Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Scheduling for Mobile Edge Computing with Multiple Links

As the explosive growth of smart devices and new applications, traffic volume has been growing exponentially. Thus, mobile edge computing (MEC) technology regarded a promising technology is proposed to enhance the computation capabilities of the networks. Considering available MEC research mostly focused on single-tier base station scenario and single offloading link between mobile devices and MEC servers connected to the macro base station, causing high transmission delay and link congestion problem. A new two-tier small cell architecture equipped with MEC servers is proposed and an efficient task scheduling is designed. Specially, we first formulate the task scheduling problem as a nonlinear mixed integer program problem which is NP-hard. Then we transform this optimization problem into two subproblems, i.e., a linear constrained quadratic programming problem and a convex optimization problem. Finally, Compared our proposed scheme with other existing schemes, the proposed scheme could not only receive 30% energy saving, but also apply to large-scale smart devices.
Modeling and Coverage Analysis of Power Control in User-Centric UAV Networks

In unmanned aerial vehicle (UAV) networks, line-of-sight (LOS) links dominate the communication among UAV transmission points (TPs) and user equipments (UEs), which tremendously improves link quality but also leads to strong interference. This paper proposes a power control strategy to combat interference in user-centric UAV networks, where a dynamic UAV transmission point group (TPG) is organized serve each user seamlessly. Based on the strategy, semi-closed expression of network downlink coverage probability is derived leveraging stochastic geometry analysis with the consideration that different TPGs can overlap with each other. Specifically, each UE would choose the closest UAV as its serving UAV to transmit data while other UAVs within the TPG will mitigate interference by reducing transmitting power. Two coefficients μ and β are applied to determine the area and intensity for power control, which can realize the tradeoff between interference mitigation and resource utilization. The results show that with the power control strategy we proposed, coverage probability can be improved at most 66% when the SIR threshold is 10 dB.
Multi-Connectivity Enabled User Association

This paper focuses on the problem of multi-connectivity enabled downlink user association to maximize the overall throughput in small cell networks, which is an NP-Hard problem. We formulate the problem of user association as a linear and integer optimization problem. Furthermore, we propose a many-to-two variation of hospitals/residents matching algorithm as a heuristic solution to the problem. In this variation, users propose to small cells and the preference lists are constructed based on the distance between users and small cells. Simulation results show that our proposed algorithm achieves higher throughput rates compared to greedy and similar throughput rates compared to mathematical iterative solutions on the majority of deployment scenarios. On the other hand, we also show that the mathematical iterative methods provide better solutions that serve a greater number of users than our proposed solution when there is not enough number of small cells to provide multi-connectivity to at least half of the users in the system.
Lightweight Semantics-Preserving Communication for Real-Time Automotive Software

The automotive industry is confronting the multi-core challenge, where legacy and modern software must run correctly and efficiently in parallel, by designing their software around the Logical Execution Time (LET) model. While such designs offer implementations that are platform independent and time predictable, task communications are assumed to complete instantaneously. Thus, it is critical to implement timely data transfers between LET tasks, which may be on different cores, in order to preserve a design's data-flow. In this paper, we develop a lightweight Static Buffering Protocol (SBP) that satisfies the LET communication semantics and supports signal-based communication with multiple signal writers. Our simulation-based evaluation with realistic industrial automotive benchmarks shows that the execution overhead of SBP is at most half that of the traditional Point-To-Point (PTP) communication method. Moreover, SBP needs on average 60% less buffer memory than PTP.
Impact of Via-Inductance on Stability Behavior of Large Gate-Periphery Multi-finger RF Transistors

In this paper, the impact of source via-inductance on stability performance of large gate-periphery RF transistors is investigated in terms of Rollett’s stability factor (K-factor) using a small-signal equivalent circuit model. The RF device-under-test studied in this work is a commercial multi-finger GaN HEMT with a considerably large gate-periphery of 10 × 90 µm. A systematic analysis of the K-factor is done by deriving its mathematical expression in terms of the equivalent circuit intrinsic and extrinsic components. While gate-to-drain capacitance is unanimously considered to be the most critical component in determining the device stability performance, due to the formation of the feedback loop, the simulation and experimental results obtained in this work reveal potential regions of device instability in the form of peaks and valleys, that emerge as a manifestation of the coupling between the via-inductance and the intrinsic drain-to-source capacitance. This study is of significance particularly to multi-finger large gate-periphery devices since they have a reduced gate-resistance and therefore are driven further into instability. This work is expected to serve as a guideline in obtaining optimized multi-finger RF transistors with regard to stability.
A Novel Semi-Autonomous Teleoperation Method for the TianGong-2 Manipulator System

Semi-autonomous teleoperation based on Learning from Demonstration is an effective method of remote operation of space manipulators, especially in the scenarios with limited communication and repeated operation problems. However, the joint trajectories generated by those methods may not be suitable for space manipulator control. In this paper, we present a novel semi-autonomous teleoperation method, which has been used for the TianGong-2 manipulator system. The proposed method can not only reproduce trajectories for the tasks according to the current environment, but also generate the smoother and smaller joint control torques. To implement the method, we first collected kinesthetic demonstrations by the space manipulator teaching platform as prior knowledge. Then, based on these kinesthetic demonstrations, we designed the joint control commands with Dynamics Constraint Learning from Demonstration algorithm. We finally evaluated our method with the simulation and on-orbit experiment by locating the dexterous hand to the pre-screwing bolt. Our results show a significant reduction of joint control torque fluctuations and peak-to-peak values, and also can reduce energy consumption.
Optimal Scheduling of Dynamic Energy Demand in Smart Grid Using Time-slotting Linear Programming

This work concentrates on the problem of load scheduling in demand-side management for residential users in smart grid. A new linear programming model is proposed to search for the optimal operating slots of the household appliances so that the total cost of energy bill is minimized. The proposed model takes into account the standby power and time-varying demand of the appliances to make the model more realistic for real-life application. The model also provides flexibilities to the residential users by allowing them to choose multiple preferable time slots for the operations of the appliances. The convex form of linear programming model guarantees an efficient search of the exact solutions. Simulation results show that the obtained optimal solution is able to reduce user's daily electricity charges.
Design and Implementation of Multi-Cloud VNFs Deployment Utilizing Lightweight LXC Virtualization

With the upcoming digital industrial revolution, the telecommunications industry has been looking for ways to become more efficient. This involves maintaining or improving its performance at a lower operational cost. This need is also aided by the fact that the industry has been experiencing declining profitability. This has resulted in the industry looking at solutions like virtualization and resource pooling or cloudification among others. Recent studies have concluded on container-based virtualization for better performance. However, there are different container providers and choosing a less performant provider increases deployment costs. Additionally, to foster sustainability and adoption of the virtualization, its management and orchestration (MANO) needs to conform to the ETSI (European Telecommunication Standards Institute) network function virtualization (NFV) MANO standards. Hence, this paper designs and implements an ETSI complaint NFV MANO for container-based virtualization using the most performant container provider, LXC. In order to cater for horizontal scaling and distributed services, we promptly incorporate multi-host, multi-cloud in the design
Simulation Analysis of Battery/Ultracapacitor Hybrid Energy Storage System for Electric Vehicle

Preservation of environment and energy conservation has caused the advancement of Electric Vehicles (EVs), Energy Storage System (ESS) plays an essential role in superior and durable performance of EV. In peak power demand condition battery will have to provide more power in a short duration. For which battery is not entirely capable of. To meet the peak power demand of the vehicle battery is combined with Ultracapacitor (UC). UC has a high power density than the battery. In this paper, battery/UC HESS is discussed. Ultracapacitor voltage is kept higher than the battery voltage in this Hybrid Energy Storage System (HESS). Battery will only provide power when ultracapacitor voltage goes below battery voltage. By using adequate power management, maximum system performance along with less power consumption can lead to extended battery life.
Introducing Computational Thinking Concept Learning in Building Cognitive Capacity and Character for Elementary Student

Computational thinking has become a interesting subject and important concept in education throughout the nation. Computational Thinking (CT) has been defined as an important skill as digital literacy that must be learned by next generation. Along with the need to make an agreed definition of CT many countries are still in the process of, or have not started, introducing CT into the curriculum at all levels of education. It was also found in Indonesia that Computer Science / Computing, which could be the most obvious place to teach CT, has not been the main subject in several schools. This research aim is to design an alternative CT learning media to study interestingly and effectively computational concept which consists of 4 elements, namely decomposition, abstraction, pattern recognition and algorithm. Research and Development followed by ADDIE development model (Analysis, Design, Development, Implementation and Evaluation) is used to capture the problem, identify the user need to be implemented in digital media. This learning media application product is equipped with an educational game based on computational thinking. The media has several features such as presentation of material and examples of problems and the solution, and small challenges in a games consisting of 3 stages. The subjects of this study were students of K-12. The data used for analysis was collected in several ways, including observation, questionnaire, pre-test and post-test in similar question. The result is the use of CT learning media is very helpful to improve students' understanding of CT in problem solving even in different topics.
Investigations on Massive MIMO Performance with Multi-Antenna Users by Ray-Tracing

The downlink data rate in a single-cell urban massive MIMO network is evaluated where the scenario comprises a base station with 256 antennas and 64 users each having two antennas. The downlink performance according to three different precoding algorithms, which can exploit multiple antennas at user terminals to improve the performance, are presented. Two different methods are used to generate the channel matrix, in particular, ray-tracing simulations and the WINNER II stochastic channel model, and the resulting downlink performances are compared.
Improving the Degree of Autarky of a 16 House Neighbourhood in the Netherlands - A case study

In this work we investigate the possibility to operate an existing neighbourhood in the Netherlands in a “soft-islanded” (near autarkic) way. For this, a multi-energy system, including optimal control, is modelled using PV panels, a combined heat and power generator and 6 kWh of battery storage per household. By utilizing the synergies between electricity, heat and bio-gas, the system provides a good solution to relieve stress on electricity grids within the context of the energy transition. Simulation studies show that the envisioned system, with control, is able to reach a Degree of Autarky of 91% for the energy consumption.
C3SRAM: In-Memory-Computing SRAM Macro Based on Capacitive-Coupling Computing

This letter presents C3SRAM, an in-memory-computing SRAM macro, which utilizes analog-mixed-signal capacitive-coupling computing to perform XNOR-and-accumulate operations for binary deep neural networks. The 256 × 64 C3SRAM macro asserts all 256 rows simultaneously and equips one ADC per column, realizing fully parallel vector-matrix multiplication in one cycle. C3SRAM demonstrates 672 TOPS/W and 1638 GOPS, and achieves 98.3% accuracy for MNIST and 85.5% for CIFAR-10 dataset. It achieves 3975× smaller energy-delay product than conventional digital processors.
Underwater Image Enhancement Using Scene Depth-Based Adaptive Background Light Estimation and Dark Channel Prior Algorithms

Due to the complexity of the underwater environment, underwater images captured by optical cameras usually suffer from haze and color distortion. Based on the similarity between the underwater imaging model and the atmosphere model, the dehazing algorithm is widely adopted for underwater image enhancement. As a key factor of the dehazing model, background light directly affects the quality of image enhancement. This paper proposes a novel background light estimation method which can enhance the underwater image. And it can be applied in 30-60m depth with artificial light. The method combines deep learning to obtain red channel information of the background light in the dark channel of the underwater image. Then, the background light is obtained by adaptive color deviation correction. Finally, the experiments of underwater images enhancement are carried out, using the dark channel prior algorithm based on the proposed background light estimation method. The results show that the proposed method effectively improves underwater image blur and color deviation, and is superior to other methods in multiple non-reference image evaluation indicators.
Holistic Modeling of Time Sensitive Networking in Component-Based Vehicular Embedded Systems

This paper presents the first holistic modeling approach for Time-Sensitive Networking (TSN) communication that integrates into a model-and component-based software development framework for distributed embedded systems. Based on these new models, we also present an end-to-end timing model for TSN-interconnected distributed embedded systems. Our approach is expressive enough to model the timing information of TSN and the timing behaviour of software that communicates over TSN, hence allowing end-to-end timing analysis. A proof of concept for the proposed approach is provided by implementing it for a component model and tool suite used in the vehicle industry. Moreover, a use case from the vehicle industry is modeled and analyzed with the proposed approach to demonstrate its usability.
3D Indoor Mapping Based on Camera Visual Odometry Using Point Cloud

Recently, 3D visualization technologies such as point cloud are growing rapidly and are in demand for environmental mapping needs because they have the ability to represent the real world in a more complete and in depth way, as well as new and more advanced visual experiences. The point cloud model is able to represent objects well through a series of points and is increasingly used in various fields of research. While 3D visualization technology is growing, several platforms such as humanoid robots, mobile robots, and automatic cars require mapping technology that is able to properly map the surrounding environment with a better display to improve intelligence and decision-making abilities. To overcome the problem of mapping, in this research an experiment was conducted to combine point cloud to form a map based on a visual odometry from stereo camera.
LOSoft: ℓ0 Minimization via Soft Thresholding

We propose a new algorithm for finding sparse solution of a linear system of equations using $\ell_{0}$ minimization. The proposed algorithm relies on approximating the non-smooth $\ell_{0}$ (pseudo) norm with a differentiable function. Unlike other approaches, we utilize a particular definition of $\ell_{0}$ norm which states that the $\ell_{0}$ norm of a vector can be computed as the $\ell_{1}$ norm of its sign vector. Then, using a smooth approximation of the sign function, the problem is converted to $\ell_{1}$ minimization. This problem is solved via iterative proximal algorithms. Our simulations on both synthetic and real data demonstrate the promising performance of the proposed scheme.
Theoretical Framework of Digital Marketing and the Future Availability in Malaysia

Digital marketing is well known in the foreign countries (America, Canada, Dubai and India) rather than in Malaysia. This is due to their advance technology, high idealistic marketing plan, population and high internet usage. Currently there are many concerns being raised by individuals in the marking sector and also the marketing experts, on the future growth of digital marketing. Nevertheless, in Malaysia the business idealistic marketing plans are very broad and mostly using traditional approach towards promoting and selling their product. Thus, the technology advancement in the Malaysia is currently in a moderate stage and not as advance as the Europe or foreign countries. Besides, the technology advancement in Malaysia, the knowledge on following the right process flow in digital marketing is very low. Furthermore, a developing country usually follow the trends in other developed foreign countries rather than establishing new trends. The suggestion of new technology with better marketing strategy other than digital marketing in the Europe or foreign countries, eventually reduce the use of digital marketing strategies. However, the aim is to ensure that the digital marketing strategies are used in Malaysia for a longer term without any extinction. Therefore, this paper proposes a strategy to deliver a correct and resourceful training with the precise process flow or steps in using digital marketing. The strategy enables users to adapt the current technology of digital marketing for betterment and innovation especially implementing Artificial Intelligence (AI) into Digital Marketing for more security and technology advancement.
Outcomes of carotid artery stenting in high-risk patients; a single center retrospective review

Ahmet Kirbas (*), Nursen Tanrikulu (**), Mutlu Cihangiroglu (***), Omer Isik (*) SUMMARY In our study, we evaluated the safety and efficacy of carotid artery stenting(CAS) in high surgical risk patients. 22 consecutive patients who underwent carotid artery stenting(CAS) between March 2009 and Decem- ber 2011 were eligible. Only symptomatic stenosis were studied in high surgical risk patients as defined by the SAPPHIRE(Stenting and Angioplasty with Protection in Patients at High-Risk for Endarterectomy) trial. The mean age was 77.8 (range 67-87). Of the study group, 17 male(77.27%) and 5 female(22, 72%). Procedural success was achieved in 21 patients(95.4%). The mean stenosis in patients was 90.68 ±5.4%. Reasons for stenting included cardiac/pulmonary risk(63%), contralateral carotid artery occlu- sion(72%), age older than 75(72%). None of the patients suffered from ma- jor stroke, MI or death due to CAS during their hospital stay. The overall rate of in-hospital adverse events (TIA, permanent bradycardia, hypotension) was 18.1 %(4 of 21). Bradycardia in 2(9.5%), hypotension in one(4.7%) and transient ischemic attack in one(4.7%) were observed during proce- dures. Our results support the use of CAS in high surgical risk patients with significant symptomatic carotid artery disease.
Stepped Frequency IRCI-Free Sliding Spotlight MIMO SAR

A stepped-frequency sliding spotlight MIMO SAR using frequency domain system identification (FDSI) algorithm is proposed to obtain ultra high resolution radar images with wide swatch coverage. In this paper, a stepped-frequency waveforms are adopted in such a way that each transmitter emits a conventional linear frequency modulated (LFM) waveform modulated by a different carrier frequency and the obtained range resolution corresponds to the total transmitted bandwidth. The proposed method has ideally zero sidelobe level in the estimated channel in the range dimension. In addition, a low PRF is used and the resulted azimuth ambiguity is removed using spatial filters along with azimuth de-ramping
Security and Privacy in the Smart Home: A Survey of Issues and Mitigation Strategies

Internet of Things (IoT) devices are becoming more and more prominent in our everyday lives and have made their way into our homes. As with all technologies, security and privacy are key points to consider when designing and choosing to use IoT devices to make our homes smarter. However due to the complexity, heterogeneity, and the wide selection of the devices used, implementing security and privacy is highly difficult. This paper surveys and synthesizes the literature on security and privacy issues that arise in a smart home. The paper continues to survey studies conducted to mitigate these flaws.
Fog-Assisted Cooperative Protocol for Traffic Message Transmission in Vehicular Networks

Traffic information exchange between vehicles and city-wide traffic command center will enable various traffic management applications in future smart cities. These applications require a secure and reliable communication framework that ensures real-time data exchange. In this paper, we propose a Fog-Assisted Cooperative Protocol (FACP) that efficiently transmits uplink and downlink traffic messages with the help of fog Road Side Units (RSUs). FACP divides the road into clusters and computes cluster head vehicles to facilitate transmission between vehicles and traffic command center or fog RSUs. Using a combination of IEEE 802.11p and C-V2X wireless technologies, FACP minimizes the time required by a vehicle to retrieve traffic information. Furthermore, FACP also utilizes cooperative transmissions to improve the reliability of traffic messages. Simulations results show that FACP improves the reception rate and end-to-end delay of traffic messages.