Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPLY-Based High-Speed Conditional Carry and Carry Select Adders for In-Memory Computing

Big data applications involved with neural networks requires frequent data transfer between memory and processing elements and thus, take a significant portion of energy. One possible method is to realize logic operations inside the memory, often known as in-memory computing. The memristor employed in a crossbar can also conduct logic operations, making it a promising candidate for enabling such designs. In-memory computing can vastly improve speed efficiency by significantly reducing memory access time and energy. Therefore, efficient operations inside the memory are crucial for data-intensive applications. This article presents two high-speed adders in memristive technology, which are derived using IMPLY-based logic operations and can be implemented on a crossbar architecture for in-memory computing. One is a carry select adder, which uses efficient ripple carry adders and multiplexers. While the other is a conditional carry adder, designed using a modified half adder and multiplexers. An IMPLY-based multiplexer and modified half adder are proposed as part of these adders. A modified half adder calculates the sum of two inputs and two carry outputs for various carry inputs. Compared to conventional adder designs, the speed of these two adders has been significantly enhanced. In particular, the proposed 32-bit Carry Select Adder and Conditional Carry Adder take 123 and 90 computational steps, respectively. The proposed adders have improved the speed by 30% and 49%, respectively, compared to the best-existing adder in IMPLY. Further, this article presents computational steps and the number of memristors required for $n$-bit adders. The proposed circuits were simulated using the VTEAM model, and energy consumed for each adder is calculated from the simulations.
An airborne low SWaP-C UAS sense and avoid system

This paper presents a low size, weight and power – cost (SWaP-C) airborne sense and avoid (ABSAA) system, which is based on a linear frequency modulated continuous wave (LFMCW) radar and can be mounted on small unmanned aircraft system (UAS). The system satisfies the constraint of the available sources on group 2/3 UAS. To obtain the desired sense and avoid range, a narrow band frequency (or range) scanning technique is applied for reducing the receiver’s noise floor to improve its sensitivity, and a digital signal integration with fast Fourier transform (FFT) is applied to enhance the signal to noise ratio (SNR). The gate length and chirp rate are intelligently adapted to not only accommodate different object distances, speeds and approaching angle conditions, but also optimize the detection speed, resolution and coverage range. To minimize the radar blind zone, a higher chirp rate and a narrowband intermediate frequency (IF) filter are applied at the near region with a single antenna signal for target detection. The offset IF frequency between transmitter (TX) and receiver (RX) is designed to mitigate the TX leakage to the receiver, especially at close distances. Adaptive antenna gain and beam-width are utilized for searching at far distance and fast 360 degree middle range. For speeding up the system update rate, lower chirp rates and wider IF and baseband filters are applied for obtaining larger range scanning step length out of the near region. To make the system working with a low power transmitter (TX), multiple-antenna beamforming, digital signal integration with FFT, and a much narrower receiver (RX) bandwidth are applied at the far region. The ABSAA system working range is 2 miles with a 1W transmitter and single antenna signal detection, and it is 5 miles when a 5W transmitter and 4-antenna beamforming (BF) are applied.
VRNavigSS: A Two-dimensionality Virtual Reality System for Depression Level Detection

Depression is a severe mental illness that can lead to negative moods and activities. The traditional clinical approach for diagnosing depression is face-to-face consultation, which is limited by time and space. Virtual Reality (VR), as a novel technology with higher accessibility and lower cost, can serve as an effective digital approach to diagnosing psychological disorders. In VR systems, users are exposed to various experimental scenarios, gaining immersive and interactive experiences. Recent research has demonstrated a relationship between depression and low spatial memory navigation ability (SMNA). Based on these considerations, we propose a VR system to detect one’s depression level by measuring spatial memory navigation performances. The system consists of three virtual scenarios with different spatial scales and dimensions. To study the system’s effectiveness, a pilot study with eight participants was conducted. The results showed differences in the participants’ spatial memory navigation performances in the three scenarios and a correlation between depression level and their spatial memory navigation performances.
An Adaptive Algorithm to Offload Task for User’s QoE in Vehicular Edge System

With the rapid development of vehicle edge offloading technology, smart device owners' quality of experience (QoE) requirements for computing offloading are gradually increasing. However, some dynamic or uncertain factors in the edge offloading scenario, such as the fluctuating network environment, battery energy consumption and other conditions, will affect users. Based on the multi-armed bandit theory, this paper proposes an adaptive learning algorithm that can dynamically sense environmental changes. Finally, the failure rate and offloading performance of large, medium and small tasks are simulated on the EdgeCloudSim simulation platform. The results show that the proposed algorithm has lower latency and energy consumption performance.
Enabling Unobtrusive Gait Analysis for Self-Health Monitoring using a Smartphone

Gait analysis states that the scientific study of body movements are responsible for locomotion in human beings. Many people can move about with abnormal gait patterns for years without knowing the odd symptoms. However, an abnormal walking style can lead to more significant health problems when someone's regular gait pattern is altered due to injury or illness. Therefore, one must self-assess and diagnose the early stages of abnormal gait. We propose an interdisciplinary implementation of the Human Gait Analysis System project (HGAS), a human pose estimation using the PoseNet machine learning model that can run on a smartphone browser. Gait analysis conducted in a laboratory environment is expen-sive and cannot be used to assess the person's gait daily. There-fore, it is desired to focus on developing a new gait analysis system that is affordable for personal use, user-friendly, and mobile. The present work explores machine learning techniques that can be used to determine basic gait parameters using observational gait analysis. The individual can utilize the developed system for a self-gait assessment, help doctors/nurses to understand the patient gait patterns for their treatment, or an individual pose assessment during yoga/exercises.
Implementation of a Cost-Effective Privacy Leakage Detection System for Hosted Programs

Posting programs to code hosting platforms such as GitHub is common for developers, but it will lead to privacy leakage issues in hosted programs. Though there are some detection methods for privacy leakage, they are difficult to be applied in practice. First, existing works mainly focus on detection algorithms, while ignoring the complete detection system from a holistic perspective. Second, the system will be blocked when acquiring programs because code hosting platforms usually have protection mechanism. Third, high-performance privacy detection algorithms need hardware devices in practice, and their effectiveness in real scenarios has not been verified since there is no public real hosted program privacy dataset.To address the above problems, we implement and commercialize a user-friendly privacy information leakage detection system for actual hosted programs. Firstly, we provide a system frame-work that can automatically complete "program acquisition-privacy detection-alert", allowing subscribers receive alerts if there is a privacy information leakage. Secondly, we propose a novel multi-random crawler scheme that can flexibly cope with the limitations of GitHub when acquiring hosted programs. Thirdly, we skillfully apply a cost-effective detection approach based on fuzzy matching, which can detect the subscriber customized privacy information with high performance. Based on this, we further provide a high-quality dataset obtained in real scenarios. Finally, we conduct comprehensive experiments to evaluate our system. Experimental results demonstrate the effectiveness of our crawler scheme and detection approach in providing a user-friendly system.
Accelerate Multi-view Inference with End-edge Collaborative Computing

Multi-view inference can utilize visual information from several views like a human being and significantly improve accuracy in some scenes, but it inevitably incurs more computing overhead than traditional DNN inference. To meet the requirement of low latency in typical scenarios, we consider utilizing model partition technique of edge computing to speed up multi-view inference, and design a multi-view end-edge co-inference execution framework (MV-IEF) which can make use of both end and edge resources for multi-view inference tasks. However, when employing the framework simply, the efficiency of multi-view inference will be constrained by network dynamics and heterogeneity of devices corresponding to multiple views. To break this constraint, we establish an optimization model based on the framework to minimize the multi-view inference time and solve it on the basis of game theory. And meanwhile, we propose a joint optimization algorithm for multi-view resource allocation and model partition (MV-JRAMP), which can make remarkable decisions of resource allocation and model partiton according to network status and computing capabilities of devices. Finally, we build a prototype and evaluate the performance of MV-JRAMP. Experiments show that MV-JRAMP can accelerate multi-view inference by up to 3.71×.
3D Reconstruction of Gastrointestinal Regions Using Single-View Methods

Capsule endoscopy is about to become an alternative to traditional colonoscopy. One uses a wireless camera to visualize the gastrointestinal (GI) tract. A 3D model based on image sequences obtained from wireless capsule endoscopy (WCE) can be helpful to diagnose or analyse areas of interests. We have therefore investigated the possibility to provide enhanced viewing for gastroenterologists by reconstructing 3D shapes from WCE images. The study is done on virtual graphics-based models of human GI regions. The shape from shading (SFS) method is applied to colon images and the quality of the reconstructed shapes is compared with ground truth models. WCE images suffer from uneven and dim illumination due to point light source. Therefore, we provide a method based on surface normals from reconstructed 3D models to enhance contrast particularity in images capturing larger depths by changing the illumination from point light to directional light. Images of different resolution are also tested to evaluate their effect on the quality of the 3D reconstruction. We have also tested the shape from focus (SFF) method, a possibility for future WCEs, and compared the results with SFS. Finally, enhanced images and 3D shapes recovered with both methods have been evaluated by gastroenterologists through subjective experiments. Objective experiments indicate that both methods are capable of reconstructing the 3D shapes of colon images successfully, but the SFF method is better at retaining details in the reconstructed models than the SFS method. Subjective experiments show that contrast enhanced images are highly preferred over original images. Also, having the reconstructed 3D models in addition to the images during evaluation is found to be very useful by gastroenterologists and sometimes even being preferred over the original image.
ZMAD: Lightweight Model-Based Anomaly Detection for the Structured Z-Wave Protocol

Smart home automation is part of the Internet of Things that enables house remote control via the use of smart devices, sensors, and actuators. Despite its convenience, vulnerabilities in smart home devices provide attackers with an opportunity to break into the smart home infrastructure without permission. In fact, millions of Z-Wave smart home legacy devices are vulnerable to wireless injection attacks due to the lack of encryption support and the lack of firmware updates. Worse yet, recent Z-Wave secure S2 devices with built-in encryption are also vulnerable to specific targeted attacks, i.e., attacking S2 devices is possible via vulnerable legacy devices or injecting malicious unencrypted packets that alter S2 devices normal operations. In this paper, we present ZMAD, a lightweight anomaly-based intrusion detection system (IDS) for monitoring and detecting wireless attacks on Z-Wave smart home devices. ZMAD uses a technique called packet formalization to address heterogeneous packets coming from various Z-Wave devices. ZMAD also uses a centralized learning approach to profile normal communication patterns of devices to increase Z-Wave Command Class coverage. By constructing a lightweight artificial neural network built from scratch in consideration of packet formalization and centralized learning, ZMAD can effectively detect abnormal behaviors in Z-Wave networks and runs on an external device to avoid network overhead. We applied ZMAD to an evaluation testbed constructed using 17 top-rated real-world Z-Wave smart home devices. From our experiments, we confirmed that ZMAD could effectively discover wireless injected packets with an accuracy of 98% for its artificial neural network. Our further analysis demonstrated that ZMAD is more effective than existing approaches, increasing the coverage of Z-Wave Command Classes by 663% while reducing five to 47 times the size of the trained model (23.1 KB) compared to existing deep learning architectures.
Bayesian Estimation for 3D-point Object Identification Based on Probabilistic Field Representation

New network services using real spatial information are expected to emerge in remote areas. For the advancement of services, it is important to understand real space from real spatial information, and for this purpose, object identification using machine learning is widely employed. Instead of directly identifying real space information using machine learning techniques, in this study, we represented real space as a field of probabilistic superposition of objects, which incorporates the results of the object identification as well as information about neighboring objects. We obtained the neighbor information based on positional relationships of objects in real space from a dataset used by the machine learning algorithm, and built an empirical knowledge base of the positional relationships. Then, we developed a method to use the empirical knowledge to modify the object identification results. Our results show that the outcomes of object identification are changed by the empirical knowledge and the accuracy of the identification is improved when confidence in the machine learning algorithm is low.
Variance-Based Exploration for Learning Model Predictive Control

The combination of model predictive control (MPC) and learning methods has been gaining increasing attention as a tool to control systems that may be difficult to model. Using MPC as a function approximator in reinforcement learning (RL) is one approach to reduce the reliance on accurate models. RL is dependent on exploration to learn, and currently, simple heuristics based on random perturbations are most common. This paper considers variance-based exploration in RL geared towards using MPC as function approximator. We propose to use a non-probabilistic measure of uncertainty of the value function approximator in value-based RL methods. Uncertainty is measured by a variance estimate based on inverse distance weighting (IDW). The IDW framework is computationally cheap to evaluate and therefore well-suited in an online setting, using already sampled state transitions and rewards. The gradient of the variance estimate is then used to perturb the policy parameters in a direction where the variance of the value function estimate is increasing. The proposed method is verified on two simulation examples, considering both linear and nonlinear system dynamics, and compared to standard exploration methods using random perturbations.
Averting Human-Elephant Conflict using IoT and Machine Learning of Elephant Vocalizations

Human-Elephant conflict causes harm to life and property and has contributed to endangering elephants. Elephant deterrent systems have been built using electric wire fences [1], improving the fences with chili [2], and sound deterrents to scare the elephants [3] and all have limited success [4]. The elephants are hunted and killed making them endangered [5]. The elephants help the ecosystem [6]. This paper outlines the approach we took toward a more sustainable world where humans can co-exist with elephants by building an elephant alertness system to predict when they are dangerously close and to alert humans. We built an IoT device using bio-acoustics and machine-learning as an early warning system to determine the proximity and behavior of elephants by classifying elephant vocalizations. We built this early warning device using a Raspberry Pi along with a microphone and an alarm system. The device identifies the presence of elephants nearby using sound sensors. We built an AI machine learning model that identifies the type of vocalization as a Chirp, Roar, Rumble, or Trumpet and predicts whether the elephants are likely to raid even when they are not visible from darkness or thick foliage. In this paper, we share the challenges we solved in building this outdoor weather-friendly Artificial Intelligence IoT (AIoT) device hosting the artificial intelligence predictive model.
A Novel Simulation Methodology for Silicon Photonic Switching Fabrics

Optical communication based on silicon photonics is a promising candidate for future networks. However, a key component that still presents challenges is a practical, silicon photonics-based, high performance switch with a high port count. The impracticality of buffering traffic in the optical domain mandates the use of circuit switching at the transmission level. This renders the photonic power penalty dependent on many factors, including architectural aspects and, most importantly, the switch load. Since the latter changes dynamically with network traffic we argue that simulating silicon photonics-based switches requires considering the photonic power penalty under dynamic workloads, which is not supported by state-of-the-art techniques. In this paper, we show how to simultaneously simulate both the overall switch as well as the photonic power penalty, by proposing a novel combination of the bufferless nature of photonic fabrics, flow-level simulation and optical beam propagation modelling. This approach enables a simulator to consider different kinds of switching fabrics and photonic components. We focus on how to model Beneš photonic switching fabrics formed with Mach-Zehnder Interferometers and consider their deployment as switching cores for top-of-rack switches. We compare our simulation with the published data from two fabricated chips and found accuracy is within 0. 5dB with respect to insertion loss, and within 3dB with respect to crosstalk. As a use-case, we evaluate the impact of routing algorithms on the photonic power penalty and found this can reduce the worst-case photonic power penalty by up to 4dB.
Information Diffusion Prediction via Exploiting Cascade Relationship Diversity

Information diffusion can be regarded as the process of multi-user collaboration to deliver information. How to predict the cascade size is a fundamental task and has many applications such as rumor detection, product marketing, etc. Recent works attempt to mine temporal and structural characteristics hidden in the information cascade based on deep learning models. As we know, the complicated interactions between nodes are critical for cascade size prediction, and these interactions are usually hidden in the multiple types of cascade relationships. However, the cascade relationship diversity has not been comprehensively exploited by current studies. In this paper, we propose a novel model named CTformer, which leverages the global receptive field of Transformer to make accurate prediction. Specifically, CTformer takes advantage of both global position encoding and bias matrices to explore cascade relationship diversity. Extensive evaluation results on multiple real-world datasets show that CTformer achieves significant performance gains over the state-of-the-art methods.
A Narrative Review of Recent Finite Element Studies Reporting References for Elastic Properties of Zirconia Dental Ceramics

For fixed dentures, new generations of zirconia with diverse characteristics and design choices are of significant interest. Although in vitro studies and finite element analysis (FEA) studies have been published, comprehension of various new methods of material testing and analysis remains insufficient. Young’s modulus and Poisson’s ratio of the desired materials must be available for investigators to validate FEA investigations that are accompanied by mechanical testing. The aim of this narrative review was to find recent FEA studies that report these values for newly developed commercial CAD/CAM zirconia restorative materials and compile them in a data list. A PubMed search was performed (English articles; 2018–2023; keywords: FEA, finite element, zirconia). Full-text articles (157) were examined, including studies (36) reporting the commercial materials’ names, Young’s modulus, and Poisson’s ratio. Only 21 studies had the source of their values referenced. A list of the materials and values used in these studies was compiled. Researchers are highly recommended to trace back references to determine the origins of these values for commercial materials. New research is encouraged to test the ever expanding list of new commercial esthetic monolithic CAD/CAM zirconia materials, as well as their different translucencies, to report their Young’s modulus and Poisson’s ratio.