Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint computing and communication resource allocation for satellite communication networks with edge computing

Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links, satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks (SCNs). By deploying edge computing servers in satellite and gateway stations, SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management. Therefore, in this paper, we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications, while two different satellite edge computing scenarios and local execution are considered. Furthermore, the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem. A game-theoretic and many-to-one matching theory-based scheme (JCCRA-GM) is proposed to achieve an approximate optimal solution. Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.
Lightweight Encryption for Multimedia in the Internet of thing(iot)

In this paper, a systematic knowledge of the knowledge regarding optical coding algorithms for all IoT based interfaces was carried out and the limitations of these IoT devices and their design limitations were better understood. This study not only identified the global applications of IoT devices with its limited resources, but also identified the security challenges and threats that could affect security related to IoT devices.
Asynchronous Online Service Placement and Task Offloading for Mobile Edge Computing

Mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading schemes. Many existing works focus on offloading tasks to the edge servers while ignoring the heterogeneity and diversity of computation services, which is also important in MEC. In this paper, we investigate the joint problem of online task offloading and service placement—downloading and deploying the service-related resources at edge servers—in the dense MEC network. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Due to the uncertainty of task demands, it is impossible to make an online long-term optimal decision. Therefore, we propose an online algorithm based on the two-timescale Lyapunov optimization without requiring the future information. By making asynchronous decisions on service placement and task offloading, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm is more competitive than benchmarks.
FIRM-COV: High-Coverage Greybox Fuzzing for IoT Firmware via Optimized Process Emulation

With the growing prevalence of the Internet of Things (IoT), related security threats have kept pace. The need to dynamically detect vulnerabilities in IoT devices cannot be overstated. In this work, we present FIRM-COV, the first high coverage-oriented greybox fuzzer for IoT firmware. FIRM-COV leverages newly optimized process emulation by targeting IoT programs and mining real-world vulnerabilities. FIRM-COV focuses on solving problems of IoT fuzzing based on empirical analyses, using the required structured input, the inaccuracy and instability of emulation, and the required high code coverage. By optimizing the existing emulation technique, FIRM-COV always maintains a stable state and achieves high accuracy when detecting vulnerabilities. We also implement a dictionary generation algorithm to provide structured input values and synergy scheduling to achieve high coverage and throughput. We compare FIRM-COV with other IoT fuzzing frameworks for eight real-world IoT devices. As a result, FIRM-COV achieves the highest coverage and throughput, finding the fastest and most 1-day vulnerabilities with almost no false-positives. It also found two 0-day vulnerabilities in real-world IoT devices within 24 h.
Analysis of the Multilayer Perceptron Parameters Impact on the Quality of Network Attacks Identification

The paper investigates the architecture of a multilayer perceptron. Using the CSE-CIC-IDS2018 dataset we analyze the quality of computer attack (CA) identification under different parameters of the multilayer perceptron. An artificial neural network (ANN) scheme is obtained empirically, using the analysis of existing works, the optimal number of the hidden layers is identified. The obtained ANN contains three modifiable parameters: activation functions on the first and the second hidden layers, a way to optimize the gradient descent algorithm. The activation functions and optimization methods for the gradient descent algorithm are discussed in this study. A comparison of the ROC-AUC classification characteristics (area under the error curve) of all the constructed ANNs showed which activation function and gradient descent algorithm optimization method is the best of the considered parameter combinations when using a multilayer perceptron scheme to solve CA identification problems.
Adaptive Self-Tuned Controller IC for Resonant-Based Wireless Power Transfer Transmitters

This article introduces an adaptive self-tuned controller IC for resonant wireless power transfer (RWPT) transmitters. The controller IC comprises an on-the-fly very-high-frequency tracking hardware with high resolution and an independent high-resolution digital pulsewidth modulator (PWM)-based (HR-DPWM) current programmed control. These facilitate precise frequency generation as well as adaptive tuning of the reactive components in the matching network, which translate into tight current/power regulation capabilities while retaining optimized power transfer conditions on the transmitting side. The controller IC enables to effectively disengage the power-delivery capabilities from the variations of the resonators, electrical circuits, and wireless medium. The controller core is based on a fully synthesizable digital architecture that has been realized through HDL tools, and several key building blocks have been developed and described in detail: a delay-line-based phase detector, high-resolution digital frequency synthesizer, and HR-DPWM. To fully exploit the benefits of digital electronics, reduce power consumption, and save area, the digital core of the controller has been designed completely through asynchronous architecture, eliminating the need of high-speed clock and its related hardware. The mixed-signal controller IC has been designed, implemented, and fabricated in 0.18-μm 5-V CMOS process, resulting in effective silicon area of 0.6 mm2. To demonstrate the mixed-signal controller IC in closed-loop operation of a wireless power system, an experimental 20-W resonant capacitive-based WTP system has been designed and validated. The effectiveness of the controller is well demonstrated and evaluated at the MHz range up to 200 mm misalignment, meeting the strict requirements of resonant-based WPT systems.
Impact of Monovalent Cation Halide Additives on the Structural and Optoelectronic Properties of CH3NH3PbI3 Perovskite

The influence of monovalent cation halide additives on the optical, excitonic, and electrical properties of CH3NH3PbI3 perovskite is reported. Monovalent cation halide with similar ionic radii to Pb2+, including Cu+, Na+, and Ag+, have been added to explore the possibility of doping. Significant reduction of sub‐bandgap optical absorption and lower energetic disorder along with a shift in the Fermi level of the perovskite in the presence of these cations has been observed. The bulk hole mobility of the additive‐based perovskites as estimated using the space charge limited current method exhibits an increase of up to an order of magnitude compared to the pristine perovskites with a significant decrease in the activation energy. Consequentially, enhancement in the photovoltaic parameters of additive‐based solar cells is achieved. An increase in open circuit voltage for AgI (≈1.02 vs 0.95 V for the pristine) and photocurrent density for NaI‐ and CuBr‐based solar cells (≈23 vs 21 mA cm−2 for the pristine) has been observed. This enhanced photovoltaic performance can be attributed to the formation of uniform and continuous perovskite film, better conversion, and loading of perovskite, as well as the enhancement in the bulk charge transport along with a minimization of disorder, pointing towards possible surface passivation.
Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters

Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can eventually increase the quality of the output voltage along with the efficiency of the MLI. The performance of the algorithm is evaluated with three different case studies involving 7, 11, and 17-level three-phase MLIs. The results are verified using both simulation and experimental studies. The results showed substantial improvement and superiority compared to other available algorithms both in terms of the harmonics reduction of harmonics and finding the correct solutions.
Co-Existence of ITS-G5 and C-V2X at an Urban Road Intersection

High mobility, low latency and high throughput requirements in intelligent transport systems (ITS) have paved the way for the development of new wireless communication technologies. Therefore, the 5.9 GHz band has been assigned to ITS applications under two main technologies. Europe's ITS-G5 is one such technology, which is based on IEEE 802.11p. The other alternative technology is 3GPP's cellular vehicle-to-everything (C-V2X). Both of these technologies have their inherent advantages and disadvantages due to dissimilarities in their physical (PHY) and media access control (MAC) layer architectures. Therefore, the applicability of each technology will vary depending on the situation. While previous work has been mainly focused on the comparison of two technologies, in this paper, we investigate the benefit of the co-existence of both of these technologies for a V2I downlink scenario in a road-side unit (RSU) placed at the center of an urban road intersection. We propose an optimization scheme to achieve the best minimum signal-to-interference-plus-noise ratio (SINR) performance for the RSU while providing connectivity to the maximum possible number of vehicles. Our analysis shows that ITS-G5 should be given priority when communicating with larger number of vehicles while C-V2X should be given priority when less number of vehicles are requesting to connect with the RSU.
Applying the Reconfigurable Computing Environment Concept to the Deep Neural Network Accelerators Development

Deep Neural Networks are widely used in many tasks where deep understanding of the domain context is required. They are able to extract high-level features of data and the dependencies between them. This is possible due to the multi-layer structure and a large number of parameters. The key disadvantage of DNN is its high computational complexity, which is a serious problem for very large models, or for devices with severe limitations on their autonomy, weight and processing delays. This paper proposes a solution to the issue by rotating the signal inside a homogeneous computing structure with dynamic reconfiguration of layers. Our implementations of the structures are based on the concept of reconfigurable computing environments (RCE). The developed models of these computing structures and their processing elements are shown. Results of timing simulation of these models and their comparison with classic non-reconfigurable alternatives are presented.
Design and Implementation of an Intelligent Control System for a Lower-Limb Exoskeleton to Reduce Human Energy Consumption

Power augmentation is known to be one of the important applications of Exoskeletons. This paper designs a control strategy to reduce the energy consumed by users in power augmentation mode. The strategy aims to calculate and apply the interaction force between humans and robots according to human intentions. To realize human intentions, the movement's kinematic characteristics and the user's muscular activity were used. The movement patterns were learned by the robot using a set of adaptive oscillators. The human movement pattern in each movement cycle was considered the basis for predicting human intention in the next cycle. Thereby, the robot's optimal path and interaction torque were calculated and applied to the robot by the internal control loop. Within this process, Electromyography (EMG) signals are used to coordinate the robot's interaction torque with human intention. This torque is modified continuously based on the EMG signals for each moment of the movement phase. Further, this control strategy's performance was first simulated and eventually evaluated by implementing them in the experimental testbed. The results confirmed that the control strategy adopted help to achieve predefined goals.
Store Edge Networked Data (SEND): A Data and Performance Driven Edge Storage Framework

The number of devices that the edge of the Internet accommodates and the volume of the data these devices generate are expected to grow dramatically in the years to come. As a result, managing and processing such massive data amounts at the edge becomes a vital issue. This paper proposes "Store Edge Networked Data" (SEND), a novel framework for in-network storage management realized through data repositories deployed at the network edge. SEND considers different criteria (e.g., data popularity, data proximity from processing functions at the edge) to intelligently place different categories of raw and processed data at the edge based on system-wide identifiers of the data context, called labels. We implement a data repository prototype on top of the Google file system, which we evaluate based on real-world datasets of images and Internet of Things device measurements. To scale up our experiments, we perform a network simulation study based on synthetic and real-world datasets evaluating the performance and trade-offs of the SEND design as a whole. Our results demonstrate that SEND achieves data insertion times of 0.06ms-0.9ms, data lookup times of 0.5ms-5.3ms, and on-time completion of up to 92% of user requests for the retrieval of raw and processed data.
Sampling-Noise Modeling & Removal in Shape From Focus Systems Through Kalman Filter

Shape from Focus (SFF) is one of the passive techniques to recover the shape of an object under consideration. It utilizes the focus cue present in the stack of images, obtained by a single camera. In SFF when the images are acquired, the inter-frame distance, also known as the sampling step size, is assumed to be constant. However, in practice, due to mechanical constraints, sampling step size cannot remain constant. The inconsistency in the sampling step size causes the problem of jitter, and produces Jitter noise in focus curves. This Jitter noise is not visible in images, because each pixel in an image (of the stack) will be subjected to the same error in focus. Thus, traditional image denoising techniques will not work. This paper formulates a model of the Jitter noise, followed by the design of system and measurement models for Kalman filter. Then, the jittering problem for SFF systems is solved using the proposed filtering technique. Experiments are performed on simulated and real objects. Ten noise levels are considered for simulated, and four for real objects. RMSE and Correlation are used to measure the reconstructed shape. The results show the effectiveness of the proposed scheme.
Glitch Compensation for a Digital-to-Analogue Converter

The resolution of precision mechatronic systems is fundamentally limited by the noise and distortion performance of digital-to-analogue converters (DACs). The sources of noise and distortion include quantisation error, non-linearity, thermal and semiconductor noise, and glitches. In precision systems it is desirable to approach the theoretical performance limit in order to maximise dynamic range and bandwidth. Currently, effective methods exist to mitigate DAC error sources, except for glitches, which is the focus of this paper. A glitch compensation technique is introduced based on an open-loop observer that utilises knowledge about glitches while quantisation error mitigation remains intact. The compensation technique attempts to minimise the glitch effects present on the analogue output via additional control effort, combined with ΔΣ-modulation which is a method for mitigating quantisation error. The method is applied to a DAC model including the glitch effects and show significant improvements over earlier efforts.
Wi-attack: Cross-technology Impersonation Attack against iBeacon Services

iBeacon protocol is widely deployed to provide location-based services. By receiving its BLE advertisements, nearby devices can estimate the proximity to the iBeacon or calculate indoor positions. However, the open nature of these advertisements brings vulnerability to impersonation attacks. Such attacks could lead to spam, unreliable positioning, and even security breaches. In this paper, we propose Wi-attack, revealing the feasibility of using WiFi devices to conduct impersonation attacks on iBeacon services. Different from impersonation attacks using BLE compatible hardware, Wi-attack is not restricted by broadcasting intervals and is able to impersonate multiple iBeacons at the same time. Effective attacks can be launched on iBeacon services without modifications to WiFi hardware or firmware. To enable direct communication from WiFi to BLE, we use the digital emulation technique of cross technology communication. To enhance the packet reception along with its stability, we add redundant packets to eliminate cyclic prefix error entirely. The emulation provides an iBeacon packet reception rate up to 66.2%. We conduct attacks on three iBeacon services scenarios, point deployment, multilateration, and fingerprint-based localization. The evaluation results show that Wi-attack can bring an average distance error of more than 20 meters on fingerprint-based localization using only 3 APs.
Optimal Linear Encryption Against Stealthy Attacks on Remote State Estimation

Defending against malicious attacks has become increasingly important in various cyber-physical systems. This article presents an encryption-based countermeasure against stealthy attacks on remote state estimation. Smart sensors transmit data to a remote estimator through a wireless communication network, in which data packets can be intercepted and compromised by attackers. The remote end is equipped with a false data detector that monitors the system. To avoid being detected, the attack should follow the stealthiness constraint. A linear encryption scheme is proposed to reduce the influence of potential stealthy attacks. For arbitrary linear encryption, the worst-case linear attack that yields the largest estimation error is derived. Accordingly, the optimal linear encryption, which minimizes the worst-case estimation error, is designed based on the Stackelberg game analysis. The above optimal strategies are considered in both the complete and partial measurement information scenarios for the attacker. Moreover, the generalization to nonlinear encryption strategies is also discussed. Comparisons of attack and encryption strategies through numerical examples are provided to illustrate the theoretical results.
Tuning-Less Injection-Locked Frequency Dividers with Wide Locking Range Utilizing 8th-Order Transformer-Based Resonator

This paper presents two tuning-less ultra-wide locking range (LR) injection-locked frequency dividers (ILFD). By utilizing an 8th-order transformer-based resonator, the LR can be improved significantly. Also, an inductive gain peaking technique is adopted to ensure start-up condition and achieve low power consumption. Two chips are fabricated in 40nm CMOS technology. The first chip exhibits a best-in-class LR of 104.5% from 28.8 to 91.9 GHz while consuming 5.8mW with a 0.9V power supply. The second chip achieves the best figure of merit (FoM) up to 26.6GHz/mW and 79.6% from 31 to 72 GHz LR with a 0.5V power supply.
AWash: Handwashing Assistance for the Elderly with Dementia via Wearables

Hand hygiene has a significant impact on human health. Proper handwashing, having a crucial effect on reducing bacteria, serves as the cornerstone of hand hygiene. For the elder with dementia, they suffer from a gradual loss of memory and difficulty in coordinating steps in the execution of handwashing. Proper assistance should be provided to them to ensure their hand hygiene adherence. Toward this end, we propose AWash, leveraging only commodity IMU sensor mounted on most wrist-worn devices (e.g., smartwatches) to characterize hand motions and provide assistance accordingly. To handle particular interference of senile dementia patients in IMU sensor readings, we design a number of effective techniques to segment handwashing actions, transform sensory input to body coordinate system, and extract sensor-body inclination angles. A hybrid neural network model is used to enable AWash to generalize to new users without retraining or adaptation, avoiding the trouble of collecting behavior information of every user. To meet the diverse needs of users with various executive functioning, we use a state machine to make prompt decisions, which supports customized assistance. Extensive experiments on a prototype with eight older participants demonstrate that AWash can increase the user’s independence in the execution of handwashing.