Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpreting Frequency Shift in Translational-Rotational Mobility Using Source-Generic Mode Channel

This paper investigates how the translational and rotational mobility influences radio link in terms of frequency shift, using the intrinsic mode channel that does not reply on particular realizations of radiating sources. Arbitrary field in source-free region can be modeled as a weighted sum of different modes of spherical vector waves. Through translation and rotation of spherical waves, the channel mapping the transmit and receive modes can be acquired analytically. The spatial frequency spectrum (SFS) is obtained from the mode-mapping channel by Fourier transform, regarding the spatial samples taking from translational or rotational movement. Effects of 1) rotating orientation (pitch, yaw, roll), 2) rotation radius, 3) carrier frequency, and 4) translational distance between source and observer coordinates, on the SFS and frequency shift are analyzed. It is found that in roll mobility that is transverse to the direction of observer, the spatial frequencies associated to the primary SFS peaks do not change with carrier frequency, rotation radius or distance between source and observe coordinates; the spatial frequencies of SFS peaks do not vary among modes either except for that when the elevation mode indexes are zero and there are no secondary SFS peaks. In pitch or yaw mobility, the SFS peaks occur more frequently with the increase of carrier frequency, increase of rotation radius, or decrease of the distance between source and observe coordinates. This work serves as a start for modeling and recognizing translational-rotational mobility in view of source-generic mode channel which is further transferable with particular source applied.
Source Identification of Asymptomatic Spread on Networks

Identifying the sources of spreading dynamics on networks has drawn extensive attention in recent years, where a variety of epidemic models have been adopted to simulate the propagation dynamics among network nodes, such as the Susceptible-Infectious (SI) model. The objective is to identify the most possible source of infection based on the observed state transition events (e.g., from susceptible to infectious) on a small set of monitored nodes. Most existing studies assumed that once a monitored node is infected, it will be immediately observed. However, in reality, it is likely that for many infectious diseases, a newly infected person becomes infectious without showing any disease symptoms. In this case, the state transition cannot be observed until symptoms arise after a time period (i.e., the incubation period). Accordingly, in this paper, we focus on investigating the source identification problem of asymptomatic spread on complex networks by monitoring only a small number of network nodes. Specifically, we adopt a continuous-time SI model with a contagious incubation period to simulate the asymptomatic spread on networks, where the length of the incubation period is assumed to be an independent and identically distributed exponential distribution. In doing so, we formulate the source identification problem as a likelihood maximization problem and solve it using the Monte Carlo approximation and importance sampling. Finally, we validate the performance of our method on both synthetic and real-world networks with different experimental settings. The results show that our method can achieve higher identification accuracy than several benchmark methods.
The relationship model construction of dynamic color and visual attention based on mobile card layout

With the rapid development of mobile application industry, the interaction between human and mobile application interface has become an indispensable part of daily life. Designers also strive to guide users' visual attention reasonably on the limited screen. This paper analyzes the design elements of dynamic color in the mobile terminal card layout, establishes the relationship model between dynamic color and visual attention in mobile end card layout, and explores the relationship between dynamic color and visual attention of users. Based on the analytic hierarchy process (AHP), this paper constructs an index system that affects users' visual attention. By designing the control group experiment, users can evaluate the importance of the index layer factors in pairs, and finally get the weight value of the influence of color, purity, lightness and speed of the design factors of dynamic color on the visual note. The model can provide a theoretical basis for the design of dynamic color of mobile card layout and a method for designers to calculate the visual attention degree of interface elements to users.
Implementation Experiment of a Rotary LED Transmitter for Improving the Transmission Rate for Image Sensor Communication

This study demonstrates a novel LED transmitter for image sensor communication (ISC) and describes its implementation experiment. In an ISC system, optical signals are transferred through the blinking of LEDs, and they are then captured using a camera. Due to the low frame rate of commercial cameras, which is generally around dozens of frames per second, ISC systems suffer from very low data rates. To overcome this problem, we developed a rotary LED transmitter that can boost the data rate of ISC systems while using a commercial camera with a low frame rate. The developed transmitter rotates LEDs that blink depending on the modulated data, faster than the shutter speed of the image sensor. In this case, the receiver can capture multiple blinking LEDs per image. To illustrate, the amount of information per image is increased by the rotating LEDs. This paper presents the prototype of the rotary LED transmitter and introduces its structure details. Moreover, we performed an implementation experiment of the transmitter, and it was found that the rotary LED transmitter can transfer 60 times the data rate of the conventional method with the same number of LEDs and the same camera frame rate.
Performance Analysis of Electrical Equivalent Circuit Models of Lithium-ion Battery

Electrical equivalent circuit models of battery helps us to understand the behavior in terms of its electrical characteristics, charging status and battery capacity to improve the system performance and increase the overall efficiency. In this paper different models of lithium-ion battery are discussed and their performance analysis is studied along with the benefits and demerits which will help us to select an ideal model which will suit best to a power electronics application. It is also observed that accurate electrical equivalent model is best suited for power applications as it takes into account the battery life time in its model. Charging and discharging characteristics of an ideal lithium-ion battery model are plotted using matlab to match the performance with the battery model specifications of lithium ion battery.
User Cooperation Scheduling in Cellular Systems

Given its capability in efficient resource/load sharing between Uu link and sidelink (SL), user equipment (UE) cooperation has been identified as a promising technology in 5G evolution to improve the network capacity, coverage and transmission latency. This paper considers the design of practical transmission scheme from industrial perspective. The challenge lies in the modeling of physical layer behaviors of UE cooperation, including cooperative scheduling between target UE (TUE) and cooperative UE (CUE) which plays an important role in cooperative UE transmission and reception. This paper investigates the UE cooperation performance with proposed scheduling schemes for both uplink (UL) and downlink (DL), where the evaluation results show that UE cooperation could significantly improve the cell average throughput and cell edge throughput.
A Compact Ridged Substrate Integrated Coaxial Line

Substrate integrated coaxial line (SICL) has been widely used for high frequency interconnects. In this paper, a ridged substrate integrated coaxial line (RSICL) is proposed. It has similar structure to the SICL but has two ridges on both sides of the inner conductor. The proposed RSICL is a quasi-shielding structure which can support the transverse electromagnetic (TEM) mode. Moreover, the introduction of ridges into SICL can significantly minimize the interconnect size. The dominant mode and higher-order modes, the characteristic impedance and the propagation constants of the RSICL are analyzed based on the full-wave simulation software. Compared with the SICL, the RSICL can reduce the width of the internal and external conductors by 22.5% and 10%, respectively. Besides, the dominant mode bandwidth of the RSICL is 11 % wider than that of the SICL.
Exploring Cross-lingual Singing Voice Synthesis Using Speech Data

State-of-the-art singing voice synthesis (SVS) models can generate natural singing voice of a target speaker, given his/her speaking/singing data in the same language. However, there may be challenging conditions where only speech data in a non-target language of the target speaker is available. In this paper, we present a cross-lingual SVS system that can synthesize an English speaker’s singing voice in Mandarin from musical scores with only her speech data in English. The pro-posed cross-lingual SVS system contains four parts: a BLSTM based duration model, a pitch model, a cross-lingual acoustic model and a neural vocoder. The acoustic model employs encoder-decoder architecture conditioned on pitch, phoneme duration, speaker information and language information. An adversarially-trained speaker classifier is employed to discourage the text encodings from capturing speaker information. Objective evaluation and subjective listening tests demonstrate that the proposed cross-lingual SVS system can generate singing voice with decent naturalness and fair speaker similarity. We also find that adding singing data or multi-speaker monolingual speech data further improves generalization on pronunciation and pitch accuracy.
A Mixed Orthogonal Frequency Division Multiplexing (X-OFDM) Waveform for Optical Wireless Communication

Optical wireless communication (OWC), with intensity modulation (IM), requires the modulated radio frequency (RF) signal to be real and non-negative. To satisfy the requirements, this paper proposes a novel mixed orthogonal frequency division multiplexing (X-OFDM) waveform. Hermitian symmetry (HS) of the sub-carriers in frequency domain, guarantees the signal in time domain to be real, which reduces the spectral efficiency to 1 /2. For the odd sub-carriers in frequency domain, the signal in time domain after inverse fast fourier transform (IFFT) is antisymmetric. For the even sub-carriers in frequency domain, the signal in time domain after IFFT is symmetric. Based on the antisymmetric and symmetric characters, the X-OFDM waveform is designed for the signal in time domain to be non-negative. With N sub-carriers in frequency domain, the generated signal in time domain is 3N/2 points, where the spectral efficiency is further reduced to 1 /3 and the direct current (DC) bias is not needed. Based on the simulations, the X-OFDM waveform achieves much higher power efficiency than traditional waveforms without DC bias, when the normalized bit rate is larger than 1.7 bits/s/Hz.
LVASM: Location-based Vehicle Accident Safety Measure

The cases of road accidents in India are increasing day by day. Due to this a significant amount of death is recorded due to the gap between emergency responders and accident victims. In this context, a number of car making companies started developing accident prevention and notification system which are costly and not able to maintain by all the vehicle users. Thus to enhance the reach of such applications for road safety measures a new system is required. In this work we present, a new Location-based Vehicle Accident Safety Measure (LVASM) system developed using the android platform. The LVASM has zero cost and can help any vehicle users by notifying the emergency responders. The proposed LVASM application based notification system works in three modules to assist the user’s, first initialization, second observation, and third is notification. The LVASM model is able to efficiently respond means detect the accident and start to send the notifications to the emergency responders. Additionally, the use of multiple responders makes it more valuable. The experimental analysis demonstrates the application accurately detects the accident and start notification automatically. The LVASM system helps to save a life during road accident scenarios. The comparison with the two similar accidental prevention systems shows the LVASM working model efficient in terms of the time of response or detection of accidental situation.
A Review of Video Generation Approaches

Generating videos from some initial frames is an appealing field of research in deep learning. There exists an ever expanding foray of approaches to generate long-range and realistic video frame series. Generating videos can help predict trajectories and even model object movements, to enhance autonomous robots. However, there are only a few comprehensive studies that review various approaches on the basis of their relative advantages, disadvantages, and evolution. Hence, this paper presents a detailed overview of Deep Learning based approaches employed to tackle the complex problem of video generation. The approaches involve Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs) and even the Transformer model. Finally, the performance of all the approaches are examined and compared on the BAIR Robot Pushing dataset.
A Differential duplex Filtering Antenna with Low Cross-polarization

A differential duplex filtering antenna with low cross-polarization is introduced in this paper. The duplex antenna consists of a radiation patch, two coupling slots and a feed filtering network. Transmitting signals pass through the feed filtering network, and then the antenna is excited by two coupling slots symmetrically etched on the ground plane. Receiving signals are received by the radiation patch, and then pass through the feed filtering network. Simulation results show that the maximum gain of the antenna is 5.86 dBi in the low frequency band (1.95 GHz-2.050 GHz) and 6.67 dBi in the high frequency band (2.38 GHz-2.51 GHz). In addition, symmetrical radiation patterns and low cross-polarization are also achieved because of the differential-feed structure.
An Effective Learning Scheme for Weighted-BP with Parallel Permutation Decoding

Weighted-BP is a method to improve the decoding performance of belief-propagation (BP) by learning its appropriate message weights using deep learning technique. For binary primitive BCH codes, it has been reported in literature that their performance can be enhanced more through parallel decoding which is achieved by exploiting the automorphism property of the codes. In this paper, an effective learning scheme for the parallel weighted-BP is proposed. While in the conventional work the weights preliminarily learned for a single weighted-BP were utilized as is even in the scenario of parallel decoding, we show that some gain can be further obtained if we take the parallel structure into account in the learning phase.
Improved Energy Efficient Structure-Free Data Aggregation and Delivery (IESDAD) Convention in WSNs

In Wireless Sensor Networks (WSNs), the power utilization is high due to recognized data transmission as compared with processing of data carried out within the sensor hub. To save the energy data aggregation methodology generally used which conserves the energy by removing the redundant information transfer in highly dense sensor networks. In this paper, proposed convention improved energy efficient structure-free data aggregation and delivery (IESDAD) collects the redundant information at the transitional nodes. In the proposed convention, every transitional node calculates waiting time for data packets very carefully due to which data collection carries out proficiently along the routing path. The recognized information packets are sent legally at the comprehension point for information collection. The IESDAD convention, the bounce hub is chosen dependent on cost work for the information transmission and delivery without open preservation of a network configuration. It also uses three-dimensional and time-based correlation for minimizing the overall redundant data transmission in wireless sensor networks to maximize the network life-time. The presentation of the proposed convention is estimated by performing widespread simulations. The simulation outcome shows that it performs well in the existing structure-free conventions in accordance with average and maximum energy consumption, throughput, delay and average energy consumption in mobile MIMO & SISO.
A Soft-Switching Transformer-Less Step-Down Converter Based on Resonant Current Balance Module

In this article, a soft-switching transformer-less step-down converter based on a resonant current balance module is proposed. An LED driver is taken as an example of the analysis and design of the converter. The converter is made up of a stacked half-bridge, resonant current balance modules, and pairs of light-emitting diode (LED) strings. The resonant current balance modules allow the proposed converter to realize automatic current sharing, soft switching, and extending to a 1/N converter. The proposed converter can operate in discontinuous conduction mode (DCM) or continuous conduction mode (CCM) mode by choosing different operation frequencies, and achieve zero current switching or zero voltage switching (ZVS), respectively. Also, by using frequency doubling technique in the half-bridge, the switching losses are reduced by half under the same conditions. A 15.8-W experimental prototype with 100 V input voltage and 25 V output voltage has been implemented in the laboratory, verifying the feasibility of the soft-switching transformer-less step-down converter.
A low-cost high linear GaAs HBT power Amplifier

In this paper, a monolithic microwave integrated circuit (MMIC) power amplifier with a single-chip MMIC and a single-path output matching network is demonstrated by adopting an on-chip linearizer. The linearizer is composed of the base-emitter diode of an active bias transistor and a capacitor to provide an RF short at the base node of the active bias transistor. The saturated output power of the power amplifier (PA) is about 34 dBm at 1 GHz. The PA achieves 1 dB compression point of output power of 31.8 dBm at 25°C and an Output 3rd order in-terceot point of about 45.5 dBm.
Applying Distance Dynamic Qlearning Partitioning Approach in Group Recommendation System Used in Social Sites

In this era, when people prefer online solution for most of their problems, recommendation system became the most successful tool to solve it. Various method have been introduced to improve the performance and quality of the search engine. We need a system which can design and develop the reporting system not just the latest product. Some other tools and technologies have to be merged with the recommendation system to make it more powerful and user friendly. In this paper, we have combined Group Recommendation System with Qlearning partitioning approach along with Stochastic function to increase the accuracy, precision ,recall time and f-measure of the Recommendation System in social sites and the experiments has been explained by using community diagram showing different approach and tools used to make recommendation system more efficient so that it can provide the user friendly environment to the consumers.
Low-Complexity Neural Networks for Baseband Signal Processing

This study investigates the use of neural networks for the physical layer in the context of Internet of Things. In such systems, devices face challenging energy, computational and cost constraints that advocate for a low-complexity baseband signal processing. In this work, low-complexity neural networks are proposed as promising candidates. They present adaptability to operating conditions, high performance to complexity ratio and also offer a good explainability, crucial in most communications systems that cannot rely on “black-box” solutions. Moreover, recent advances in dedicated hardware for neural networks processing bring new perspectives in terms of efficiency and flexibility that motivate their use at the physical layer. To illustrate how classical baseband signal processing algorithms can be translated to minimal neural networks, two models are proposed in this paper to realize single-path equalization and demodulation of M-QAM signals. These models are assessed using both simulation and experimentation and achieve near optimal performances.
Double Moving Average Methodology for Smoothing of Solar Power Fluctuations with Battery Energy Storage

Recently, there has been a dramatic increase in the need to meet the excessive energy demand with environmentally friendly renewable energy resources. Large scale penetration of variable Photovoltaic (PV) power leads to multiple instabilities such as voltage fluctuations, frequency deviations, and reduced overall output power quality. In order to counteract these erratic behaviors, storage systems such as a Battery Energy Storage System (BESS) is included into the electrical grid. However, these storage systems are expensive and so power smoothing filters are coupled with BESS for optimal operation and cost reduction. Traditional smoothing filters, namely Low Pass Filters (LPFs) and Moving Average (MA) filters have issues related with power lagging, high charging/discharging power and battery State of Charge mismanagement. This research introduces the Double Moving Average filter for smoothing of PV power fluctuations and assesses its efficiency with respect to the LPF and the MA filter. Smoothed power output facilitates the greater infiltration of solar PV power in the grid.
Sentiment Analysis on Twitter Data Using Deep Learning approach

The recent developments of many social networking websites have created large collections of product reviews and polarity of opinions etc, data for customer around the world. Data collection from these social media can be utilized to solve objectives such as market prediction, product recommendation, and reviewer’s sentiment. It is very difficult task to manage of unstructured data available on social media. To handle such type of data, Deep learning algorithms is an appropriate solution for analysis these challenges. In this paper, we propose a CNN- LSTM based deep learning method with pre trained embedding approach learns to extract feature automatically for analysing sentiments and classification of reviews or opinions labelled into two polarity as positive or negative. Our proposed model to implement the result gives better performance on benchmark dataset. The performance of CNN-LSTM based deep learning method has compared with baseline machine learning methods.