Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Smart Sleeve Control Mechanism for Active Assisted Living

This paper describes the development of a Smart Sleeve control mechanism for Active Assisted Living. The Smart Sleeve is a physically worn sleeve that extends the user’s control capability within his/her intelligent built-environment by enabling (1) direct actuation and/or (2) teleoperation via a virtual interface. With respect to the first, the user may point his/her sleeve-wearing arm towards a door or a window and actuate an opening or a shutting; or towards a light and effect its turning on and off as well as regulating its intensity; or even towards a particular region to initiate ventilation of it. With respect to the second, the user may engage actuations in systems beyond his/her field of vision by interacting with a virtual representation of the intelligent built-environment projected on any screen or surface. For example, the user is able to shut the kitchen’s door from his/her bedroom by extending the Smart Sleeve towards the door of a virtual representation of the kitchen projected (via a standard projector and/or television monitor) on the bedroom’s wall. In both cases, the Smart Sleeve recognizes the object which the user wishes to engage with—whether in the real or the virtual worlds—(a) by detecting the orientation of the extended arm via an Accelerometer / Gyroscope / Magnetometer sensor; and (b) by detecting a specific forearm muscle contraction via Electromyography caused by the closing and opening of the fist, which serves to select the object. Once the object is identified and selected with said muscle contraction, subsequent arm gestures effect a variety of possible actuations for a given object (e.g., a window may be opened / shut or dragged to differing degrees or aperture; a light’s intensity may be increased or decreased, etc.). In cases of ambiguous selections, the user my use voice-commands to explicitly identify the desired object of selection. Furthermore, due to this voice-based recognition mechanism, which recognizes both spoken commands as well the identity of the speakers, different rights to actuation may be assigned to different users. The Smart Sleeve is yet another mechanism integrated into an on-going development of a highly intuitive Active and Assisted Living implementation and its features, ones explicitly designed to enhance user-experience as well as to promote user well-being via intuitive interactions between human and non-human agents within the intelligent built-environment.
Neural Session-Aware Recommendation

Recommender systems help users find items they are likely to interact within the near future, such as products to buy in e-commerce or songs to play in music websites. The Traditional recommendation methods make predictions based on long-term user profiles, i.e., the items a user interacted with in the past while ignoring the time and order of the interactions. Recent findings, however, suggest that users may exhibit interest to items in a certain order depending on situations and more recent items in a sequence have a larger impact on the subsequent choices. Moreover, in many practical applications, user–item interactions are organized into short sessions, where each session reflects the user’s short-term interest in addition to long-term preferences. Leveraging both long-term user profiles and short-term sequential patterns from sessions can lead to more accurate models known as the session-aware recommendation methods. In this paper, we explore various strategies to integrate user long-term preferences with session patterns encoded by recurrent neural networks (RNNs). The strategies include integrating user embeddings with input and output of session RNNs, integrating with fixed or adaptive contributions of the user and session components by using a specially designed gating mechanism. We conducted an empirical evaluation of three publicly available datasets. The results indicate that combining user long-term profiles with the output of session RNNs yields improved predictions and the proposed adaptive integration model outperforms the state-of-the-art sequential and session-aware recommendation methods.
Low-Cost IoT Surveillance System Using Hardware-Acceleration and Convolutional Neural Networks

Reliable object detection is crucial for intelligent surveillance systems. Despite the abundance of available systems, these are often expensive or require cloud connectivity which restricts their usage. In this paper, we present a complete low-cost solution using embedded devices. Person detection is performed locally, using an efficient implementation of a state-of-the-art convolutional Single Shot Detector. The proposed model is fine-tuned to the surveillance task at hand and tested using a Raspberry Pi and the Up Squared Board devices. Additionally, the benefits of hardware-acceleration are evaluated using an Intel® Movidius™ Neural Compute Stick (NCS). The final model performs person detection with an average precision of 0.48. The Raspberry Pi is capable of acquiring and processing images at a rate of 0.4 to 5.3 FPS without/with NCS, whereas the Up Board achieves 2.1 to 7.3 FPS. Further tests demonstrate how the system performs when multiple cameras and multiple NCS devices are deployed. In conclusion, the proposed architecture is suitable for real-time, affordable surveillance systems in home and commercial settings.
Shipwrecked Victims Localization and Tracking using UAVs

Unmanned Aerial Vehicles (UAV) can be considered as an efficient wireless communication alternative in regions where the terrestrial infrastructure is absent. In this paper we propose to use drones for public safety, especially, as a rescue solution for shipwrecked. The detection and the localization of the victims are carried out by a group of drones which receive SOS signals transmitted by their safety jackets. The proposed localization technique provides a good estimate of the target positions based only on the RSSI measurements of the received signals. In fact, using mobile UAVs allows to create a large number of virtual anchors so we can better localize victims. Simulations results show very low localization errors and good tracking capabilities of our algorithm for various scenarios. A performance enhancement is also noted when using multiple drones.
An IoT and Blockchain-Based Multi-Sensory In-Home Quality of Life Framework for Cancer Patients

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient’s situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one’s home environment and securely share it with one’s community of interest. Using our proposed framework, both transactional records and multimedia big data – consisting of a user’s physiological as well as mental states – can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient’s data available from an in-home secure monitoring system for a better understanding of a patient’s current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development.
State of the art on Privacy Risk Estimation Related to Android Applications

Mobile applications have access to users data and may leak it to third parties without users consent. Googles Android platforms include a permission model that restricts applications access to users personal information. However, Android users have a big decision to make before they install an app from the Google Play Store. Will they accept the full list of permissions and have faith that it will not overstep its boundaries, or refuse to install it at all? Users are not aware of how their personal data would be used once applications are installed and permissions granted. This raises a significant privacy risk for Android users. This paper presents an overview and discusses the implications of the findings in current research focusing on their approaches towards users awareness of the privacy risk involved with granting permissions to Android applications.
New Sensing Matrices Based On Orthogonal Hadamard Matrices For Compressive Sensing

Compressive sensing is a new methodology to reconstruct sparse signals from a few number of measurements. In this paper, we propose new sensing matrices for compressive sensing using orthogonal Hadamard matrix. The conventional sensing matrices based on orthogonal Hadamard matrix give acceptable performance but have some drawbacks. In contrast, the proposed sensing matrices are more suitable to compressive sensing compared with the conventional ones. Because they surpass their conventional drawbacks and give higher performance simultaneously. Extensive simulations on both synthesized signals and real images are conducted to show the power of the proposed sensing matrices compared with the conventional ones using convex optimization algorithms.
Efficient Atomistic Simulation of Heterostructure Field-Effect Transistors

In this paper, atomistic-level quantum mechanical simulations are performed for nanoscale field-effect transistors (FETs) with lateral or vertical heterojunction, within the non-equilibrium Green’s function formalism. For efficient simulation of such heterostructure FETs, a novel approach is developed where the Green’s functions are calculated by complementarily using the two algorithms of the recursive Green’s function and the <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>-matrix. The <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>-matrix algorithm is extended to seamlessly combine the two methods on the open system and an algorithm for the electron correlation function based on the extended <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>-matrix algorithm is also developed. The proposed method significantly reduces simulation time, making rigorous atomistic simulations of heterojunction FETs possible. As an application, device simulations are performed for the germanane/InSe vertical tunneling FET (VTFET) modeled through the first-principles density functional theory. Our simulation results reveal that the germanane/InSe VTFET is a promising candidate for future low power applications.
Security Issues in Internet of Things: Vulnerability Analysis of LoRaWAN, Sigfox and NB-IoT

One of the key enablers of an increased Internet of Things (IoT) roll-out is Low-Power Wide Area Network (LP-WAN) - a family of technologies tailored for resilient and energy-efficient communication of thousands of devices over large distances (even up to 100km). Under the pressure from both the business and the society to provide ubiquitous connectivity as soon as possible, new IoT deployments are conducted with haste and often by inexperienced people. Consequently, the aspect of communication security traditionally remains a secondary matter, even though the potential harm of a successful hacker attack can be enormous. Therefore, this paper presents an analysis of LP-WAN vulnerabilities, as well as several Proof-of-Concept (PoC) attacks toward LoRaWAN (packet forging), Sigfox (replay with DoS) and NB-IoT (attack using malicious UE), that confirm the existence of the vulnerabilities in both the standards and off-the-shelf hardware and services.
Smart System for Monitoring and Control of Swimming Pools

This paper proposes a new scheme for monitoring and controlling the swimming pool’s quality through a low-cost system based on wireless sensor networks, which can reduce the requirements for human intervention in the swimming pool maintenance. The main purpose of this system is to provide resources savings for the final user in financial and natural resources, contributing to a sustainable environment. This article also presents a mobile application for interacting with the proposed system which enables users with administrator permissions to control some actions in the swimming pool, in order to stabilize some required parameters for its good quality.
QoS-by-Design in reconfigurable IoT ecosystems

Attractive Internet-of-Things (IoT) ecosystems need to cope with fast edge hardware evolutions. Offering flexibility to IoT infrastructure owners when disrupted or broken sensors and actuators need to be replaced is of major importance. It must be possible to plug in commercial-of-the-shelf alternatives that are probably more reliable and/or cheaper at a given time. Decoupling the application from the underlying infrastructure during software design increases flexibility and reconfigurability. Application developers no longer create applications tailored to one specific sensor or actuator. However, quality concerns imposed by the application may restrict feasible paths that can be taken by infrastructure providers. Hasty decisions can result in improperly functioning IoT ecosystems. This paper proposes a structured approach to embed Quality-of-Service (QoS) challenges in the development and operations cycle of advanced IoT environments, thereby incorporating clear separation-of-concerns between app developers and infrastructure providers. Its practicality is demonstrated by means of a smart office case.
Observer-Based Command Filtered Adaptive Neural Network Tracking Control for Fractional-Order Chaotic PMSM

In this paper, an observer-based command filtered adaptive neural network tracking control problem is addressed for a fractional-order chaotic permanent magnet synchronous motor (PMSM) with the immeasurable state, parameter uncertainties, and external load disturbance. First, the Chebyshev neural networks are introduced to approximate the nonlinear and unknown functions. Next, a neural network reduced-order state observer is designed to obtain the unmeasured state. Then, the command filtering approach based on the first-order Levant differentiator is developed to solve the “explosion of complexity” issue of backstepping, and a novel fractional-order error compensation mechanism is employed, which can remove the filtering errors in finite time. After that, the continuous frequency distributed model is investigated to design proper Lyapunov function, and it is demonstrated that the proposed control method not only ensures that all signals in the fractional-order PMSM system are bounded but also suppresses chaotic oscillation. Finally, the simulation studies are provided to verify the correctness and effectiveness of the proposed scheme.
Simple Autonomous Flight Control of a UAV Flying Above a UGV Using Onboard Camera Vision*

An autonomous flight control method of UAV(: Unmanned Aerial Vehicle) which flying just above the UGV(: Unmanned Ground Vehicle) using only the information of the onboard camera of UAV is proposed in this paper. In the conventional research, motion capture system, GPS, and laser range finder are often used to measure the position and posture of UAV. Although the conventional measurement system is effective in certain conditions and limited environments, it is not always applicable outdoors. There have also been researched about the cooperation of UAV and UVG, but much previous research, its effectiveness was shown in by numerical simulation or experiments using many sensors. By using our proposed system, it is possible to select a feature point on the UGV placed on the ground by using only a UAV onboard camera, and to fly the UAV just above the UGV. Besides, if the UGV moves along a trajectory, UAV can fly above the UGV. In our experiment, we use ROS for an image processing and calculation of control inputs. To operate the UAV automatically, we apply a simple PID control method and its gains are individually adjusted. The effectiveness of our proposed method is shown by experiment.
Occupancy Estimation using WiFi: A Case Study for Counting Passengers on Busses

Occupancy estimation in public busses is growing in importance due to its usefulness in planning public transport systems effectively. Bus occupancy data can be used in real time by transport companies to better estimate demand, improve customer satisfaction while also enabling long-term analyses for the purpose of route optimisation. Additionally, in scenarios where busses are used to replace other modes of public transport, such as metropolitan trains during schedule/unscheduled stoppages, occupancy estimation can help operators determine the deployment requirements of replacement busses. In this paper, we present a low cost WiFi-based system for occupancy estimation in busses. In particular, we propose a novel algorithm for occupancy estimation that reduces the problem of overestimation encountered in WiFi-based occupancy estimation approaches. We validate the accuracy and efficiency of the proposed algorithm and the WiFi-based occupancy estimation solution via a real-world experiment.
Simulating Social Control Mechanisms in Changing Intentions to Implement Transformational Adaptation

This paper investigates by simulation the change of farmers’ intention to shift from implementing incremental to transformational adaptation. An agent-based model is developed to simulate the change of farmers’ intention. The intention level, which is composed mainly of opinion and information variables about incremental and transformational adaptation, is the main determinant of farmers’ decisions of when to shift. Farmers update their opinion according to their connections in opinion networks layer and adjust their information according to their connections in information networks layer. The results showed that an increase in the number of farmers implementing transformational adaptation occurred when a preparatory phase was introduced. A preparatory phase is a phase to encourage further the farmers’ opinion to implement transformational adaptation. This paper contributes to the understanding of the transition process to transformational adaptation and the modeling of the change of farmers’ intention based on opinion and information of others in a different layer of network connections.
A Physical Model-Based FDTD Field-Circuit Co-Simulation Method for Schottky Diode Rectifiers

So far, plenty of microwave power circuits such as microwave diode rectifiers are mainly designed and analyzed by conventional electromagnetic (EM) co-simulation method based on the semiconductor equivalent circuit models. However, the simplified equivalent circuit model may contribute to loss of precision at high frequencies or under high power. Compared with the equivalent circuit model, the semiconductor physical model provides a means for studying the physics of electron transport, and thus, better describes the semiconductor device. This paper explores analyzing microwave diode rectifiers by employing a physical model-based field-circuit co-simulation method. This method combines the physical model-based circuit simulation to the finite-difference time-domain (FDTD)-based field-circuit co-simulation and thus, achieves accurate and effective hybrid full-wave field-circuit co-simulation. For validation, two diode rectifiers working at S- and C-band, respectively, are simulated and analyzed by the proposed method. The simulation result agrees well with measurement and shows higher accuracy than the equivalent circuit model-based simulation.
Designing An IoT Cloud Solution for Aquaculture

Farming has been changing through multiple technological transformations in recent years, in parallel with the earth’s growing population and demands. The Internet of Things (IoT) provides very remarkable technological innovations on farming by bringing a new paradigm. Aquaculture in fresh or salt water is a rapidly growing food sector to produce aquatic plants or animals. IoT can also produce encouraging solutions for the problems in the implementation and maintenance of aquaculture systems. In this paper, we present a use case with an IoT cloud solution after summarizing the potential technologies and tools to use in accordance with the use case problem as identified in the Horizon2020 Project, the intelligent management system for integrated multi-trophic aquaculture (IMPAQT) in which we have been participating in.
Soil sampling mobile platform for Agriculture 4.0

Soil testing is essential for modern agriculture: to optimise the production, protect the environment from fertilisers overuse, save money and energy during the production. The purpose of this work was to develop an autonomous mobile platform with soil sampling device for agriculture. Soil samples are analysed to determine the composition, characteristics or nutrient levels of the soil. Smallholder farmers can use simple hand-held field-testing kits. However, in the case of large farms where plants are grown upon hundreds of hectares, the autonomous mobile platform with a soil sampler would be the optimal solution. Precision farming is a concept of using new production and management methods that use all kind of data collected about specific locations and crop variety. The mentioned robot can increase resource and cost efficiency in acquiring the required data. Information about the soil properties can be retrieved from the field using the robot's onboard systems, enabling farmers to respond to abrupt changes in real time. Data technologies and collecting soil system allows an efficient production process. Use of robots on farms is associated with the progressive digitisation of all areas of our lives, and agriculture is no exception. After the presentation of available commercial solutions to soil sampling and data storage solutions, we will describe the core design of the mentioned soil sampling robot. The mobile robot has a form of a six-wheeled platform with the all-terrain suspension system. The soil sampling device is placed onboard and consists of a drill pipe with custom flap design on the end of it, to keep the soil within, preserving whole cross-section. The storage of soil samples and its analysis are currently being developed. Significant observations after tests and studies point out that such a robot could increase production efficiency. Collected samples provide accurate data of nutrient-supplying capacity.
Communication Connectivity in Multi-agent Systems with Multiple Uncooperative Agents

We consider a multi-agent system that consists of two group of agents: clients and routers. Clients move according to their own agenda. Routers’ control policy needs to be designed to maintain communication connectivity for others. A control policy is designed for the routers to maintain the communication connectivity between clients. The control policy consists of periodically computing desired positions in a distributed manner and then steering the routers to those desired positions. First, desired positions of routers are determined by an optimization problem which minimizes the length of the longest edge connecting a client and a router on a tree corresponding to the communication relationship between agents. Then, the routers are steered to those desired positions by motion control. Simulations are done assuming that all routers are quadrotor drones to illustrate the results.