We use the sum-product algorithm (SPA) on factor graphs to enable joint detection and decoding over multipath channels in single-carrier modulation formats. We simulate the multipath channels as accurately as possible at the receiver side by discretizing channel parameters, such as channel paths and attenuation coefficients, to compute means of Gaussian observations. We assign these parameters Bernoulli random variables and consider priors of 0.5 for them. We propose a novel implementation strategy to reduce the complexity of the receiver considerably.

Short-form Video Addiction (SVA), a novel digital addiction of the modern world, proliferates among young adults and is not formally diagnosable. SVA detection from resulting bio-signals is crucial to prevent its adverse impacts. Existing formal methods involve large and expensive neuro-imaging devices in laboratory setups that are intrusive and not feasible to use in daily life. A possible non-intrusive solution can be using wearable sensors which is challenging due to the resulting noisy and faint signals. To address this problem, we investigate multi-modal wearable sensing technology to detect SVA in a non-intrusive fashion. However, fusing multi-modal sensors effectively presents different challenges due to the presence of signal heterogeneity. In this study, we propose a novel multi-modal temporally coherent domain adaptation method to effectively detect SVA using Electroencephalogram (EEG) and Electrodermal Activity (EDA) sensors. We also investigate the nature and properties of SVA with the help of different components of EEG and EDA signals. We evaluate our proposed method for SVA detection and fatigue assessment tasks. Experimental evaluation posits the proposed model’s superior performance (+10% accuracy) over state-of-art domain adaptation models.

Supervised learning requires labeled data to train models and then make predictions from new input data. Deep Learning (DL) methods require immense amounts of training data and processing power to provide reasonable results. In computer vision applications, and more specifically in despeckling SAR (Synthetic Aperture Radar) images, due to the speckle content, there is no ground truth available. To test the performances of despeckling filters, the common approach is tocorrupt synthetic images with a suitable speckle model and then, after filtering, well-known metrics are obtained. Then, filters are tested on actual SAR data, and specific metrics for SAR are evaluated. However, even the most elaborated speckle models are far from accounting for the complex mechanisms related to SAR images. In this paper, a methodology to design a realistic dataset to overcome these limitations is proposed. Actual SAR images of the same scene but acquired with the same sensor on different dates are downloaded from one of the available satellite platforms. Images are properly co-registered and averaged to get a ground truth-like reference image to objectively evaluate the performance of a despeckling method. To show the benefits of the proposed methodology, an on-the-shelf deep learning approach is used to filter the data, and compared with the standard approach using synthetic corrupted images with a speckle model. The final validation on actual SAR data not included in the training phase validates the proposed dataset. From the results shown, it is recommended to test filters on the proposed more realistic dataset and abandon the usual approach.

This paper discusses the process of estimation of the effective relative permittivity for 3D-printed dielectric slabs with varying infill densities. The dielectric slabs with varying infill densities were placed inside rectangular waveguides with varying dimensions that correspond to different frequency ranges to determine the relative permittivities of the dielectric slabs. The results from the measurement of relative permittivity for these dielectric slabs with varying infill density indicated that the relative permittivity of 3D-printed dielectric slab decreases as the infill density decreases. This reduced relative permittivity of the slab has been coined as effective relative permittivity (εr.eff) throughout the paper. The relationship between the εr.eff and the infill density which was found to be linear in our previous simulation study was validated for higher infill densities through extensive measurements conducted in this study.

It will show prevalence of computer applications and software in our daily routines. It discusses the significance of studying the impact of these applications on different aspects of daily life, emphasizing the potential for improved the efficiency, convenience, and quality of lives

Efficient and accurate prediction of surrounding vehicles’ trajectories over time is crucial for autonomous vehicle decision-making and planning. While the Transformer method has been widely used for interactive vehicle trajectory prediction due to its ability to consider multi-vehicle trajectories in parallel, this parallel computation mechanism causes computational exponential overload in Long Sequence Time-series Forecasting (LSTF) problems. In response, this paper proposes an improved Transformer method, known as the structural Informer, which can achieve accurate and efficient long-term trajectory prediction of the target vehicle (TV). Specifically, the proposed method considers not only the temporal and spatial features of the interaction trajectory, but also the impact of vehicle state changes on the trajectory. To reduce computational redundancy and complexity while improving memory usage and prediction accuracy, the ProbSparse self-attention mechanisms and attention distillation operations are employed. The method is validated and evaluated using the NGSIM dataset, and the results demonstrate that the proposed structural Informer achieves satisfactory accuracy and time cost in long-term prediction of the TV compared with various interactive trajectory prediction methods.

This work explores a nonlinear extension of the conventional Maxwell equations inwhich the concept of charge density is expressed in terms of the fields.  Solutions are found to exist which consist of localized concentrations of electromagnetic energy such as exist in real particles.  Some additional features of the model for interacting particles are discussed.

Vertically-mounted bifacial module farms (VBF), which are ideal for power generation projects of traditional industries wherein photovoltaics are applied (i.e., ‘PV +’ mode), have some unique advantages such as extremely high land utilization, anti-fouling ability, and grid peak shaving ability. However, VBF also suffer from shortcomings such as low power generation efficiency and high levelized cost of energy (LCOE). This paper proposes the VBF-bifacial companion method (VBF-BCM) for light supplementation and efficiency enhancement of VBF. A reflective device is added to the PV brackets in the VBF, and supplementary light is applied to the dark side of the module in the adjacent row. The technical and economic feasibility of VBF-BCM were determined and calculations showed that the VBF-BCM system can significantly increase system power generation worldwide. The maximum power generation gain ratio was 90%, and the global annual average was 70%. Compared with the latitude-tilted mounted monofacial module farms (LTMF), the total power generation of the VBF-BCM system can be increased by more than 35% globally. Furthermore, VBF-BCM can reduce the LCOE of the original VBF solution by up to 40%, which renders VBF-BCM a perfect PV system that is ideal for the ‘PV +’ mode

With the ever increasing use of cloud computing, the need of energy also increases drastically yielding its most vulnerable adverse effects on the environment. This ultimately requires to conserve or to minimize the energy consumption laying the foundation for Green cloud Computing. In other words, Green Cloud Computing (GCC) is the data center architecture over internet whose objective is to decrease its power consumption without compromising performance from user’s perspective. A key component of Green Computing is the ability to conserve energy or reduce carbon footprints. It enables a wide range of live virtual machine migration, online monitoring, and VM placement optimization. This type of system adapts to periods of peak demand and adjusts the availability of cloud-based resources based on their expansion or contraction. Thus, this paper analyzes the extensive literature on GCC and identifies research gaps for further exploration in this area that has abundant research potential.

A systematic Review of YOLO for medical Object Detection (2018 - 2023)

Multivariate time series generative models mainly focus on generating time series from complete data, while few works generate complete multivariate time series from incomplete data, which is challenging to capture the feature correlation and temporal dependence simultaneously, due to the missing features and time segments. Missing values disrupt the feature correlation and temporal dependence learned by existing models, which deteriorates the distribution of missing values, resulting in low utility of synthetic data. Moreover, these methods capture feature correlation and temporal dependence separately, causing the joint distribution deviation of feature and time dimensions, thus reducing the synthetic data fidelity. In this work, we present a conditional diffusion model TimeDiff to generate time series with high fidelity and utility. This method randomly masks possible missing values in observed data as condition to conduct self-supervised learning of the missing value distribution. Meanwhile, uniting the feature and time dimensions, a multi-dimension temporal-feature network is proposed to capture the correlation of time variation among features. Experiments on real-world time series datasets demonstrate that TimeDiff reaches state-of-the-art results for generating high-utility multivariate time series from incomplete data with up to 80% missing rates.

The presented work is motivated to offer speaker placement architecture and a low-cost Edge-IoT-based solution to poultry farmers. That will directly improves the health of birds and profitability of the farmer.

A document by Siu Wai Ho . Click on the document to view its contents.

Here I will show the L-currents formulated for the necessary charge required in string vibrations for the particle generator scheme taken as ∇_(p,q).

LEO satellites are to be equipped with mMIMO in the near future. Introducing the beam-based positioning paradigm marks the advent of a new research topic that is concerned with exploiting 5G beamforming features of LEO satellites’ communications to infer UT position in Earth’s coordinates. Considering the passive SoO nature of the proposed method, the simulation results (MAE = 0.22 km with 100 satellites) are considered very promising, as positioning is possible even in poor GDOP situations. Furthermore, the proposed method does not require high-accuracy measurements at the receiver side, thus reducing costs and sensitivity to interference. As future work, more sophisticated simulation environment will be built to model mMIMO beamforming components, the communication channel, and various sources of noise and interference. In addition, LEO satellite orbits and realistic mega constellations will be modelled. Additional positioning algorithms will be developed and analyzed for enhanced accuracy and reliability. The proposed ideas within this article are currently under the process of IPR patenting (Finnish patent application number: 20235545).

A document by Wen Zhou . Click on the document to view its contents.

A document by Claude Carlet . Click on the document to view its contents.

Robotic lightweight-exoskeletons show great potential to enhance human motion capabilities for the disabled, elderly, and healthy. Active human-robotic interfaces with integrated actuation and sensing allow high quality measurements and an optimal fit when worn. We present a compact actuator-sensor unit to be embedded in most interfaces that provides a controllable force impact using shape memory alloy strips in a compliant parallel configuration. The sensing of interaction forces through the user's clothing is enabled using a thin layer based capacitive force sensor. The unit was characterized in a test setup and attached to a subject's limb. We could demonstrate controllable actuation in the force range of up to 62 N with effective force offsets of 13.7 N from arbitrary contact force requiring less than 10 W. The unit showed reliable sensing, revealing characteristic patterns of basic limb movements. The actuation controls the initial contact forces and enables robust interaction force monitoring and can thereby facilitate further signal processing. For future work, we will miniaturize the unit further and investigate compensation of interface displacements during activities of daily living in a user study.