Bistatic multi-input multi-output (MIMO) radar has the capability of measuring the transmit angle from the receiving array, which means the existence of information redundancy and benefits data association. In this paper, a data association decision for bistatic MIMO radar is proposed and the performance advantages of bistatic MIMO radar in data association is analyzed and evaluated. First, the parameters obtained by receiving array are sent to the association center via coordinate conversion. Second, referencing the nearest neighbor association (NN) algorithm, an improved association decision is proposed with the transmit angle and target range as association statistics. This method can evade the adverse effects of the angle system errors to data association. Finally, data association probability in the presence of array directional error is derived and the correctness of derivation result is testified via Monte Carlo simulation experiments. Besides that performance comparison with the conventional phased array radar verifies the excellent performance of bistatic MIMO Radar in data association.

As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

In this letter, a quantization error-aware Tomlinson-Harashinma Precoding (THP) is proposed based on the equivalent zero-forcing (ZF) criterion in Multiuser Multiple-Input Single-Output (MU-MISO) systems with limited feedback, where the transmitter has only quantized channel direction information (CDI). This precoding scheme is robust to the channel uncertainties arising from the quantization error and the lack of channel magnitude information (CMI). Our simulation results show that the new THP scheme outperforms the conventional precoding scheme in limited feedback systems with respect to Bit Error Ratio (BER).

The issues of comparing the similarity or dissimilarity (distance) between structures have been studied. Especially, several distances between trees and their efficient algorithms have been proposed. However, all of the tree distances are defined based on mapping between vertices only, and they are helpless to compare the tree structures whose vertices and edges hold information. In this paper, we will propose a mapping between rooted and unordered trees based on vertex translation and edge translation, and then define a distance based on proposed mapping, and develop an efficient algorithm for computing proposed distance. Proposed distance can be used to compare the similarity or distance between two natural language sentences.

This paper discusses the largest common similar substructure (in short, LCSS) problem for trees. The problem is, for all pairs of "substructure of A and that of B," to find one of them, denoted by A and B', such that A is most similar to B' and the sum of the number of vertices of A and that of B' is largest. An algorithm for the LCSS problem for unrooted and unordered trees (in short, trees) and that for trees embedded in a plane (in short, Co-trees) are proposed. The time complexity of the algorithm for trees is O (max (ma, mb)2 NaNb) and that for CO-trees is O (mambNaNb), where, ma (mb) and Na (Nb) are the largest degree of a vertex of tree Ta (Tb) and the number of vertices of Ta (Tb), respectively. It is easy to modify the algorithms for enumerating all of the LCSSs for trees and CO-trees. The algorithms can be applied to structure-activity studies in chemistry and various structure comparison problems.

Several distances between trees have been proposed. However, most of the reports on distances have dealt with rooted and ordered trees. This paper proposes two distances between unrooted and cyclically ordered trees (CO-trees) and their computing methods. A CO-tree is a tree embedded in a plane. These distances are defined based on Tai's mapping (TM) and a strongly structure preserving mapping (SSPM) between CO-trees. The time complexities to compute the distances between two CO-trees Ta and Tb are OT (N 2aN 2b) for the distance based on a TM and OT(mambNaNb) for that on an SSPM, respectively, where ma(mb) and Na(Nb) are the largest degree of a vertex and the number of vertices of Ta(Tb), respectively. The space complexities of both methods are Os(NaNb). Those distances can be applied to the clustering of CO-trees.

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.

Erasure codes have been considered as one of the most promising techniques for data reliability enhancement and storage efficiency in modern distributed storage systems. However, erasure codes often suffer from a time-consuming coding process which makes them nearly impractical. The opportunity to solve this problem probably rely on the parallelization of erasure-code-based application on the modern multi-/many-core processors to fully take advantage of the adequate hardware resources on those platforms. However, the complicated data allocation and limited I/O throughput pose a great challenge on the parallelization. To address this challenge, we propose a general multi-threaded parallel coding approach in this work. The approach consists of a general multi-threaded parallel coding model named as MTPerasure, and two detailed parallel coding algorithms, named as sdaParallel and ddaParallel, respectively, adapting to different I/O circumstances. MTPerasure is a general parallel coding model focusing on the high level data allocation, and it is applicable for all erasure codes and can be implemented without any modifications of the low level coding algorithms. The sdaParallel divides the data into several parts and the data parts are allocated to different threads statically in order to eliminate synchronization latency among multiple threads, which improves the parallel coding performance under the dummy I/O mode. The ddaParallel employs two threads to execute the I/O reading and writing on the basis of small pieces independently, which increases the I/O throughput. Furthermore, the data pieces are assigned to the coding thread dynamically. A special thread scheduling algorithm is also proposed to reduce thread migration latency. To evaluate our proposal, we parallelize the popular open source library jerasure based on our approach. And a detailed performance comparison with the original sequential coding program indicates that the proposed parallel approach outperforms the original sequential program by an extraordinary speedups from 1.4x up to 7x, and achieves better utilization of the computation and I/O resources.

A new hybrid brain-computer interface (BCI), which is based on sequential controls by eye tracking and steady-state visual evoked potentials (SSVEPs), has been proposed for high-speed spelling in virtual reality (VR) with a 40-target virtual keyboard. During target selection, gaze point was first detected by an eye-tracking accessory. A 4-target block was then selected for further target selection by a 4-class SSVEP BCI. The system can type at a speed of 1.25 character/sec in a cue-guided target selection task. Online experiments on three subjects achieved an averaged information transfer rate (ITR) of 360.7 bits/min.

This paper provides a prototype neural prosthesis system dedicated to restoring continence and micturition function for patients with lower urinary tract diseases, such as detrusor hyperreflexia and detrusor-sphincter dyssynergia. This system consists of an ultra low-noise electroneurogram (ENG) signal recording module, a bi-phasic electrical stimulator module and a control unit for closed-loop bladder monitoring and controlling. In order to record extremely weak ENG signal from extradural sacral nerve roots, the system provides a programmable gain from 80 dB to 117 dB. By combining of advantages of commercial-off-the-shelf (COTS) electronics and custom designed IC, the recording front-end acquires a fairly low input-referred noise (IRN) of 0.69 μVrms under 300 Hz to 3 kHz and high area-efficiency. An on-chip multi-steps single slope analog-to-digital converter (ADC) is used to digitize the ENG signals at sampling rate of 10 kSPS and achieves an effective number of bits (ENOB) of 12.5. A bi-phasic current stimulus generator with wide voltage supply range (±0.9 V to ±12.5 V) and variable output current amplitude (0-500 μA) is introduced to overcome patient-depended impedance between electrode and tissue electrolyte. The total power consumption of the entire system is 5.61 mW. Recording and stimulation function of this system is switched by control unit with time division multiplexing strategy. The functionality of this proposed prototype system has been successfully verified through in-vivo experiments from dogs extradural sacral nerve roots.

This paper discusses the problems of largest similar substructures (in short, LSS) in rooted and unordered trees (in short, R-trees) and those in unrooted and unordered trees (in short, trees). For two R-trees (or trees) Ta and Tb, LSS in Tb to Ta is defined, and two algorithms for finding one of the LSSs for R-trees and that for trees are proposed. The time and space complexities of both algorithms are OT (m3NaNb) and OS(mNaNb), respectively, where m is the largest degree of a vertex of Ta and Tb, and Na(Nb)is the number of vertices of Ta(Tb).