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This paper focuses on the FPGA implementation of a slew-rate reduction (SR) shaping function for envelope tracking (ET) power amplifiers (PAs). The SR envelope has been proved effective to trade-off power efficiency and linearity in ET PA systems where the envelope tracking modulator (ETM) is bandwidth limited. However, the implementation issues need to be addressed when targeting high clock rates to cope with current 5G new radio wide-band signals. This paper shows the FPGA implementation of the SR envelope generation. We explore the use of high-level synthesis (HLS) for the SR envelope generation to evaluate the performance and resource usage of the hardware architecture. The HLS design is also compared with a hand-written hardware description language (HDL) version. An in-depth analysis shows strengths and limitations of the HLS design to meet the timing constraints when considering a throughput of 614.4 MSa/s. © 2022 IEEE.

This paper presents a new approach for achieving bitrate and pixel rate reduction in the MPEG immersive video coding setting. We demonstrate that it is possible to avoid the transmission of some depth information in the Test Model for Immersive Video (TMIV) by estimating it at the receiver's side. Although the transmitted information in TMIV is considered as non-redundant, we show that it is possible to improve this algorithm. This method provides 3.4%, 9.0%, and 12.1% average BD-rate gain for natural content on high, medium, and low bitrate, respectively, with up to respectively 12.3%, 16.0%, and 18.4% peak reductions. Moreover, it preserves the perceptual quality as measured with MS-SSIM and VMAF metrics. Additionally, it decreases the pixel rate by 8.3% for each test sequence.

This paper implements the infection process of 2019 Novel Coronavirus Diseases (COVID-19) in an agent-based model and compares the effectiveness of multiple infection prevention measures. In the model, 1120 virtual residents agents live in two towns where they commute to office or school and visiting stores. The model simulates an infection process in which they were exposed to the risk of transmission of the novel coronavirus. The results of the experiments showed that individual infection prevention measures (commuting, teleworking, class closing, contact rate reduction, staying at home after fever) alone or partially combined them do not produce significant effects. On the other hand, if comprehensive measures were taken, it was confirmed that the number of deaths, the infection rate, and the number of severe hospitalised patients per day was decreased significantly at the median and maximum respectively.

Plants are everywhere around the Earth. Plant identification is a very important problem for environmental protection and exploration. But getting to know various plants requires taking into account a large number of features which might be easy for a botanist but not easy for ordinary people. So having an automatic Plant identifier which uses a leaf to identifying plants will help many people. Here, a method is proposed where Convolutional Neural Network (CNN) technique is used to classify plants using leaf images. Using Adam optimizer and automatic Learning Rate reduction technique the model gave promising accuracy. This system was trained on 3600 RGB leaf images of 2 categories for 6 different plant species. The model reported promising results with validation accuracy was 95.86% and training accuracy was 96.54%. Different pre-processing techniques such as background whitening, noise removal are used. In the convolutional networks activation function ReLU is used in the hidden layer and Softmax for output layer.

In ordinary near infrared qualitative identification, maize seed were not covered with seed coating agent. While, in actual agricultural market, maize seeds always should be covered by seed coating agents to resist diseases invasion and pests, improve germination rate, and increase yield. The kinds of seed coating are many and varied, and it is hard to determine their components. Therefore it is usually necessary to build identification model by maize seeds without seed coating, and then use the model to recognize seeds with seed coating. The maize seeds coating usually mixed by insecticides, fungicides, fertilizer, plant growth regulators, etc. These components often include hydrogen group organic compounds, which have certain absorption to near infrared spectrum. So the seed coating agent has an interference on near infrared spectroscopy qualitative identification effect. It will reduce the performance of conventional machine learning methods significantly. To reduce the influence caused by seed coating, a method of near infrared spectroscopy qualitative modeling based on deep learning method has been proposed in this paper. Firstly, maize seed spectrum without seed coating agent were used as training set, then a qualitative analysis model is constructed by stack auto encoder algorithm and Softmax classifier. With this deep learning model, maize seeds with seed coating can be identified. The experimental results indicated with the method based on deep learning, maize varietal authenticity recognition rate reduction caused by seed coating is controlled within 3%.

Deep neural networks recently became the state-of-the-art in many pattern recognition tasks, including speech recognition. Currently the de facto standard in these networks is to use the rectified linear activation function, but there is an ongoing quest for even more efficient functions for this purpose. In parallel, many authors experiment with augmenting the network structures with extra connections with the goal of building deeper networks. Here, we combine the two approaches and propose the very simple technique of composing the layers of the network both from rectified and linear neurons. While this solution has basically no cost, in our speech recognition experiments these linearly augmented networks attained a modest, but significant error rate reduction over the standard rectifier network in all cases. In comparison with a more involved activation function, we found that the proposed method performs equivalently or slightly better than a maxout network when trained on a larger data set, while it is computationally simpler.

We propose a simple and cost effective DRoF-based optical video transmission system. Experiments on actual multiple terrestrial broadcasting signals verify the feasibility of the system and its new rate reduction method.

This paper considers a network consists of multiple primary users (PUs) and secondary users (SUs). To seek for the pre- and post-coding matrices of the PUs and SUs that improve the system output, the author applies Max-SINR algorithm for the PUs and cognitive interference alignment (IA) for the SUs. Then by counting the number of variables and the number of equations in the IA condition, this paper analyzes the achievable DoF of all users. Simulation results show that the PUs using max-SINR achieve higher rate than using standard IA with negligible SUs rate reduction.

Anti-reflective coatings (ARC) are commonly used within the solar industry as a means to increase overall photovoltaic (PV) module efficiency. A PV module with ARC will increase light absorption, allowing for enhanced performance when compared to PV modules without ARC. Beyond increasing module efficiency by increasing overall light absorption, ARC modules can provide additional benefits due to environmental interactions when compared to non-ARC modules. A common environmental consideration for utility-scale plant performance is the impact of soiling accumulation on plant performance. With the widespread adoption of ARC modules comes an opportunity to understand and quantify the impact of soiling on both ARC and non-ARC PV Modules during a field deployment. In this study we show how soil accumulation and soiling rates vary between both ARC and non-ARC First Solar CdTe PV modules in various climatic regions in North and South America. Results suggest that First Solar ARC CdTe modules can provide up to a 60 percent anti-soiling accumulation rate reduction benefit throughout the year when compared to First Solar non-ARC CdTe modules.

This paper presents a generalized bi-prediction (GBi) technique to extend the notion of the existing weighted bi-prediction to prediction unit (PU) level. GBi allows bi-prediction weights to be transmitted at the coding unit (CU) level and used for the bi-predicted PUs within that CU. The candidate set of weights in the GBi mode includes a total of 7 weights, including 1/2 used for conventional bi-prediction. An index pointing to the entry of a weight value in the candidate weight set is signaled. At most one index per CU is signaled and the corresponding weight values are shared across all the bi-prediction PUs and all color components in that CU. Besides, GBi can also be extended to inter coding tools, such as, merge mode and affine prediction that support bi-prediction. Experimental results show that GBi performs consistently better than the JEM-2.0 anchor in all rate points, with an average Y Bjentegaard delta rate reduction of 1% and no extra decoding burden introduced.

In cellular networks, the Shortest Remaining Processing Time first (SRPT) principle, which is known to be optimal for sharing jobs on a single-server system, can deliver significant advantages over conventional scheduling algorithms. Transmissions are finished earlier, which improves user experience and enables operators to allow more users in their networks. However, this comes at the cost of reduced cell throughput and a penalty for large objects in peak traffic situations. We therefore propose an extension to SRPT that combines it with opportunistic scheduling and allows to tradeoff the advantages of both sides. We evaluate and compare the performance of SRPT and conventional schedulers by simulation with a realistic traffic scenario. An important metric is the transmission duration of application layer objects as it is central for the users' Quality of Experience (QoE). The results show that the proposed scheduler provides the superior QoE of SRPT for short, interactive transmissions while keeping the rate reduction for larger traffic objects at an acceptable level.

This paper summarizes our recent efforts made to automatically transcribe call center conversations in real-time. Data sparseness issue is addressed due to the small amount of transcribed training data. Accordingly, first the potentials in the inclusion of additional non-conventional training texts are investigated, and then morphological language models are introduced to handle data insufficiency. The baseline system is also extended with explicit models for non-verbal speech events such as hesitation or consent. In addition, all the above techniques are efficiently combined in the final system. The benefit of each approach is evaluated on real-life call center recordings. Results show that by utilizing morphological language models, significant error rate reduction can be achieved over the word baseline system, which is preserved across experimental setups. The results can be further improved if nonverbal events are also modeled.

In this paper, we present multi-hop relay communication strategies for half-duplex decode-and-forward relay networks. The model for the channel that we consider includes pathloss, shadowing, and fast fading. For this channel model, we propose a full spatial reuse multi-hop (FSRM) relay communication scheme, which allows relays to transmit their data using every other time slot. With the FSRM scheme, the end-to-end rate reduction factor of multi-hop relay communication is fixed at 1/2, regardless of the number of hops. We analyze the outage probability of the proposed scheme for a directional antenna system and an omnidirectional antenna system. For a directional antenna system, the analysis reveals that the FSRM scheme achieves a lower outage probability than the traditional orthogonal multi-hop relaying scheme. For an omnidirectional antenna system, we find the SNR region of the proposed scheme that achieves better performance. We further analyze various power allocation methods to manage interference and determine the optimal operation scheme in terms of the SINR.

Traditional rate-distortion theory is focused on how to best encode a signal using as few bits as possible and incurring as low a distortion as possible. However, very often, the goal of transmission is to extract specific information from the signal at the receiving end, and the distortion should be measured on that extracted information. In this paper we examine the problem of encoding signals such that sufficient information is preserved about their pair wise distances. For that goal, we consider randomized embeddings as an encoding mechanism and provide a framework to analyze their performance. We also propose the recently developed universal quantized embeddings as a solution to that problem and experimentally demonstrate that, in image retrieval experiments, universal embedding can achieve up to 25% rate reduction over the state of the art.

Fluctuating global economic trends impact product demand, which in turn impact assembly materials volume and inventory levels in the factories leading to excessive materials scrap due to shelf life expiry during demand downturns. Therefore, the need to drive for assembly direct materials shelf life extension has become a critical focus area to improve materials inventory management. This paper describes an approach to extend the shelf life of assembly direct materials (epoxy underfill and Flip Chip Ball Grid Array (FCBGA) substrate materials) to minimize the impact of dynamic volume changes on materials scrap and provide flexibility plus cost savings in managing materials inventory. 33% scrap rate reduction was achieved for underfill and significant cost savings were realized for FCBGA substrates.

This paper describes a new approach for significantly improving overall defect coverage for CMOS-based designs. We present results from a defect-oriented cell-aware (CA) library characterization and pattern-generation flow and its application to 1,900 cells of a 32-nm technology. The CA flow enabled us to detect cell-internal bridges and opens that caused static, gross-delay, and small-delay defects. We present highvolume production test results from a 32-nm notebook processor to which CA test patterns were applied, including the defect rate reduction in PPM that was achieved after testing 800,000 parts. We also present cell-internal diagnosis and physical failure analysis results from one failing part.

Orthogonal frequency division multiplexing (OFDM) suffers from significant out-of-band radiation due to high sidelobes of modulated subcarriers. In a cognitive radio (CR) system this effect results in high interference between secondary and primary users (PUs). Existing methods for reducing this interference have the drawbacks of significant data transmission rate reduction or power peak to average ratio (PAPR) growth. In this paper an adaptive optimal method is proposed that treats on OFDM signal in both time and frequency domains. Simulation results show that by using this algorithm, it is possible to achieve a high value of sidelobe suppression while the PAPR value of the transmitted signal is unchanged and the transmission rate reduction is lower than similar methods.

A system normally has a finite useful life. The new system in each replacement cycle seldom has exactly the same characteristics and properties. However, maintenance problems are usually defined over the infinite time span where the system is assumed to have the same conditions and costs in each replacement cycle. This assumption might not be practical. Therefore, the purpose of this research is to develop the optimal preventive maintenance (PM) policies with degradation rate reduction over a finite time span under a warranty consideration by minimizing the expected total maintenance cost. Two cases are studied and compared, one is no warranty provided and the other is with a given warranty period. For the second case, two policies, performing and not performing PM within the warranty period, are examined. In this paper, the algorithm for searching the optimal solution is presented. Examples are given and the sensitivity analysis is also provided.

It is expected that the soft error rate (SER) of combinational logic will increase significantly. Previous solutions to mitigate soft errors in combinational logic suffer from delay penalty or area/power overhead. In this paper, we proposed an output remapping technique to reduce SER of critical paths. Experimental results show up to about 20X increase in Qcritical. So the SER is reduced significantly. This method does not introduce any delay penalty. The area/power overhead is limited as well. The output remapping method is based on our novel glitch width model. The analysis shows that output remapping technique works well along with technology scaling.

We present theory, design and simulation results for a silicon neuron circuit that achieves extremely low spike rates and small footprint by exploiting the low current characteristics in floating gate structures. As in biological counterparts, the spike rate is compressed against stimulant current. Simulations confirm sub-Hertz spike rates in steady state with a stimulant current of 7pA and below, and up to 100x spike rate reduction at InA. With reasonable device variation modelling, Monte Carlo simulation shows that spike rate varies by a standard deviation of 25%.

Robustness in the presence of various types and severities of environmental noise has been researched extensively over the past several years, however this remains one of the main problems facing automatic speech recognition systems. This paper describes a noise-robust ASR front-end that employs a new variable frame rate analysis, based upon the first-order difference of the log energy for each frame (DE). Compared with previous variable frame rate methods, this delta energy approach is simpler and achieves similar recognition accuracy improvements but at reduced complexity. Recognition experiments on the Aurora II connected digits database reveal that the proposed front-end achieves an average digit recognition accuracy of 68.69% for a model set trained from clean data and 85.82% for a model set trained from data with multiple noise conditions. Compared with the ETSI standard Melcepstral front-end, the proposed front-end obtains a relative error rate reduction of around 20% for the clean model set, achieved consistently across nearly all signalto-noise ratios and noise conditions tested.

The Large Hadron Collider at CERN offers unprecedented challenges to the design and construction of detectors and trigger/data acquisition systems. For ATLAS, a three level trigger system has been developed to extract interesting physics signatures with a 106 rate reduction. To accomplish this, components of physics analysis traditionally deferred to offline physics analysis must be embedded within the online trigger system. For the muon trigger, the specific off-line algorithms MOORE (muon object oriented reconstruction) and MuId (muon identification) have been adopted so far for the on-line use, imposing an operation in a Bayesian-like environment where only specific hypotheses must be validated. After a short review of the ATLAS trigger, the paper shows the general strategy of the muon identification and selection accessing the full event data, or being seeded from results derived at a previous stage of the trigger chain

Summary form only given. The author describes how the price to beat is one of the key linchpins in ensuring that robust retail competition develops in the restructured Texas electric marketplace. The price to beat is intended to both ensure that residential and small commercial customers see a rate reduction from regulated utility rates, as well as ensure that there is an adequate margin for new competitors.

Quantization errors of a sigma-differential pulse code modulation (SDPCM), a multibit version of sigma-delta modulation, are analyzed in the time domain. It is shown that a certain type of sampling rate reduction always reduces the worst-case quantization error r/sup N/ times, where r is a factor of rate reduction and N is the order of the SDPCM. >

This paper discuss two methodologies for screening potential VLSI CMOS infant mortality failures, caused ether by the in-circuit components or the overall circuit performance degradation. The first is based on the direct input/output buffers leakage testing, the second employs the power supply current measurement, while no internal node states are toggling. Experimental data, showing strong correlation between rejected devices and early failure rate reduction is demonstrated. Finally, early failure screening technique, efficient both in cost ant time, utilizing existing test equipment is suggested for production testing as well as for process reliability monitoring.

A system is presented for transform coding of imagery. Specifically, the system uses the 2-D discrete cosine transform (DCT) in conjunction with adaptive classification, entropy-constrained trellis-coded quantization, optimal rate allocation, and adaptive arithmetic encoding. Adaptive classification, side rate reduction, and rate allocation strategies are discussed. Entropy-constrained codebooks are designed using a modified version of the generalized Lloyd algorithm. This entropy-constrained DCT-based system is shown to achieve outstanding coding performance as compared to other DCT-based systems.

The relation between yield and early failures as encountered in the field was investigated with several high volume IC's manufactured in several processes. A clear correlation between yield and reliability was found that obeys a simple model. The model also provides the ability to predict the FIT or PPM level of a given IC based on its yield. Using these results we illustrate how a wafer fab may improve the reliability level of its products in a controlled way.