Stream Data Rates Research Articles

Compressive Sensing SAR Imaging Algorithm for LFMCW Systems

Linear frequency-modulated (LFM) continuous-wave (CW) radar is usually the first choice in synthetic aperture radar (SAR) imaging missions due to its relatively low cost and hardware simplicity. However, the use of continuous-wave unnecessarily introduces the problem of intrapulse motion. Furthermore, a large amount of data generated by the CW imaging process may overburden the onboard communication system with its high streaming data rate. On the other hand, a large amount of data may, indeed, not be necessary for high-resolution SAR imaging. In this article, we took full consideration of the intrapulse motion in LFMCW radar systems and modeled the phase preserving extended frequency scaling algorithm (EFSA) reconstruction process with far fewer data samples as compressive sensing problem and used subgradient descent algorithm with optimal step size to realize compressive sensing reconstruction. Our compressive sensing problem is different from the traditional direct inversion-based problem in that our reconstructed results contain both mainlobe and sidelobes. Comparisons were made with mainstream iterative soft thresholding-based reconstruction algorithm to demonstrate its capability to reconstruct large imaging scenes with high resolution. Both simulations and experiments with measured data have verified our proposed algorithm.

Scaling-Up Distributed Processing of Data Streams for Machine Learning

Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the learned models is essential for improved inference in these applications. Further, these applications often involve data that are either inherently gathered at geographically distributed entities or that are intentionally distributed across multiple machines for memory, computational, and/or privacy reasons. Training of models in this distributed, streaming setting requires solving stochastic optimization problems in a collaborative manner over communication links between the physical entities. When the streaming data rate is high compared to the processing capabilities of compute nodes and/or the rate of the communications links, this poses a challenging question: how can one best leverage the incoming data for distributed training under constraints on computing capabilities and/or communications rate? A large body of research has emerged in recent decades to tackle this and related problems. This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates. In particular, it focuses on methods that solve: (i) distributed stochastic convex problems, and (ii) distributed principal component analysis, which is a nonconvex problem with geometric structure that permits global convergence. For such methods, the paper discusses recent advances in terms of distributed algorithmic designs when faced with high-rate streaming data. Further, it reviews guarantees underlying these methods, which show there exist regimes in which systems can learn from distributed, streaming data at order-optimal rates.

Rate types for stream programs

We introduce RATE TYPES, a novel type system to reason about and optimize data-intensive programs. Built around stream languages, RATE TYPES performs static quantitative reasoning about stream rates -- the frequency of data items in a stream being consumed, processed, and produced. Despite the fact that streams are fundamentally dynamic, we find two essential concepts of stream rate control -- throughput ratio and natural rate -- are intimately related to the program structure itself and can be effectively reasoned about by a type system. RATE TYPES is proven to correspond with a time-aware and parallelism-aware operational semantics. The strong correspondence result tolerates arbitrary schedules, and does not require any synchronization between stream filters.We further implement RATE TYPES, demonstrating its effectiveness in predicting stream data rates in real-world stream programs.

Stream Processing Health Card Application

In this paper, we propose a data stream management system embedded to a smart card for handling and storing user specific summaries of streaming data coming from medical sensor measurements and/or other medical measurements. The data stream management system that we propose for a health card can handle the stream data rates of commonly known medical devices and sensors. It incorporates a type of context awareness feature that acts according to user specific information. The proposed system is cheap and provides security for private data by enhancing the capabilities of smart health cards. The stream data management system is tested on a real smart card using both synthetic and real data.

가용대역폭 관리를 통한 영상 스트리밍 서비스 품질 제어 시스템 설계 및 구현

본 논문은 차세대 통합망 환경에서 IPTV와 같은 영상 스트리밍 서비스를 위해 종단간 가용대역폭 모니터링을 통하여 영상 품질을 제어할 수 있는 영상 품질 제어 시스템(VQCS)을 제안한다. IPTV 서비스를 통해 영상, 음성 및 게임과 같은 다양한 멀티미디어 서비스들을 제공할 수 있으며 이런 서비스들은 대용량의 대역폭을 소비하게 된다. 이때 네트워크 가용대역폭이 부족할 경우 영상 끊김, 형태 왜곡 및 잔상과 같은 영상 품질 저하가 발생하게 된다. 영상 스트리밍 서비스의 품질을 안정적으로 제어하기 위해서는 가용대역폭 모니터링 방안이 요구된다. 이에 우리는 단말 내의 영상 품질 제어 시스템에서 IP 헤더 내의 전체길이(Total Length) 필드를 이용하여 단위시간 동안 링크를 점유하고 있는 패킷 양 계산을 통해 가용 대역폭을 측정하였으며, 가용대역폭 정보를 기반으로 네트워크 내의 스케일러빌리티 추출기는 영상 스트림 데이터율을 적응적으로 선택하여 대역폭 부족으로 인한 영상 스트리밍의 품질 저하를 막아 서비스 품질을 향상시킬 수 있다. In this paper, we propose video quality control system(VQCS) which can control video service quality through the monitoring of end-to-end available bandwidth for video streaming service like IPTV in NGN convergence network. Various multimedia services such as video, voice and gaming service can be provided by IPTV, and these services require large amounts of bandwidth. At this time, video quality degradation like video jerkiness, block distortion and blurring is caused when network available bandwidth is insufficient. Available bandwidth monitoring method is need to stably control video streaming quality. So, we periodically calculate the amount of the packets in link and measure available bandwidth by using total length field in IP header at terminal. Scalability extractor in network selects suitable video streaming data rate based on available bandwidth and transports video streaming with adaptive data rate to prevent video quality deterioration.

Playout buffer and rate optimization for streaming over IEEE 802.11 wireless networks

Most streaming rate selection and buffer optimization algorithms are developed for wired networks and can perform poorly over wireless networks. Wireless MAC layer behavior, such as rate adaptation, retransmissions, and medium sharing, can significantly degrade the effectiveness of current streaming algorithms. This article presents the Buffer and Rate Optimization for Streaming (BROS) algorithm to improve streaming performance. BROS uses a bandwidth estimation tool designed specifically for wireless networks and models the relationship between buffer size, streaming data rate, and available bandwidth distribution. BROS optimizes the streaming data rate and initial buffer size, resulting in a high data rate but with few frame losses and buffer underflow events, while still keeping a small initial buffer delay. BROS is implemented in the Emulated Streaming (EmuS) client-server system and evaluated on an IEEE 802.11 wireless testbed with various wireless conditions. The evaluation shows that BROS can effectively optimize the streaming rate and initial buffer size based on wireless network bandwidth conditions, thus achieving better performance than static rate or buffer selection and jitter removal buffers.

Detecting suspended video streams through variance-time analysis

To detect suspended video streams, an algorithm is proposed which analyzes traffic data to sense changes in variances. The objective of the algorithm is to help restart suspended streams as quickly as possible in cooperation with network administrators, mechanisms to change routes, etc. The algorithm handles traffic data of a number of streams all together to detect one or more suspended streams without using the information on the number of streams and on each stream's data rate. It works with the same parameter values for both LAN and WAN traffic streams, even if their packet interarrival-time distributions are different. Experiments with 41 live TV streams showed that suspension is detectable when the traffic rate decrease caused by the suspension is 1.6%.

In-service monitoring of 16 port x 32 wavelength bi-directional WDM-PON systems with a tunable, coded optical time domain reflectometry

We propose a multi-port, multi-wavelength supervisory system for the in-service transmission line monitoring of a bidirectional WDM-PON system. Identifying unique requirements for the performance monitoring of a real field WDM-PON system, we define the architecture for the supervisory system and utilize the most up-to-date technologies (Simplex coding, tunable source, and optical switches) to demonstrate a successful interrogation of a transmission line up to 16 ports x 32 nodes (512 user) capacity. Monitoring of individual branch traces up to 60 km was achieved with the application of a 127-bit simplex code corresponding to a 7.5dB SNR coding gain. In-service transmission experiments showed negligible penalty from the monitoring system to the transmission signal quality, at a 2.5Gbps / 125Mbps (down / up stream) data rate.

Architectures and technologies for high-speed optical data networks

Current optical networks are migrating to wavelength division multiplexing (WDM)-based fiber transport between traditional electronic multiplexers/demultiplexers, routers, and switches. Passive optical add-drop WDM networks have emerged but an optical data network that makes full use of the technologies of dynamic optical routing and switching exists only in experimental test-beds. This paper discusses architecture and technology issues for the design of high performance optical data networks with two classes of technologies, WDM and time division multiplexing (TDM). The WDM network architecture presented stresses WDM aware Internet protocol (IP), taking full advantage of optical reconfiguration, optical protection and restoration, traffic grooming to minimize electronics costs, and optical flow-switching for large transactions. Special attention is paid to the access network where innovative approaches to architecture may have a significant cost benefit. In the more distant future, ultrahigh-speed optical TDM networks, operating at single stream data rates of 100 Gb/s, may offer unique advantages over WDM networks. These advantages may include the ability to provide integrated services to high-end users, multiple quality-of-service (QoS) levels, and truly flexible bandwidth-on-demand. The paper gives an overview of an ultrahigh-speed TDM network architecture and describes recent key technology developments such as high-speed sources, switches, buffers, and rate converters.