Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving Authentication in Wireless IoT: Applications, Approaches, and Challenges

Wireless IoT is a promising area in which large quantities of data and information are collected, exchanged, and stored constantly. While these data and information are utilized for good purposes in wireless IoT, great risks arise in that attackers are seeking to exploit the merits of this new technology for their own benefits. In such a resource-constrained environment, devices are easily exposed to various attacks. Confidential private information is as valuable as it is vulnerable. It is difficult for end users to trust wireless IoT and to adopt related applications due to the lack of satisfactory privacy-preserving mechanisms, which has become the major obstacle for the adoption and popularization of wireless IoT. In this article, we first introduce the classical application scenarios of wireless IoT, along with related security and privacy attack models. We then present a brief overview of privacy-preserving schemes of wireless IoT. Based on the classification of application scenarios, we further present recent advances in privacy-preserving mechanisms in wireless IoT. Finally, we discuss open issues and future research directions for these application scenarios in wireless IoT.
Time-Varying Social-Aware Resource Allocation for Device-to-Device Communication

With the rapid development of wireless mobile communication, local content sharing has become the emerging demand for users who are geographically close. Device-to-device (D2D) communication allows two users to communicate directly with each other. In order to achieve more effective content distribution and content spread by allocating better spectrum resources to users with better social networking and content diffusion capabilities, we propose an optimization scheme for resource allocation of the D2D communication by utilizing the potential social relations that are embedded in the communication devices. The degree of intimacy between users is abstracted from the call records to quantify social-relation strengths. Considering the time-varying property of social relations, the auto-regressive integrated moving average model is applied to map the call records into time sequence to predict users’ social relations. Besides, instead of individual utility or the overall network utility, each user aims to maximize its social-community utility which takes other social related D2D users into consideration. Potential game is utilized to solve the social-community utility maximization problem of resource allocation for the D2D communication due to its outstanding mapping nature and always has the Nash equilibrium. Finally, a social-aware distributed resource allocation algorithm is proposed, and the algorithm achieves convergence and stability. Numerical results show that our proposed scheme increases the overall utility over 30% compared with coalition game scheme, and over 50% compared with random selection scheme without loss of the fairness.
Coupling-Matrix-Based Systematic Design of Single-DC-Bias-Controlled Microstrip Higher Order Tunable Bandpass Filters With Constant Absolute Bandwidth and Transmission Zeros

This paper presents a systematic design procedure of microstrip higher order tunable bandpass filters (BPFs) that can realize a constant absolute frequency bandwidth (ABW) and transmission zeros (TZs) with the minimum number of varactors and a single controlled dc-bias voltage. To this end, intrinsic frequency dependence of interresonator couplings is employed instead of external controls, but the design has faced a difficulty of realizing ideal frequency curves of coupling coefficients at all the coupling regions, including nonadjacent couplings. To overcome such a difficulty, this paper introduces a frequency-curve shape design approach considering upward- or downward-curved convex that is dependent on the types of interresonator coupling. As illustrative examples, fourth- and sixth-order tunable BPFs are proposed and designed, starting from a coupling-matrix synthesis. Their structural parameters can be efficiently designed with the proposed design method. The frequency agility with keeping a constant ABW, acceptable low insertion loss, a good in-band return loss level, and TZs is numerically and experimentally demonstrated for both fourth- and sixth-order tunable BPFs.
Development of Tendon Driven Under-Actuated Mechanism Applied in an EMG Prosthetic Hand with Three Major Grasps for Daily Life

This paper presents a lightweight (<250 g) and low-cost (<350 USD) biomimetic prosthetic hand with two actuators embedded in the palm. One of them is employed for flexion/extension of the five digits, and the other one is used for the adduction/abduction of thumb. Thus, the hand can achieve major grasping tasks that account for about 85% of activities in daily life. The unique transmission provides various advantages such as a compact structure, weight saving, and short driven distance. Furthermore, by using 3D printing technology, most parts of the prosthetic hand were made to be much lighter and have a humanlike appearance, compared with conventionally manufactured artificial hand. Finally, the performance and practical applicability of the proposed design was verified experimentally through both of a motion verification and an intuitive control test by a healthy subject and a transradial amputee.
Failure Detection Using Proprioceptive, Auditory and Visual Modalities

Handling safety is crucial to achieve lifelong autonomy for robots. Unsafe situations might arise during manipulation in unstructured environments due to noises in sensory feedback, improper action parameters, hardware limitations or external factors. In order to assure safety, continuous execution monitoring and failure detection procedures are mandatory. To this end, we present a multimodal failure monitoring and detection system to detect manipulation failures. Rather than relying only on a single sensor modality, we consider integration of different modalities to get better detection performance in different failure cases. In our system, high level proprioceptive, auditory and visual predicates are extracted by processing each modality separately. Then, the extracted predicates are fused. Experiments on a humanoid robot for tabletop manipulation scenarios indicate that the contribution of each modality is different depending on the action in execution, and multimodal fusion results in an overall performance increase in detecting failures compared to the performance attained by unimodal processing.
Study on Frequency Splitting of SS Magnetic Resonance Wireless Charging

With the development of wireless charging technology and people’s constant attention to environmental protection, electric vehicles will gain rapid popularity. The efficiency and power during charging are very important issues. For the problem of low output power due to frequency splitting in magnetic resonance wireless charging system, the mutual inductance coupling model of the circuit is analyzed in this paper. The expression of load current and efficiency is also obtained, and the parameters of the primary and secondary sides are not identical in this paper. Then the frequency characteristics are also obtained. A parameter selection method to avoid over coupling is proposed. The simulation results verify the correctness of the method and provide guidance for parameter selection, and the experimental prototype is designed to verify the theoretical analysis. Finally, a universal process is described to design parameters for wireless charging system.
A Training Utility for Estimating the Bowling Speed of a Cricketer Using Accelerometer Data

This prototype system known as the Wearable Instantaneous Ball Speed Estimator (WIBASE) was designed to measure the bowling speed of a cricketer during training. When fast bowlers are training, coaches have to assess their ability to bowl consistently fast balls even when they are required to perform long bowling spells, hence the need for reliable, accessible and affordable equipment for measuring their bowling speed cannot be over emphasised. The WIBASE seeks to fill in this gap. It is made up of two hardware components; a computer and a wrist-worn electronic board that houses among other components, a 3-dimensional (3D) acceleration sensor. The system tracks the three-axis acceleration generated by the movement of the arm when delivering the ball and stores these values. The raw sensor data from three different sensors namely accelerometer, gyroscope and magnetometer is processed by a Digital Motion Processor (DMP) on the board in a process known as Sensor Fusion before it is sent via Bluetooth to the computer. The computer runs a Python script that receives the filtered acceleration which consists of both static acceleration and dynamic acceleration. The acceleration is numerically integrated over a minute period of time around the release point using the Trapezoidal method of integrating numerical data to derive the speed of the cricket bowler. The results obtained from the three sets of experiments that were conducted show that the WIBASE can track the 3D acceleration of the hand when bowling, derive the speed of the bowlers and display the speed on a computer while logging all the data into a file.
RG-Trees: Trajectory-Free Feedback Motion Planning Using Sparse Random Reference Governor Trees

Sampling based methods resulted in feasible and effective motion planning algorithms for high dimensional configuration spaces and complex environments. A vast majority of such algorithms as well as their application rely on generating a set of open-loop trajectories first, which are then tracked by feedback control policies. However, controlling a dynamic robot to follow the planned path, while respecting the spatial constraints originating from the obstacles is still a challenging problem. There are some studies which combine statistical sampling techniques and feedback control methods which address this challenge using different approaches. From the feedback control theory perspective, Reference Governors proved to be a useful framework for constraint enforcement. Very recently, Arslan and Koditschek (2017) introduced a feedback motion planner that utilizes Reference Governors that provably solves the motion planning problem in simplified spherical worlds. In this context, here we propose a “trajectory-free” novel feedback motion planning algorithm which combines the two ideas: random trees and reference governors. Random tree part of the algorithm generates a collision-free region as a set of connected simple polygonal regions. Then, reference governor part navigates the dynamic robot from one region to the adjacent region in the tree structure, ensuring it stays inside the current region and asymptotically reaches to the connected region. Eventually, our algorithm robustly routes the robot from the start location to the goal location without collision. We demonstrate the validity and feasibility of the algorithm on simulation studies.
Special Power Electronics Converters for Photovoltaic Nano-Grids Applications

This paper proposes novel approaches to create two converters for PV Nano grids and Smart homes applications which will empower the human future life. The first one can take a one input from PV panel and converts it into numerous outputs to supply different dc loads. The second one is a cascaded PV inverter to supply ac loads. As shown in this paper, success in designing a DC-DC converter with this criteria is achieved. This included designing a converter receiving a single input voltage for usage of a nano-grid. The single input leads to outputs where each are loads of different values. Then, successful design and simulation for a multi-level inverter (cascaded inverter) is achieved. The inverters are done for both single level and cascaded level. The simulation design is done visa H-bridge design for the single and cascaded inverter. Both the converter are showing good results and dependability for future small-scale power grids. These successful preliminary results allowed nano-grid to act as a valued future power generation option.
NoCo: ILP-Based Worst-Case Contention Estimation for Mesh Real-Time Manycores

Manycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared caches and memories and hence concentrates most of the contention that tasks suffer, with effects on the worst-case contention delay (WCD) of packets and tasks' WCET. While several proposals minimize the impact of individual NoC parameters on WCD, e.g. mapping and routing, there are strong dependences among these NoC parameters. Hence, finding the optimal NoC configurations requires optimizing all parameters simultaneously, which represents a multidimensional optimization problem. In this paper we propose NoCo, a novel approach that combines ILP and stochastic optimization to find NoC configurations in terms of packet routing, application mapping, and arbitration weight allocation. Our results show that NoCo improves other techniques that optimize a subset of NoC parameters.
Determining Key Factors that Lead to the Adoption of Password Managers

Passwords form part of our daily routine and even though there are alternative authentication mechanisms, such as biometrics, passwords stubbornly persist. Passwords have been around for the last few decades, but also the various problems associated with users trying to create passwords that are strong and secure. Users are faced with a cognitive burden in managing passwords which often leads to poor password practices or users recycling passwords across various accounts. While there is no anticipated end to the use of passwords, scholars have identified that passwords need to be better supported – one such method is using a password manager. There is a wealth of technical research relating to password managers, which has led to drastic improvements and the maturing of the technology. However, there is a little research on why people would choose to adopt password managers. To explore these factors, this research uses an adapted version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) that includes trust as an additional construct. Using empirical data, the results of the study show that performance expectancy, habit, and trust are key factors in the intention to adopt a password manager.
Power Transistor Fault Diagnosis in SRM Drives Based on Indexes of Symmetry

To reduce the negative impacts of power converters on a machine such as switched reluctance motor (SRM) a fast and accurate fault detection method is required. Several fault types could occur in SRM drives, such as, open and short-circuit transistor faults. Thus, this paper presents a new fault detection and diagnosis method for transistor faults in a SRM drive. The method is based on symmetry indexes that are created from the analysis of the currents patterns. The proposed approach results in a fast and robust method, presenting immunity to different mechanical conditions. The characteristics of the proposed method will be verified through several simulation tests.
Fall detection and activity recognition system for usage in smart work-wear

Human activity measurement and classification has been hot research topic for several years. Most of the solutions are based on the mobile phones, however there are also some wearable device implementations that have very specific functionality. The aim of this paper is to propose a human activity recognition and fall detection solution that provides extra safety for people working in challenging conditions. The system is integrated with the monitoring solution that provides real-time information about all workers and raises automatically an alarm in case of accidents or abnormal conditions.
Hardware Support of the Periodic Component Detection Algorithm Based on Zero-Crossing

Paper proposes a hardware support of method for detecting a periodic component in a measurement signal, based on the calculation of the signal's zero-crossing numbers. On the basis of the considered method, an algorithm with low computational costs is proposed. The algorithm can be used to indicate of the progressing of resonant phenomena, to determine the presence of a carrier signal, and so on. The structures of the basic modules required for constructing hardware support devices of the proposed algorithm are proposed. The proposed modules are relatively easy to implement and can be easily manufactured for example using FPGA technology. An example of a block diagram of a detection device of periodicity with a known frequency is given. A description of the operation of the device is given and the main time diagrams are given.
Identification of disturbance sources based on random forest model

With more and more disturbance sources such as high-speed railway and renewable energy generation, the power quality problem has become increasingly complex, which seriously affects the reliable operation of the power grid. Identifying the types of disturbance sources that cause power quality events based on power quality monitoring data will support for targeted control disturbance sources, also provide evidences for determining contribution between customers and operators. This paper proposes a method for identification of disturbance sources based on random forests. Firstly, it chooses analysis indices and extracts both temporal and statistical characteristics of selected indicators from historical data. After balancing datasets, the data are used as eigenvectors of training random forests. Secondly, combining with OOB(out-of-bag), one of evaluation indicators, adjustment of random forest parameters in a closed-loop is used to construct cost-optimized random forest classifier. Thirdly, the type of disturbances is on-line identified using the classifer. Based on data from a power quality monitoring system in a regional grid of China, it is verified that the method has a high accuracy for identifying disturbance sources such as electric railways, converter stations, wind power, photovoltaics, and smelters.
FSG: A statistical approach to line detection via fast segments grouping

Line extraction is a preliminary step in various visual robotic tasks performed in low textured scenes such as city and indoor settings. Several efficient line segment detection algorithms such as LSD and EDLines have recently emerged. However, the state of the art segment grouping methods are not robust enough or not amenable for detecting lines in real-time. In this paper we present FSG, a fast and robust line detection algorithm. It is based on two independent components. A proposer that greedily cluster segments suggesting plausible line candidates and a probabilistic model that decides if a group of segments is an actual line. In the experiments we show that our procedure is more robust and faster than the best methods in the literature and achieves state-of-the art performance in a high level robot localization task such as vanishing points detection.
GKFP: A New Fuzzy Clustering Method Applied to Bearings Diagnosis

This paper proposes a new clustering method called Gustafson-Kessel with Focal Point (GKFP). The proposal aims at benefiting from the advantage of using Gustafson-Kessel clustering technique leveraged by the use of a Focal Point which enables obtaining partitions with different levels of granularity. Thus the method identifies clusters with uncorrelated or strongly correlated data while it allows the user to explore different regions of the feature space with different levels of detail. Due to the possibility of dealing with correlated data, a regularization procedure might be necessary. Therefore, the paper also briefly describes a Bayesian regularization which can be associated with GKFP. Experiments from bearing fault diagnosis show that GKFP outperforms three other clustering techniques, i.e., the popular fuzzy c-means (FCM), Gustafson-Kessel (GK), and the state of the art FCMFP, for two different bearing data sets.
Augmented-Reality Computer-Vision Assisted Disaggregated Energy Monitoring and IoT Control Platform

The aim of this research is to develop an innovative low cost and affordable platform for smart home control and energy monitoring interfaced with augmented reality. This method will educate people about energy use at a time when fuel costs are rising and create novel methods of interaction for those with disabilities. In order to increase the awareness of energy consumption, we have developed an interactive system using Augmented Reality to show live energy usage of electrical components. This system allows the user to view his real time energy consumption and at the same time offers the possibility to interact with the device in Augmented Reality. The energy usage was captured and stored in a database which can be accessed for energy monitoring. We believe that the combinations of both, complex smart home applications and transparent interactive user interface will increase the awareness of energy consumption.
Leader-Follower Formation Control of Mobile Sensor Robots for Cable Tunnel Inspection

In this paper, we propose an inspection scheme based on leader-follower formation for mobile sensors robots to accomplish the task of inspecting a cable tunnel to detect potentially danger and provide warning mechanism. We adopt leader-follower formation control to achieve formation and artificial potential field method to avoid the occasional appeared obstacle. The leader possesses abilities of mission planning and formation changing to conduct the entire inspection and the followers simply track the movement of the leader. Unlike the usual tunnel inspection with a single robot, the strategy we proposed in this paper has advantages of wider inspection range, higher accuracy and higher tolerance through the utilization of multi robots system. Simulation results are presented to validate the effectiveness of the proposed strategy.
Exploiting Low Complexity Beam Allocation in Multi-User Switched Beam Millimeter Wave Systems

Switched-beam systems offer a promising solution for realizing multi-user communications at millimeter wave (mmWave) frequencies. A low-complexity beam allocation (LBA) algorithm has been proposed to solve the challenging problem of maximizing sum data-rates. However, there are practical limitations in mmWave systems, such as restrictions in the number of available radio frequency transceiver chains at the base station, sensitivity to sidelobe interference and the beam generation techniques. In this paper, using generalized beam-patterns, we present the maximum sum data-rates achievable in switched-beam mmWave systems compared with fixed-beam systems by applying LBA. Then, the impact on maximum sum data rates of actual beam-patterns, obtained from a practical mmWave lens antenna, which have higher and non-uniform sidelobes compared with the theoretical beams, is assessed. Finally, as a guide for practical wireless system design, benchmarks are established for relative sidelobe levels that provide acceptable sum data-rate performance when considering generalized beam patterns.