Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted Least-square Method for A Novel 3D Localization Scheme Using 1D AOA Measurements

Traditional Angle of Arrival (AOA) based localization methods require both the azimuth and elevation angle measurements to locate the source in 3D space. For that reason, sensors have to equip different kinds of 2D arrays. However, in some practical situations, the sensors are only allowed to be equipped with linear arrays for its portability or the space limitation. In those cases, traditional Angle of Arrival based localization methods turn to locate a target in 2D plane by using the azimuth angles measured by several linear arrays. In this paper, we propose a novel 3D localization technique using 1D AOA measured from linear arrays. The localization problem is formulated as a weighted least squares (WLS) optimization problem. In order to reduce the computation complexity, pseudo-linear approximation is used to deal with the nonlinearity of the problem. The Cramer-Rao Lower Bound (CRLB) is analyzed to evaluate the performance. To show the robustness, random positions of sensors and directions of linear arrays are considered in this paper. Simulation results show that the proposed method is very close to the CRLB with a certain number of sensors when the measurement noise is small.
Characterization of Four-Wave Mixing in Specialty Silica Fibers at 2 μm Using Continuous-Wave Pumps

We report the characterization of continuous-wave four-wave mixing (FWM) in SM1950 and SMF-28 silica fibers in the <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula> wavelength band. Maximum conversion efficiencies of −21 dB and −25 dB are achived for SM1950 and SMF-28 fibers, respectively. In order to explore multichannel wavelength conversion at <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula>, we characterize the wavelength conversion of a three-tone signal, which is generated by intensity modulation of a laser using a 10 GHz radio frequency (RF) signal. For all the three channels, we achieve a conversion efficiency of −25 dB using a 21.8 dBm pump. Recent demonstrations of efficient splicing of hollow-core fibers, which have been proposed for optical communications at <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula>, with silica-based fibers paves the way for all-optical processing of multi-channel signals such as orthogonal frequency-division multiplexing (OFDM).
CPA: Accurate Cross-Platform Binary Authorship Characterization Using LDA

Binary authorship characterization refers to the process of identifying stylistic characteristics that are related to the author of an anonymous binary code. The aim is to automate the laborious and error-prone reverse engineering task of discovering information related to the author(s) of binary code. This paper presents <monospace>CPA</monospace>, a novel approach for characterizing the authors of program binaries. Instead of using generic features such as n-grams, <italic>CPA</italic> proposes a set of new features based on collections of various aspects of author style, including author code traits, code structure characteristics, and author expertise in solving coding tasks. It employs the Latent Dirichlet Allocation (LDA) algorithm to generate author style signatures to help identify similar author style characteristics in other binaries. We evaluated <monospace>CPA</monospace> on large datasets extracted from selected open-source C/C++ projects in GitHub and Google Code Jam events, and it successfully attributed a large number of authors with a significantly higher <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score: around 91% when the number of authors was 1,500. In addition, the false positive rate was low, around 1.5%. When the code was subjected to refactoring techniques or code transformation or was processed using different compilers/compilation settings, there was no significant drop in accuracy, demonstrating the robustness of our tool. Finally, in the case of code written by multiple authors, <monospace>CPA</monospace> was able to identify the authors with a high <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score, around 89%.
Building Dynamic Mapping with CUPS for Next Generation Automotive Edge Computing

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. There are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud; (ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. The capabilities of our system have been quantified. The experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.
15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices

Nonvolatile computing-in-memory (nvCIM) can improve the latency (tAC) and energy-efficiency (EFMAC) of tiny AI edge devices performing multiply-and-accumulate (MAC) computing after system wake-up. Prior nvCIMs have proven effective for binary input (IN) and weight (W), and 3b output (OUT) [1], 1-8-1b IN-W-OUT [2], and 2-3-4b IN-W-OUT [3] neural networks; however, the higher precision (4-4b IN-W) for MAC operations is needed for multi-bit CNNs to achieved high-inference accuracy [4]. As Fig.15. 4.1 shows, improving the precision of nvCIM macros involves various challenges. (1) A large number of activated WLs provides a wide range of BL current (IBL) resulting in an inaccurate BL-clamping voltage (VBLC); as well as a large (IBL) requiring a large array area due to the need for wide metal lines to support high-current density. (2) Previous “WL = input” approaches suffer from: (a) few parallel inputs (IN#) due to (1), and (b) long (tAC) in multiple cycles of binary WL inputs on 1T1R cells for multibit inputs. (3) Previous positive-negative-split weight-mapping consumes high total (lBL) and area overhead (needing 2x(m-1) cells for a signed m-bit weight) for cell arrays with high-weight precision. (4) Long (tAC) and a large number of reference currents (IREF#) for high-precision outputs. To overcome these challenges, this work proposes: (1) a BL-IN-OUT multibit computing (BLIOMC) scheme using a single WL-on and input-aware multibit BL clamping (IA-MBC) to shorten (lBL) for multibit inputs, increase IN#, and reduce the (lBL) range/size for accurate (VBLC) and a compact array area. (2) Scrambled 2's complement (S2C) weight mapping (S2CWM), input-aware source-line (SL) voltage biasing (IA-SLVB), and an S2C value combiner (S2CVC) to reduce area overhead and lBL in the cell array. (3) A dual-bit small-offset current-mode sense amplifier (DbSO-CSA) to reduce IREF# and tAC. A fabricated 22nm 2Mb ReRAM-CIM macro presents the first 4b-input nvCIM macro, featuring a 9.8-18.3ns tAC and an EFMAC of 121.3-28.9TOPS/W from binary to 4bIN-4bW-11bOUT compute precisions.
Real Time Detection of Roadside Speed Breakers and Obstacles upto Knee-level

Navigation Guide refers to frameworks that helps people with vision misfortune to navigate safely. This paper proposes such a navigation guide that would assist visually impaired people in their navigation. The proposed device detects speed breakers on the street and other obstacles at floor level and knee level around individuals. The system includes six infrared sensors, Arduino Uno, power supply, three motors for vibration and a earphone. The framework's overall accuracy is 91% which reflects its acceptability by its domain of users. Experimental study shows the effectiveness of the proposed method with respect to existing methods.
10.6 A 4G/5G Cellular Transmitter in 12nm FinFET with Harmonic Rejection

5G New Radio (NR) technology provides much higher data rate and system capacity compared to 4G LTE. This is achieved, among other techniques, by increasing the maximum channel bandwidth per component carrier (CC) from 20MHz in LTE to 100 MHz in NR and by introducing new frequency bands in the range of 3.3 to 5GHz [1]. A 4G/5G multimode multiband cellular transceiver must support this wide bandwidth and wide tuning range while maintaining excellent EVM for high-order modulation and high linearity and low noise for challenging LTE scenarios.
An Improved Indoor Localization Algorithm with Neighbour Fusion

Accurate reading of a mobile in an indoor environment is very important in effectively providing a wide range of location based services (LBS). Thus, an improved localized position fine-tuning technique is proposed in view of a more accurate overall localization. The key idea behind the proposed algorithm is the improvement to the position fine-tuning step where it uses both time and space correlation among multiple raw position estimates. This technique allows obtaining higher levels of accuracies in localization without additional hardware. Simulation results verify the accuracy improvements associated with the proposed improved fine-tuning technique.
Research on Architecture of Typical Accident Prevention and Control and Early Warning System for High Temperature Molten Metal Operation

In view of the present typical high temperature molten metal homework frequent accidents, the lack of scientific and reasonable accident prevention and control and early warning technology, this article through to the related to cloud computing technology and the Internet of things technology were analyzed, and put forward a scheme based on paas cloud platform and industrial-grade sensor of the high temperature molten metal construction operation of typical accident prevention and control and warning system. The scheme with high temperature molten metal work related laws, specifications and standards as the basis, using the Bow-Tie analysis method to build up high temperature molten metal work typical accident prevention and control system, through the paas cloud platform to achieve the prevention and control system of information. Users can conduct typical accident risk cognition, static and dynamic enterprise risk assessment, job training through the system. The system has realized the parameters of the high temperature molten metal work accident early warning through the paas platform open API interface connection within the enterprise related sensors. The construction of the system provides a scientific and reasonable tool for enterprises to prevent the occurrence of accidents and reduce the loss of accidents.
A Novel Visualization Malware Detection Method based on Spp-Net

In recent years, the threat of malware in Internet is extremely serious, malware detection becomes one of the most challenging task in cyberspace. In this paper, we propose a novel malware detection method using code image and nerual network. We enhanced traditional gray code image with visible string and PE file structure to a RGB image, and then we put the image into the top 13 layer of VGG16, finally we use Spp-Net to adapt the feature size. In order to make label reliable, we use various Virustotal result to get a voting result, and using the algorithm to train and predict the malware. We test the proposed method in real malware datasets, experimental results show that our method has a better performance.
A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

Multivariate Time Series Forecasting (MTSF) has recently emerged its growing importance in many industries. However, how to reduce the influence of the noise components existing in time series on prediction and extract features effectively are still two challenges in MTSF. This paper focuses on those two challenges and proposes a new prediction method based on decomposition-ensemble framework called adaptive sub-series clustering-stacked residual LSTMs-multi-level attention mechanism (ASC-SRLSTMs-MLAttn). The method consists of three stages: decomposition, prediction, and ensemble. In the decomposition stage, the target series is decomposed by Ensemble Empirical Mode Decomposition into multiple sub-series, which will be clustered and reconstructed by the ASC algorithm to reduce the complexity and the time consumption of prediction. In the prediction stage, the sub-series and correlation series will be fed into SRLSTMs-MLAttn for sub-series prediction. The model is based on the encoder-decoder architecture with stacked residual LSTMs as the encoder, which can effectively capture the dependencies among multi variables and the temporal features from multivariate time series. Besides, a multi-level attention mechanism (MLAttn), which makes full use of the encoding information of the encoder, has been introduced to further improve the prediction performance of the model. In the ensemble stage, the predicted values of each sub-series will be summed to obtain the final prediction of the target time series. We also demonstrate the superiority and effectiveness of our proposed method on four public datasets via the conducted comparison experiment and ablation study.
Exploring Computational Inference of Microbial Interactions and their Dynamics

Next Generation Sequencing has increased detailed studies of microbial communities. Understanding microbial interactions is crucial for understanding these communities. Microbial Interaction Networks (MINs) have been inferred using microbial abundance profiles, using multiple methods and frameworks. In this paper we explore current approaches in inferring microbial interactions and demonstrate that they are dynamic in nature, and propose a visualisation approach for observing dynamic nature of MINs. Our work highlights that even very rare microbiota have bursts of high abundance at some time points. Further, we suggest possible improvements in the field of computational inference of MINs.
Design of Low Transconductance Voltage to Current Converter in CMOS

For biomedical applications low power and low transconductance linear transconductor is required. In this paper a low transconductance and high gain transconductance is designed based on based on gate driven input stage for basic Miller OTA. The circuit is then modified to achieve very low transconductance using series parallel PMOS differential current pair technique. The circuits are designed in gpdk 180nm CMOS technology.
Performance Optimization of NOMA Based SWIPT Protocol for Full-Duplex Massive MIMO Systems

This paper studies the performance optimization of hybrid simultaneous wireless information and power transfer (SWIPT) protocols for full-duplex (FD) massive multiple-input multiple-output (MIMO) systems. A wireless energy transmission method combining time-switching (TS) and power-splitting (PS) is proposed to enable the sensor to receive more energy from the base station (BS). Non-orthogonal multiple access (NOMA) is applied during the information transfer phase in the case of relatively concentrated sensor or user distribution. System performance is maximized by optimizing the time switching (TS) ratio and precoding matrix. Simulation results show that the proposed optimized NOMA based SWIPT protocol obtains the best system performance when compared with the existing transmission protocols.
3.6 A CMOS Resistor-Based Temperature Sensor with a 10fJ·K2 Resolution FoM and 0.4°C (30) Inaccuracy From −55°C to 125°C After a 1-point Trim

Energy efficiency and accuracy are important specifications of CMOS temperature sensors. BJT -based sensors achieve state-of-the-art accuracy [1], while Wheatstone-bridge (WhB) sensors achieve lower accuracy but state-of-the-art energy efficiency [2], [3]. This paper presents a WhB sensor that is read out by an energy-efficient continuous-time delta-sigma modulator (CTDSM). Compared to [2], [3], the modulator achieves better energy efficiency with the help of a return-to-CM (RCM) DAC and an OTA with a tail-resistor linearization scheme. Moreover, better accuracy is achieved by embedding the DAC in the bridge and by using more sensitive silicided-diffusion resistors instead of silicided-poly resistors. Compared to the state-of-the-art [3], the proposed sensor achieves a 2× improvement in resolution FoM (10fJ.K2), and a 2× improvement in inaccuracy (0.4° C(3σ) from −55° C to 125°C after a 1-point trim).
Redesigning The Interaction of Twitter's Content Preferences Feature Using User-Centered Design Approach

Social media has become an important part in the way of people communicate. One of the most popular social media in Indonesia is Twitter. Twitter users can send short messages called tweets to a set of other users who receive their messages called followers. Twitter users can also manage their tweets preferences using content preferences feature to set what content they want to see on their timeline. After doing a research using questionnaire method, some problems which are related to content preferences were found. One of the problems that users find using the feature is that they often see tweets they are not interested in seeing such as negative tweets that contain sensitive topic and spam. Twitter has mute feature to anticipate this problem by hiding tweets that contain specific keyword and sent by specific user. But user research shows that the mute feature is not effective enough to hide tweets that do not match to user's preferences. In this paper, we answer and fix those problems by redesigning Twitter's content preferences feature using user-centered design approach in ISO 9241-210:2010. The outcome of this whole research is to create high-fidelity prototype that has some usability and user experience goals and later be validated using usability testing.
Localization Methods for Internet of Things: Current and Future Trends

Internet of things (IoT) is a network which consists of billions of small sensors which communicate with each other to obtain solutions for environmental problems. The location information is very important to know where the data come from. This paper introduces and analyzes different categories of localization in IoT from different views. The advantages and disadvantages of each technique and which application it is suitable for will be demonstrated.
Channel Polarization Based Block Compressive Sensing SoftCast System

The existing image or video transmission system of joint blocked compressive sensing and SoftCast (BCS-SoftCast) has an advantage in energy or bandwidth, but the peak signal to noise ratio (PSNR) is still poor in the case of low signal to noise ratio (SNR). We find that the indexes of the non-zero data, which correspond to the columns of the measurement matrix, have an impact on system performance. In this paper, by using the idea of channel polarization, we propose two column selection methods. For the first method, namely polarization column selection (PCS), the columns of measurement matrix are selected based on the column reliability and non-repetition. In order to further improve the performance in low SNR, the second method, namely hybrid column selection (HCS), partially modifies the first method. That is to say, some low reliability columns selected in the first method are replaced by the columns with the high weight. The simulation results show that both methods can improve the PSNR performance of BCS-SoftCast. Especially, in the low-SNR region, the PSNR performance with the HCS construction dramatically outperforms that with the conventional method.
18.7 A DC to 35MHz Fully Integrated Single-Power-Supply Isolation Amplifier for Current- and Voltage-Sensing Front-Ends of Power Electronics

Increased switching frequency of power devices for power electronics allows for compact and lightweight circuit implementations for variety of applications, including power factor corrections, DC/DC converters, and motor drive inverters. In particular, recent advances in GaN/SiC devices have pushed the switching frequency into the MHz range, which requires soft switching operation to minimize switching losses caused by the increased frequency. To achieve accurate soft switching operation in the MHz range, wideband isolated current and/or voltage sensing with bandwidth of at least 10MHz is mandatory, but few existing solutions have such a wide bandwidth and the ones that do are expensive.
Virtual Network Embedding Based on Hybrid Adaptive Genetic Algorithm

Virtual network embedding (VNE) is one of the central parts of network virtualization, and its main objective is to maximize the revenue of substrate networks(SN). In the previous studies, heuristic algorithms are widely used to solve the embedding problems, but they are liable to trap in local optimum. In addition, they also have slow convergence rates which are caused by ignoring the intrinsic characteristics of the VNE. In this paper, on the first hand, we improve the cross operator of traditional genetic algorithm (GA) to an adaptive cross operator, in order to increase the convergence speed; on the other hand, a simulated annealing algorithm is used to replace the mutation operation, and the simulated annealing idea is adopted to modify the selection operation to avoid the local optimization. The simulation results show that the proposed algorithm has a significant improvement in the VNE acceptance rate and the revenue-to-cost ratio.
Toward Developing Fog Decision Making on the Transmission Rate of Various IoT Devices Based on Reinforcement Learning

In recent years, the focus on reducing the delay and the cost of transferring data to the cloud has led to data processing near end devices. Therefore, fog computing has emerged as a powerful complement to the cloud to handle the large data volume belonging to the Internet of Things (IoT) and the requirements of communications. Over time, because of the increasing number of IoT devices, managing them by a fog node has become more complicated. The problem addressed in this study is the transmission rate of various IoT devices to a fog node in order to prevent delays in emergency cases. We formulate the decision making problem of a fog node by using a reinforcement learning approach in a smart city as an example of a smart environment and then develop a Qlearning algorithm to achieve efficient decisions for IoT transmission rates to the fog node. Although to the best of our knowledge, thus far, there has been no research with this objective, in this study two more approaches, random-based and greedy-based, are simulated to show that our method performs considerably better (over 99.8 percent) than these algorithms.