Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Underwater Vehicle Control

Earth, the third planet from the sun is the only planet on which life is known to exist. Seventy percent of the earth is covered by oceans. These oceans have been playing a very crucial role in transporting of goods even before the road. At the same time it cannot be ignored the possibilities of the negative role of the oceans- a very serious threat to human society and damage of man-made infrastructure through the natural phenomenon such as hurricanes and tsunamis, the latest being Hudhud in Visakhapatnam and Nilam in Chennai. Autonomous Underwater Vehicles (AUV) and Remotely Operated Vehicles (ROV), are considered as marine robots which are very useful for study of ocean resources such as aquaculture, offshore mining, ocean survey etc.,. This paper considers the dynamic modeling of an Autonomous Underwater Vehicle (AUV) which is used to track the AUV in the presence of ocean currents and other underwater environmental disturbances. Model Predictive Control (MPC) has been attempted on AUV for control purpose. The main advantage of using MPC is it can easily handle multi input multi output systems very effectively. Constraints on inputs, states and outputs can also be considered using this technique. It has the ability to handle model mismatch and disturbances. Sensor noise can be estimated using filter and Kalman filtering plays an important role in AUV navigation. AUV sensors play an important role in data collection and remote sensing. Global Positioning satellite (GPS) may not work in underwater environment for remote sensing. Inertial Measurement Unit (IMU) sensor is used to find position of a vehicle and it may also helpful for remote sensing. Mathematical modeling and MPC formulations are the contributions of this paper. The results presented in this paper can be summarized in three points as dynamic modeling of AUV, stability issues, and formulation of MPC. All the simulations are carried out in MATLAB environment. These results help to explain the AUV control concept which is essential for ocean studies to monitor the underwater environment.
Design of an Integrated Gripper with a Suction System for Grasping in Cluttered Environment

Development of an integrated gripping system for grasping various objects in different work environments is very useful in many practical applications, such as warehouse automation. In this paper, we propose a linkage-driven underactuated gripper combined with a suction mechanism. This underactuated gripper has two fingers which can be controlled independently and each finger is constructed by stacking one five-bar mechanism over one double parallelogram. The special architecture allows for installing all of the actuators on the base. The suction mechanism is used to grasp objects in narrow space and enhance the grasp stability. The ability of grasping various objects is confirmed through practical grasping experiment on a commercial robot arm.
Power Coordination System between Multiple Sources in Electric Vehicle Application

This study presents a power management scheme for application in hybrid electric vehicle (EV) systems. For various advantageous features like, high efficiency, and high reliability photovoltaic (PV) system is chosen as the main power source of EV. However, according to mitigate the intermittency problem of solar cell, auxiliary source is required to be incorporated along with PV. Hence, an energy storage system (Battery) is associated with the PV system which can ensure an uninterrupted power supply. Through the bidirectional DC-DC converter battery cells are used to store excess solar energy and provide them to the load when required. As per requirement the battery is charged and discharged by controlling the operation mode of the bidirectional converter i.e. bucking and boosting, respectively. The MATLAB simulation results are presented to show the behavior of the responses even in presence of variation in load as well as in different illumination condition of PV module.
Towards Power Efficient High Performance Packet I/O

Recently, high performance packet I/O frameworks continue to flourish for their ability to process packets from high-speed links. To achieve high throughput and low latency, high performance packet I/O frameworks usually employ busy polling. As busy polling will burn all CPU cycles even if there's no packet to process, these frameworks are quite power inefficient. However, exploiting power management techniques such as DVFS and LPI in the frameworks is challenging, because neither the OS nor the frameworks can provide information (e.g., actual CPU utilization, available idle period, or the target frequency) required by these techniques. In this article, we establish a model that can formulate the packet processing flow of high performance packet I/O to help and address the above challenges. From the model, we can deduce the information needed for power management techniques, and gain the insights to balance the power and latency. After suggesting to use pause instruction to reduce CPU power within short idle period, we propose two approaches to conduct power conservation for high performance packet I/O: one with the aid of traffic information and the other without. Experiments with Intel DPDK show that both approaches can achieve significant power reduction with little latency increase.
A Novel Method for Person Re-Identification: Conditional Translated Network Based on GANs

The main challenge of person re-identification (re-id) lies in the strikingly discrepancy between different camera views, including illumination, background and human pose. Existing person re-id methods rely mostly on implicit solutions, such as seeking robust features or designing discriminative distance metrics. Compared to these methods, human solutions are more straightforward. That is, imagine the appearance of the target person under different camera views before matching target person. The key idea is that human can intuitively implement viewpoint transfer, noting the association of the target person under different camera views but the machine failed. In this paper, we attempt to imitate such human behavior that transfer person image to certain camera views before matching. In practice, we propose a conditional transfer network (cTransNet) that conditionally implement viewpoint transfer, which transfers image to the viewpoint with the biggest domain gap through a variant of Generative Adversarial Networks (GANs). After that, we obtain hybrid person representation by fusing the feature of original image with the transferred image then perform similarity ranking according to cosine distance. Compared with former methods, we propose a human-like approach and obtains consistent improvement of the rank-1 precision over the baseline in Market-1501, DukeMTMC-ReID and MSMT17 dataset by 3%,4%,4%, respectively.
Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.
A Survey on Efficient Converter Driver Techniques for LED LIGHTING Applications

This review article focuses on various proposed LED driver technologies and different converter topologies which will help to increase efficiency, reliability, and lifespan of lighting systems. These classifications of LED drivers are presented for the both AC and DC voltages and are described evidently. The different methods to balance the currents at the output side of the driver, its pros and cons are explained in detail. This paper gives a detailed overview about the various power conversions and different control strategies like electrolytic capacitor less, soft switching, PWM dimming, current balance techniques to enhance the performance of LED drivers. The main goal of this paper is to give correct awareness to choose suitable LED driver for various smart lighting applications.
Ultrasensitive room-temperature chemical sensors by Ag-decorated ultraporous ZnO nanoparticle networks

Highly sensitive room temperature gas sensors consisting of ultraporous ZnO nanoparticle networks decorated with Ag nanoparticles (NPs) were fabricated by nanoparticle aerosol self-assembly and sequential sputtering. Optimization of the AgNPs loading and the thickness of ultraporous ZnO networks lead to a sensor response, defined as the ratio of resistance change, of 1.8 and 7.4 at 0.1 and 1 ppm ethanol concentrations, respectively, at room temperature under light irradiation. This is ~ 10 times higher than that of pure ultraporous ZnO film under the same experimental conditions. Furthermore, the optimal AgNPs-decorated ultraporous ZnO films can detect as low as 5 ppb of ethanol gas at room temperature under light illumination. The high sensitivity of AgNPs-decorated ZnO film can be ascribed to the synergistic effects of the ultraporous nanoparticle network morphology, AgNPs sensitization and light-assisted photo-excited gas-sensing process. These provide directions for the design of high sensitive metal-oxide semiconductor-based gas sensors capable to operate at room temperature.
Optimized Data Association Based on Gaussian Mixture Model

Data association is the foundation of state estimation in mobile robot simultaneous localization and mapping. Aiming at the problems of false association, high computational complexity in joint compatible branch and bound algorithm, we propose an optimized joint compatible branch and bound data association algorithm based on Gaussian mixture clustering. Firstly, the local association strategy is adopted to limit data association in local region, so as to reduce the number of features involved in data association at the current moment. Secondly, the Gaussian mixture clustering algorithm is used in local areas to group the observed values at the current moment, so as to get several groups that have little correlation with each other. Finally, joint compatible branch and bound data association algorithm is used in each group for data association, and the optimal solution is obtained according to mutual exclusion criteria and optimal criteria. The experiment results verify that the algorithm improved the accuracy of data association, reduced the computational complexity and improved the efficiency of data association.
A Cantilever Type Bistable MEM Nonvolatile Memory with Piezoelectric Deactuation for High-Temperature Applications

This paper reports a mechanism of introducing bistability in the cantilever switch for the microelectromechanical system based non-volatile memory (MEMS-NVM). The bistability in cantilever switch is a fine play of adhesion and restoring force. The innovation lies in the adopted method to control the switch contact adhesion force. The prototype MEM-NVM imbeds piezoelectric slabs, which on biasing alters the surface roughness and thus the adhesion force. The prototype bistable cantilever switch is fabricated and tested for its bistability and memory applications. The obtained SET/RESET voltages, to toggle in two logic states is 10 volts at ~250 °C. The memory SET/RESET time is ~1µs. The idea is not limited to non-volatile memories but can be extended to any of the adhesion failed MEMS devices.
Improving the Dynamic Force Control of Series Elastic Actuation Using Motors of High Torque-to-Inertia Ratios

A series elastic actuator (SEA) includes an elastic spring in series with an actuator. SEAs provide more accurate force and impedance control than conventional rigid actuators. They are ideal for robots and machines that need to interact safely with the environment. The majority of existing SEAs uses brushless or brushed DC motors as the actuators. The advantages of using step motors as the actuators of SEAs have not received enough attention. Step motors have much higher torque-to-weight ratio and torque-to-inertia ratio than other DC motors. Hence they can provide better stability and high-speed accuracy of force control while maintaining lightweight. When the rotor position feedback is used, step motors can achieve accurate dynamic position response smoothly. This paper develops the dynamic model of a linear series elastic step motor and presents its prototype. Force and impedance control responses will be provided to show the advantages due to the high torque-to-inertia ratio of step motors. It is expected that the results presented here can offer a better actuator selection of SEAs when high-performance dynamic force control is required.
Deep Learning based AC Line Fault Classifier and Locator for Power System

Power system fault classification is one of the critical procedure in assessing the security of the power system and restoring it to the normal (stable) operation. This paper presents a novel architecture of a deep neural network (DNN) applied to both fault type and location prediction problem. Proposed DNN architecture uses four densely connected hidden layers, each layer having 40 units with hyperbolic tangent as the activation function. This paper compares the performance of four advanced optimizers (Adam, RMSProp, Nadam, AdaMax) used for training the deep neural network. Preliminary results show that the proposed DNN architecture has high accuracy for classifying the power system faults and their location.
The Electronic Throttle Controller Based on the Model Reference Adaptive Nonlinear Triple-step Method

Electronic throttle control system is extremely important for engine intake control. Due to the nonlinear structure of the electronic throttle and the change of friction during the rotation of the throttle baffle, the angle of the electronic throttle controlled by the Electronic Control Unit (ECU) will cause a large control error. In order to control the electronic throttle more accurately and quickly, this article builds an electronic throttle control system based on dSPACE Rapid Control Prototyping (RCP). This paper first establishes a mathematical model based on the mechanism of the electronic throttle. Next the particle swarm optimization algorithm is applied to identify the parameters of the electronic throttle system. Then the controller based on discrete model reference adaptive control (MRAC) nonlinear triple-step method is designed. Finally, dSPACE RCP is applied for experimental verification. Experimental results demonstrate the efiectiveness of the MRAC nonlinear triple-step method controller with prescribed transient response.
Sampled-Data Adaptive ESO Based Composite Control for Nano-Positioning Systems

This work studies a novel sampled-data adaptive extended state observer (ESO) based composite control approach for the digital nano-positioning systems with sampling restrictions. In the proposed control architecture, a sampled-data ESO with an output predictor is designed to estimate states and disturbances within a continuous period of time, where the observer gains are adaptively updated. To deal with the inter-sample disturbances, a multirate disturbance compensator is then worked out by upsampling, with which a discrete composite feedback controller with linear and nonlinear parts is employed aiming at improving the transient performance of positioning. Moreover, the estimation convergence and the stability are analyzed by means of Lyapunov methods. The proposed control method is then applied to the simulations and experiments in a piezoelectric nano-positioning system so as to validate its effectiveness.
Cooperative Update Mechanism of Cache Update Method Based on Content Update Dynamic Queries for Named Data Networking

In Named Data Networking (NDN), a client requests a content object by its content identifier, instead of the server's IP address. When a router or a node around the client stores the content in the cache, it transfers the content from the cache. As a result, it is possible to provide information quickly and efficiently to many users. In NDN, a content distributor can configure the content's expiration time at each cache. The expiration time should be long enough to use the cache effectively and decrease the amount of traffic. However, if the expiration time is too long this increases the possibility of receiving old content. A shorter expiration time reduces this possibility but this causes the amount of traffic to increase instead. In order to solve this trade-off relationship, we proposed an automatic cache update method where each router queries the version number of all content in the cache to automatically remove old content. Since it is too difficult to set an appropriate inquiry interval for all the content, we also proposed a cache update method based on content update dynamic queries to dynamically change the inquiry interval when each content object is not updated. This paper improves this method by introducing a cooperative update mechanism to reduce the amount of traffic in the network and the number of times old content is received. It also evaluates the method on the generated topology that is similar to real topology and shows that, for example, when the cache size on all routers is large enough, although the difference in the average aggregated traffic is larger than that of the author's first method where the inquiry interval was 270 seconds by 49.1–84.9 GBytes for the experimental period (7 days) (= 4.9–8.4 MBytes per minute), the number of inquiries all routers generated is reduced by 131232.6– 1159687.9 times over 7 days (= 13.0–115.0 times per minute).
An iron filings filter for desulfurization of biogas

Biogas has great potential as a source of clean and cheap energy. However, during production also hydrogen sulfide is generated, which must be removed to avoid damage to equipment and danger to users and environment. Among the various configurations available for the removal of hydrogen sulphide from biogas, iron filings it is one of the options available for further consideration for this process. Therefore, this article proposes the development of a filtering system in two stages for the removal of hydrogen sulfide. Tests confirm the potential of iron filings as an effective and economical agent for removing H2S. A filter with polyvinyl chloride (PVC) body was developed and biogas flowed through it. The most significant results show the reduction of $H_{2}S$ levels, from a concentration of 155 ppm in filter admission to 0.9 ppm at the outlet, which was measured by a portable hydrogen sulfide analyzer. A removal efficiency greater than 90% were observed for most of the runtime.
A Survey of Mid-Air Ultrasonic Tactile Feedback

This paper contains a succinct survey on the recent major advances in contactless ultrasonic tactile feedback. It is a haptic technology which enables easy mid-air interactions with rich multisensory feedback and creates effects which are otherwise impossible. It can become a disruptive technology for multimedia interfaces, applications, and mobile computing. We summarize and discuss the advantages, problems and applications of the technology. This survey provides an introduction to the topic for anyone interested in applying or researching it.
ARTDL: Adaptive Random Testing for Deep Learning Systems

With recent breakthroughs in Deep Learning (DL), DL systems are increasingly deployed in safety-critical fields. Hence, some software testing methods are required to ensure the reliability and safety of DL systems. Since the rules of DL systems are inferred from training data, it is difficult to know the implementation rules about each behavior of DL systems. At the same time, Random Testing (RT) is a popular testing method and the knowledge about software implementation is not needed when we use RT. Therefore, RT is very suitable for the testing of DL systems. And the existing mechanisms for testing DL systems also depend heavily on RT by the labeled test data. In order to increase the effectiveness of RT for DL systems, we design, implement and evaluate the Adaptive Random Testing for DL systems (ARTDL), which is the first Adaptive Random Testing (ART) method to improve the effectiveness of RT for DL systems. ARTDL refers to the idea of ART. That is, fewer test cases are needed to detect failures by selecting the test case with the furthest distance from non-failure-causing test cases. Firstly, we propose the Feature-based Euclidean Distance (FED) as the distance metric that can be used to measure the difference between failure-causing inputs and non-failure-causing inputs. Secondly, we verify the availability of FED by presenting the failure pattern of DL models. Finally, we design ARTDL algorithm to generate the test cases that are more likely to cause failures based on the FED. We implement ARTDL to test top performing DL models in the field of image classification and automatic driving. The results show that, on average, the number of test cases used to find the first bug is reduced by 62.74% through ARTDL, compared with RT.
Hand gesture recognition based on convolutional neural network using a bistatic radar system

Recently, hand gesture recognition based-on radar has attracted many researchers in the field of human–computer interfaces. However, the number of kinds of hand gestures and recognition accuracy can be still increased. In this paper, we propose a hand gesture recognition approach based on convolutional neural network (CNN) using a bistatic radar system. Firstly, we build a bistatic radar system which consists of two pulse radars and define an active area of hand gesture called gesture desktop. Then, two time-distance maps are obtained by signal pre-processing, and we build a Bistatic-CNN with two branches of convolutional layers as a classifier to recognize 14 hand gestures. The bistatic radar system can offer us much more information of hand gesture from different perspectives and achieve much higher hand gesture recognition accuracy than single radar. The experimental results based on the measured data show that the proposed approach can recognize 14 hand gestures with average accuracy over 98%.