Miracle-dz/UniFAR-FTUs huggingface dataset:
A Latent Variable Model State Estimation System for Image Sequences

Self-driving cars need to be able to assess and understand the state of their surroundings. To achieve this goal, it is necessary to construct a model which holds information about the state of the environment based on sensor measurements. In common state estimation systems like Kalman filters, it is necessary to explicitly model state transitions and the observation process. These models have to match the internal dynamics of the observed system as closely as possible to yield reliable estimation results. In this work, we propose a method that can learn an approximation of the internal dynamics of a system, without the need to explicitly model these processes. Our system even works on highly complex data like frames of a video sequence. The approach is based on a latent variable model with a continuous hidden state space. To deal with the fact that the estimated processes are sequential, we use recurrent neural networks. As an example to show the potential of this system, resulting predicted future frames of short video sequences are shown. The proposed system shows a general approach for state estimation without the need for any knowledge about the underlying state transition or observation processes.
Adaptive Fuzzy Compensation Control of MIMO Stochastic Nonlinear Systems with Input Hysteresis

In this paper, an observer-based adaptive fuzzy compensation control scheme of multi-input and multi-output (MIMO) strict-feedback nonlinear systems is developed, where stochastic disturbances, actuator faults and input hysteresis are considered at the same time. The design difficulty of unknown system functions is eliminated via the universal approximators, i.e., fuzzy logic systems, and a reduced-order observer is constructed to estimate the unmeasurable state variables. By applying the backstepping design framework, an adaptive fuzzy controller is constructed that can compensate for the effects of actuator faults/failures and hysteresis nonlinearities when the system operates. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the output of a subsystem follows the same trajectory with the corresponding reference signal. Furthermore, a simulation result is demonstrated the validity of the designed scheme.
Augmented DTSCR With Fast Turn-On Speed for Nanoscale ESD Protection Applications

In this brief, two novel diode-triggered silicon-controlled rectifiers (DTSCRs) with fast turn-on speed have been presented. By embedding current gain amplifier modules (i.e., Sziklai pair and Darlington pair) into the conventional DTSCR, the current gain of parasitic bipolar junction transistors (BJTs) on the silicon-controlled rectifier (SCR) path in the new devices becomes much larger than that of the conventional counterpart, thus resulting in a faster turn-on speed and better quasi-static <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics. As such, the quasi-static triggering characteristic of the new DTSCR improves by 80%, and the turn-on speed improves by 42%–93% for different current levels.
Optimal Parameters of Nonstatistical Sensor Data Aggregation Minimizing Latency in IoT Gateway

A data aggregation function in Internet-of-Things (IoT) systems summarizes a large amount of small segments of sensor data, and is effective for saving limited communication resources such as radio frequency and sensor node energy. In a previous study, we analyzed two fundamental statistical data aggregation schemes--constant interval and constant number schemes--and derived the Laplace-Stieltjes transforms of the system time and latency distributions for each scheme. Moreover, we derived simple and accurate estimation formulae for the optimal aggregation interval and number. In this paper, we analyze nonstatistical data aggregation with the constant number scheme. We analytically derive the Laplace-Stieltjes transforms and the averages of the system time and latency distributions. We find that the average latency can be minimized by tuning the traffic intensity in the transmission process that depends on the aggregation number, arrival rate, and transmission time of the data unit. Based on the clarified characteristics, we derive estimation formulae for the optimal aggregation number and optimal latency according to the traffic intensity that minimizes the average latency. Our main contribution is the theoretical basis for further research on the performance evaluation and design of communication devices, such as IoT gateways, which will be essential to realize latency- critical IoT applications.
PTC: Pick-Test-Choose to Place Containerized Micro-Services in IoT

In the presence of the Internet of Things (IoT) devices, the end-users require a response within a short amount of time which the cloud computing alone cannot provide. Fog computing plays an important role in the presence of IoT devices in order to meet such delay requirements. Though beneficial in these latency-sensitive scenarios, the fog has several implementation challenges. In order to solve the problem of micro-service placement in the fog devices, we propose a framework with the objective of achieving low response time. This problem has been formulated as an optimization problem to improve the response time by considering the time-varying resource availability of the fog devices as constraints. We propose an orchestration framework named Pick-Test-Choose (PTC) to solve the problem. PTC uses Bayesian Optimization based iterative reinforcement learning algorithm to find out a micro-service allocation based on the current workload of the fog devices. PTC employs containers for service isolation and migration of the micro-services. The proposed architecture is implemented over an in-house testbed as well as in iFogSim simulator. The experimental results show that the proposed framework performs better in terms of response time compared to various other baselines.
A Magnetic Field Sensor Array as an Universal Smart Home Control Device

This paper presents a smart home control device based on magnetic field sensors. The proposed device can distinguish between separate gestures performed with a permanent magnet in the sensing area. The device is designed without the necessity of a software interface or an internet connection in order to increase the acceptance by elderly people. The suggested approach is versatile in its realization as well as application and can enable additional security features. A prototype with a reduced complexity is presented that is able to function as a virtual dimmer or thermostat using a raspberry pi within a neuronal network.
A Study of Level-Shifted PWM Single-phase 11-Level Multilevel Inverter

This paper presents a modified shifted-level pulse width modulation (PWM) for an 11-level multilevel inverter (MLI) that used for distributed generation systems and medium and high power energy conversion systems applications because of the flexibility in the voltage level that this converter has been introduced. This converter employs five independent DC sources to generate the eleven output voltage levels considering a reduced number of switching devices. The proposed switching control arrange the switching devices to synthesize the multilevel output voltage. The MMLI structure consisted of traditional H-Bridge as the main converter and aiding circuit that used to form the multilevel of the output voltage according to the proposed switching technique. The gains of this converter is that it presents a topology for increasing the output voltage levels in the simultaneously with a decrements of the switching devices, the switching losses as well as low level of THD. The validity of the presented MLI are verified by both computer simulation using MATLAB Simulink© and real-time simulation (RTS) with the aid of dSPACE hardware.
Intrusion object recognition and space distance measurement in safety area of high voltage transmission lines

In this paper, the research work of image recognition is carried out in the face of the detection of illegal intrusion objects in power transmission lines. By analyzing the current intelligent monitoring and real-time prediction related technologies, Based on the image data of binocular camera, the method of identifying illegal foreign objects is mainly studied under the condition of constructing line space equation. The research on identification method proposes the use of continuous inter-frame difference method and background difference method to detect foreign object recognition, which can effectively overcome the problem of missed detection of static foreign objects, shadow misdetection and inability to locate foreign objects in the foreign object detection algorithm based on image difference. And using the point cloud to calculate the spatial distance, it can accurately obtain the distance from the invading foreign object to the line. And the feasibility of the verification method on site.
Sparsity Constrained Regression For High-Speed Signal Integrity Modeling

High-speed signal integrity (SI) modeling usually involves modeling non-linear subsystems. Traditional approaches like response surface modeling (RSM), artificial neural networks (ANN) and support vector machine (SVM) suffer from two major issues. Either they don’t scale well when the number of dimensions or variables increase in the non-linear model or they are sensitive to the initial training data used to create the models. For example: RSM approach requires large design of experiments (DoE) when the number of variables increase. With ANN, the designer has to spend a lot of time tuning or optimizing the hyperparameters. In this paper, a new approach based on sparsity constrained regression (SCR) is proposed. This approach can scale even when the non-linear problem has large number of variables and it only has one hyper-parameter to tune making it easy to train and test. A test case based on printed circuit board (PCB) stack-up modeling is used in this paper to test the efficiency of SCR approach. The results are compared against traditional RSM and ANN approaches.
Mitigating the Impact of Channel Tapering in Vertical Channel 3-D NAND

The channel tapering from top to bottom in vertical channel 3-D NAND is a major concern. This leads to nonuniformity in the NAND string performance, including cell current (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {cell}}$ </tex-math></inline-formula>) and threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula>) variation along the vertical NAND string. In this article, we show that the variation in the electric field due to the difference in the channel radius from bottom to top is the root cause behind the <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> variation along the string. For the first time, we propose novel techniques to minimize the adverse effects of the channel tapering on <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> variations. It is shown that graded channel doping (~10<sup>18</sup> cm<sup>−3</sup> at the bottom to ~10<sup>15</sup> cm<sup>−3</sup> at the top) results in the narrowing of the <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> variation by ~90% along the vertical string. Further, we propose that a nonuniform block oxide thickness along the string can be used to enhanced uniformity of <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> distribution from bottom to top. Additionally, we investigate and show that the uniformity in <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> distribution among word line (WL) transistors can also be achieved by optimizing the amplitude and duration of the position-dependent program/erase voltages. The proposed techniques in this article have a high potential for designing variation tolerant and reliable 3-D NAND memories having enhanced uniformity in the program/erase <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${I}_{\text {cell}}$ </tex-math></inline-formula> distribution.
Spherical Flux Concentration Transmitter for Omnidirectional Wireless Power Transfer with Improved Power Transmission Distance

In this paper, we propose a spherical flux concentration structure for omnidirectional wireless power transfer. Omnidirectional wireless power transfer technology is a method that can transmit power to a transmitter located in arbitrary position in a two-dimensional or three-dimensional space. However, in order to improve power transfer distance in wireless power transfer system, the diameter of the coil or the number of windings must increase, thereby increasing the size of the transmitter. The transmitter structure proposed in this paper adds a ferrite core inside the transmitter coil so that the magnetic flux generated by the transmitter is directed in the position of the receiver. As a result, the flux linkage increases and the mutual inductance increases. By implementing the omnidirectional wireless power transfer system using the proposed structure, the power transfer distance can be improved by 65% compared to the existing system without increasing the size of the transmitter. Simulation shows that the proposed spherical flux concentration structure increases the mutual inductance when applied to omnidirectional wireless power transmission system.
Ranked Rule Based Approach for Sentiment Analysis

Today, large population use social networking sites like Facebook, Twitter, LinkedIn etc. Through social media, people share messages, photos. They also impart information about a particular event or specific situation. There is limited research on crowd management to handle a disaster. We should focus on Crowd Management using Sentiment Analysis as a tool for safety in some events or situations. People convey their emotion about crowd using social sites. Crowd-related issues encountered day to day life such as stations, shopping malls, and stadiums or some events like marriage which may cause congestion and due to that some people may be injured or causes death. Peoples post their sentiments through Twitter, LinkedIn etc.In this paper, we consider traffic jam event where traffic will be able to move or will not be able to move. For this purpose, tweets are collected from social networking site Twitter. Human expressions are expressed through Natural Language Processing and then calculate polarity of sentiment using rule-based approach. User’s opinion is classified into positive, negative or neutral Sentiment. Polarity score of sentence is calculated through SND pattern. Users may enter false tweets which will decrease accuracy of system. To increase accuracy of system along with polarity score, we also consider polling based on user ranking in our proposed system.
Multimodal Encoder-Decoder Attention Networks for Visual Question Answering

Visual Question Answering (VQA) is a multimodal task involving Computer Vision (CV) and Natural Language Processing (NLP), the goal is to establish a high-efficiency VQA model. Learning a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions is the heart of VQA. In this paper, a novel Multimodal Encoder-Decoder Attention Networks (MEDAN) is proposed. The MEDAN consists of Multimodal Encoder-Decoder Attention (MEDA) layers cascaded in depth, and can capture rich and reasonable question features and image features by associating keywords in question with important object regions in image. Each MEDA layer contains an Encoder module modeling the self-attention of questions, as well as a Decoder module modeling the question-guided-attention and self-attention of images. Experimental evaluation results on the benchmark VQA-v2 dataset demonstrate that MEDAN achieves state-of-the-art VQA performance. With the Adam solver, our best single model delivers 71.01% overall accuracy on the test-std set, and with the AdamW solver, we achieve an overall accuracy of 70.76% on the test-dev set. Additionally, extensive ablation studies are conducted to explore the reasons for MEDAN’s effectiveness.
Comparative study of Narrowband and Wideband Opportunistic Spectrum Access Techniques

The radio spectrum is a limited natural resource which is precious due to the drastic growth in the wireless communication. The static allocation of the spectrum has led to an underutilization of the spectrum. Cognitive radio is the promising technology for the dynamic allocation of the spectrum which improves the spectrum utilization. In cognitive radio, an unlicensed (secondary) user can opportunistically access the spectrum when the licensed (primary) user is not transmitting, without causing any harmful interference to the primary user's signal. Spectrum sensing is the key element of cognitive radio, which provides the crucial information regarding the status of the spectrum. The spectrum sensing algorithms aims at detecting whether the primary user is transmitting over the spectrum or the spectrum is empty. Based on the results of the spectrum sensing the secondary user can opportunistically access the spectrum when the spectrum in empty. In this paper, a brief survey of narrow band and wide band spectrum sensing techniques have been discussed.
Design and implementation of SALVUS women safety device

India being considered itself as a well developing nation and an economic core, faces several of evils known from ages like dowry, crime against women, sexual assault, abduction and the worst amongst all is rape. In spite of India's literacy rate, the women don't privilege their right to equality and justice in this society. Women suffering these atrocities are even denied the basic human rights as mentioned in the Indian penal code. Women not being as fit (physically) as men need to be protected from the hands of evils of the society. The implementation of the acts and techniques for a safer women is not sufficient and has to be enhanced well enough. In order to overcome all these problems, we should aim on a promising society where women feel safer and secure throughout the world. This proposed idea describes the steps and process as to how the safety device works in ensuring the welfare of the women by instantly sending a message and the location of the victim to the pre stored contacts. This paper also describes as to how the assailant gets scared by the loud noise and the vibration created so as to let go off the victim.
On the Pedestrian Flow Analysis through Passive WiFi Sensing

The proliferation of mobile devices, including smartphones and tablets, has been enabling new possibilities for inferring information about the positions, behavior and activities of the users carrying these devices. For instance, by leveraging the WiFi probes sent out by mobile devices in public spaces (such as shopping malls, metro stations, etc.), even if pedestrians do not have their mobile devices to be associated with any WiFi access point (AP), it is attractive to conduct pedestrian analysis in a passive sensing approach to facilitate the efficient management of public infrastructures as well as convenient customer services. This paper considers the problem of pedestrian flow analysis by implementing a pedestrian surveillance system in the transfer channel of a metro station in Guangzhou China. Firstly, a fingerprint database is generated through a Gaussian process regression (GPR) approach. On these grounds, a pedestrian number estimation method based on linear regression is presented by making use of the fingerprint-based localization method to refine the number of mobile devices residing in the surveillance area, and a pedestrian velocity estimation method is proposed based on particle filter and the inverse distance weighted (IDW) method. According to the dataset obtained in real scenarios, the effectiveness and advantages of the proposed two methods are confirmed.
Joint Block Length and Pilot Length Optimization for URLLC in the Finite Block Length Regime

In this paper, we maximize the system throughput of a point-to-point ultra-reliable low-latency communications (URLLC) system by jointly optimizing its block length and pilot length under the constraints of latency and block error probability. A finite block length (FBL) is adopted to enable low transmission latency. We prove that the throughput is approximately concave with respect to pilot length, given a block length, and that there exists a unique optimal block length in terms of throughput, with a given pilot length. Closed-form expressions are derived for the near-optimal pilot length with a given block length, as well as the asymptotic block error probability with respect to both block length and pilot length. A low-complexity iterative algorithm is proposed for joint optimization of block length and pilot length, which converges within only 1-3 iterations. Simulation results show that the proposed joint optimization scheme achieves a near-optimal throughput performance of an FBL URLLC system, with a much lower complexity than exhaustive search. It also significantly outperforms the previous approaches that considered either block length optimization or pilot length optimization only.
Evaluation of Three-Phase Transformerless DC-Bypass PV Inverters for Leakage Current Reduction

In transformerless photovoltaic (PV) systems, the leakage current reduction is one of the most important issues. Many interesting single-phase dc-bypass transformerless PV inverters have been proposed for the leakage current reduction, and there is a well-known conclusion that the clamping topology is better than the unclamping one in terms of leakage current reduction. However, is this conclusion correct for three-phase dc-bypass ones? The main objective of this article is to investigate the impact of clamping and unclamping dc-bypass switches on the leakage current reduction capability of three-phase transformerless inverters. The findings reveal that the conclusion is different from the single-phase dc-bypass ones. The theoretical analysis and experimental results are provided to verify the new insights.
Design of Contract-Based Sponsorship Scheme in Stackelberg Game for Sponsored Content Market

Per sponsored content policy, a content provider can pay the network operator on behalf of mobile users to lower the data usage fees so as to generate more advertising revenue. Under such a scheme, how to offer proper sponsorship to the users in response to varying data prices becomes an important issue. Furthermore, the information asymmetry between the content provider and users makes the problem more challenging. In this paper, we propose a Stackelberg game based framework to tackle this challenge. In the framework, the network operator determines the data price first as the leader of the game, and the content providers as well as users make the decisions based on the data price as the followers. Specifically, the decision making process of the followers with the presence of asymmetric information is formulated as a contract game. In the contract game, the content provider designs a contract that contains sponsoring strategies toward all types of the users. After obtaining the optimal contract that maximizes the profit of the content provider, we also derive the optimal pricing of the network operator through backward induction. The Stackelberg equilibrium is proved to be unique, and numerical results are presented for performance evaluation.