Block Compressive Sensing Single-View Video Reconstruction Using Joint Decoding Framework for Low Power Real Time Applications

Several real-time visual monitoring applications such as surveillance, mental state monitoring, driver drowsiness and patient care, require equipping high-quality cameras with wireless sensors to form visual sensors and this creates an enormous amount of data that has to be managed and transmitted at the sensor node. Moreover, as the sensor nodes are battery-operated, power utilization is one of the key concerns that must be considered. One solution to this issue is to reduce the amount of data that has to be transmitted using specific compression techniques. The conventional compression standards are based on complex encoders (which require high processing power) and simple decoders and thus are not pertinent for battery-operated applications, i.e., VSN (primitive hardware). In contrast, compressive sensing (CS) a distributive source coding mechanism, has transformed the standard coding mechanism and is based on the idea of a simple encoder (i.e., transmitting fewer data-low processing requirements) and a complex decoder and is considered a better option for VSN applications. In this paper, a CS-based joint decoding (JD) framework using frame prediction (using keyframes) and residual reconstruction for single-view video is proposed. The idea is to exploit the redundancies present in the key and non-key frames to produce side information to refine the non-key frames’ quality. The proposed method consists of two main steps: frame prediction and residual reconstruction. The final reconstruction is performed by adding a residual frame with the predicted frame. The proposed scheme was validated on various arrangements. The association among correlated frames and compression performance is also analyzed. Various arrangements of the frames have been studied to select the one that produces better results. The comprehensive experimental analysis proves that the proposed JD method performs notably better than the independent block compressive sensing scheme at different subrates for various video sequences with low, moderate and high motion contents. Also, the proposed scheme outperforms the conventional CS video reconstruction schemes at lower subrates. Further, the proposed scheme was quantized and compared with conventional video codecs (DISCOVER, H-263, H264) at various bitrates to evaluate its efficiency (rate-distortion, encoding, decoding).

Performance on Joint Turbo Coding for Overloaded MIMO-OFDM Systems

To achieve a sen sational mistake adjusting capability, the 0.33 generation Partnership challenge (3GPP) prolonged haul improvement (LTE) makes use of speedy codes as its in advance blunder rectifying (FEC) modern day. moreover, to benefit better throughput, a LTE moreover executes numerous records various yield (MIMO) systems..no matter the way that a traditional rapid unwinding plan offers appealing execution within the best MIMO frameworks, significant execution degradation takes place in over-load MIMO frameworks at the same time as the amount of transmitting wires is greater outstanding than that of gathering mechanical assemblies. on this paper, a joint decoding plan for quicker codes, proposed with AWGN channel. In joint faster deciphering, calculations of sensitive statistics are driven for each combination of bits from all streams in place of freely among every circulate. to enroll within the trellis plots from each the streams, a incredible-trellis diagram is used. The numerical outcomes prepared through pc propagation show off that the proposed scheme offers tremendous execution over the usual affiliation in particular via distinct characteristic of an over-stacked MIMO frameworks. in the MIMO frameworks with 4 transmit and recipients, as much as as a minimum one.0 dB execution development can be gained at a bit mistake charge (BER) of . The proposed plot also accomplishes advanced throughput with a throughput/(SNR/flow into) quantity close to the theoretical furthest reaches of over-problem MIMO framework

Bidirectional Transition-Based Dependency Parsing

Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the prediction of subsequent actions. In this paper, we propose a simple framework for bidirectional transitionbased parsing. During training, we learn a left-to-right parser and a right-to-left parser separately. To parse a sentence, we perform joint decoding with the two parsers. We propose three joint decoding algorithms that are based on joint scoring, dual decomposition, and dynamic oracle respectively. Empirical results show that our methods lead to competitive parsing accuracy and our method based on dynamic oracle consistently achieves the best performance.