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A dual-wavelength Tm super( 3+):ZBLAN fibre laser operating around 1480 nm is demonstrated. A linear cascaded cavity with bidirectional upconversion pumping at 1064 nm was used; alternate single wavelength and dual-wavelength operation is achieved by simple adjustment of the pump powers. A wavelength spacing of only 0.6 nm is obtained for dual-wavelength operation.

Owing to the constantly rising energy demand, Internal Combustion Engine (ICE)-equipped vehicles are being replaced by Electric Vehicles (EVs). The other advantage of using EVs is that the batteries can be utilised as an energy storage device to increase the penetration of renewable energy sources. Integrating EVs with the grid is one of the recent advancements in EVs using Vehicle-to-Grid (V2G) technology. A bidirectional technique enables power transfer between the grid and the EV batteries. Moreover, the Bidirectional Wireless Power Transfer (BWPT) method can support consumers in automating the power transfer process without human intervention. However, an effective BWPT requires a proper vehicle and grid coordination with reasonable control and compensation networks. Various compensation techniques have been proposed in the literature, both on the transmitter and receiver sides. Selecting suitable compensation techniques is a critical task affecting the various design parameters. In this study, the basic compensation topologies of the Series–Series (SS), Series–Parallel (SP), Parallel–Parallel (PP), Parallel–Series (SP), and hybrid compensation topology design requirements are investigated. In addition, the typical control techniques for bidirectional converters, such as Proportional–Integral–Derivative (PID), sliding mode, fuzzy logic control, model predictive, and digital control, are discussed. In addition, different switching modulation schemes, including Pulse-Width Modulation (PWM) control, PWM + Phase Shift control, Single-Phase Shift, Dual-Phase Shift, and Triple-Phase Shift methods, are discussed. The characteristics and control strategies of each are presented, concerning the typical applications. Based on the review analysis, the low-power (Level 1/Level 2) charging applications demand a simple SS compensation topology with a PID controller and a Single-Phase Shift switching method. However, for the medium- or high-power applications (Level 3/Level 4), the dual-side LCC compensation with an advanced controller and a Dual-Side Phase-Shift switching pattern is recommended.

The study presents a non-isolated bidirectional DC–DC converter, which has simple circuit structure. The control strategy is easily implemented. Also, the synchronous rectifier technique is used to reduce the losses. The voltage gain of the proposed converter is the half and the double of the conventional bidirectional DC–DC buck/boost converter in the step-down and step-up modes, respectively. Therefore the proposed converter can be operated in wide-voltage-conversion range than the conventional bidirectional converter. The voltage stresses on the switches of the proposed converter are a half of the high-voltage side. In addition, the operating principle and steady-state analyses are discussed. Finally, a prototype circuit is implemented to verify the performance of the proposed converter.

With the launch of Landsat 9 in September 2021, an optimal opportunity for in-flight cross-calibration occurred when Landsat 9 flew underneath Landsat 8 while being moved into its final orbit. Since the two instruments host nearly identical imaging systems, the underfly event offered ideal cross-calibration conditions. The purpose of this work was to use the underfly imagery collected by the instruments to estimate cross-calibration parameters for Landsat 9 for a calibration update scheduled at the end of the on-orbit initial verification (OIV) period. Three types of uncertainty were considered: geometric, spectral, and angular (bidirectional reflectance distribution function—BRDF). Differences caused by geometric uncertainty were found to be negligible for this application. Spectral uncertainty was found to be minimal except for the green band when viewing vegetative targets. BRDF models derived from the MODIS BRDF product indicated substantial error could occur and required development of a mitigating methodology. With these three contributions of uncertainty properly addressed, it was estimated that the total cross-calibration uncertainty for underfly data could be kept under 1%. The data collected during the underfly were filtered to remove outliers based on uncertainty analysis. These data were used to calculate the TOA reflectance and radiance cross-calibration values for each spectral band by taking the ratio of Landsat 8 average pixel values to Landsat 9. Initial results of this approach indicated the cross-calibration may be as accurate as 0.5% in reflectance space and 1.0% in radiance space. The initial results developed in this study were used to refine the cross-calibration of Landsat 9 to Landsat 8 at the end of the OIV period.

The Meteorological Synthesizing Centre-West (MSC-W) of the European Monitoring and Evaluation Programme (EMEP) has been performing model calculations in support of the Convention on Long Range Transboundary Air Pollution (CLRTAP) for more than 30 years. The EMEP MSC-W chemical transport model is still one of the key tools within European air pollution policy assessments. Traditionally, the model has covered all of Europe with a resolution of about 50 km × 50 km, and extending vertically from ground level to the tropopause (100 hPa). The model has changed extensively over the last ten years, however, with flexible processing of chemical schemes, meteorological inputs, and with nesting capability: the code is now applied on scales ranging from local (ca. 5 km grid size) to global (with 1 degree resolution). The model is used to simulate photo-oxidants and both inorganic and organic aerosols. In 2008 the EMEP model was released for the first time as public domain code, along with all required input data for model runs for one year. The second release of the EMEP MSC-W model became available in mid 2011, and a new release is targeted for summer 2012. This publication is intended to document this third release of the EMEP MSC-W model. The model formulations are given, along with details of input data-sets which are used, and a brief background on some of the choices made in the formulation is presented. The model code itself is available at www.emep.int, along with the data required to run for a full year over Europe.

This paper prescribed the design of controller for electrical vehicle to Grid power, by using this controller improve the power requirement of grid and reactive power compensation capability. Bidirectional converter is very helpful during on peak load demand. During off peak load demand grid will supply the power to the battery and charge the battery. During on peak load demand excess power of battery will supply to the grid. The concept aggregator is depicted in the figure 2. (Aggregator collects the power from all electrical vehicle first then it supply to the grid). This modern electrical vehicle technology proposed the distribution generation Methodology. All the control strategies of modern electrical vehicle to grid is proposed like smart charging or discharging of batteries during off peak load demand and On peak load demand respectively. V2G controller allow the active power it act as an ancillary services to grid. Electrical vehicle controller has ability to exchange the active or reactive power capability. Simulation of bidirectional AC/DC and DC/DC controller and their control circuit are analyzed by using matlab Simulink software.

Accurately evaluating the quality of bidirectional reflectance distribution function (BRDF) models is essential for photo-realistic rendering. Traditional BRDF-space metrics often employ numerical error measures that fail to capture perceptual differences evident in rendered images. In this paper, we introduce the first perceptually informed neural quality metric for BRDF evaluation that operates directly in BRDF space, eliminating the need for rendering during quality assessment. Our metric is implemented as a compact multi-layer perceptron (MLP), trained on a dataset of measured BRDFs supplemented with synthetically generated data and labelled using a perceptually validated image-space metric. The network takes as input paired samples of reference and approximated BRDFs and predicts their perceptual quality in terms of just-objectionable-difference (JOD) scores. We show that our neural metric achieves significantly higher correlation with human judgments than existing BRDF-space metrics. While its performance as a loss function for BRDF fitting remains limited, the proposed metric offers a perceptually grounded alternative for evaluating BRDF models.

Human activity recognition (HAR) has become a significant area of research in human behavior analysis, human–computer interaction, and pervasive computing. Recently, deep learning (DL)-based methods have been applied successfully to time-series data generated from smartphones and wearable sensors to predict various activities of humans. Even though DL-based approaches performed very well in activity recognition, they are still facing challenges in handling time series data. Several issues persist with time-series data, such as difficulties in feature extraction, heavily biased data, etc. Moreover, most of the HAR approaches rely on manual feature engineering. In this paper, to design a robust classification model for HAR using wearable sensor data, a hybrid of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is used. The proposed multibranch CNN-BiLSTM network does automatic feature extraction from the raw sensor data with minimal data pre-processing. The use of CNN and BiLSTM makes the model capable of learning local features as well as long-term dependencies in sequential data. The different filter sizes used in the proposed model can capture various temporal local dependencies and thus helps to improve the feature extraction process. To evaluate the model performance, three benchmark datasets, i.e., WISDM, UCI-HAR, and PAMAP2, are utilized. The proposed model has achieved 96.05%, 96.37%, and 94.29% accuracies on WISDM, UCI-HAR, and PAMAP2 datasets, respectively. The obtained experimental results demonstrate that the proposed model outperforms the other compared approaches.

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

Urdu and English are widely used for visual text communications worldwide in public spaces such as signboards and navigation boards. Text in such natural scenes contains useful information for modern-era applications such as language translation for foreign visitors, robot navigation, and autonomous vehicles, highlighting the importance of extracting these texts. Previous studies focused on Urdu alone or printed text pasted manually on images and lacked sufficiently large datasets for effective model training. Herein, a pipeline for Urdu and English (bilingual) text detection and recognition in complex natural scene images is proposed. Additionally, a unilingual dataset is converted into a bilingual dataset and augmented using various techniques. For implementations, a customized convolutional neural network is used for feature extraction, a recurrent neural network (RNN) is used for feature learning, and connectionist temporal classification (CTC) is employed for text recognition. Experiments are conducted using different RNNs and hidden units, which yield satisfactory results. Ablation studies are performed on the two best models by eliminating model components. The proposed pipeline is also compared to existing text detection and recognition methods. The proposed models achieved average accuracies of 98.5% for Urdu character recognition, 97.2% for Urdu word recognition, and 99.2% for English character recognition.