The recent scale-up of GPU hardware through the integration of multiple GPUs into a single machine and the introduction of higher bandwidth interconnects like NVLink 2.0 has enabled new opportunities of relational query processing on multiple GPUs. However, due to the unique characteristics of GPUs and the interconnects, existing hash join implementations spend up to 66% of their execution time moving the data between the GPUs and achieve lower than 50% utilization of the newer high bandwidth interconnects. This leads to extremely poor scalablity of hash join performance on multiple GPUs, which can be slower than the performance on a single GPU. In this paper, we propose MG-Join, a scalable partitioned hash join implementation on multiple GPUs of a single machine. In order to effectively improve the bandwidth utilization, we develop a novel multi-hop routing for cross-GPU communication that adaptively chooses the efficient route for each data flow to minimize congestion. Our experiments on the DGX-1 machine show that MG-Join helps significantly reduce the communication overhead and achieves up to 97% utilization of the bisection bandwidth of the interconnects, resulting in significantly better scalability. Overall, MG-Join outperforms the state-of-the-art hash join implementations by up to 2.5x. MG-Join further helps improve the overall performance of TPC-H queries by up to 4.5x over multi-GPU version of an open-source commercial GPU database Omnisci.

In recent years, we have witnessed significant efforts to improve the performance of Online Analytical Processing (OLAP) on graphics processing units (GPUs). Most existing studies have focused on improving memory efficiency since memory stalls can play an essential role in query processing performance on GPUs. Motivated by the recent rise of just-in-time (JIT) compilation in query processing, we investigate whether and how we can further improve query processing performance on GPU. Specifically, we study the execution of state-of-the-art JIT compile-based query processing systems. We find that thanks to advanced techniques such as database compression and JIT compilation, memory stalls are no longer the most significant bottleneck. Instead, current JIT compile-based query processing encounters severe under-utilization of GPU hardware due to divergent execution and degraded parallelism arising from resource contention. To address these issues, we propose a JIT compile-based query engine named Pyper to improve GPU utilization during query execution. Specifically, Pyper has two new operators, Shuffle and Segment , for query plan transformation, which can be plugged into a physical query plan in order to reduce divergent execution and resolve resource contention, respectively. To determine the insertion points for these two operators, we present an analytical model that helps insert Shuffle and Segment operators into a query plan in a cost-based manner. Our experiments show that 1) the analytical analysis of divergent execution and resource contention helps to improve the accuracy of the cost model, 2) Pyper significantly outperforms other GPU query engines on TPC-H and SSB queries.

Traditionally, FPGAs were programmed using low-level Hardware Description Languages (HDLs) like Verilog or VHDL, which made it extremely difficult to design, build and maintain systems for FPGAs. However, the recent release of OpenCL SDKs by FPGA vendors like Xilinx and Altera have significantly improved the programmability of FPGAs and have brought new research opportunities for query processing systems on FPGAs. It remains an open question whether and how we can optimize OpenCL based database engines for FPGAs. There is a gap on optimizations and tuning between OpenCL and FPGA, since OpenCL is mainly designed for parallel multi-/many-core architectures. In this paper, we attempt to answer this question under the context of pipelined query execution. For this, we first perform a detailed study of database engines on the latest generation of FPGAs. We then design an FPGA based shared pipeline query execution system (FADE) which exploits the hardware features of FPGAs and minimizes inefficiencies like the high communication reconfiguration overhead. Our experiments show that our design achieves significant performance speedup over existing approaches for pipelined query executions on FPGA. Finally, we also present the challenges and opportunities for query processing on the latest generation FPGAs.

Image outlier detection has been an important research issue for many computer vision tasks . However, most existing outlier detection methods fail in the high-dimensional image datasets. In order to address this problem, we propose a novel image outlier detection method by combining autoencoder with Adaboost (ADAE). By ensembling many weak autoencoders, our method can better capture the statistical correlations among the features of normal data than the single autoencoder . Therefore, the proposed ADAE is able to determine the outliers efficiently. In order to reduce the many parameters in ADAE, we introduce the Sparse Group Lasso (SGL) constraint into the learning objective of ADAE. We combine Adagrad with Proximal Gradient Descent to optimize this additional learning objective. We also propose the multi-objective evolutionary algorithm to determine the best penalty factors of SGL. By evaluating on several famous image datasets, the detection results testify to the outstanding outlier detection performance of ADAE. The evaluation results also show SGL can make the detection model more compact while maintaining the similar detection performance.

Advanced processor architectures have been driving new designs, implementations and optimizations of main-memory hash join algorithms recently. The newly released Intel Xeon Phi many-core processor of the Knights Landing architecture (KNL) embraces interesting hardware features such as many low-frequency out-of-order cores connected on a 2D mesh, and high-bandwidth multi-channel memory (MCDRAM). In this paper, we experimentally revisit the state-of-the-art main-memory hash join algorithms to study how the new hardware features of KNL affect the algorithmic design and tuning as well as to identify the opportunities for further performance improvement on KNL. Our experiments show that, although many existing optimizations are still valid on KNL with proper tuning, even the state-of-the-art algorithms have severely underutilized the memory bandwidth and other hardware resources.

In geographically distributed data centers, data analytics systems have recently been developed and optimized for such geo-distributed environments. With respect to various system operators' requirements on data analytics, existing studies have optimized systems for individual goals such as resource efficiency, per-job latency and fairness. However, the optimizations with multiple objectives simultaneously have been overlooked. Even worse, some objectives can be translated to discordant actions and their relationship can be impacted by the unique features of geo-distributed data analytics systems. For example, we have observed clear trade-off between fairness and resource efficiency. In this paper, we develop an efficient framework for multi-objective optimizations on geo-distributed data analytics systems. Specifically, we develop GeoSpark, an extension to Spark, which automatically performs a multi-objective optimization according to the system operators' preferences on different objectives. The multi-objective optimization is inherently intractable especially for large-scale workloads. Therefore, we propose an efficient online heuristic to approximate the optimal scheduling plan while achieving a lower bound guarantee in the worst case. Evaluation using synthetic workload shows that GeoSpark effectively performs the multi-objective optimizations based on system operators' preferences on different objectives. GeoSpark achieves up to 30% makespan reduction, 28% job latency reduction and better fairness guarantee compared with existing schedulers in Apache Spark in the geo-distributed setting.

Geo-distributed clouds are becoming increasingly popular for cloud providers, and data centers with different regions often offer different prices, even for the same type of virtual machines. Resource provisioning in geo-distributed clouds is an important and complicated problem for budget and performance optimizations of scientific workflows. Scientists are facing the complexities resulted from various cloud offerings in the geo-distributed settings, severe cloud performance dynamics and evolving user requirements on performance and cost. To address those complexities, we propose a declarative optimization engine named Geco for resource provisioning of scientific workflows in geo-distributed clouds. Geco allows users to specify their workflow optimization goals and constraints of specific problems with an extended declarative language. We propose a novel probabilistic optimization approach for evaluating the declarative optimization goals and constraints to address the cloud dynamics. Additionally, we develop runtime optimizations to more effectively utilize the cloud resources at runtime. To accelerate the solution finding, Geco leverages the power of GPUs to find the solution in a fast and timely manner. Our evaluations with four common workflow provisioning problems demonstrate that, Geco is able to achieve more effective performance/cost optimizations in geo-distributed cloud environments than the state-of-the-art approaches.

Recently, Intel Xeon Phi is emerging as a many-core processor with up to 61 x86 cores. In this demonstration, we present PhiDB, an OLAP query processor with simultaneous multi-threading (SMT) capabilities on Xeon Phi as a case study for parallel database performance on future many-core processors. With the trend towards many-core architectures, query operator optimizations, and efficient query scheduling on such many-core architectures remain as challenging issues. This motivates us to redesign and evaluate query processors. In PhiDB, we apply Xeon Phi aware optimizations on query operators to exploit hardware features of Xeon Phi, and design a heuristic algorithm to schedule the concurrent execution of query operators for better performance, to demonstrate the performance impact of Xeon Phi aware optimizations. We have also developed a user interface for users to explore the underlying performance impacts of hardware-conscious optimizations and scheduling plans.

Energy efficiency is a major design and optimization factor for query co-processing of databases in embedded devices. Recently, GPUs of new-generation embedded devices have evolved with the programmability and computational capability for general-purpose applications. Such CPU-GPU architectures offer us opportunities to revisit GPU query co-processing in embedded environments for energy efficiency. In this paper, we experimentally evaluate and analyze the performance and energy consumption of a GPU query co-processor on such hybrid embedded architectures. Specifically, we study four major database operators as micro-benchmarks and evaluate TPC-H queries on CARMA, which has a quad-core ARM Cortex-A9 CPU and a NVIDIA Quadro 1000M GPU. We observe that the CPU delivers both better performance and lower energy consumption than the GPU for simple operators such as selection and aggregation. However, the GPU outperforms the CPU for sort and hash join in terms of both performance and energy consumption. We further show that CPU-GPU query co-processing can be an effective means of energy-efficient query co-processing in embedded systems with proper tuning and optimizations.

Resource provisioning for scientific workflows in Infrastructure-as-a-service (IaaS) clouds is an important and complicated problem for budget and performance optimizations of workflows. Scientists are facing the complexities resulting from severe cloud performance dynamics and various user requirements on performance and cost. To address those complexity issues, we propose a declarative optimization engine named Deco for resource provisioning of scientific workflows in IaaS clouds. Deco allows users to specify their workflow optimization goals and constraints of specific problems with an extended declarative language. We propose a novel probabilistic optimization approach for evaluating the declarative optimization goals and constraints in dynamic clouds. To accelerate the solution finding, Deco leverages the available power of GPUs to find the solution in a fast and timely manner. We evaluate Deco with several common provisioning problems. We integrate Deco into a popular workflow management system (Pegasus) and show that Deco can achieve more effective performance/cost optimizations than the state-of-the-art approaches.

Generally, H.264/AVC video coding standard with hierarchical bipredictive picture (HBP) structure outperforms the classical prediction structures such as “IPPP...” and “IBBP...” through better exploitation of data correlation using reference frames and unequal quantization setting among frames. However, multiple reference frames (MRFs) techniques are not fully exploited in the HBP scheme because of the computational requirement for B-frames, unavailability of adjacent reference frames, and with no explicit sorting of the reference frames for foreground or background being used. To exploit MRFs fully and explicitly in background referencing, we observe that not a single frame of a video is appropriate to be the reference frame as no one covers adequate background of a video. To overcome the problems, we propose a new coding scheme with the HBP, which uses the most common frame in scene (McFIS), generated by background modeling, as a long-term reference (LTR) frame for the third unipredictive reference frame, so that foreground and background areas are expected to be referenced from the two frames in the HBP structure and the McFIS, respectively. There are two approaches to generate McFIS under the proposed methodology. In the first approach, we generate a McFIS using a number of original frames of a scene in a video and then encode it as an I-frame with a higher quality. For the rest of the scene, this generated I-frame is used as an LTR frame. In the second approach, we generate an McFIS from the decoded frames and then use it as an LTR frame, without the need to encode the McFIS. The first and the second approaches are suitable for a video with static background and dynamic background, respectively. In general, the second approach requires more computational time than that of the the first approach. The experiments confirm that the proposed scheme outperforms three state-of-the-art algorithms by improving the image quality significantly with reduced computational time.

The CSMA/CA protocol leaves a idle/busy period signature in its band of operation that can easily be profiled by energy detectors. In this paper, we present a hypothesis on the protocol's idle period distribution and verify its exactness through simulation. This distribution may be useful in spectrum sensing applications where there is a need to fingerprint such networks, or to infer the number of nodes actively participating in the system using simple, protocol-independent circuitry.

In this paper, we formulate the throughput of a spatially-optimized CSMA/CA in two interference-limited co-channel cells, and validate it using statistics obtained via simulation. This special variant of the protocol incorporates a MAC/PHY cross-layer mechanism which aborts the reception of the payload portions of frames from co-channel cells in interference-limited multi-cell deployments. For physical transceivers relying solely on preamble detection for carrier sensing, this optimization adequately mitigates the exposed node syndrome, thereby allowing nodes to enjoy nearly the maximum throughput as provided by CSMA/CA in single cell or carrier-sensing threshold (CST) isolated scenarios.

In this paper, we propose a low-complexity video coding scheme based upon 2-D singular value decomposition (2-D SVD), which exploits basic temporal correlation in visual signals without resorting to motion estimation (ME). By exploring the energy compaction property of 2-D SVD coefficient matrices, high coding efficiency is achieved. The proposed scheme is for the better compromise of computational complexity and temporal redundancy reduction, i.e., compared with the existing video coding methods. In addition, the problems caused by frame decoding dependence in hybrid video coding, such as unavailability of random access, are avoided. The comparison of the proposed 2-D SVD coding scheme with the existing relevant non-ME-based low-complexity codecs shows its advantages and potential in applications.

Discrete cosine transform (DCT) is the orthogonal transform that is most commonly used in image and video compression. The motion-compensation residual (MC-residual) is also compressed with the DCT in most video codecs. However, the MC-residual has different characteristics from a nature image. In this paper, we develop a new orthogonal transform-rotated orthogonal transform (ROT) that can perform better on the MC-residual than the DCT for coding purposes. We derive the proposed ROT based on orthogonal-constrained L1-Norm minimization problem for its sparse property. Using the DCT matrix as the starting point, a better orthogonal transform matrix is derived. In addition, by exploring inter-frame dependency and local motion activity, transmission of substantial side information is avoided. The experiment results confirm that, with small computation overhead, the ROT is adaptive to change of local spatial characteristic of MC-residual frame and provides higher compression efficiency for the MC-residual than DCT, especially for high- and complex-motion videos.

In H.264/advanced video coding (AVC), lossless coding and lossy coding share the same entropy coding module. However, the entropy coders in the H.264/AVC standard were original designed for lossy video coding and do not yield adequate performance for lossless video coding. In this paper, we analyze the problem with the current lossless coding scheme and propose a mode-dependent template (MD-template) based method for intra lossless coding. By exploring the statistical redundancy of the prediction residual in the H.264/AVC intra prediction modes, more zero coefficients are generated. By designing a new scan order for each MD-template, the scanned coefficients sequence fits the H.264/AVC entropy coders better. A fast implementation algorithm is also designed. With little computation increase, experimental results confirm that the proposed fast algorithm achieves about 7.2% bit saving compared with the current H.264/AVC fidelity range extensions high profile.

Multiple description coding (MDC) is one of the widely used mechanisms to combat packet-loss in non-feedback systems. However, the number of descriptions in the existing MDC schemes is very small (typically 2). With the number of descriptions increasing, the coding complexity increases drastically and many decoders would be required. In this paper, the compressive sensing (CS) principles are studied and an alternative coding paradigm with a number of descriptions is proposed based upon CS for high packet loss transmission. Two-dimentional discrete wavelet transform (DWT) is applied for sparse representation. Unlike the typical wavelet coders (e.g., JPEG 2000), DWT coefficients here are not directly encoded, but re-sampled towards equal importance of information instead. At the decoder side, by fully exploiting the intra-scale and inter-scale correlation of multiscale DWT, two different CS recovery algorithms are developed for the low-frequency subband and high-frequency subbands, respectively. The recovery quality only depends on the number of received CS measurements (not on which of the measurements that are received). Experimental results show that the proposed CS-based codec is much more robust against lossy channels, while achieving higher rate-distortion (R-D) performance compared with conventional wavelet-based MDC methods and relevant existing CS-based coding schemes.

With the wide applications of saliency information in visual signal processing, many saliency detection methods have been proposed. However, some key characteristics of the human visual system (HVS) are still neglected in building these saliency detection models. In this paper, we propose a new saliency detection model based on the human visual sensitivity and the amplitude spectrum of quaternion Fourier transform (QFT). We use the amplitude spectrum of QFT to represent the color, intensity, and orientation distributions for image patches. The saliency value for each image patch is calculated by not only the differences between the QFT amplitude spectrum of this patch and other patches in the whole image, but also the visual impacts for these differences determined by the human visual sensitivity. The experiment results show that the proposed saliency detection model outperforms the state-of-the-art detection models. In addition, we apply our proposed model in the application of image retargeting and achieve better performance over the conventional algorithms.

Selective attention in the human visual system is performed as the way that humans focus on the most important parts when observing a visual scene. Many bottom-up computational models of visual attention have been devised to get the saliency map for an image, which are data-driven or task-independent. However, studies show that the task-driven or top-down mechanism also plays an important role during the formation of visual attention, especially with the cases of object detection and location. In this paper, we proposed a new computational visual attention model by combining bottom-up and top-down mechanisms for man-made object detection in scenes. This model shows that the statistical characteristics of orientation features can be used as top-down clues to help for determining the location for salient objects in natural scenes. Experiments confirm the effectiveness of this visual attention model.

H.264/AVC video coding standard with hierarchical bipredictive picture (HBP) generally outperforms the other prediction structures such as ‘IPPP…’ and ‘IBBP…’ through better exploitation of data correlation using the preceding and succeeding reference frames. However, due to the different coding order of frames, the HBP scheme could not fully exploit the data correlations using multiple reference frames for occluded background, repetitive motion, etc. In this paper, we propose a new HBP scheme which uses the most common reference frame in scene (McFIS) as a third reference frame with other two closest bipredictive reference frames assuming that foreground and background areas of the current frame are referenced from the two bipredicted frames and the McFIS respectively. The experimental results confirm that the proposed scheme outperforms two state-of-art algorithms by improving significant image quality with comparable computational time.

The general problem addressed in this paper is tracking the 2-D direction of arrival (DOA) of an acoustic source signal by using a single acoustic vector sensor (AVS). A Bayesian framework and its particle filtering implementation are introduced to adapt to the underwater ambient noise environment, in which both the interference and background noise exist. Several innovations are explored here: 1) a particle filtering based acoustic source tracking algorithm for AVS is developed; and 2) by using a multi-modality likelihood model to model the source detection and false alarm separately, the algorithm is able to alleviate the effect due to noise and interference. Particularly, by employing additional acoustic information, the proposed approach is able to track the 2-D DOA by using a single AVS. The performance of proposed approach is fully investigated under different simulated ambient noisy environments. Experiment results show that the proposed algorithm outperforms the traditional Capon beamforming approach and is able to lock on the 2-D DOA of the source even in a very challenging environment.

In video coding, an intra (I)-frame is used as an anchor frame for referencing the subsequence frames, as well as error propagation prevention, indexing, and so on. To get better rate-distortion performance, a frame should have the following quality to be an ideal I-frame: the best similarity with the frames in a group of picture (GOP), so that when it is used as a reference frame for a frame in the GOP we need the least bits to achieve the desired image quality, minimize the temporal fluctuation of quality, and also maintain a more consistent bit count per frame. In this paper we use a most common frame of a scene in a video sequence with dynamic background modeling and then encode it to replace the conventional I-frame. The extensive experimental results confirm the superiority of our proposed scheme in comparison with the existing state-of-art methods by significant image quality improvement and computational time reduction.

The H.264 video coding standard exhibits higher performance compared to the other existing standards such as H.263, MPEG-X. This improved performance is achieved mainly due to the multiple-mode motion estimation and compensation. Recent research tried to reduce the computational time using the predictive motion estimation, early zero motion vector detection, fast motion estimation, and fast mode decision, etc. These approaches reduce the computational time substantially, at the expense of degrading image quality and/or increase bitrates to a certain extent. In this paper, we use phase correlation to capture the motion information between the current and reference blocks and then devise an algorithm for direct motion estimation mode prediction, without excessive motion estimation. A bigger amount of computational time is reduced by the direct mode decision and exploitation of available motion vector information from phase correlation. The experimental results show that the proposed scheme outperforms the existing relevant fast algorithms, in terms of both operating efficiency and video coding quality. To be more specific, 82 ~92% of encoding time is saved compared to the exhaustive mode selection (against 58 ~74% in the relevant state-of-the-art), and this is achieved without jeopardizing image quality (in fact, there is some improvement over the exhaustive mode selection at mid to high bit rates) and for a wide range of videos and bitrates (another advantages over the relevant state-of-the-art).

Motion estimation (ME) and motion compensation (MC) using variable block size, fractional search, and multiple reference frames (MRFs) help the recent video coding standard H.264 to improve the coding performance significantly over the other contemporary coding standards. The concept of MRF achieves better coding performance in the cases of repetitive motion, uncovered background, non-integer pixel displacement, lighting change, etc. The requirement of index codes of the reference frames, computational time in ME&MC, and memory buffer for pre-coded frames limits the number of reference frames used in practical applications. In typical video sequence, the previous frame is used as a reference frame with 68~92% of cases. In this paper, we propose a new video coding method using a reference frame (i.e., the most common frame in scene (McFIS)) generated by the Gaussian mixture based dynamic background modelling. The McFIS is not only more effective in terms of rate-distortion and computational time performance compared to the MRFs but also error resilient transmission channel. The experimental results show that the proposed coding scheme outperforms the H.264 standard video coding with five reference frames by at least 0.5 dB and reduced 60% of computation time.

H.264/AVC FRExt (Fidelity Range Extensions) and Motion JPEG2000 are the latest inter-frame and intra-frame video coding standards, respectively. It is well known that an inter-frame method achieves higher coding efficiency compared with an intra-frame one, and the Motion JPEG2000 has been selected for digital cinema compression. In this paper, we attempt to compare these two different schemes with theoretical and experimental analysis for super-HD (high definition) visual signals. One additional contribution of the paper is that the impact of block partition, motion search range and skipped block size for inter-frame coding is discussed. Based on the analysis, we extend the standard H.264/AVC FRExt by using larger block size and search range. The experimental results show that this extension leads to higher coding efficiency and makes the H.264/AVC FRExt more suitable for super-HD video coding.

In this paper, we propose a low-complexity video codec based on two-dimensional Singular Value Decomposition (2D-SVD). We exploit the common temporal characteristics of video without resorting to motion estimation. It has been demonstrated that this codec has higher coding efficiency than the relevant existing low complexity codecs. Moreover, the proposed codec performs well to deal with packet loss that is unavoidable in error-prone transmission. Therefore it is with advantages and good potential for wireless video applications such as mobile video calls and wireless surveillance.

1The pattern-based video coding (PVC) outperforms the H.264 through better exploitation of block partitioning and partial block skipping. In the PVC scheme the best pattern is determined against the moving regions (MRs) in a macroblock (MB) of the current frame against the co-located MB in the reference frame; motion estimation (ME) and motion compensation (MC) are carried out using the pattern covered MRs, and the rest of the regions are treated as skipped areas. The MRs can be due to the object areas and the uncovered background (UCB) areas. Thus, the ME & MC by the pattern for the MRs of the UCB would not be accurate if there is no similar region in the reference frame. As a result no coding gain can be achieved for the UCB. Recently a dynamic background frame termed as the McFIS (the most common frame of a scene) has been generated using Gaussian mixture models for object detection. In this paper we propose a new PVC technique which will use the McFIS as a reference frame to determine the MRs where only object areas will be captured as the MRs. Thus, the proposed technique overcomes the mismatch problem of the UCB for ME&MC. The experimental results confirm the superiority of the proposed scheme in comparison with the existing PVC and McFIS-based methods by achieving significant image quality gain.

The existing image compression methods (e.g., JPEG2000, etc.) are vulnerable to bit-loss, and this is usually tackled by channel coding that follows. However, source coding and channel coding have conflicting requirement. In this paper, we address the problem with an alternative paradigm, and a novel compressive sensing (CS) based compression scheme is therefore proposed. Discrete wavelet transform (DWT) is applied for sparse representation, and based on the property of 2-D DWT, a fast CS measurements taking method is presented. Unlike the unequally important discrete wavelet coefficients, the resultant CS measurements carry nearly the same amount of information and have minimal effects for bit-loss. At the decoder side, one can simply reconstruct the image via l1minimization. Experimental results show that the proposed CS-based image codec without resorting to error protection is more robust compared with existing CS technique and relevant joint source channel coding (JSCC) schemes.

The conventional Intra (I-) frame is used for error propagation prevention, backward/forward play, random access, indexing, etc. This frame is also used as an anchor frame for referencing the subsequence frames. To get better rate-distortion performance a frame should have the following quality to be an ideal I-frame: the best similarity with the frames in a GOP, so that (i) when it is used as a reference frame for a frame in the GOP we need less bits to achieve the desired image quality; (ii) if any frame is missing at the decoding end we can retrieve the missing frame from it. In this paper we will generate a most common frame of a scene (McFIS) in a video sequence using dynamic background modelling and then encode it to replace the conventional I-frame. By using McFIS as an I-frame, we not only gain the above mentioned two benefits but also ensure adaptive GOP for better rate-distortion performance compared to the existing coding schemes. The experimental results confirm the superiority of our proposed scheme in comparison with the existing state-of-art methods by significant image quality and computation time.

The performance of a Brain-Computer Interface (BCI) depends on reliable feature extraction and accurate classification. Motor imagery has been successfully used in BCI for communication and control. During motor imagery, for EEG based BCI, it was known that the discriminative frequency bands are subject-specific. Moreover, such discriminative frequency bands for each subject might vary from time to time. In this paper, we investigate the variability of discriminative spectral ranges and its impact on classification accuracy. It is found that for each subject, his discriminative frequency bands changes significantly from session to session, but keeps almost stable within a session. We then propose a method to adaptively update the discriminative frequency bands using Time-Frequency fisher ratio. From the experimental analysis, it is found that we can reduce the average error rate by 11.50% compared to the case where fixed discriminative frequency bands obtained from calibration session are used.

The main application of Cognitive Radio (CR) is for the broadband wireless access in the rural areas which demand adequate data rate and multi user support. IEEE 802.22, the first standard evolved for CR suggests Orthogonal Frequency Division Multiple Access (OFDMA) as the physical layer technique. In this paper wavelet packet based multi carrier multiple access scheme is proposed which can be an equally promising candidate for high data rate multi user CR applications. The performance of the proposed system is evaluated using standard wavelet bases and is compared with OFDMA.

Brain computer interface (BCI) provides a direct communication pathway between a human and an external device. In this paper, we propose a new dasiadiscriminative filterbank common spatial pattern (DFBCSP)psila algorithm to select the subject-specific filters automatically during training for a motor imagery based BCI. The subject-specific filters are selected using the fisher ratio values of filtered electroencephalogram (EEG) signal. The channel dasiaC3psila alone could give sufficient information to select the discriminative filterbank for the proposed system. We have also explored the possibility of boosting the system performance by including dynamic temporal features. Fusion of static and dynamic features in the proposed DFBCSP frame work gave an average test accuracy of 92.44%, which is significantly better than conventional filterbank based common spatial pattern algorithms.

One of the major issues in cognitive radio is obtaining an adaptive transmitting waveform based on environmental measurements. An efficient Radio frequency (RF) carrier waveform generation based on approximate prolate spheroidal wave function and bandpass filters is proposed in this paper. Bandpass filters are designed using interpolation and masking technique which gives a sharp cut off at lower order. The bit error performance of the waveform is evaluated. The proposed system will be useful in accessing TV spectrum and has good spectral utilization efficiency.

The transmitted reference (TR) receiver provides a good alternative to the good-yet-expensive RAKE receiver for ultra wideband impulse radio (UWB-IR). TR receiver is however inefficient due to the necessity in transmitting a reference prior to the data symbol. In this paper, the performance of TR receiver is investigated under high data rate transmission and compared to that of RAKE receiver. It is shown that the performance of basic TR is worse than a fractionally spaced selective RAKE receiver. A simple selective TR transceiver is then proposed to improve the performance of TR under ISI. It is shown that the proposed approach achieves almost similar performance to that of selective RAKE receiver while still maintains the simplicity of TR.

Despite being well out of the physical layer interference range, cells of CSMA/CA-based wireless networks suffer unnecessary throughput degradation when they are within the carrier sensing range. To mitigate this medium access control layer-specific interference problem, many schemes have been proposed, of which transmit power and carrier sensing threshold control schemes stand out. In this paper, we propose a scheme which not only works out on its own but also improves the performance of these two schemes: Based on a 'cell identifier' imbedded in the header portion of all radio frames, nodes drop the payload portion of frames which number do not match that of their respective cells while their drop rates are within some tolerance limit. Using a realistic IEEE 802.11b simulator as our validation platform, we show that this scheme has some merit especially in the presence of path-loss or signal power measurement uncertainties.

This paper presents a semi-Markov model that evaluates the performance of TCP connections with asymmetric Additive Increase and Multiplicative Decrease (AIMD) congestion control settings involved in sharing of a common drop-tail router. We study the fairness of the connections and their individual bandwidth utilizations as well as packet loss rates. We confirm that certain asymmetric AIMD settings may achieve fairness in bandwidth sharing. We also found that while connections with asymmetric AIMD settings operate at different bandwidth utilizations, they generally experience similar packet loss rate.

In mobile ad hoc networks (MANETs), packets hop from a source to a series of forwarding nodes until they reach the desired destination. Defining the hop length to be the distance between two adjacent forwarding nodes, we observe that the two adjacent forwarding nodes tend to be farther away from each other with a higher probability in a one-dimensional MANET. We derive the probability density functions for the hop lengths to confirm our observation. Applying the developed results, we further formulate the relationship between the mean number of hops and the distance between the source and the destination.

Impulse radio system has an ultra wide bandwidth, which makes it a viable solution for short range high data rate system. With the introduction of the FCC UWB spectral mask, numerous works have produced pulse shapes whose spectrum closely fits the FCC spectral mask. Such pulse shapes have better energy per bandwidth ratio and thus perform better against noise. This paper shows that aside from the energy, there are other factors that also affect the performance under high speed indoor channel. The authors also show a simple yet efficient pulse shape hopping (PSH) method to improve the performance of the system, by exploiting orthogonal pulses.

TFRC is TCP-friendly rate control protocol with TCP Reno's throughput equation based. It is designed to mainly provide optimal service for unicast multimedia flow operating in the best-effort Internet environment. However, due to Reno's significant performance degradation in wireless networks, TFRC has also led to unavoidable performance suffering from wireless links due to the poor performance of Reno throughput. This paper proposes an enhancement of TFRC based on the differentiating method used in TCP Veno. Specifically, it utilizes Veno's state differentiator to distinguish congestion losses and non-congestion losses during transmission. By discounting the impact of those non-congestion losses on the throughput calculation, it can effectively alleviate the throughput degradation caused by wireless links. The simulation results have shown that, our proposal can achieve throughput improvement up to 70% as compared to the original TFRC. Meanwhile, it maintains other merits of the original TFRC, such as sending rate smoothness, fairness, and TCP-friendliness.

TCP Veno was proposed to eliminate TCP performance suffering from wireless links. Real network measurements and live Internet results have validated TCP Veno's significant throughput improvement in wireless networks and its harmonious co-existence with TCP Reno connections in wired networks. In this paper, we develop a simple analytic approach to characterize TCP Veno behavior in both wire and wireless situations. Being different from the equation of TCP Reno, a more general close formula is derived, taking into account of the refined multiplicative decrease algorithm in Veno, to model the throughput for a bulk transfer of TCP Veno flow. Our simulation and experimental results demonstrate that such an equation is able to accurately predict TCP Veno throughput over different network scenarios, ranging from very low lossy links to very heavy lossy links.

TCP Veno was recently proposed to eliminate TCP performance suffering from wireless links. Real network measurements and live Internet results have validated Veno's significant throughput improvement in wireless networks and its harmonious coexistence with TCP Reno connections in wired networks. We demonstrate the out-of-phase synchronization of Veno in one-way traffic, as opposed to the in-phase synchronization of Reno. The detailed studies of this behavior and its interaction with Reno are reported. Moreover, our careful study shows that this out-of-phase synchronization benefits network link utilization, and reduces the occurrence of congestion loss.

The computational complexity of linear phase finite impulse response (LPFIR) filters used in the channelizer of a wideband receiver is dominated by the number of adders (subtracters) employed in the multipliers. Common subexpression elimination (CSE) is a well-known technique for minimizing the number of adders in LPFIR filters. An improved CSE method is proposed in this paper, which is used to implement the channel filters of a filter bank channelizer (FBC). In the FBC, each modulated bandpass filter extract one channel from the input wideband signal. The reduction in number of adders is obtained by eliminating redundant multiplications of common subexpressions that exist among the channel filters of the FBC with the input signal. Design example of the channel filters employed in the digital advanced mobile phone system (D-AMPS) show that the proposed method offers considerable reduction in the number of full adders when compared with conventional CSE methods.

The flexibility of a software-defined radio (SRR) depends on its capability to operate in multi-standard wireless communication environments. The most computationally intensive part of wideband receivers is the channelizer, which extracts multiple narrowband signals from adjacent frequency hands. In an SDR receiver, the compatibility of the channelizer with different communication standards is guaranteed by its reconfigurability. This paper presents an efficient channelizer that has a reconfigurable architecture based on quadrature mirror filter bank (QMF) trees. We show that the channelizer can he efficiently implemented using common subexpression based filter structures. An example of dual-mode global system for mobile communication (GSM)/personal digital cellular (PDC) channelizer is discussed to illustrate the proposed design methodology.

The probability q/sub i/ of successful reception in a nonfading mobile radio channel with i contending mobiles transmitting to a central base station is studied for a number of different capture and spatial distribution models. It is shown that a generalized capture model can be used to estimate q/sub i/'s for a simplified example system which uses noncoherent frequency shift keying modulation. This model can be applied to other systems as well. An example of the use of the q/sub i/'s in the throughput evaluation of a finite population slotted ALOHA system is given. In most practical systems, the mobiles cannot get arbitrarily close to the base station. The effect of this constraint on q/sub i/ is examined. Finally, the dependence of the capture probability for a test mobile on its distance from the base station is obtained.>

To improve performance on the inbound (mobile-to-base-station) channel of a packet radio system consisting of a base station and a number of mobile users, the area around the base station is divided into M sectors. Signals originating from users in different sectors are received by different directional antennas at the base station. It is shown that, if the number of receivers at the base station is less than M, the selection of the antennas to be connected to the receivers becomes an issue. A number of antenna selection schemes are compared for three different channel models, assuming an ideal antenna pattern. It is found that the scheme that selects the antennas with the largest received signal powers is nearly optimum. The effects of a more practical nonideal antenna pattern are discussed.>

An automatic repeat-request (ARQ) scheme with memory and soft error detectors has been recently proposed by Benelli. Its performance was studied mainly through computer simulation. In this paper, a generalized version of this ARQ scheme is examined. The selection of certain thresholds and weights to minimize the bit error rate in systems using a fixed number of packet repeats is considered. Finally, the evaluation of the average number of transmissions per packet in systems in which negatively acknowledged packets are retransmitted until successfully received is described.