Miracle-dz/UniFAR-FTUs huggingface dataset:
Encoding, Model, and Architecture: Systematic Optimization for Spiking Neural Network in FPGAs

Spiking neural network (SNN) has drawn research interests as it mimics dynamic activities of human brain and has the potential to perform real-time cognitive tasks. However, latency, throughput and flexibility of existing hardware implemented SNNs are limited. The conventional rate coding is inefficient in terms of accuracy and latency. Oversimplified SNN models adopted by neuromorphic hardware discard characteristics such as neuron dynamics and filter effects etc., which are critical for neural information processing. Recent research advancements show that the potential of SNN can be better utilized by moving beyond rate-based model and considering temporal information embedded in the spike sequences. However, these works employ complex biologically realistic SNN models, posing challenges to hardware complexity. Furthermore, most existing neuromorphic hardware are developed for specific SNN models, or aiming at replicating biological behaviors. There is a lack of general methodology for SNN design optimization. Novel hardware architecture and systematic optimization techniques are required for efficient FPGA implementation and support flexible SNN models. To address above issues, in this work we proposed a holistic optimization framework for encoder, model, and architecture design of FPGA based neuromorphic hardware. We present an efficient neural coding scheme and training algorithm, which can optimize encoder parameters to enable fast inference. A flexible and hardware-friendly model is proposed, in which SNNs are represented as a network of Infinite Impulse Response (IIR) filters. Finally, an end-to-end framework is developed to optimize and deploy FPGA implementation. Experimental results show our work achieves state-of-the-art accuracy in various classification tasks, and outperforms various platforms including CPU, GPU and dedicated neuromorphic processors in terms of latency and throughput.
Biometric Authentication with Combined Iris and Sclera Information by YOLO-based Deep-Learning Network

In this work, the effective YOLO-based iris and sclera deep-learning classifier is studied for biometric authentication. By joint the partial iris and sclera information, the traditional sclera and iris segmentation pre-process is not required. By the YOLO-based model, the visible-light eye images are labelled with the jointly partial iris and sclera region, and the identity classifier is trained to inference the person's identity. By the UBIRIS image database for performance evaluations, the proposed YOLOv2 based joint iris and sclera identity classifier achieves the mean average precision (mAP) up to 99.83%, and the average inference time per one image of the YOLO model can be also reduced by using less number of anchor boxes. Compared with the previous works, the proposed design is more effective without any iris and sclera segmentation computations.
Rapid 2D Positioning of Multiple Complex Objects for Pick and Place Application Using Convolutional Neural Network

Robot guidance in an industrial environment is an important task to be solved in modern production facilities. A pick and place task is definitely one of the most common robot guidance issues to solve. In the beginning of the pick and place task, we need to perform a precise positioning of the objects of interest. In this contribution, an innovative engineering approach to multiple object positioning is proposed. The approach consists of two consecutive steps. At first, the original scene with objects of interest is transformed using a neural network. The output of this transformation is a schematic image, which represents the positions of the objects with gradient circles of various colors. Then, the positions of the gradient circles are determined by finding local maxima in the transformed image. The proposed approach is tested on a legitimate positioning problem with more than 99.8 % accuracy.
Low-cost portable system prototype for breast cancer detection using UWB signals

This work reports the realization of a microwave imaging system prototype focused on low-cost and portability. A power loss study in the several breast layers has been done, pointing to a 5.0 cm penetration system capacity. A mechanical and dielectric coupling medium was proposed together with a phantom made to mimic the internal breast tissues. Firmware, software and protocol were developed to work on an embedded UWB transceiver platform. The scattered collected signals were applied to the improved confocal algorithm, and the preliminary results showed that the system was feasible to detect 1.4 cm tumors located at 3.3 cm deep in the breast with a signal to clutter ratio of 2.17 dB and signal to mean ratio of 13.68 dB. This prototype will serve as a guideline towards a low-cost portable microwave imaging system that can be mass distributed in medical offices, stepping up the total of people with access to an early breast cancer diagnosis.
DALEX: Explainers for Complex Predictive Models in R

Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters - a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k. a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method irrespective of its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented here works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different models and gives additional tools for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended.
Toward Automated Tissue Classification for Markerless Orthopaedic Robotic Assistance

A markerless computer aided orthopaedic platform will require a complex computer vision system to isolate and track rigid bodies used to localize a robot to a patient. Isolating rigid bodies such as bone requires accurate segmentation and this study explores using diffuse laser reflectivity to accurately classify tissue. Lasers (red at 650nm and infrared – IR – at 850nm) intersected four material types; cartilage, ligament, muscle and metal surgical tools within a controlled cadaveric setup. Images were captured with an infrared CMOS sensor, pre-processed to isolate laser centers, and resized to test information requirements. Images for both laser types were scaled from <inline-formula> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> pixels to <inline-formula> <tex-math notation="LaTeX">$30\times30$ </tex-math></inline-formula> pixels and trained on a convolutional neural network, GoogLeNet. At sizes above <inline-formula> <tex-math notation="LaTeX">$15\times15$ </tex-math></inline-formula> pixels, the IR laser had a higher classification accuracy, reaching 97.8% at <inline-formula> <tex-math notation="LaTeX">$30\times30$ </tex-math></inline-formula> pixels, whereas the red laser peaked at 94.1%. It was shown as not possible to qualitatively identify materials that were not trained in the network based on their probability outputs. Further work will be performed to classify multiple points in a single scene as a step toward segmenting entire surgical views for markerless Computer Assisted Orthopedic Surgery (CAOS) systems.
Communication Systems in Distributed Generation: A Bibliographical Review and Frameworks

The exhaustion of natural energy and oil reserves has initiated the concept of renewable energy systems (RESs). This has expanded the vision of energy sector towards a diversified power grid while introducing the distributed energy resources (DERs) and distributed generation (DG). Though, this diversification is achieved by adding new energy generation sources and a two-way power flow, it opens the channel of production and trading with alternating current (AC) and direct current (DC) energy formats. But DC-based energy, due to its sporadic nature, can be further stored easily by energy storage devices. However, in recent years, a compelling need has arisen to understand the communication systems in distributed generation (DG) for better performance management, control and parallel power transfer. In this article, a bibliographic review on communication systems in distributed generations (DGs) is provided. The study identifies various communication technologies, standards, and protocols used in AC and DC-based DGs. Moreover, it contains the classification of different frameworks and methods involved. The methodology of different approaches and their likely combination are discussed for different types of communication networks. This study also represents useful information for readers, thereby demonstrate the complete life-cycle of digital data in sensors/actuators, transmitter, receiver, filter, decoder for control of DG elements and identifies future challenges as well. A comprehensive list of publications to date are compiled to provide a complete picture of different developments in this area.
Plasma Heme Scavengers Alpha-1-Microglobulin and Hemopexin as Biomarkers in High-Risk Pregnancies

Women with established preeclampsia (PE) have increased plasma concentration of free fetal hemoglobin. We measured two hemoglobin scavenger system proteins, hemopexin (Hpx) and alpha-1-microglobulin (A1M) in maternal plasma using enzyme-linked immunosorbent assay during the late second trimester of pregnancy in women with high and low risk of developing PE. In total 142 women were included in nested case-control study: 42 women diagnosed with PE and 100 controls (49 randomly selected high-risk and 51 low-risk controls). The concentration of plasma A1M in high-risk controls was higher compared to low-risk controls. Women with severe PE had higher plasma A1M levels compared to women with non-severe PE. In conclusion, the concentration of plasma A1M is increased in the late second trimester in high-risk controls, suggesting activation of endogenous protective system against oxidative stress.
A Non-invasive Automatic Skin Cancer Detection System for Characterizing Malignant Melanoma from Seborrheic Keratosis

Due to the complexity of skin cancer treatment at later stages, the investigation of an efficient non-invasive automated system can help in guiding diagnosis. This paper proposes a non-invasive automatic system for characterizing malignant melanoma from seborrheic keratosis (BKL) using pixel-based segmentation and feature extraction techniques. The proposed system utilizes the pixel-based features to capture the main characteristics that discriminate BKL and malignant melanoma (MEL). The pixel-based technique enabled single-pixel distributions for color and texture that results in good discrimination of pigmented skin lesions from unaffected skin regions in the processed image. In the experimental results, the obtained characterization result using gradient boosted trees (GBT) is promising and outperformed other state-of-the-art techniques, which had an accuracy equaled to 97.5%, Dice measure equaled to 98.5%, sensitivity equaled to 98.3%, and specificity equaled to 92.1%.
Virtual Reality on Product Usability Testing: A Systematic Literature Review

Research has shown that virtual reality (VR) technology has significant relevance in the product development process, revealing the user’s increasing interactive capacity with the virtual product. This study aims to characterize the scientific knowledge that address the application of usability testing for product development in the automotive sector using virtual reality technology. Based on a systematic literature review (SLR) on virtual reality and product usability evaluations, it was conducted a search in Science Direct, Springer, IEEE Xplore, and Google Scholar knowledge bases, that identified six studies related to this topic. The results indicated that, although the user experience using virtual reality in usability testing has some limitations related to the tactile sense, its use is providing results capable of identifying design errors, bringing benefits in agile corrections, and cost reduction with physical prototypes. Additionally, user experience tends to be more realistic with the adoption of haptic devices. On this basis, virtual reality has the potential to innovate the product development process, which through usability testing, could verify and validate the human-machine interaction within the secure digital environment.
Hierarchy of Hybrid Materials—The Place of Inorganics-in-Organics in it, Their Composition and Applications

Hybrid materials, or hybrids incorporating both organic and inorganic constituents, are emerging as a very potent and promising class of materials due to the diverse, but complementary nature of the properties inherent of these different classes of materials. The complementarity leads to a perfect synergy of properties of desired material and eventually an end-product. The diversity of resultant properties and materials used in the construction of hybrids, leads to a very broad range of application areas generated by engaging very different research communities. We provide here a general classification of hybrid materials, wherein organics–in-inorganics (inorganic materials modified by organic moieties) are distinguished from inorganics–in–organics (organic materials or matrices modified by inorganic constituents). In the former area, the surface functionalization of colloids is distinguished as a stand-alone sub-area. The latter area—functionalization of organic materials by inorganic additives—is the focus of the current review. Inorganic constituents, often in the form of small particles or structures, are made of minerals, clays, semiconductors, metals, carbons, and ceramics. They are shown to be incorporated into organic matrices, which can be distinguished as two classes: chemical and biological. Chemical organic matrices include coatings, vehicles and capsules assembled into: hydrogels, layer-by-layer assembly, polymer brushes, block co-polymers and other assemblies. Biological organic matrices encompass bio-molecules (lipids, polysaccharides, proteins and enzymes, and nucleic acids) as well as higher level organisms: cells, bacteria, and microorganisms. In addition to providing details of the above classification and analysis of the composition of hybrids, we also highlight some antagonistic yin-&-yang properties of organic and inorganic materials, review applications and provide an outlook to emerging trends.
Learning to Predict Risky Driving Behaviors for Autonomous Driving

The most critical issue in the autonomous car is safety. Many kinds of research were proposed in recent years, such as car accident, obstacle, lane detection, and sign recognition, to study this issue. However, we can observe that some clues can be seen before the crash occurs. Several large-scale datasets were established by different research groups in recent years to study the driving behaviors to obtain better driving experience for the autonomous car. However, no dataset focuses on risky driving behaviors. risky and dangerous driving behavior will directly lead to car accidents. Once we can discover the risky driving behaviors in advance, it is possible to make more response time. In this paper, we collect 400 our own videos with car accidents and carefully annotate the dangerous behaviors, car accidents, and object contextual information for each video. We also investigate the preliminary approach to discover the cues of the common risky behaviors from the collected dataset. The initial experiments also show that the common dangerous behaviors mining can effectively increase the response time before the car accident occurs.
Proximal Actuation of an Elastically Loaded Scissors Mechanism for the Leg Design of a Quadruped Robot

Spring Loaded Pantographs (SLPs) are frequently used in designing lightweight limbs for multi-legged robots. Quadruped robots that incorporate cable-pulled SLP legs have proven to be agile, robust and capable of conserving energy during their gait cycle. In such designs, the extension of the distal segments via the knee joint is dependent upon the length of the cable. In this article we propose the use of an Elastically Loaded Scissors Mechanism (ELS Mechanism or ELSM), which is a variant of the SLP. Driven by ’pulling’ onto the proximal joint of the scissors as opposed to the distal joint, this proposed leg utilizes the increased mechanical advantage of the scissors mechanism to ’amplify’ input angles to larger output displacement by the knee joint. Analysis and Simulations reveal that the proposed mechanism achieves increased motion speed as compared to the SLP mechanism. This, however, comes at the cost of higher load on the actuator which serves as an engineering trade-off. This is validated by experimentation using motion capture and load motor techniques of the SLP and ELS configurations in a physical quadruped robot.
FPGA and ASIC Implementation and Comparison of Multipliers

The multipliers find extensive use in today’s digital world and are even fundamental components in many signal processing applications. This paper presents the implementation and comparison of three types of 4-bit and 8-bit multipliers i.e. Array, Wallace and Baugh Wooley. The implementation is done on both the FPGA and ASIC to compare the designs. The FPGA implementation is done on NEXYS 4 DDR which is equipped with Xilinx Artix-7 FPGA. The ASIC implementation is done using Cadence and Standard Cell Library 90 nm gpdk (generic process design kit). The results indicate that the Baugh Wooley incurs the minimum delay in the FPGA implementation while as the Wallace tree incurs minimum Power Delay Product in the ASIC implementation. The Baugh Wooley is the least area consuming architecture for both the FPGA and the ASIC implementation.
Anti-Malware Efficiency Evaluation Framework

Anti-malware software is one of many countermeasures applied in organizations, installed on standalone personal computers, or across a network to detect malware and malicious software. This software performs critical security functions, and vendors utilize various techniques to perform the detection processes. However, a continuous assessment of these products' processes is mandatory to ensure antivirus programs execute their functions effectively. Most evaluation approaches target the security features without including other factors that influence the evaluation results, which may not present adequate evidence of product efficiency. This paper introduces a strategic approach to select the proper criteria based on the security and operational features of anti-malware software to align the evaluation process with organizational objectives.
Simple Assessment of Spatio-Temporal Evolution of Salt Marshes Ecological Services

A number of previous research studies have addressed the enormous role played by biodiversity and ecosystems in human well-being and have placed particular emphasis on the consequences resulting from the reduction or loss of these services. A handful of studies have implemented practical methodologies to quantify the variability of limiting factors leading to reductions in these services. The aim of this article is to document the limited number of studies that have analyzed coastal ecosystem services and acknowledge the impacts of physical changes in habitat provision. In one example, it is clear that the maintenance of salt marshes depends on sedimentary supply and consequent morphological variability in spite of the fact that there is usually no recurrent integration of habitat time-space dynamics (sediment availability) during the quantification and monetization of marsh services (i.e. monetary valuation of salt marsh services). This means that one key challenge facing the analysis of salt marsh (or other ecosystem) services in a global climate context is to predict future value, based on past trends, while at the same time guaranteeing conservation. Research in this field has been very broad and so the use of long-term evolutionary datasets is proposed here to explain future habitat provision. An empirical approximation is also presented here that accounts for service provision and enables time-space analysis. Although improvements will be required, the equation presented here represents a key first step to enable managers to cope with the constraints of resource limitations and is also applicable to other habitats.
An Improved Recycling Folded-Cascode Amplifier with High Unity-Gain Frequency

An improved recycling folded cascode (IRFC) operational transconductance amplifier (OTA) is described in this paper. By applying the damping-factor-control (DFC) compensation technique for recycling folded cascode amplifier, the unity-gain frequency and dc gain are improved significantly. The proposed amplifier has been implemented using TSMC 0. 18µm CMOS process. According to the simulation results, the proposed amplifier achieved 157MHz in unity-gain frequency and 90.1dB in dc gain while driving the same 5.6pF load.
A Thermal-aware Optimization Framework for ReRAM-based Deep Neural Network Acceleration

Resistive RAM (ReRAM) is widely regarded as a promising platform for deep neural network (DNN) acceleration. However, the ReRAM device suffers from severe thermal problems that degrade the lifetime and inference accuracy of the ReRAM-based DNN accelerator. To address the issues, we propose a thermal-aware optimization framework for accelerating DNN on ReRAM (TOPAR). TOPAR includes 3-stage offline thermal optimization and online thermal-aware error compensation. Offline thermal optimization consists of thermal-aware weight decomposition, thermal-aware column reordering, and fine-grained weight adjustment to reduce the temperature of the ReRAM-based DNN accelerator. For online thermal-aware error compensation, we compensate conductance change according to the temperature variation. With TOPAR, the endurance degradation due to temperature rise improves up to 2.39×, and inference accuracy is preserved without harming the performance of the ReRAM-based DNN accelerator.
MINLP-Based Routing for Electric Vehicles with Velocity Control in Networks with Inhomogeneous Charging Stations

Battery electric vehicles (BEVs) are playing an increasingly important role in personal mobility due to the wish to counteract climate change and political regulations concerning carbon dioxide emissions. Nevertheless, there are obstacles that need to be overcome. Especially long-distance journeys are problematic due to long charging stops and range anxiety It is a driver’s wish to fulfill a given driving task in a time-optimal way. But in the BEV case, driving faster does not necessarily lead to a decreased total travel time. The vehicle routing and charging problem is formulated as a mixed-integer nonlinear program (MINLP) and solved using mathematical optimization methods. First, time-minimizing vehicle routing with charging stations providing different power levels is discussed. The program returning the exact result is significantly faster than previous ones. Afterwards, the model is extended: driving speed becomes adjustable. A combined time minimal optimization of which route to take, how fast to drive, where and how much to recharge is the result. The combination of these four parameters has never been studied before. It is shown that up to 14.48% of driving time can be saved in our examples by incorporating the choice of a driving speed.