Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Machine Learning and Deep Learning Techniques in Text Based Emotion Detection

Due to social media platforms there are lots of comments every day. Generally the comments are friendly and have good intentions. But sometimes comments convey the feelings of anger, sadness, fear, etc. Negative comments provide evidence that you cannot succeed at your goals, which can also be demotivating. So our task is to predict the various emotions of the comments. For this work, machine learning like Decision Tree, Random Forest, SVM, Logistic Regression using tf-idf and count vectors based model and deep learning model have been used. Among all deep leaning model have performed better than any other machine learning models with an accuracy of 92%.
On the Impact of Residual Transmitter Distortions in 5G Massive MIMO Base Stations

The low-cost hardware components, commonly assumed to be used as building blocks of Massive MIMO (multiple-input multiple-output) base stations, could limit the achievable data rates. In this paper, the impact of residual transmitter distortions on downlink transmission in channels with frequency-selective fading is evaluated using link-level simulations. Nonlinear distortions, modeled here with clipping, are demonstrated to have significantly different behavior, due to directional radiation patterns, compared with additive white gaussian noise (AWGN). In a transmission with clipping distortions to a single user equipment (UE), increasing the number of antennas on the base station side leads to a negligible reduction (about 1 dB visible for 512 antennas) of the error vector magnitude (EVM) for channels with a strong line-of-sight (LoS), while for non-line-of-sight (NLoS) channels a noticeable reduction (about 4 dB for 16 antennas) can be observed. In a multi-user MIMO transmission in LoS channels, increasing the number of UEs leads to improving the EVM for clipping distortions (about 4 dB reduction visible for 8 UEs). However, for AWGN-modeled distortions sharing the transmission power between multiple UEs results in an EVM degradation, especially if the power is distributed between UEs based on their path losses to achieve comparable quality of service.
Semi Embedded Architecture for a mobile indoor localization system

The Internet of Things (IoT) is one of the most in-demand technologies due to its ability to simplify complex tasks through low-cost devices connected to the internet. Indoor positioning is one of the most sought-after indoor technologies, as most indoor services require position information to provide highquality, low-latency services. As a result, the indoor positioning field is also contributing to the rapid development of the IoT by using objects as sources of information to assist in tracking. This paper describes the development and evaluation of a lowcost indoor positioning approach using the Internet of Things (IoT) and received signal strength (RSS) based on UWB-OFDM technology. The proposed approach aims to simplify complex tasks and improve the quality of indoor services by providing accurate and low-latency position information. We introduce the Mobile Access Point Model (MAPM), an optimization algorithm that reduces the negative impact of multipath on the system quality through crowdsourcing. We evaluated the MAPM model in a real-world setting using a Wi-Fi node, which scans the user-reinforced access point and calculates the distance using the scanned RSSI. The node sends the necessary data to the server for software processing, and the server uses the received distances to calculate the position information and store the data in the system database. The evaluation results showed that the proposed MAPM model reduced the negative impact of multipath (reduces NLOS conditions) by 63%, and the root mean square error (RMSE) of the proposed approach using a Wi-Fi node was 69.52 cm. This paper presents a low-cost and effective indoor positioning approach using IoT devices, which can contribute to the rapid development of IoT technology.
A Novel Labeling Scheme for Neural Belief Propagation in Polar Codes

Recently, deep learning has been adopted to improve the performance of the belief propagation algorithm in polar codes, namely, the neural belief propagation decoder. By attaching trainable parameters to each edge on a tanner graph, this decoder effectively weakens the effect of short cycles. In this paper, we will show that the decoder can be further improved with our newly-designed labeling scheme. Instead of groundtruth transmitted codewords, our scheme utilizes codewords generated from a high-performance decoder (i.e., noise-aided belief propagation list decoder) to construct loss functions. Besides, to reduce the labeling complexity, the ground-truth transmitted codewords are integrated into the decoder as a list and compared with the other lists with the principle of minimum Euclidean distance. Numerical simulation results demonstrate that the proposed labeling scheme can improve the decoding performance by up to 0.2 dB with the same run time complexity and model size.
A VLP scheme for Underground River Measurement USVs

Based on Visible Light Positioning (VLP), this paper proposes an algorithm to track the pose of Unmanned Surface Vehicles (USVs) for the river flow discharge correcting in closed environments. In river flow discharge measurement, the USVs tracking method is more flexible than the bridge and fixing ship method in different circumstances. Tracking USVs’ pose using VLP could work without Global Navigation Satellite System (GNSS) in closed environments such as underground rivers, canyons, glaciers, and sewer networks. Therefore, this paper proposes a VLP algorithm to track USVs pose using Unscented Kalman Filter (UKF) while ensuring lower computational complexity. The algorithm is verified by simulation. The results show that the algorithm has high accuracy and strong robustness in the pose tracking of USVs.
Predicting Customer Quality of Service for a Large Fixed Broadband Service Provider

The number of fixed broadband customers has continued to increase over the past decade in Brazil. In spite of its demand, customers face numerous issues with broadband services. Thus, in this paper, we partner with TIM, one of the largest fixed broadband service providers in Brazil, to analyze customers’ Quality of Service (QoS) parameters and predict customers’ download rates. We consider 3.4 million logs collected from 5% of the total customers for 31 days starting from May 22nd, 2021. We build a framework using Error-Correcting Output Codes (ECOC) and H2O’s Automatic Machine Learning (AutoML) that accurately predicts the quality of service, particularly the download rate, achieved by the customers using features related to customer location, internet plan, and equipment. Our experiments demonstrate that our model achieves around 83% accuracy on average on our dataset. Our framework can be used by TIM to improve its fixed broadband services.
QoS-OLSR 2.0: A Quality-of-Service Optimized Link State Routing protocol for Mesh Networks

This paper tackles the problem of frequent disconnections in the Quality-of-Service Optimized Link State Routing (QoS-OLSR) protocol due to mobility in mesh networks. Different routing protocols are proposed for mesh networks, such as Better Approach to Mobile Ad-hoc Networking (BATMAN), Optimized Link State Routing (OLSR), and QoS-OLSR 1.0. QoS-OLSR 1.0 improves the performance compared to the other protocols by incorporating cluster head formation and a Quality-of-Service metric used in cluster head selection. However, it still does not mitigate the impact of mobility in the formulated QoS metric. In this work, we propose and implement a cluster-based QoS-OLSR protocol for mesh networks that accounts for mobility. The work proposes a global QoS metric for nodes, calculated as the average quality of all neighbors’ links. Also, a relative QoS metric is proposed and calculated based on the direct link with the neighbor and its global QoS. Based on the relative QoS, nodes in the network select cluster heads. In case of disconnection from the selected cluster head, the nodes join an existing cluster head to recover from disconnection. Cluster heads in the proposed protocol are responsible for determining Multi-point relays (MPRs) to 2-hop and 3-hop away cluster heads to enhance connectivity. The proposed protocol is implemented as a Linux-compatible protocol for emulations using Mininet-WiFi and compared to BATMAN and QoS-OLSR 1.0. The proposed protocol outperforms the benchmark protocols in terms of throughput, Packet Delivery Ratio, and Round Trip Time with low and high mobility nodes part of the network.
Low-effort VR Headset User Authentication Using Head-reverberated Sounds with Replay Resistance

While Virtual Reality (VR) applications are becoming increasingly common, efficiently verifying a VR device user before granting personal access is still a challenge. Existing VR authentication methods require users to enter PINs or draw graphical passwords using controllers. Though the entry is in the virtual space, it can be observed by others in proximity and is subject to critical security issues. Furthermore, the in-air hand movements or handheld controller-based authentications require active user participation and are not time-efficient. This work proposes a low-effort VR device authentication system based on the unique skull-reverberated sounds, which can be acquired when the user wears the VR device. Specifically, when the user puts on the VR device or is wearing it to log into an online account, the proposed system actively emits an ultrasonic signal to initiate the authentication session. The signal returning to the VR device’s microphone has been reverberated by the user’s head, which is unique in size, skull shape and mass. We thus extract head biometric information from the received signal for unobtrusive VR device authentication.Though active acoustic sensing has been broadly used on mobile devices, no prior work has ever successfully applied such techniques to commodity VR devices. Because VR devices are designed to provide users with virtual reality immersion, the echo sounds used for active sensing are unwanted and severely suppressed. The raw audio before this process is also not accessible without kernel/hardware modifications. Thus, our work further solves the challenge of active acoustic sensing under echo cancellation to enable deploying our system on off-the-shelf VR devices. Additionally, we show that the echo cancellation mechanism is naturally good to prevent acoustic replay attacks. The proposed system is developed based on an autoencoder and a convolutional neural network for biometric data extraction and recognition. Experiments with a standalone and a mobile phone VR headset show that our system efficiently verifies a user and is also replay-resistant.
A SAR-assisted Incremental $\Sigma\Delta$ ADC with Accumulation-based S/H Circuit for Shunt Current Measurements

This paper presents a SAR-assisted first order incremental $\Sigma\Delta$ analog-to-digital converter (ADC) equipped with an accumulation based sample and hold (S/H) circuit to effectively limit its kT/C noise contribution, potential bottleneck for the resolution performance of the converter. This system has been designed for shunt current measurements in the electrical drives battery management framework; the sensed and digitized current information is needed for a proper monitoring and regulation of the behaviour of these essential devices for automotive applications. The converter has been developed at the behavioural level in the MATLAB Simulink environment with special attention to the S/H circuit which has been analyzed in Cadence Virtuoso; the complete system features a high common mode voltage (HCMV) in the order of tens of Volts, a peak-to-peak 50 mV differential input range and a target resolution of 12 bits.
Predicting Lumpy Skin Disease using Various Machine Learning Models

As infection with the virus of lumpy skin disease, which causes acute or subacute sickness in cattle and the population of water buffalo, poses a serious threat to the cattle industry. Machine learning models gives the direction to make ease of testing the lumpy disease. To predict Lumpy skin disease various machine learning models are available. Various parameters are considered to find out the numerous values such as accuracy, MAE, MSE and RMSE. In this paper distinguish machine learning models are considered to make prediction level high. This paper works for the different ML models on the lumpy disease prediction to forecast the cattle disease. Random forest model gives the highest accuracy which is 97.10% and poly-F is at second position which is 96.77% accuracy score. Random forest gives MAE closet value to 0 which is 0.028 that indicates the best ML model with respect to error as well. The next ML model is decision tree, the value is 0.037. For MSE value, poly-F model gives remarkable output which is 0.025 and random forest with having 0.028 value. For RMSE, poly-F model gives remarkable output which is 0.16, random forest with having 0.017 value and decision tree is at 0.19.
Analysing and Visualising trends for Supply Chain Demand Forecasting

Forecasting and prediction analysis have recently become a vital part of the supply chain management especially for perishable items being produced. The paper aims to deliver the importance of intervening the resources and abilities of data analysis and visualization to determine accurately the expected sales values with the supply chain management process for improving the overall business. A dataset with record of five years of actual sales data is used. On this data four prediction models- Seasonal Naïve, Holt-Winters Triple Exponential Smoothing, Seasonal ARIMA and Linear Regression, were trained and their results were analysed. Each model focused on its on characteristic way of determining outputs and were capable of overall delivering good results with error rate not exceeding 27%. The paper determines the reason behind the variations in the results and concludes with how the right forecasting model can help supply chain managers to improve the performance of the business.
A realistic network traffic forecasting method based on VMD and LSTM network

Global internet usage is growing dramatically in recent years, making it difficult for operators to allocate resources rationally and maintain network security. If the network traffic can be accurately and timely forecasted, it can help operators relieve this pressure. However, due to the non-linearity and non-stationarity of network traffic, it's difficult to capture dynamic characteristics to obtain excellent prediction results for large number of traditional methods. To address this issue, we propose a novel long short-term memory (LSTM) neural network combined with variational mode decomposition (VMD) for network traffic forecasting. It firstly applies VMD to decompose the time series according to frequency information, extracting nonlinear and non-stationary features in the sequence, and then LSTM exploited to capture the long-term dependencies of network traffic data. We combine the decomposition algorithm with LSTM to establish a mapping between historical network traffic data and future ones. Experimental results based on real-world datasets demonstrate that the prediction accuracy of our model overperforms the existing state-of-the-art algorithms,
A 28V-to-3.3V, 2 A, Fully Monolithic 3-Level Hybrid Converter in 130 nm HV-CMOS

This paper presents a fully monolithic 3-Level Hybrid Flying Capacitor (3LHFC) converter based on a 130 nm technology node. The design targets a 28V-to-3.3V, 2 A, step-down dc-dc converter. An analog phase-shift modulator keeps the flying capacitor under control, and a dedicated startup circuit brings the flying capacitor to its target value in cold-start conditions. Integration of the full power stage on chip operating at 1 MHz switching frequency results in high conversion efficiency and in the reduction of the external passive components. The presented 3LHFC converter has been validated in silicon. Experimental results are shown and compared with the current state of the art.
Towards Mutual Trust-Based Matching For Federated Learning Client Selection

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.
D-S Evidence Theory Information Fusion Methods and Applications

In this paper, a D-S evidence theory information fusion method is proposed along with the basic concept of D-S evidence theory and the synthesis law. By applying this information fusion method to the condition monitoring of generating units through simulation experiments, the real-time operating status of the units can be reflected from different aspects, and a comprehensive and correct understanding of the environment or characteristics can be derived to make more accurate and complete estimates and decisions, thus improving the robustness of the whole system. From the experimental results, it can be seen that the D-S evidence theory information fusion method effectively reduces the uncertainty of fault diagnosis and improves the credibility of fault state identification.
Twenty-Five Years of Evolution in Speech and Language Processing

In this article, we summarize the evolution of speech and language processing (SLP) in the past 25 years. We first provide a snapshot of popular research topics and the associated state of the art (SOTA) in various subfields of SLP 25 years ago, and then highlight the shift in research topics over the years. We describe the major breakthroughs in each of the subfields and the main driving forces that led us to the SOTA today. Societal impacts and potential future directions are also discussed.
Modeling and Simulation of Electrical Circuits to Determine Human Body Impedance

A wide variety of body parameters, such as body mass, total body water content, and body fat composition, can be studied and analyzed by extracting impedance values from the human body. BIA (bioelectrical impedance analysis) is a process to measure the body's impedance. It is a helpful technique for obtaining information about the body's internal tissues. There are numerous methods for expressing the electrical equivalent circuit for these impedance measurements. However, these methods are simplistic and mostly fail to realize the biological nature of the tissues. Using fractional calculus, we have proposed an electrical equivalent circuit of the impedance of the human body. The circuit has been designed by emphasizing the dynamic character of the body tissues. The proposed circuit has been found to have the maximum fitting with the impedance values of the experimental impedance curve. This emphasizes using a fractional component in the electrical equivalent circuit of human body impedance. This equivalent circuit can be used for various types of in-silico analysis.
MispredTable: A Side Branch Predictor to TAGE in Multithreading Processors

Tagged geometric history length (TAGE) branch predictor shows good prediction accuracy with sufficient storage budget. However, shrinking TAGE's storage budget dramatically reduces its prediction accuracy. In this paper, we propose MT (Misprediction Table)-TAGE, which adds a side predictor to the TAGE branch predictor. This side branch predictor will record the branches with the highest misprediction rate during the running process of the program and modify the prediction result of the branch when the number of mispredictions is higher than dynamic threshold. We also propose a new algorithm that enables MT-TAGE to be implemented in multithreaded processors. This work is written in SystemVerilog and tested on a RISC-V multithreaded processor. The experimental results show that in a quad-thread processor, the misprediction rates of MT-TAGE under the storage budget of 32kbits and 8kbits are 5.03MPKI (misprediction per kilo-instructions) and 5.54MPKI, respectively, which are 12.5% and 15.5% less than TAGE under the same area.
A Low Complexity Mixed-Field Sources Parameter Estimation Framework at Sub-Nyquist Sampling Rates

In order to solve the problem of multi-parameter joint estimation of multi-band signals in mixed-field (MF), a subNyquist sampling structure is designed and a multi-parameter joint estimation framework based on symmetric uniform linear array (ULA) is proposed. Under this framework, multiparameter estimation of MF signals can be achieved at sub- Nyquist sampling rates. The frequency, direction of arrival (DOA) and distance parameters of the MF signals are automatically paired and each step is the estimation of one-dimensional parameters, which simplifies calculation complexity while avoiding parameter pairing ambiguity.