Selection Of Compression Ratio Research Articles

Information aggregation and data compression based on PLC for distributed photovoltaic integration

The massive integration of distributed photovoltaics (PVs) into power grid causes rapid growth of data collection, which has put significant pressure on power line communication (PLC). Edge computing enables efficient data compression on edge-side gateway to reduce data size. However, it still faces challenges such as the coupling between transmission delay and compression delay, as well as the coupling between data compression ratio selection and gateway selection. In this paper, we formulate the information aggregation and data compression problem to minimize the total delay, and develop a delay-aware upper confidence bound (UCB) algorithm. It sets the negative value of delay as the learning reward to achieve delay-aware learning, and jointly optimizes gateway and data compression ratio selection based on upper confidence bound ranking and Karush-Kuhn-Tucker (KKT) conditions. Simulation results demonstrate the superior performance of the proposed algorithm.

Online policies for throughput maximization of backscatter assisted wireless powered communication via reinforcement learning approaches

In this paper, we consider the design of online policies in a backscatter assisted wireless powered communication system, aiming at maximizing the longterm average throughput. We consider a complete data life cycle, from sampling, compression, transmission to reception and decompression. Practical constraints including finite battery capacity, time-varying uplink channel and nonlinear energy harvesting model are considered. An optimization problem is formulated in a Markov decision process framework to maximize the longterm average throughput by a hybrid of mode switching, time and power allocation, and compression ratio selection. Capitalizing on this, we first adopt value iteration (VI) algorithm to find offline optimal solution as benchmark. Then, we propose Q-learning (QL) and deep Q-learning (DQL) algorithms to obtain online solutions without prior information. Simulation results demonstrate the effectiveness of the hybrid transmission mode with flexible data compression. Furthermore, DQL-based online solution performs the closest to the optimal VI-based offline solution and significantly outperforms the other two baseline schemes QL and random policy. Insight analysis on the structure of the optimal policy is also provided.

Dynamic Compression Ratio Selection for Edge Inference Systems With Hard Deadlines

Implementing machine learning algorithms on the Internet-of-Things (IoT) devices has become essential for emerging applications, such as autonomous driving and environment monitoring. But the limitations of computation capability and energy consumption make it difficult to run complex machine learning algorithms on IoT devices, especially when the latency deadline exists. One solution is to offload the computation-intensive tasks to the edge server. However, the wireless uploading of the raw data is time consuming and may lead to deadline violation. To reduce the communication cost, lossy data compression can be exploited for inference tasks but may bring more erroneous inference results. In this article, we propose a dynamic compression ratio selection scheme for edge inference system with hard deadlines. The key idea is to balance the tradeoff between the communication cost and inference accuracy. By dynamically selecting the optimal compression ratio with the remaining deadline budgets for queued tasks, more tasks can be timely completed with correct inference under limited communication resources. Furthermore, information augmentation that retransmits less compressed data of task with erroneous inference, is proposed to enhance the accuracy performance. While it is often hard to know the correctness of inference, we use uncertainty to estimate the confidence of the inference and based on that, jointly optimize the information augmentation and compression ratio selection. Finally, considering the wireless transmission errors, we further design a retransmission scheme to reduce performance degradation due to packet losses. The simulation results show the performance of the proposed schemes under different deadlines and task arrival rates.

Temperature, Heat Distribution and Performance Analysis in a Variable Compression Ratio Internal Combustion Engine

The internal combustion engines are also known as heat engines because of the utilization of heat energy of the fuel to convert it in to mechanical energy of the engine which finally runs the vehicle. Therefore, it is necessary to examine the generation and utilization of heat inside and outside the engine. For the above mentioned purpose, a variable compression ratio engine was used to perform experiment. The heat distribution of the used engine was investigated to recognize the heat loss areas from the engine. The experiments were performed by changing compression ratio from 15 to 21 and an optimum compression ratio was found. The experiments are further extended to find an optimum load value at standard compression ratio of the engine. The analysis was performed by using various factor of heat utilization such as heat supplied by fuel, heat equivalent to brake power, heat loss to exhaust gases, heat loss to engine cooling water and heat unaccounted. The proposed research will be useful to overcome the challenges during selection of compression ratio for design of an engine in industries.

SPIHT Algorithm With Adaptive Selection of Compression Ratio Depending on DWT Coefficients

In mobile multimedia devices, the frame memory compression (FMC) technique by embedded compression (EC) is becoming an increasingly important video-processing method for reducing the external data bandwidth requirement, which, in turn, results in power savings. Among various EC schemes, the combination of discrete wavelet transform (DWT) and set partitioning in hierarchical trees (SPIHT) is widely used for FMC because it achieves high compression efficiency with low computational complexity. However, there is room for improvement in the conventional DWT and SPIHT algorithm because it compresses all blocks with the same compression ratio without taking into account the correlation between DWT coefficients and the SPIHT algorithm. This study proposes a novel one-dimensional (1-D) DWT and SPIHT algorithm, which enhances the quality of the compressed video by internally applying an adaptive compression ratio for the SPIHT algorithm based on DWT coefficients while keeping the same bit-stream size. The block complexity is predicted from the distribution of DWT coefficients. Then, simple blocks are aggressively compressed with a low compression ratio, while the complex blocks are compressed with a high ratio. Furthermore, to achieve the best video quality, each compression ratio is decided by an optimization technique based on mathematical formulation. Precisely, the logarithm of mean squared error by the SPIHT algorithm is assumed to be linearly correlated with the logarithm of processed DWT coefficients. Experimental results are provided that support the aforementioned model. Compared to the conventional 1-D DWT and SPIHT algorithm, the proposed scheme remarkably improves the video quality by an average of 2.23 dB in peak signal-to-noise ratio when the target compression ratio for the SPIHT algorithm is 5/16.

A review of design considerations for light-duty diesel combustion systems

Practical aspects of light-duty diesel combustion system design are reviewed, with an emphasis on design considerations reported by manufacturers and engine design consultancies. The factors driving the selection of compression ratio, stroke-to-bore ratio, and various aspects of combustion chamber geometry are examined, along with the trends observed in these parameters in recently released engines. The interactions among geometric characteristics, swirl ratio, and the fuel injection nozzle parameters are also reviewed.

The discrete modal transform and its application to lossy image compression

This paper introduces the discrete modal transform (DMT), a 1D and 2D discrete, non-separable transform for signal processing, which, in the mathematical sense, is a generalization of the well-known discrete cosine transform (DCT). A 3D deformable surface model is used to represent the image intensity and the introduced discrete transform is a by-product of the explicit surface deformation governing equations. The properties of the proposed transform are similar to those of the DCT. To illustrate these properties, the proposed transform is applied to lossy image compression and the obtained results are compared to those of a DCT-based compression scheme. Experimental results show that DMT, which includes an embedded compression ratio selection mechanism, has excellent energy compaction properties and achieves comparable compression results to DCT at low compression ratios, while being in general better than DCT at high compression ratios.

Effect of compression ratio on speech recognition and speech-quality ratings with wide dynamic range compression amplification.

This project examined the effect of varying compression ratio on speech recognition and quality. Both listeners with mild-to-moderate sensorineural hearing loss and a control group of listeners with normal hearing participated. Test materials were sentences from the Connected Speech Test (R. M. Cox, G. C. Alexander, & C. Gilmore, 1987) which were digitally processed with linear amplification and wide dynamic range compression amplification with 3 compression ratios. Speech-recognition scores were obtained with sentences in quiet and in noise at a 10-dB signal-to-noise ratio for each amplification condition. Additionally, the participants rated each amplification condition in terms of clarity, pleasantness, ease of understanding, and overall impression. Results indicated that, for speech in quiet, compression ratio had no effect on speech-recognition scores; however, speech-quality ratings decreased as compression ratio increased. For speech in noise, both speech-recognition scores and ratings decreased with increasing compression ratio for the listeners with hearing loss. These results suggest that selection of compression ratio on the basis of speech-quality judgments does not compromise speech recognition.

Effect of compression ratio in a slow-acting compression hearing aid: Paired-comparison judgments of quality

Paired-comparison judgments of quality were obtained from 20 hearing-impaired listeners (half with a small dynamic range and half with a large dynamic range) for speech-in-noise (vent, apartment, and cafeteria) processed through a slow-acting compression hearing aid. Compression ratio was varied (1, 1.5, 2, 3, 5, and 10:1). Compression threshold, attack time, and release time were fixed. Sound quality judgments were significantly affected by compression ratio, noise, and dynamic range. Preference decreased with increasing compression ratio. The selection of compression ratio. The selection of compression ratios < or = 2:1 was significantly higher than of compression ratios > 3:1. Less compression (no compression or 1.5:1) was preferred with the highest level noise (cafeteria noise) than with the lower level noises (vent or apartment). In particular, the small dynamic range group preferred compression with the vent and apartment noises (noise below the compression threshold), but preferred a linear hearing aid with the cafeteria noise (above the compression threshold). The large dynamic range group showed a slightly greater preference for the linear hearing aid for all three noises.