Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Labelled Indoor Mapping using SLAM and Object Detection

In this paper, an in-house developed robot, named as ExploDroid, is introduced with details of its construction, mapping, object-detection implementation and functional demonstration. It is developed using the Robot Operating System (ROS) platform. The proposed application of this robot is to obtain a labelled 2D map of an unknown static indoor environment using Simultaneous Localization and Mapping (SLAM) algorithm, for creation of a map and also for object-detection in it. The objects would then be appropriately labelled. The end result is a 2D map of the environment, with labelled objects. The SLAM algorithm is implemented using the open-source Gmapping package, which is integrated into the ROS Framework. Besides, it is also used alongside the RViz Visualization tool for viewing the maps created. By employing low-cost hardware, the cost of building such a platform has been made quite economical while retaining the precision of the map obtained. Experimental trials in real world environments yielded accurate and verifiable object-detection and labelling results.
Localisation using UAV in RFID and Sensor Network Environment:Needs and Challenges

Study in paper accentuates the need of utilising Unmanned Aerial Vehicles(UAVs) in an Radio Frequency Identification (RFID) Sensor Network environment to develop mission critical applications. UAVs can act as a data logger and enable localisation in such an integrated environment, that can help us develop mission critical applications which can be deployed in harsh environments. The paper focuses on investigation and comparison of localisation techniques using UAVs in the given technology space. The need to establish localisation using UAVs, which demands rigorous mathematical models, approaches and experimental setups, is envisaged.
Quadcopter Control via Onset Eye Blink Signals: A BCI Study

Owing to the advancements in the field of brain technology, numerous researches have been done to explore the prospects of application of brain signals in numerous ways. This work presents a novel approach to control a quadcopter using brain activity for eye blinking signals. EEG signals generated as a result of eye blinking are transformed into useful input to the quadcopter to perform respective commands. The work covers the execution of 8 commands using a 4-sec time window via 2 EEG electrodes. Each task to be executed in a 1-sec window. For more fast and easier control, an additional interface is provided to make the commands handier and more useful for the subject to choose between the commands. Classification accuracy shows promising results which can ensure better control for other devices like a quadcopter. Thus, this work provides a user-friendly approach for control of a quadcopter via eye blinking signals using a low cost and non-invasive technique using a wearable EEG headset.
Arduino Based Voice Controlled Robot

In this research paper, a system is being proposed, which focuses on the concept of how a robot can be controlled by the human voice. Voice control robot is just a practical example of controlling motions of a simple robot by giving daily used voice commands. In this system, an android app is used as a medium for the transmission of human commands to microcontroller. A controller can be interfaced with the Bluetooth module through the UART protocol. The speech is received by the android app and processed by the voice module. Voice is then converted to text. [1] The microcontroller will further process this text, which will take suitable action to regulate the robot. The objective is to design a robotic car whose basic movements such as moving forward, turning to left or right can be controlled by the human voice. The Hardware Development board used here is the ATmega Arduino board. The software part is done in Arduino IDE using Embedded C. Hardware is implemented, and software porting is done. Generally, recognition of human voice using some kind of module cost way too much. After performing an ample amount of studies on controlling robots, we came to the conclusion that yes, there exists a simple and very efficient way to manipulate robots through our voice. This is an ergonomic approach for the ease of robotic application. Such types of robots will provide great helping hands while performing multiple tasks. The result of our studies also shows that there still exists plenty of space for further research and development. [2]
Joint radar and communication: A survey

Joint radar and communication (JRC) technology has become important for civil and military applications for decades. This paper introduces the concepts, characteristics and advantages of JRC technology, presenting the typical applications that have benefited from JRC technology currently and in the future. This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration. Compared to previous surveys, this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC. Specifically, we explore an open research issue on radar and communication operating with mutual benefits based on collaboration, which represents the fourth stage of JRC evolution. This paper provides useful perspectives for future researches of JRC technology.
Logic carrying network building method based on link load balancing

Taking into account the K short path link mapping not considering load balance and is prone to bottleneck link, a link mapping strategy was proposed which is based on link load balancing. Through the bandwidth of link being used to judge link busy idle degrees, by calculating the link load balancing degree to determine the mapping of the link and depend on the strategy which a logic carrying network building method was given. The experiment results show that the method not only higher resource utilization and the success building rate, but also makes a more balanced network load, avoiding the bottleneck resources appear. Making physical network can meet more of the follow-up network construction requests.
CAT-TWO: Counter-Based Adaptive Tree, Time Window Optimized for DRAM Row-Hammer Prevention

Row-hammering flips bits in a victim DRAM row by frequently activating its adjacent rows, compromising DRAM integrity. Several studies propose to prevent row-hammering by counting the number of activates to a DRAM row and refreshing the corresponding victim rows before the count surpasses a row-hammer threshold. However, these approaches either incur a significant area overhead or a large number of additional activations (ACT) that could degrade the system performance. In this paper, we propose CAT-TWO, a time-window-optimized version of the existing Counter-based Adaptive Tree (CAT) scheme for row-hammer prevention. We first ensure that the victim rows are always refreshed at the last level of the tree without counter overflow by configuring the threshold and the number of CAT-TWO counters based on the fact that the maximum number of ACTs is limited within the refresh window. We further reduce the size and latency of CAT-TWO by applying high-radix rank-level CAT-TWO with multiple tree roots. CAT-TWO incurs less than 0.7% energy overhead on a baseline DDR4 DRAM device, and generates less than 0.03% additional ACTs to refresh victim rows in the worst case, which hardly affects system performance.
RoboMapper: An Automated Signal Mapping Robot for RSSI Fingerprinting

With the ever-increasing numbers of mobile devices, location-based services became a crucial part of mobile development. Many indoor location detection systems are developed to solve positioning problem where satellite-based solutions prone to failure. Among many proposed solutions, fingerprinting technique proved to be the most reliable approach for indoor location. However, it comes with a cost; it entails a time-consuming learning phase which should be repeated many times during the system's life time to preserve system accuracy. Thus, we propose an automated signal mapping robot called RoboMapper to alleviate time-consuming nature of the learning phase of fingerprinting technique. With the help of its accurate distance keeping mechanisms, RoboMapper can construct the signal map of the environment so that the created map can be used for user positioning. Our findings indicate that using RoboMapper 2.68-meter positioning accuracy with 70% probability can be achieved.
Artificial Neural Network enabled P2D Model Deployment for End-of-Line Battery Cell Characterization

The production chain of lithium-ion battery cells is an intricate process with manifold process-product interdependencies leading to a complex product, the battery cell. In order to assess the quality and the performance of the battery cell in the end-of-line characterization, battery cell models need to be fit to experimental data in order to estimate unmeasured physical parameters of the cell. This procedure is laborious and error prone to the model complexity and the large number of physical parameters and is therefore presently not suitable as an end-of line test in a large-scale battery cell production. In this paper, a battery cell model is used to generate data by varying measured and unmeasured parameters in order to train an artificial neural network model for a fast and reliable diagnostics. The presented artificial neural network model is capable of fitting the battery cell model in less than a second to make it more suitable for end-of-line characterization.
Accelerating Fine-Grained Spatial-Textual Trajectory Similarity Joins with GPGPUs

The increasing volume of spatial-textual data generated from check-ins and reviews facilitates many practical applications. In these applications, similarity joins are the basic operation which further supports a wide range of similarity queries. However, this compute-intensive join operation calls for more efficient and effective algorithm to support real-time queries. Exploiting general purpose GPUs (GPGPUs) is one natural solution. Nevertheless, existing join algorithms utilizing GPGPU lack considerations on unique features of spatial-textual trajectories, especially the textual domain, thus resulting in poor performance. In this paper, we first propose a baseline spatial-textual trajectory similarity join algorithm, which is essentially based on a traditional algorithm for relational joins on GPGPUs. Then, by analyzing the bottleneck of this baseline, we further develop a fine-grained join algorithm which consists of memory access techniques and optimizations. We propose a strategy of flip to ensure coalesced memory accesses, and a batch scheduler for dividing the whole task into batches to fit the global memory while alleviating imbalanced throughput. Extensive experiments have been conducted over real-life datasets and the results show that our fine-grained algorithm performs the best and reduces the latency effectively.
Reduced-order Model and Mechanism of Dynamic and Stability Issues for Equivalent Source-Load Two-terminal DC System in DC Voltage Control Timescale

This paper investigates reduced-order model and mechanism of dynamic and stability issues for equivalent source-load two-terminal DC system in DC voltage control timescale. The main contributions of this work can be drawn as: 1) A physical model has been proposed with a concise parallel circuit structure of resistor, inductor and capacitor for dynamic and stability analysis of dc voltage control timescale in equivalent source-load two-terminal DC system. 2) With this model, a reduced second-order characteristic equation has been derived for dynamic and stability mechanism analysis, which can provide clear and intuitive insight into system damping and oscillation frequency with system and control parameters. 3) In DC voltage control timescale, increasing the DC voltage control loop proportional gain will increase the damping, but reduce the damping oscillation frequency; increasing the DC voltage control loop integral gain will reduce the damping and increase the damping oscillation frequency; increasing the DC capacitance will reduce the damping and damping oscillation frequency. Comparison of eigenvalues between detailed small-signal model and reduced-order model and detailed simulation results based on PSCAD/EMTDC verify the analysis.
Plant Leaves Region Segmentation in Cluttered and Occluded Images Using Perceptual Color Space and K-means-Derived Threshold with Set Theory

Presence of clutters and occluding objects within agricultural farm environments challenges accurate segmentation of plant leaves, a prerequisite for an effective machine-vision-based automation of agricultural tasks. In this paper, we propose a plant leaves segmentation method that can be integrated into vision-based robotic harvester and quality inspection systems. The proposed method combines the discriminatory power of color-based technique with the simplicity and computational efficiency of threshold-based technique. Clutters and occluding objects are eliminated by infinitesimal angular displacement of the threshold image, followed by the application of set theory. Performance evaluation shows that the proposed method demonstrate strong robust features and computational efficiency.
Application Analysis of Resistor Superconducting Fault Current Limiter in MMC-HVDC System

When a dc short-circuit fault occurs, the fault current rises rapidly, which has a strong impact on power electronic switches. It brings stability problems to the system as well. Recently, with the development of superconducting technology, superconducting fault current limiter(SFCL) has become an ideal device to limit short-circuit fault current, which provides a new idea for reducing the fault current of HVDC, and prolongs the use time of switching equipment in the system [1]–[3]. This paper summarizes the types and applications of SFCL, and takes the Resistor Superconducting Fault Current Limiter(RSFCL) as an example to analyze its principle and build a simulation model in PSCAD/EMTDC, then analyze its application effect in the MMC-HVDC system, it can offer useful advice on setting value of corresponding protection schemes.
Triple-Shapelet Networks for Time Series Classification

Shapelets are discriminative subsequences for time series classification (TSC). Although shapelet-based methods have achieved good performance and interpretability, they still have two issues that can be improved. First, previous methods only assess a shapelet by how accurately it can classify all the samples. However, for multi-class imbalanced classification tasks, these methods will ignore the shapelets that can distinguish minority class from other classes and will tend to use the shapelets that are useful for discriminating the majority classes. Second, the shapelets are fixed after the training phase and cannot adapt to time series with deformations, which will lead to poor matches to the shapelets. In this paper, we propose a novel end-to-end shapelet learning model called Triple Shapelet Networks (TSNs) to extract multi-level feature representations. Specifically, TSN learns the most discriminative shapelets by gradient descent similar to previous methods. In addition, it learns category-specific shapelets for each class by using auxiliary binary classifiers. Finally, it uses a shapelet generator to produce sample-specific shapelets conditioned on subsequences of the input time series. The addition of category-level and sample-level shapelets to the standard model improves the performance. Experiments conducted on extensive time series data sets show that TSN is state-of-the-art compared to existing shapelet-based methods, and the visualization analysis also shows its effectiveness.
Machine Learning based Decision Support System for Post Mortem Inspection of Pig Health

This paper presents a decision support system for post mortem inspection of slaughtered pigs to help pig abattoirs in producing quality pork. Valuable information on pig health can be achieved by means of computer application. A noble method for decision making on post mortem inspection of pigs using different machine learning techniques is presented here. It is important to make an accurate decision for pork consumption from the post mortem finding of the pig to prevent consumption of unhealthy meat. The proposed system collects the comprehensive information regarding the post mortem decisions related to pig from the veterinary experts. Different models of Machine Learning Algorithms are trained in this system to perform a comparative study in terms of different performance measures. It is found that the predictive model with Support Vector Machine(SVM) is the best performing model for making a decision on the post mortem health of a pig for the pig datasets. By using the developed predictive machine learning model, it is able to take a decision on normal, partial condemnation or total condemnation of a post mortem pig with high accuracy.
Electronic Trace Data and Legal Outcomes: The Effect of Electronic Medical Records on Malpractice Claim Resolution Time

Information systems generate copious trace data about what individuals do and when they do it. Trace data may affect the resolution of lawsuits by, for example, changing the time needed for legal discovery. Trace data might speed resolution by clarifying what events happened when, or they might slow resolution by generating volumes of new and potentially irrelevant data that must be analyzed. To investigate this, we analyze the effect of electronic medical records (EMRs) on malpractice claim resolution time. Use of EMRs within hospitals at the time of the alleged malpractice is associated with a four-month (12%) reduction in resolution time. Because unresolved malpractice claims impose substantial costs on the entire healthcare system, our finding that EMRs are associated with faster resolution has broad welfare implications. Furthermore, as we increasingly digitize society, the ramifications of trace data on legal outcomes matter beyond the medical context. This paper was accepted by Teck Ho, information systems.
RFdesk: Record Your Objects on Desktop Using COTS RFID Devices Contactlessly

Desktop is a reliable and amicable object carrier that accompanies us in our daily life, while working, eating and even entertaining. In this work, we devise a contactless but accurate object tracking system on desktop with commercial RFIDs. Comparing with conventional vision or acoustic based solutions, our system needs less computational resources and could be mucheasier for deployment. Moreover, ours could record the true positions for each device instead of the relative positions delivered in most of the previous studies. To this end, recording the user's access to the object on the desktop allows the user to interact with the smart device with simple actions. We present RFdesk, a contactless object location system that accurately locates every objects on the desktop. However, compare to tracking object withcontacted tag, several challenges are tackled before we make the system work. First of all, the signal employed for contactless tracking gets reflected twice which is thus much weaker, making the signal more susceptible to environmental noise and multipath. Another well-known challenge for contactless tracking is multi-target tracking as the signals reflected from multiple targets get mixed and interact. Extensive experiments show that RFdesk canflexibly deploy devices and tags, antennas, and localization itemscan be deployed on different planes. A median error of 3cm can be achieved for target tracking without attaching tags to the targets, even if there are 4 positioning objects on the desktop. Moreover, the average localization error can still be kept within 5.6cm outperforming the state-of-the-art systems by 100%.
A New FlexibleAntenna Array Design for Hyperthermia Treatment of Bone Cancer

This work presents a new flexible meandered dipole antenna array designed specifically to treat, with thermal ablation, bone tumors. Electromagnetic and thermal simulations were performed in the CST (computer simulation technology) program. The performance of the antenna arrays was tested for flexibility and different input powers. A three-layer bone phantom was created, including skin, fat, muscle. The antenna performance was evaluated in terms of return loss and directivity characteristics. Variable input power (1W − 5W) was applied. The specific absorption rate (SAR) was calculated through the electromagnetic simulations and it was used as an input for the thermal simulations. According to the results of the thermal simulations performed on the phantom, temperature rise on tumor tissue (15mm) placed in the bone was examined. The effectiveness of the antenna array, designed for use in the treatment of bone tumors, was examined according to different input powers for 20 min exposure time.
Active and Adaptive Application-Level Flow Control for Latency Sensitive RPC Applications

The Remote Procedure Call (RPC) frameworks are widely deployed in industry. Applications supported by RPC frameworks are often latency-sensitive which strictly require to be responded before the deadline. For meeting this requirement, RPC frameworks adopt the application-level flow control mechanism. This mechanism gives an appropriate threshold determining the number of RPC requests that the server can process, thus avoids missing the deadline. However, this threshold at the application-level is a fixed empirical value so that it is hard to obtain respectable performance because an endpoint's processing capacity can take a huge quantity of values by varying workload and different hardware configurations. While other methods based on specialized transport protocols are adaptive, they will introduce extra costs for message reporting from server to client. Furthermore, adopting specialized transport protocols will also introduce extra transplanting efforts for TCP-based applications. In this paper, we provide an active and adaptive application level flow control mechanism at the client side. We first design an algorithm to find the appropriate threshold to achieve the desired response time. Then based on this algorithm, we control the threshold to bound the response time as expected. We implement our flow control mechanism using a memcached testbed. Experiments prove that our mechanism can accurately reduce the mean and 99th percentile response time by at least 71.3% and 69.4% respectively, while keeping a relatively high QPS. Furthermore, compared to static-threshold mechanism, our flow control mechanism is more efficient under low latency constraints.
Improving the Reliability of TMR With Nontriplicated I/O on SRAM FPGAs

Triple modular redundancy (TMR) with repair is a commonly employed mitigation strategy used on SRAM field-programmable gate arrays (FPGAs) to reduce the effects of ionizing radiation and improve a circuit’s sensitive cross section. This article examines TMR circuits, where the I/O ports of the circuit have not been triplicated, but the internal circuitry has. Such circuits introduce single-point failures (SPFs) into the circuit that limit the neutron cross-sectional improvement offered by TMR to only <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> for the b13 benchmark circuit used in this article. This article proposes two different mitigation techniques to address SPFs, which alter the placement and routing of the circuit. These mitigation techniques reduce the neutron cross section by <inline-formula> <tex-math notation="LaTeX">$26\times $ </tex-math></inline-formula> over the unmitigated circuit while minimally affecting the circuit’s maximum clock frequency and resource utilization.