Miracle-dz/UniFAR-FTUs huggingface dataset:
7.4 A 256×128 3D-Stacked (45nm) SPAD FLASH LiDAR with 7-Level Coincidence Detection and Progressive Gating for 100m Range and 10klux Background Light

3D vision is an increasingly important feature in many applications of consumer, automotive, industrial, and medical imaging. Long range, high depth resolution, high spatial resolution, and high frame rates, are often conflicting requirements and difficult to be simultaneously achieved, especially in extreme ambient light conditions. In order to address range and depth resolution, direct time-of-flight has emerged as a powerful technique to perform light detection and ranging (LiDAR), thanks to advances in low-jitter optical detectors, such as single-photon avalanche diodes (SPADs), and accurate time-to-digital converters (TDCs) [1]–[5]. High spatial resolution can be achieved using scanning, at a cost of system complexity and somewhat lower frame rates [1], [3], while FLASH sensors [2], 4, [5] offer an alternative for both high frame rates and large pixel counts, but at limited ambient light conditions, due to typically long exposure times.
A Non-Iterative Crosswords-Inspired Approach to the Recovery of 2-D Discrete Signals From Phaseless Fourier Transform Data

We propose an innovative approach to the phase retrieval of 2-D discrete complex signals. The solution strategy profitably exploits some fundamental results available for the phase retrieval of one-dimensional discrete signals by following a simple yet effective philosophy resembling the solution of crossword puzzles. The resulting procedure is completely deterministic and allows identifying all the solutions of the problem by using just the spectral amplitude data, knowledge of the support of the source, and a few additional information. As a distinguishing characteristic, it does not exploit neither global-optimization algorithms nor iterative techniques. Numerical examples witnessing the interest of the proposed ideas are given for antenna applications.
Research on Novel Normal Fuzzy Kalman Filter for Speech Enhancement in Noisy Environment

The filter design plays a vital role in digital signal processing. A novel filter for reducing the noisy signal’s effect was developed for analyzing a noisy speech signal in the time-domain and frequency-domain. The novel filter, namely normalized fuzzy Kalman (NFLK) filter, was based on the Normalized least mean squares (NLMS) filter, fuzzy logic filter, and Kalman filter. Through mathematical derivation, the numerical model of the NFKL filter was developed. The NFKL filter was validated using a speech signal with noise and compared with three standard filters as KALMAN filter, NLMS filter, and recursive least squares (RLS) filter. The spectrum signal comparison of before-after filter used the signal to noise ratio to quantify each filter’s efficacy. The comparison results proved that the NFLK filter could effectively improve the noisy speech signal with a slight SNR decrease. Thus, the novel NFLK digital filter achieved the desired specified requirements for the noise reduction of the speech signal, and its speech enhancement performance better than traditional filters.
Heart Rate Monitoring System

Telemedicine online systems are beneficial as they offer reliable and efficient health care services. These electronic healthcare networks tend to be based on sophisticated and specialized wireless and wearable sensor technologies. A rapid technological development has expanded the reach of many remote health surveillance systems. Over the past decade, the growing usage of mobile computing devices equipped with HD digital cameras and touchscreen electronic visual displays and sensors, such as smartphones and tablets, has allowed software developers to build real-time monitoring and early warning applications for cardiovascular and diabetic disorders. Patient Health monitoring systems allow Doctors to take note of Safety of patients from a distant location. That helps to prevent a lot of life-threatening circumstances. PHM (Patient Health Monitoring) systems allow doctors to assess a person's necessary data while keeping him out of hospital and in a comfortable and healthier environment. With the support of Internet and cloud infrastructure, this can be accomplished with ease these days.
Principles for Re-architecting Software for Heterogeneous Platforms

The demands on software continues to increase through the constant addition of functionalities and high expectations from users. In particular, performance has been the focus in many projects with the goal of fulfilling complex and hard requirements across a variety of domains. One way to achieve satisfactory levels of performance is through heterogeneous computing, i.e., systems that contain more than one type of processing unit, such as CPUs, GPUs, and FPGAs. However, applications are typically designed to be executed on CPUs, and re-architecting software for execution on such heterogeneous hardware architectures entails several challenges that must be addressed. In this paper, we propose a framework that supports engineers in the process of making architectural decisions to re-architect software for execution on heterogeneous platforms. We present several relevant aspects that should be addressed in the process, along with suggestions on how to create design solutions using different existing approaches. The framework was developed based on multiple interactions with three industrial partners and evaluated through a computer vision application in the automotive domain.
Aircraft Autonomous Positioning Based on a Coarse to Fine Multi-modal Image Registration Scheme

In this paper, an autonomous positioning method based on coarse-to-fine multi-modal image matching is proposed for UAV navigation in GPS denied environment. Coarse image matching refers to roughly determining the approximate position of the real-time image in the reference image. Fine image matching refers to iterative matching around the position obtained by coarse image matching to achieve more accurate matching results. Besides, RANSAC is adopted to eliminate the outliers of image matching. After getting the result of image matching, we use PNP to estimate the position of the aircraft. The proposed position method has the advantages of high accuracy and reliability. The experimental results show that the average error of the position of the proposed method is 59%.
A Review on Digital Image Watermarking Using Transformation and Optimization Techniques

Now-a-days multimedia data is being generated very frequently in text format, image format, audio format etc. Security is the major issue of the multimedia data. Multimedia data can be very easily modified by the intruders. By using Watermarking and Steganography techniques the multimedia data can be protected from unauthorised access. Watermarking technique hides the data in the carrier signal. In the process of Steganography, the digital data (e.g. digital files like audio, video, text etc) can be embedded into some other digital file. The concept of watermarking is to keep the watermark robust from various attacks. Whenever the intruder tries to temper with the digital signal and removes the watermark, the digital data becomes useless as the quality is degraded. This paper discusses about various applications of watermarking, characteristics of Watermarking and basics of image processing. This paper also provides a comprehensive study of available literature on digital image watermarking. Some of the optimization techniques have also been discussed.
Coding-Based Distributed Congestion-Aware Packet Spraying to Avoid Reordering in Data Center Networks

Modern Data Center Networks (DCNs) are commonly based on Clos topologies with a large number of equal-cost multiple paths to provide high bisection bandwidth. The existing Random Packet Spraying (RPS) scheme spreads each flow of packets to all available parallel paths in order to achieve good load balancing under symmetric topologies. However, under asymmetric topologies caused by traffic dynamics or link failures, RPS potentially suffers from serious out-of-order problem. Therefore, to avoid packet reordering, we propose a Coding-based Distributed Congestion-aware Packet Spraying mechanism called CDCPS. At the sender end, CDCPS encodes packets using forward error correction (FEC) technology and adaptively adjusts the coding redundancy according to the asymmetric degree of multiple equal-cost paths. To make full use of link bandwidth, CDCPS randomly spreads encoded packets to all available paths at the switches. The original packets can be recovered immediately once enough encoded packets from uncongested paths arrive at the receiver, even if some encoded packets are blocked on congested paths. The test results of NS2 simulation showed that CDCPS eliminates out-of-order packets completely and effectively reduces the average and $99^{th}$ flow completion time by up to 73% and 78% over the state-of-the-art load balancing scheme.
Determination of 9(10H)‐Acridone by HPLC with Fluorescence Detection

Abstract Acridone alkaloids are important bioactive compounds found mainly in the Rutaceae family. An easy to use, rapid, and accurate high performance liquid chromatography (HPLC) method with fluorescence detection was developed for determination of 9(10H)‐acridone. The HPLC assay was performed on a reversed phase C18 column with a gradient of methanol in the water as the mobile phase, and eluent flow rate of 1.0 mL min−1. The wavelengths of excitation and emission were 395 and 435 nm, respectively. The linear relationship between the peak area and the 9(10H)‐acridone concentration with the correlative coefficient of 0.999 was observed. This method is highly sensitive with a detection limit of 0.67 ng mL−1 (S/N=3), and comparison of the 9(10H)‐acridone content in the citrus roots among four varieties proved that it is suitable for the detection of acridone in plant tissues.
A Postprocessing-Technique-Based Switching Loss Estimation Method for GaN Devices

Gallium nitride (GaN) high electron mobility transistor (HEMT) is a promising candidate for the high-density power converter applications. Due to the low switching loss, the GaN HEMT may lead to a new horizon in the applications, such as fast charger, wireless charging, and 5G power amplifier. However, the fast switching speed of GaN HEMT makes it extremely sensitive to parasitic parameters, especially for the low-voltage GaN devices with small packages. Conventional switching loss estimation methods, which are normally based on the calibration fixture or modification of test circuit, cannot be effectively utilized for GaN HEMT. This article presents an accurate switching loss estimation method using the postprocessing technique. The proposed switching loss technique consists of three parts of signal processing, which are the wavelet denoising process to minimize the influence of background noise, the voltage–current ($ V-I$) alignment process to reduce errors from probe propagation delay, and the linear interpolation process for further improvement of alignment accuracy. In addition, a modified SPICE model is proposed to investigate the influence of parasitic parameters on switching losses by the circuit simulation. Based on the theoretical switching characteristics, the switching loss estimation method is finally validated experimentally on a commercial 40-V/10-A GaN HEMT in a double-pulse test.
An Active-Routing Authentication Scheme in MANET

Mobile ad-hoc networks (MANET) is a network mode that does not depend on network infrastructure and central access. The fast and flexible networking mode of MANET renders its wide applications in specific scenarios. However, rapidly changing topology and open channels bring potential security problems. In this paper, we proposed an active-routing authentication scheme (AAS) based on the characteristics of active routing protocols. We formally demonstrated that the AAS is effective against selective forwarding attack, false routing attack, byzantine attack and route spoofing attack using the BAN logic considering the possibility of malicious nodes mingling in MANET. Experimental results show that the AAS is compatible with multiple active routing protocols and it is able to increase the packet delivery rate by 33.9%, with an average increase of 18.4% in the network containing some malicious nodes. Furthermore, the AAS is robust which remains the average network connection rate reach 1.6 times of the collusion attack prevention-OLSR(Cap-OLSR) protocol and preserves 79.2% of the network performance in simulation experiments with attacks from malicious nodes.
ANTIOXIDANT AND ANTHELMINTIC ACTIVITY OF TERMINALIA ARJUNA ROXB. STEM BARK EXTRACTS

In the present investigation, four complementary assays, total reductive power, total antioxidant activity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and metal chelating ability for ferrous ions were used to screen the antioxidant property of Terminalia arjuna bark extracts. The extracts showed significant antioxidant activity in correlation to the phytoconstituents present in the extrac ts. The phytochemical analysis revealed that the major bioactive components of the plants are terpenoides, polyphenols, saponins, tannins and glycosides. Live earthworms Pheretima posthuma were used for screening anthelmintic activity of bark extract. All extracts showed effective anthelmintic activity at 20 mg ml-1 concentration.
C-Band Hybrid 3-D-Printed Microwave Isolator

This article presents the design and fabrication of a microstrip $C$ -band microwave isolator using additive technology. Such fabrication requires multimaterial 3-D printing (dielectric and absorber) and the embedding of ferrite ceramic inside the substrate. First, our design demonstrated that an efficient microstrip load can be made by 3-D printing in a single process: not only the dielectric but also the absorber, which takes the form of steps in the substrate. Then, a circulator with a composite dielectric/ferrite resonator was designed. Finally, circulator and load were integrated in a single component to optimize the isolator. The device was then 3-D-printed with a pause during the fabrication to allow the ferrite disk to be embedded. Microwave measurements demonstrated that minimum insertion losses of 0.76 dB were achieved, together with a −15 dB bandwidth of 11.9%. To our knowledge, this is the first demonstration of the feasibility of fabricating a non-reciprocal microwave component by additive technology.
32.8 A 98.4fs-Jitter 12.9-to-15.1GHz PLL-Based LO Phase-Shifting System with Digital Background Phase-Offset Correction for Integrated Phased Arrays

Achieving long-distance, high data-rate wireless connections at the 5G millimeter-wave (mm-wave) frequency bands requires ultra-low-jitter local oscillators (LOs) [1]–[3] and phased-arrays with very accurate beam-steering capability [4], [5]. In regard to jitter, digital bang-bang phase-locked loops (BBPLLs) have been recently shown as capable of satisfying the very stringent requirements, while at the same time occupying less area than their analog counterparts [3]. This makes them particularly well-suited to the so-called localized LO-generation approach, where a synthesizer is placed in each of the individual transceiver elements, to avoid routing of a global high-frequency signal across large ICs and to leverage the jitter suppression resulting from an equivalent over-the-air combination of the outputs [1]. To achieve accurate beam-steering with LO phase-shifting, highly linear mm-wave phase-shifters are then conventionally placed on each of the LO signals [4], [5], leading, unfortunately, to a substantial power, noise, and area overhead.
A Survey on Training Free 3D Texture-less Object Recognition Techniques

Local surface feature based 3D object recognition is a rapidly growing research field. In time-critical applications such as robotics, training free recognition techniques are always the first choice as they are free from heavy statistical training. This paper presents an experimental analysis of 3D texture-less object recognition techniques that are free from any training. To our best knowledge, this is the first survey that includes experimental evaluation of top-rated training free recognition techniques on the datasets acquired by an RGBD camera. Based on the experimentation, we briefly present a discussion on potential future research directions.
A new intelligent retail container system with a dual neural network model design

In recent years, image recognition technology based on deep learning has become the main solution for intelligent retail containers (IRC). This article introduces a new intelligent retail container system with a dual neural network model. Compared with the previous single-model design, the new one has significantly improved its detection recall and classification accuracy besides reducing greatly the model's retraining time caused by the increasing in new retail varieties. First, using the Faster RCNN model to complete the rough detection of retail categories (classified by outer package) to improve the detection recall; second, using the ResNet50 model to complete the fine classification of retail subcategories (classified by goods variety) to promote classification accuracy. At the same time, a variety of ablation experiments are carried out on the hard samples set of our project by means of several data augments. Some design methods and practical experience proposed in this article can be helpful for the CV (computer vision) incubation projects in the landing stage.
A Model Ensemble Approach for Sound Event Localization and Detection

In this paper, we propose a model ensemble approach for sound event localization and detection (SELD). We adopt several deep neural network (DNN) architectures to perform sound event detection (SED) and direction-of-arrival (DOA) estimation simultaneously. Generally, the DNN architecture consists of three modules stacked together, i.e, a High-level Feature Representation module, a Temporal Context Representation module, and a Fully-connected module in the end. The High-level Feature Representation module usually contains a series of convolutional neural network (CNN) layers to extract useful local features. The Temporal Context Representation module aims to model longer temporal context dependency in the extracted features. There are two parallel branches in the Fully-connected module with one for SED estimation and the other for DOA estimation. With different combinations of implementation in the High-level Feature Representation module and Temporal Context Representation module, several network architectures are used for the SELD task. At last, a more robust prediction of SED and DOA is obtained by model ensemble and post-processing. Tested on the development and evaluation datasets, the proposed approach achieves promising results and ranks the first place in DCASE 2020 task3 challenge. Index Terms: sound event localization and detection, deep neural network, model ensemble
Illegitimate Trade Detection for Electricity Energy Markets

This paper proposes a novel algorithm for detecting trade activities suspected to be illegitimate across day-ahead and real-time energy markets. Illegitimate trades bring severe risks to the financial health of energy markets and the operation stability of power grids. It is of critical importance for the operators to identify such activities in a sound and timely manner. The proposed algorithm firstly generates a set of legitimate trade feature samples using historical trade profiles of energy markets based on the comparison of day-ahead and real-time trade profiles and related environmental conditions. Then a set of illegitimate trade feature samples are created using a Genetic-algorithms based negative selection procedure based on those legitimate trade feature samples. The created illegitimate samples are further labeled with specific illegitimate trade types by comparing with pre-defined typical trade feature samples for each illegitimate trade type. After that, deep learning is employed to learn the relationship between the trade features and associated legitimacy labels from the sets of legitimate and illegitimate trade feature samples, and predict the legitimacy status for incoming trade profiles according to corresponding trade features. The effectiveness of the proposed algorithm has been demonstrated using sample trade profiles obtained from New England ISO.
Improving Robustness and Time Efficiency with Weight Function in Occlusion Face Recognition

The need for occlusion face recognition in daily life, professional fields like criminal investigation and the recent epidemic is increasing, so it is still a hot issue in the current research and many researchers are pursuing the technical excellence for the face recognition of random occlusion. In the previous research, RRC model is proposed for the excellent performance in the occlusion face recognition and the IR3C algorithm is adopted to solve this model. Among them, the weight function in this algorithm plays a big role, which will directly affect the recognition rate and running time of the experiment. In this paper, three weight functions: Inverse trigonometric function, Hyperbolic tangent function and Log function are proposed for the purpose of improving the robustness and time efficiency of IR3C algorithm. Applying these functions on the comparable types and levels of random occlusion and then comparing them with the previous research, the experimental results indicate that the inverse trigonometric function is superior to other functions with better recognition rates and shorter running time.
Mapping complex soil patterns with multiple‐point geostatistics

The commonly used variogram function is incapable of modelling complex spatial patterns associated with repetitive, connected or curvilinear features, because it is a two‐point statistic. Because this was strongly limiting to petroleum‐ and hydrogeologists, they developed multiple‐point geostatistics (MPG), an approach that replaces the variogram by a training image (TI). However, soil scientists also face complex spatial patterns and MPG might be of use to them as well. Therefore, this paper aims to introduce MPG to soil science and demonstrate its potential with a case study of polygonal subsoil patterns caused by Weichselian periglacial frost cracks in Belgium. A high‐resolution proximal soil sensing survey provided a reference image from which a continuous (655 sensor data) and a categorical (100 point observations) dataset were extracted. As a continuous TI, we used the geophysical data of another part of the field, and as categorical TI we used a classified photograph of an ice‐wedge network in Alaska. The resulting MPG maps reconstructed the polygonal patterns very well and corresponded closely to the reference image. Consequently, we identify MPG as a promising technique to map complex soil patterns and suggest that it should be added to the pedometrician's toolbox.