Miracle-dz/UniFAR-FTUs huggingface dataset:
A Memory-Bandwidth-Efficient Word2vec Accelerator Using OpenCL for FPGA

Word2vec is a word embedding method that converts words into vectors in such a way that the semantically and syntactically relevant words are closed to each other in the vector space. FPGAs can be used to design low-power accelerators for Word2vec. FPGAs use highly parallel computations which require parallel data access. Since FPGAs generally have a small external memory access bandwidth compared to CPUs and GPUs, the processing speed is often restricted. We evaluate the trade-off between bandwidth and accuracy using different fixed-point formats, and propose a memory-bandwidth-efficient FPGA accelerator by utilizing 19-bit fixed-point data. We have implemented the proposed accelerator on an Intel Arria 10 FPGA using OpenCL, and achieved upto 28% bandwidth reduction without any degradation to the computation accuracy. Since the reduced bandwidth allows us to access more data without any data access bottleneck, it is possible to increase the processing speed by increasing the degree of parallelism.
User Recognition Based on Human Body Impulse Response: A Feasibility Study

Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the $k$ -nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR.
Long Term Load Forecasting using Grey Wolf Optimizer - Artificial Neural Network

This paper presents a new technique namely Grey Wolf Optimizer- Artificial Neural Network (GWO-ANN) as a technique to forecast electrical load. GWO is a meta heuristic technique inspired by the hierarchy of leadership of the grey wolf hunting mechanism in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling are also imitated in the algorithm. GWO is utilized to determine the optimal momentum rate and learning rate of ANN for accurate prediction. In the ANN configuration, the temperature, humidity, wind speed, maximum power, and average power were used as the input data. While total power was used as the output data. ANN is trained by adjusting the parameters of momentum rate and learning rate until the output data matches the actual data. The performance of GWO-ANN was compared to the performance of ANN and Particle Swarm Optimization - Artificial Neural Network (PSO-ANN). The results showed GWO-ANN provide better result in terms of the Mean Absolute Percentage Error (MAPE) and coefficients of determination (R2) as compared to other methods.
Love wave biosensor to diagnose modification of cell viscoelasticy

In this work we present the development of an integrated microfluidic platform addressing several challenges. The sensor array combines a dual Rayleigh wave and Love wave for the diagnosis of the modification of cell viscoelasticity. Based on simulations taking into equations of motion and Navier-Stokes equation for the viscous liquid region, we evaluate and correlate the Love surface acoustic wave sensing part in order to enhance the sensitivity. The results corroborate the idea that the whole measurement system must be considered to develop an integrated optimized analysis system.
A Fast Timing Separation of Events Algorithm for Concurrent Systems

In this work, we present a fast, polynomial time implementation of the Timing Separation of Events (TSE) Algorithm, with complexity $O(E\times(V+E))$. TSE computation is a fundamental problem in the analysis of event-driven, asynchronous systems, when uncertainty is present, and event delays are specified as [min, max] intervals. The maximum or minimum TSE may be needed, based on the timing analysis required. In this work, we present a novel approach to solving the TSE computation problem, which utilises: (1) the Primal-Dual Method Algorithm of [1] to compute the system period and critical cycle, as well as annotate timing offsets to events, based on the minimum delay values, and (2) an unfolding scheme, on the event graph, to compute the maximum delay between source and target. We show that only a maximum of three unfoldings are necessary, based on the relative delays between source and target events. We present results on a set of classical TSE examples. Our results are correct and identical to [2], but our approach resolves the issue of deciding how many unfoldings are needed to achieve periodic behaviour, and is sianificantly faster than all other methods.
Playful Interaction with Humanoid Robots for Social Development in Autistic Children: a Pilot Study

Children with a diagnosis of Autism Spectrum Disorder (ASD) have serious difficulties in the development of their communicative and social skills. In recent years, robots have been tested in the therapy of autistic children as a promising tool for increasing their interest and motivation in the activities. In this paper we present the results of a pilot study with playful robot-child interaction developed for the therapy of diagnosed children aged between 3 and 5. The children were separated into an intervention and a control group. Their progress in development was measured before and after the intervention. Although the experience was unanimously considered as positive by parents and caregivers, we have found no significative differences between the intervention and control groups. Some observed trends demand more caution and additional studies for identifying not only the advantages but also the possible pitfalls of the use of robots in the therapy of autistic children.
Trajectory Optimization of Continuum arm robots

Rigid manipulators are applicable for a very structured environment and standard applications. For real world applications, continuum manipulators are used which has required high degrees of freedom, and compliance. The current work focus on the trajectory optimization of continuum robot for a specified application to minimize energy usage. To achieve this task Lagrangian mechanics is used to develop the mathematical model of the continuum robot with the payload. In this case the trajectory optimization has been carried out by treating the problem as a nested optimization problem. The outer optimization task is to optimize the trajectory using minimization of input force as primary goal where initial and final configurations of the arm are already available. Here, Genetic Algorithm is used as the optimizer for the selected tasks. The purpose of inner optimization loop is to find the feasible inverse solution for the manipulator that is required to calculate input forces which is further required to optimize the trajectory of the arm. A constrained non-linear optimization algorithm is used for the task. The optimization results show 30-80 % decrease in the input force required for the specified trajectories of the arm. The current paper shows that various tasks can be optimized using the formulated strategy to save the energy required by the arm to execute specified task.
An ANN Based Sequential Detection Method for Balancing Performance Indicators of IDS

In recent years, the number of cyber attacks has been increasing rapidly and network security has become an important issue. As a vital component of defense against network threats, intrusion detection system (IDS) was introduced and machine learning algorithms have been widely used in such systems for high detection performance. There are several evaluation indices such as false positive rate, false negative rate, and so on. A problem is that these indices are often related to each other. For example, while we try to decrease the false positive rate, the false negative rate often tends to increase, and vice versa. In this study, we proposed an ANN based sequential classifier method to mitigate this problem. Specifically, we try to train ANN to have a low false positive rate, despite which may lead to high false negative rate. Then, the reported negative instances are sent to the next ANN to make a further investigation, where the false negative instances reported at the previous ANN may be classified correctly. In this way, the final false negative rate can also be improved greatly. The results of the experiment shows that the proposed method can bring lower false negative rate and higher accuracy of detection while making the false positive rate at an acceptable level. Moreover, the optimum number of ANNs for our proposal is also investigated and discussed in this study.
A 12-bit Domino ADC with a Background Offset Calibration Scheme

This study presents a background offset calibration scheme for domino analog-to-digital converters (ADCs). The domino ADC uses more-than-one comparators to achieve both high-speed and low-power operation. The offset deviation among comparators must be mitigated to improve the effective number of bits (ENOB). This helps the resolution of the domino ADC to be improved from 6-bit to 10/12-bit. In the domino ADC, each comparator acts as both a quantizer and a DAC controller. Therefore, the operation speed can be faster than successive-approximation-register ADCs. In this study, using the proposed offset calibration scheme on a 12-bit domino ADC, the worst ENOB is improved from 7.2b to 11.87b.
Divided Localization of Multiple Targets Based on Compressive Sensing Algorithm

This paper is concerned with the multi-target localization based on compressive sensing (CS). If orthogonal matching pursuit (OMP) is applied as the signal reconstruction algorithm to locate targets, there will always be a situation that only part of targets are located successfully. To solve this problem, divided localization of multiple targets is proposed. In data preparation stage, the results of single and multiple localization are calculated under two kinds of partitioning granularity. Then, in divided localization stage, according to the data from data preparation stage, part of targets can be predetermined by finding the clusters. Also, the remaining targets can be located in the possible falling area. Finally, simulation results are provided to demonstrate that, compared with other schemes, the proposed algorithm obviously improves the positioning accuracy and the anti-noise ability.
Spectral and Energy Efficiency of Hybrid Precoding for mmWave Massive MIMO With Low-Resolution ADCs/DACs

In this paper, we investigate the spectral efficiency (SE) and energy efficiency (EE) of hybrid precoding in the point-to-point mmWave massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). First, considering the quantization noise of ADCs/DACs, an approximation expression for the SE is derived. Then, in order to solve the problem of non-convex hybrid precoding design, based on the derived analytical approximation expression, we propose a two-stage alternating minimization scheme to obtain the optimal hybrid precoder matrix. And the trade-offs between SE and EE with the full digital precoding and hybrid precoding are investigated. Numerical results verify that the system with the proposed scheme has nearly the same SE performance as with the full digital precoding. The fully connected hybrid precoding can be more energy-efficient compared to the full digital precoding, and the proposed hybrid precoding scheme can achieve the better trade-off between the SE and EE. Moreover, the increase in the resolution of ADCs/DACs can effectively improve the SE performance of the system, and 4-bits ADCs/DACs achieve the best EE performance for the hybrid precoding.
A 10.6-mW 26.4-GHz Dual-Loop Type-II Phase-Locked Loop Using Dynamic Frequency Detector and Phase Detector

This paper reports a millimeter (mm)-wave type-II dual-loop phase-locked loop (PLL) with low-power and low-complexity design for improving jitter-power performance and power efficiency. Unlike the typical type-II single-loop PLL using a tri-state phase-frequency detector (PFD) plus a charge pump (CP) that has several limits in high-speed operation, our proposed PLL features a dual-loop scheme to enhance its performance and operating speed at low power. Specifically, we propose a dynamic frequency detector (FD) and a phase detector (PD) in conjunction with voltage-to-current converters (VICs) to avoid the typical current-mode-logic (CML) circuitry for static power reduction. Prototyped in 65-nm CMOS process, the entire PLL dissipates 10.6 mW, of which the dynamic FD and PD merely consume 0.28 mW. The integrated jitter is 415.6 fsrms (10 kHz to 100 MHz) and the reference spur level is −53 dBc at a 26.4-GHz output.
Ultra-thin polymer spray coating for advanced adhesive bonding applications

Adhesive bonding using an ultra-thin polymer layer is getting more attention for more than Moore applications. However some challenges of ultra-thin layer coating remain and are subject to further research. For this purpose diluted BCB adhesive layers were evaluated to create bonding layers down to 30 nm adhesive thickness. BCB is a well-known and thus favourable candidate for this evaluation due to it’s physical properties, such as low curing temperature, high degree of planarization, high optical clarity, along with good compatibility with various metallization systems.
Multilevel Approach to HPC Center Workload and User Applications Efficiency Analysis

Every HPC Center holder wants his systems to be productive, and every supercomputer user tries to achieve his research goals as fast as possible, thus wanting his jobs to run efficiently. There are lots of possible approaches for solving such problems. This paper shares the experience of tackling these challenges at the supercomputer center of Moscow State University, the largest HPC center in Russia, hosting Lomonosov-2 system with 4.95 PFlops of peak performance and some smaller systems aimed at providing computing services for hundreds of research projects, running about one thousand jobs per day. The proposed approach covers various levels of role-specific analysis scenarios, from the overall resource utilization and job state distribution down to the precise efficiency analysis of particular jobs including detection of possible performance issues. This approach is being implemented using modules for the Octoshell HPC center management system and used by system administrators and regular users in their daily practice.
Estimation of the Collision Risk of Autonomous Vehicle using Clustering

In this paper the clustering of different road-shapes like straight road or T-junction (i.e. three way road) or cross junction (i.e. four way road) has been presented using TFSOM×SOM algorithm. A trajectory path of a vehicle on different shapes of roads has been drawn which describes the degree of risk of collision on the road. The main aim of this work is to classify the different road-shapes and also to anticipate the possibility of the density of objects on the basis of different kinds of shapes of the road. The dependency of risk of collision is on the density of objects available on the road i.e. if number of available objects is more, the risk of collision is excessive and vice-versa. This prediction of high risk and low risk of collision can be used to set an alarm to alert driving system to avoid collision.
Effects of Reward Terms in Agent-Based Box-Manipulation Animation Using Deep Reinforcement Learning

Agent-based box manipulation has wide applications in computer animation and robotics. Deep reinforcement learning can be applied to generate animations of agent-based box manipulation. This paper focuses on push-manipulation in an agent-based animation. A policy is learned in a learning session in which an agent receives a reward that is a combination of different types of reward terms. Based on the received reward, the policy is improved gradually. In this paper, we investigate the effects of each reward term in-depth in a framework that is integrated with deep reinforcement learning. We also propose a simple way to produce different animation types. We performed several examples and analyzed our findings in details.
Convolutional Neural Network based Regression for Leaf Water Content Estimation

With the advancements in precision farming, crop sensing is gaining importance for timely crop health management. Leaf water content (LWC) is key component to determine vegetation health and nourishment. Timely estimation of LWC could save us from hazardous damage by pre-planning: drought stress on plants, irrigation and prediction of woodland fire. The retrieval of LWC from visible to shortwave infrared (VSWIR: 0.39 to 2.5 µm) mid- and thermal-infrared (MIR and TIR: 2.50 to 14.0 µm) windows of electromagnetic spectrum has been investigated using different statistical algorithms. Deep learning is modernizing the fast growing field of machine learning and image processing. The convolutional neural network (CNN) is ultramodern technique of deep learning that learns and extracts features directly from data. This research is focused on the extraction of different features of different plant species by using CNN for Regression. The modeled CNN architecture automatically detects prominent features to estimate LWC in plant species from its reflectance spectra, recorded for varying amount of LWC. Previous methods applied on same dataset yielded accuracy of 93% and Root Mean Square Error (RMSE) of 7.1, however, CNN resulted in better and swift results with an accuracy of 98.4% and RMSE of 4.183. This study helps in identifying the important spectral regions for quantifying water stresses in vegetation. The outcomes of this study can enable the future space missions to foresee water content of different plant species on the basis of their spectral signatures for illustrating vegetation stresses.
CRowNet: Deep Network for Crop Row Detection in UAV Images

Nowadays, the development of robots and smart tractors for the automation of sowing, harvesting, weeding etc. is transforming agriculture. Farmers are moving from an agriculture where everything is applied uniformly to a much more targeted farming. This new kind of farming is commonly referred to as precision agriculture. However for autonomous guidance of these agricultural machines and even sometimes for weed detection an accurate detection of crop rows is required. In this paper we propose a new method called CRowNet which uses a convolutional neural network (CNN) and the Hough transform to detect crop rows in images taken by an unmanned aerial vehicle (UAV). The method consists of a model formed with SegNet (S-SegNet) and a CNN based Hough transform (HoughCNet). The performance of the proposed method was quantitatively compared to traditional approaches and it showed the best and most robust result. A good crop row detection rate of 93.58% was obtained with an $IoU$ score per crop row above 70%. Moreover the model trained on a given crop field is able to detect rows in images of different types of crops.
Cyclic Symmetry in Volume Integral Formulations for Eddy Currents: Cohomology Computation and Gauging

This contribution addresses the solution of eddy-current problems by means of a volume integral formulation based on the electric vector potential on a computational domain that exhibits a cyclic symmetry. Even if grids discretizing the domain are typically composed of tetrahedral or hexahedral elements, the proposed approach also works for general polyhedral meshes, such as those ones obtained by subgridding. In this article, an algorithm to compute a set of suitable cohomology generators needed when the conductors are not simply connected is introduced first. Besides being purely combinatorial, with linear-time worst case complexity and suitable with polyhedral meshes, it reuses a code that computes generators for triangular surface meshes, with obvious advantages concerning the implementation effort. Second, the formulation and the algorithm for cohomology computation are tweaked to be able to solve eddy-current problems with cyclic symmetry reserving specific attention to the construction of suitable tree–cotree decomposition for the problem gauging.
Formal Modelling and Verification of Real-Time Self-Adaptive Systems

This paper describes a formal approach to modelling and verification of self-adaptive real-time systems. Such systems can dynamically be affected by exception events either originated in the operational environment or in the internal status, which require to be dealt with through adaptation actions which have to fulfil timing constraints. The approach is based on Time Basic (TB) Petri nets, a formalism well-suited to the specification of time-critical systems. Although some specialcase tools have been developed to support the analysis of TB net models, the original contribution of this paper is an embedding of TB nets into the popular Uppaal toolbox based on timed automata, which makes it possible both non-deterministic exhaustive analysis by model checking and/or a quantitative analysis of model properties through statistical model checking. The paper demonstrates the application of TB net modelling and analysis through a self-healing time-critical system.
Tactile Sensors for Minimally Invasive Surgery: A Review of the State-of-the-Art, Applications, and Perspectives

Minimally invasive surgery has been one of the most significant evolutions in medicine. In this approach, the surgeon inserts specially-designed instruments through a small incision on the patient’s skin into the body cavities, abdomen, veins or, arteries and performs the surgery on organs. As a major limitation, surgeons lose their natural tactile perception due to indirect touch on the organs. Since the loss of tactile perception compromises the ability of surgeons in tissue distinction and maneuvers, researchers have proposed different tactile sensors. This review is to provide researchers with a literature map for the state-of-the-art of tactile sensors in minimally invasive surgery, e.g. in robotic, laparoscopic, palpation, biopsy, heart ablation, and valvuloplasty. In this regard, the pertinent literature from the year 2000 on sensing principles, design requirements, and specifications were reviewed in this study. The survey showed that size, range, resolution, variation, electrical passivity, and magnetic-resonance-compatibility were the most critical specification to study for tactile sensors. Based on the results, some of the requirements, e.g., magnetic-resonance-compatibility and electrical passivity are of less generality and more application-dependent; however, size, resolution, and range specifications differ for various applications and are of utmost importance.