Miracle-dz/UniFAR-FTUs huggingface dataset:
MicroSTARLING: A Millimeter Scale Self-Organizing Optical Communication Processor for Embedded Multihop Sensor and Computational Networks

In the development of small scale, networked nodes, design inspiration can be found in the decentralized and simplistic communication used by starlings in flight. Here, we present a novel node processor based on simple nearest neighbor connections to yield a self-organized linear optical sensor network. We present the processor state machine design and evaluation of the Verilog code. Each node, termed a MicroSTARLING, contains a processor fabricated using 180nm CMOS technology. The chips are 1.2mm <inline-formula> <tex-math notation="LaTeX">$\times1.2$ </tex-math></inline-formula>mm and the digital processor occupies 0.063 mm<sup>2</sup> of the chip. We assemble three test bench nodes to evaluate the processor function in wired, wireless communication and fully wireless circuits. When nodes are directly wired, the MicroSTARLING processor functions with a 25MHz input clock enabling a linear multihop network readout of 8 devices in 1.8ms. With a <inline-formula> <tex-math notation="LaTeX">$20~\mu \text{W}$ </tex-math></inline-formula>, 524KHz input clock, the processor requires <inline-formula> <tex-math notation="LaTeX">$12~\mu \text{W}$ </tex-math></inline-formula> at 1.2V. We evaluate the processor function under optical communication obtaining 6-hops between 7 nodes with little noticeable reduction in communication quality. Our fully wireless node required 3mW of power harvested via a 940nm light emitting diode (LED) and communicated via separate LEDs. To our knowledge this is the first demonstration of more than 3 hops of linear wireless communication with a mm scale self-organizing node processor. The processor size and power make possible the concept of smart dust networks with multihop communication between nodes for an embedded wireless sensor network in biological tissue structures or as the foundation of an optical neural network for edge computing.
Information Distribution in Multi-Robot Systems: Adapting to Varying Communication Conditions

This work addresses the problem of application-layer congestion control in multi-robot systems (MRS). It is motivated by the fact that many MRS constrain the amount of transmitted data in order to avoid congestion in the network and ensure that critical messages get delivered. However, such constraints often need to be manually tuned and assume constant network capabilities. We introduce the adaptive goodput constraint, which smoothly adapts to varying communication conditions. It is suitable for long-term communication planning, where rapid changes are undesirable. We analyze the introduced method in a simulation-based study and show its practical applicability using mobile robots.
An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments

Path loss is the primary factor that determines the overall coverage of networks. Designing reliable wireless communication systems requires accurate path loss prediction models. Future wireless mobile systems will rely mainly on the super-high frequency (SHF) and the millimeter-wave (mmWave) frequency bands due to the massive available bandwidths that will meet projected users’ demand, such as the needs of the fifth-generation (<inline-formula> <tex-math notation="LaTeX">$5G$ </tex-math></inline-formula>) wireless systems and other high-speed multimedia services. However, these bands are more sensitive and exhibit a different propagation behavior compared to the frequency bands below <inline-formula> <tex-math notation="LaTeX">$6~GHz$ </tex-math></inline-formula>. Hence, improving the existing models and developing new models are vital for characterizing the wireless communication channel in both indoor and outdoor environments for future SHF and mmWave services. This paper proposes an efficient improvement of the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model. Real measured data was taken for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in a typical indoor corridor environment at three selected frequencies within the SHF band, namely <inline-formula> <tex-math notation="LaTeX">$14~GHz$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$18~GHz$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$22~GHz$ </tex-math></inline-formula>. The research finding of this work reveals that the proposed models have better performance in terms of their accuracy of fitting real measured data collected from measurement campaigns. In addition, this work studies the impact of the angle of arrival and the antenna heights on the current and improved CI and FI models. The results show that the improved models provide better stability and sensitivity to the change of these parameters. Furthermore, the mean square error between the models and their improved versions were presented. Finally, this paper shows that shadow fading’s standard deviation can have a notable reduction in both the LOS and NLOS scenarios (especially in the NLOS), which means higher precision in predicting the path loss.
Comparing ANN and SVM Algorithms for Predicting Exercise Routines of Diabetic Patients

Today, various mobile applications and wearable devices support the management of diabetes by offering early and remote monitoring facilities. However, most of the available products recommend the activity/exercise level for patients based on standard data about the impact of exercise on calories burnt and blood Glucose levels. There is a risk associated with such products due to lack of customization to the individual patients. In this paper, we propose to use an Internet of Medical Things (IoMT) architecture to predict the level of activity required each day by the patient to maintain the recommended level of blood Glucose. We compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for their prediction accuracy. The proposed model takes pre-exercise Glucose level as input parameter and recommends the duration and intensity of the physical activity required by the patient each day. ANN has been observed to perform better for its classification accuracy.
Exploiting Artificial Swarms for the Virtual Measurement of Backlash in Industrial Robots

The backlash is a lost motion in a mechanism created by gaps between its parts. It causes vibrations that increase over time and negatively affect accuracy and performance. The quickest and most precise way to measure the backlash is to use specific sensors, that have to be added to the standard equipment of the robot. However, this solution is little used in practice because raises the manufacturing costs. An alternative solution can be to exploit a virtual sensor, i.e., the information about phenomena that are not directly measured is reconstructed by signals from sensors used for other measurements.This work evaluates the use of bio-inspired swarm algorithms as the processing core of a virtual sensor for the backlash of a robotic joint. Swarm-based approaches, with their relatively modest occupation of memory and low computational load, could be ideal candidates to solve the problem. In this paper, we exploit four state-of-the-art swarm-based optimization algorithms: the Dragonfly Algorithm, the Ant Lion Optimizer, the Grasshopper Optimization Algorithm, and the Grey Wolf Optimizer. The four candidate algorithms are compared on 20 different datasets covering a range of backlash values that reflect an industrial case scenario. Numerical results indicate that, unfortunately, none of the algorithms considered provides satisfactory solutions for the problem analyzed. Therefore, even if promising, these algorithms cannot represent the final choice for the problem of interest.
Ultra-High Q-factor Through Fused-silica Via (TFV) Integrated 3D Solenoid Inductor for Millimeter Wave Applications

In this work, a substrate integrated 3D solenoid inductor with an ultra-high quality factor (Q-factor) is demonstrated using the combination of Through Fused-silica Vias (TFVs) for low substrate loss and Copper (Cu) / Cobalt (Co) metaconductors for low conductor loss for millimeter wave (mmWave) applications. Especially, a Cu/Co metaconductor based 1 turn 3D solenoid inductor is integrated on a $300\ \mu\mathrm{m}$ thick fused-silica substrate with a small form factor of $170\ \mu\mathrm{m}\times 200\ \mu\mathrm{m}$. The Cu/Co inductor shows a peak Q-factor of 204 while a solid Cu counterpart does a Q-factor of 88.9 at 28.4 GHz, showing 130% Q-factor improvement. Modeling and optimization of the inductors has been performed using a lumped element circuit model and High Frequency Structure Simulator (HFSS) simulation. Design, fabrication, and characterization of the inductors is detailed.
Calibration of Visibility Samples for Real-Time Passive Millimeter Wave Imaging

A Ka-band 1024-channel passive millimeter wave (PMMW) imager with 1 GHz bandwidth called BHU-1024 has been developed by Beihang University for security screening. BHU-1024 uses linear phased array to obtain resolution in the horizontal direction and uses aperture synthesis to obtain resolution in the vertical direction. The non-ideal characteristics of the hardware cause the decrease of system sensitivity and always blur the reconstructed image, hampering applications where high resolution and accurate recognition are emphasized. Hence, effective calibration is a prerequisite to ensure the quality of the millimeter wave image. In this paper, the overall calibration approach is presented, focusing on the main errors relevant to the BHU-1024 instrument design: The basic correlator counts are preprocessed to correct for comparators offset and quadrature error. The residual offset is estimated by continuously sweeping the local oscillator (LO) phase. And for the baseline error, a novel and effective calibration approach, which makes use of an external noise source mounted on the metal frame, is proposed to tackle this problem. The results of simulation and measurement demonstrate the validity of the overall calibration approach. Finally, the actual calibrated data is used to reconstruct the millimeter wave image, which further validates the calibration process.
A Comparative Study of Microservices Frameworks in IoT Deployments

The Internet of Things (IoT) has grown dramatically in both size and complexity since its inception in 2009. One of the areas that has seen interesting developments is that of resource-constrained devices. Evidently such devices require careful engineering to better manage resources such as energy and memory, whilst still ensuring acceptable performance. The choice of software architecture and implementation are two key decisions for the engineering of such systems. The microservices architecture has been proposed, by a number of researchers, as a promising approach for IoT. However, limited research has been done on the implementation of microservices in IoT and resource-constrained devices. The aim of this paper is to compare a number of microservices frameworks in the context of IoT. The microservices frameworks are listed and compared using various criteria, thereby providing an overview of the advantages and disadvantages of each of the frameworks.
Multiwinding Transformer Leakage Inductance Optimization for Power Flow Decoupling in Multiport DC-DC Converters

Isolated multiport DC-DC converters manifest some prominent advantages over usual multiple two-port DC-DC converters such as smaller holistic magnetics and higher power density. However, the operation of such a converter is tied to the power flow decoupling capability in the magnetic medium frequency transformer (MFT). This paper targets to optimize the decoupling between the multiwinding MFT ports by means of multi-objective optimization of the leakage inductance network. Three different multi-objective cost functions are proposed and solved by Genetic Algorithm (GA). The obtained results show that a winding topology where primary winding is sandwiched by the two secondaries and are tightly wound to the core, provides the minimum possible leakage inductance without interleaving the windings. The obtained topologies from solving different objectives can be used as a benchmark in design and manufacturing of mutliwinding transformers. Experimental results are provided to verify the obtained optimum design.
Review of Algorithms for Artificial Intelligence on Low Memory Devices

The aim of the article is to conceptualise a more compact and efficient version of algorithms for artificial intelligence (AI). The core objective is to construct the design for a self-optimising and self-adapting autonomous artificial intelligence (AutoAI) that can be applied for edge analytics using real-time data. The methodology is based on synthesising existing knowledge on AI (i.e., knowledge modelling, symbolic reasoning, modal logic), with novel concepts from neuromorphic engineering in combination with deep learning algorithms (i.e., reinforcement learning, neural networks, evolutionary algorithms) and data science (i.e., statistics, linear regression, Bayesian methods). Far-reaching implications are expected from the unique integration of approaches in neuromorphic engineering and edge analytics.
Resilient Indoor Localization System Based on UWB and Visual–Inertial Sensors for Complex Environments

In this article, a resilient tightly coupled ultra-wideband (UWB) visual–inertial indoor localization system (R-UVIS) is developed to obtain accurate and robust localization performance in complex scenes, even in the case when sensors fail. More specifically, three schemes are designed for the proposed system. First, we introduce the line and image patch features to improve the precision and robustness of the visual features. Besides, we propose accurate loop closure and relocalization methods based on multifeatures to improve the performance of the localization system. Second, we introduce the UWB sensor into the system to suppress the localization drifts in the complex scenes and provide a fixed reference frame. Third, we propose a resilient multisensor fusion method based on an optimization framework to fuse the UWB, visual and inertial measurements in a tightly coupled manner. This data fusion approach improves the robustness of the system, making the localization system seamlessly switch among different localization modes depending on the specific scenes. In addition, an initialization process along with three sensors is also designed for the whole system. We conduct extensive experiments on the public dataset and in real-world scenarios to evaluate the proposed R-UVIS. The experimental results show that the proposed R-UVIS can provide the accurate and robust localization results in a fixed coordinate system for the complicated indoor scenes, even in the case when visual tracking fails or the UWB anchors are unavailable.
Ultrahigh Quality Factor Photonic Nanojets Generated by Truncated Microtoroid Structures

A photonic nanojet (PNJ) is a highly confined light beam that focuses from the shadow side of microparticles. In <inline-formula><tex-math notation="LaTeX">$46.47\lambda $</tex-math></inline-formula>this work, we propose a PNJ with ultrahigh quality factor formed by dielectric truncated microtoroid. The key properties of PNJ, such as the maximum intensity, the length of PNJ, the full-width at half maximum (FWHM), are studied in detail using finite-difference time-domain (FDTD) analysis. The results show that a PNJ with an enhanced intensity of 55.21 times to the incident light, superlong length of <inline-formula><tex-math notation="LaTeX">$46.47\lambda $</tex-math></inline-formula> and subwavelength FWHM of <inline-formula><tex-math notation="LaTeX">$0.77\lambda $</tex-math></inline-formula> is formed by semi-microtoroid, thus, an ultrahigh quality factor of 3308.68 is achieved. More importantly, the properties of the PNJ are tunable by changing the truncated proportions of the microtoroid. The structure we proposed has the advantages of compact structure and simple experimental operation, which is expected to apply in many research fields, including optical detection, optical data storage, super-resolution image, nanopattern, nanolithography, and so on.
Pallet detection and localization with RGB image and depth data using deep learning techniques

This paper presents a novel approach of pallet identification and localization algorithm (PILA) based on RGB image and depth data. The algorithm is implemented in C++ for real-time running and the RGB and depth data from low-cost RGB-D camera. Deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. The pallet's point cloud data is correlated with the labeled region of interest (ROI) in the RGB images through RGB-D fusion. The pallet's front-face plane is extracted and the orientation of the pallet is obtained at the same time. The triangle centric points of pallet's front-face could be determined with extracting x and y lines at the edge by the simple geometrical rules. Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated with natural pallet surface without any artificial markings. The results show that the pallet could be located with the 3D localization accuracy of 1cm and the angle resolution of 0.4 degree at the distance of 3m. The end-to-end running time is less than 700 ms from CAN-IO interface and this is a promising solution for autonomous pallet picking instrument and self-driving forklift applications.
Improving Heart Transplant Rejection Classification Training using Progressive Generative Adversarial Networks

Cardiac allograft rejection is a life-threatening complication that can occur in patients following heart transplantation. Endomyocardial biopsies, the current gold-standard for monitoring rejection, require manual identification of samples by experts; however, this can be subjective, costly and time-consuming. Computer-aided diagnosis models can potentially provide an automated analysis that offers accurate and consistent detection of biopsy samples. Unfortunately, the lack of a large dataset of rejection pathology signs, due to time consuming clinical annotations, limits the classification performance of such conventional AI-based models. In this paper, we developed a generative adversarial network (GAN) that creates synthetic tissue tiles from heart transplant whole slide images (WSIs) to serve as data for training rejection classifier models. To generate synthetic rejection histology regions, we used inspirational image generation (IIG) with a single rejection reference image. Additionally, to demonstrate objective improvement in image classification using synthetic rejection regions, we use a pretrained VGG-19 classifier to differentiate between rejection versus nonrejection tiles. We greatly improved classification performance, achieving an increase in the Matthews correlation coefficient (MCC) from 0.411 to 0.790 when the training set was augmented with our synthetic rejection tiles. Our model was able to create visibly realistic rejection tissue tiles which was used to augment the rejection tile database and enhanced automated rejection detection.
Advanced Single-phase Passive Immersion Cooling Solution with Natural Convection for Outdoor Edge Servers

To meet the increasing demand for AI (artificial intelligence), especially workloads requiring shorter response time like autonomous driving, out-door cloud edge server has drawn more attention and will become widely adopted and deployed in the future. The edge servers deployed in out-door environment need to operate in a wide temperature range ( -40°C to 55 °C) and humidity range (10% to 90%). In order to meet such hash requirements for out-door environment while delivering competitive computing power and density, immersion cooling is one of the efficient solutions. In this topic, a single-phase passive immersion cooling reference solution including system architecture as well as engineering design BKM (Best Known Methods) of both natural convection and force air cooling is presented, which is also purposed to sparkle ecosystem partners of innovation collaboration for more advanced system and thermal solutions for the emerging higher computing demand applications of data center and edge environments.
Deep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor Networks

Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46 % and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.
Touch Sensing for a Projected Screen Using Slope Disparity Gating

This paper introduces a novel touch sensing system for a projected screen with a single camera. To achieve touch sensing with camera image, there are two challenges to overcome. One is to establish robustness against image processing being misled by projected contents. The other is to realize both touch detection and fingertips localization with a single-camera image. In this work, we use Slope Disparity Gating and capture only the region slightly above the projected screen. This selected region is set geometrically so that a part of the finger is captured when touching, and no finger is captured when not touching. A key feature of this study is that the projector can enable this selective imaging capability in the camera while still displaying a normal projected image to the user’s eyes. The projector is both a programmable light source for capturing system and an interface display device for the users. We built a prototype system and touch sensing algorithm, evaluated the touch accuracy, demonstrated robustness to a projected content.