deployable frame-synchronized hybrid time division multiplexing (TDM) dual-function radar and communication (DFRC) framework named FSHT-DFRC is proposed in this letter to tackle this issue by taking both the frame and chirp of FMCW radar into account. In the design of frame, a new synchronized hybrid TDM transmit waveform design is proposed to embed the communication sequences into the FMCW waveform through binary phase-shift keying (BPSK). In the design of chirp, an innovative communication signal processing scheme utilizing the global phase information, which merges the fast- and slow-time, for decoding is developed to alleviate the synchronization bias between chirps elicited by radar hardware differences. The results of simulations and real-world experiment validate the effectiveness and superiority of proposed framework.

So far, big data analytics have proved worth by reaping fruitful achievements in business intelligence, health care and so on, which aims to reveal efficiently hidden and unique information from pre-existing large datasets. Therefore, big data analytics are sprouting in almost all areas, expecting to find new values from musty archives or continuous wave of newly generated data. This paper introduces how we utilize big data technologies to establish an object-oriented analytic architecture for IT operations, which evolves from traditional and coarse statistics into fine-grained and in-depth analysis. Moreover, this paper demonstrates applying the architecture to peek inside practical problem of the server performance dead zone. The peering process consists of applying various analysis models iteratively on large set of server logs. Our work can be considered as an attempt to exploit how object-oriented big data analytic benefits IT system operations and optimization.

As more and more multi-tenant applications emerge in clouds, people increasingly recognize the importance of multi-tenant applications in realizing cloud benefit maximization. Service Level Agreement (SLA) is advocated widely to monitor these applications for multiple tenants to meet their service quality requirements. However, so far these applications provide limited multi-tenant monitoring supports, which prevents the applications from guaranteeing tenants' interests efficiently. In this paper, we propose an aspect-oriented approach to monitor multi-tenant applications based on tenant SLAs. Our approach includes monitoring code generation and runtime management. During code generation, the approach proposes an SLA feature model for tenants to specify variable requirements. Based on the requirements the approach selects code snippets, which are implemented as templates in advance, and splices them into an monitoring aspect. During runtime, the approach prioritizes aspects to determine execution order and updates monitoring status in term of tenant. An implemented prototype is used to evaluate the approach by case studies, which demonstrate the approach effectiveness in common situations.

As multi-tenant applications spring up in clouds, more and more people advocate using Service Level Agreement (SLA) in service delivery to fit tenants' non-functional needs e.g. Response time and budget limit. However, most of the present application optimizations based on SLA focuses on virtual machine-based (VM-based) computing service, while other services such as storage and cache are often neglected. In this paper, we propose an SLA-driven application optimization for cache service to help to meet tenants' needs better and improve cost-effectiveness, which can be taken as complementary to the existing work. The proposed approach, built on top of Platform-as-a-Service (PaaS), pays attention to evicted data. It considers both tenant SLA-evaluated status and data performance when weighting the evicted data with re-cache likelihoods, and then adjusts their re-cache priorities. At the beginning of every cycle it predicts tenant status and evicted data performance for the coming cycle by Holt-Winters double exponential smoothing. Our simulation experiments demonstrate the optimization effectiveness in improving cache cost-effectiveness and satisfying tenant SLAs.

With the popularity of pads, large touch screens, notebooks and smart mobiles, it is a reasonable requirement that modelers use such devices to models. However, there are few modeling tools that take full advantage of the devices. This paper discusses a Web based UML modeling tool with touch screens, based on our full analysis of human-machine interaction modes for software modeling. The tool provides more input means, i.e. combining gesture input and traditional keyboard and mouse input, and supports cross-platforms modeling by using HTML 5.

Cloud computing encourages application to migrate into it for economic of scale, where they rent shared resources to deliver services. Service Level Agreements(SLA) plays an important role in assisting various applications providing high-quality services to end users in cloud's complex and uncertain environments. Most of the existing work tries to support application claimed quality by help cloud make decisions of computing resources allocation during runtime. In this paper, we propose an approach for applications to maintain quality requirements by runtime cache adjustment in consideration of service level objectives (SLOs) and unpredictable workload in cloud, which can be taken as a complement to the existing work. Our approach includes application SLO modeling and mapping to monitor metrics during runtime, and an algorithm to adapting caches according to runtime status and SLOs. The approach has been applied to a real-world SNS application which proves effectiveness of our approach.

Estimating the cost is important for cloud application developers to services in clouds, and becomes even important when it needs remaining a certain service level at the same time. Though currently much work has been down to predict cost and performance of cloud applications, most of them perform either before application design or after the application construction, which leads to either imprecise estimation or irreparable design fault. In this paper, we propose an approach to estimate the cost of running typical applications in Amazon Web Service (AWS) cloud during design phase. We propose an UML Activity-extended model (AeModel) to describe execution of application service and introduce an extraction algorithm to extract information contained in the AeModel automatically. We propose a cost model on AWS, which can help developers to estimate operating cost during design phase and satisfy performance needs, with an algorithm to produce suitable purchase solutions automatically. We perform case studies using a web-based business application to show effectiveness of our approach, and find that our approach can help developers lessen cost by adjusting application models.