Miracle-dz/UniFAR-FTUs huggingface dataset:
A 180o Phase Shift Biasing Technique for Realizing High PSRR in Low Power Temperature Sensors

The paper presents a low-cost method (no extra LDO, Op-amp) to increase the PSRR of composite pair based temperature sensor. Composite pair is a commonly used circuit to generate a linear PTAT voltage for a wide temperature range. A 180° phase shift, voltage biasing technique is introduced at the gate of the NMOS transistor to obtain a supply independent bias current in composite pair. The proposed technique thus achieves a high supply noise rejection. The circuit as a whole does not require additional circuitry, therefore it saves power and area. A 6$\mu$W Temperature sensor with 1mV/°c slope is designed in TSMC 180nm technology and achieves PSRR of -80dB. Post-layout simulation results show that the temperature sensor achieves inaccuracy of ±0.1°C from −55°C to 125°C, PSRR of −80dB at 105Hz and -70dB at 200kHz at 1V power supply, and the line regulation of 0.016%/V in a supply voltage range of 1V to 2.5V. Monte-Carlo simulation result for the output PSRR shows −80.56dB as the mean-value with a maximum deviation of ±1.5dB.
Performance Analysis of Body-Worn Antenna for Wearable Textile Substrates

This paper proposes the effect of closeness of the human body phantom model to the textile antenna (TA) where fabric materials (wash cotton, curtain cotton, and polycot) have been utilized as substrates. The antennas are designed to operate at 2.45 GHz frequency for on body wireless communication. Here, circular shape TA with curtain cotton provided return loss (−52.43 dB), bandwidth (107.4 MHz at frequency range 2.3982 to 2.5056 GHz), peak directivity 8.83 dBi with a peak gain of 7.11 dBi when body phantom is applied at 2 mm distance from the TA at 2.452 GHz and the radiation efficiency recorded is 68.07%. The overall antenna performance of each antenna without body phantom is satisfactory for the desired application. The specific absorption rate (SAR) for CMPA (curtain cotton) is 0.029 W/kg< 2 W/kg averaged over 10 g tissue.
Data classification algorithm based on Information Granules included by AFS*

Data classification problem in the field of machine learning and pattern recognition is an important research content, but the classic data classification algorithms mainly use numeric as the basis of classification modeling or put the high accuracy as the only index of classification model, and these classification algorithms usually are unable to construct classification model for easy understanding. As a basic abstract structure supporting human-centered granular computing methods, information granules can construct easy-to-understand geometric structures for different types of data. In order to solve the above problems, this paper proposes a classification algorithm based on AFS(axiomatic fuzzy set) and information granules to enhance the interpretability of models so that people can better understand the relationship between different types of data. The model can be divided into four stages. In the first stage, each class of data is divided into different data subsets. In the second stage, the AFS membership degrees of the corresponding prototypes are calculated based on axiomatic fuzzy set theory and a series of data blocks are generated on each class based on these degrees. The third stage mainly generates supersphere information granules according to the reasonable granularity principle theory and confidence levels. In the final stage, the activation level is calculated by the distance between samples and the hypersphere information granules and confidence levels to determine the "If-Then" rules.
Unsafe Maneuver Classification From Dashcam Video and GPS/IMU Sensors Using Spatio-Temporal Attention Selector

In this paper, we propose a novel deep learning architecture to classify unsafe driving maneuvers from dashcam and IMU data. Such architecture processes the output of an object detection algorithm in combination with raw video frames and GPS/IMU data. At the core of the architecture there is a novel Spatio-Temporal Attention Selector (STAS) module, which (1) extracts features describing the evolution of each object in the scene over time and (2) leverages multi-head dot product attention to select the relevant ones, i.e., the dangerous ones or the ones in danger, to perform classification. We also introduce a simple but effective methodology to increase the benefit of fine-tuning the backbone network. Our method is shown to achieve higher performance than other approaches in the literature applying attention over single frames.
Entrepreneurial Orientation on Clinical Development in Pharmaceutical Industry: Fixed-Model Analysis

Success in pharmaceutical clinical development requires organizational management capabilities. “Entrepreneurial Orientation (EO)” is a strategic construct that reflects the extent to which firms are innovative, proactive, and risk taking in their behavior and management philosophies. This study elucidates how EO influences clinical development in pharmaceutical firms. Panel-data analysis was conducted using a fixed-effect model. The panel data consists of 29 global pharmaceutical firms focusing on novel drug development for 14 years from 2004 to 2017. Accumulative numbers of novel drug approval in the United States are set as a dependent variable. Independent variables are EO components, stock beta value, and annual turnover. The EO consists of three entrepreneurial factors of innovativeness (R&D expense per sales), proactiveness (R&D expense per EBITDA), and risk taking (Stock volatility). The study results indicate that innovativeness and risk taking showed a significant positive association with an increase in the cumulative number of new product approvals. Risk taking also showed positive association both when excluding bio-tech firms and in biotech firms, but proactiveness was negative only when excluding bio-tech firms. As a result, the study showed that EO impacts novel drug approval in the pharmaceutical industry. Traditional large firm should take risks in separate small organization.
High precision detection of small hepatocellular carcinoma using improved EfficientNet with Self-Attention

Small hepatocellular carcinoma (SHCC) is among the most fatal cancers, and spotting SHCC symptoms in the early stage is vital for conducting timely treatments. Thus, auxiliary detection algorithms have been developed, especially after convolutional neural networks (CNN) made great progress in processing medical images. However, their performance is confined by dataset, resulting in limitations to accurately detect SHCC appearing small and diffusive in CT images. In our work, Self-Attention mechanism has been introduced as the front end and EfficientNet as our backbone network, contributing to a novel SHCC detection algorithm able to automatically spot subtle lesions through our image-wise annotated dataset in a weakly supervised manner. In our model, the Self-Attention module extracts ROI and background features from original CT images and generates weighed feature map to the EfficientNet. In our backbone network, the EfficientNet learns from input feature maps and is weakly supervised under image-wise annotations. With the pre-process of Self-Attention, our data size for EfficientNet has been reduced, thus enhancing learning efficiency and reducing time consumption. After training on over 1.5k CT images, our model has achieved decent detection performance comparing to other state-of-the-art methods while remaining acceptable complexity.
Patenting Strategies of Domestic and Foreign Players in the Indian Machine Tool Industry: A Comparative Study Using Multidimensional Scaling Approach

Capital goods industry, termed mother industry, has been garnering the attention of developing economies for several decades. Given the strategic importance of the machine tool industry in the manufacturing sector, the Indian Government has been supporting the industry with policy impetus founded on the self-reliance principle to close the import-export gap. The present study analyzes the trends in the machine tools imports and also the patents filed in India. Comprehensive research studies to explore various facets of macro-micro linkage at national and firm levels have been warranted to augment technology foresight exercises. We propose to investigate patenting strategies of foreign firms in the domestic market. The study involves a review of 1180 patents on CNC metal cutting machine tools filed in India. The analysis of patents-related data using the multidimensional scaling (MDS) approach provides insights into the R&D capabilities and technology positioning of the machine tool firms – both domestic and foreign. The MDS utilizes three patent indicators, namely, priority year, IPC classification codes and inventor count. CNC metal cutting machine tool segment has been selected for exemplification of the multivariate approach adopted for the study.
Deep Learning for Network Slicing and Self-Healing in 5G Systems

The 5th generation cellular network works on a user-centric methodology rather than operator-centric as in 3G or service-centric as seen for 4G. Efficient allocation of resources is possible in 5G networks, something which was not feasible in the previous network generations. The network was equally allocated to all users, whereas it should be allocated depending on the usage. A user playing AR/VR games should be given more bandwidth than a user who is just sending text messages. The main objective of this paper is to develop a solution that can manage the network slice for incoming network requests from unidentified devices. We have compared various machine learning algorithms based on their accuracy to predict the network slice. Also, a Hybrid CNN-LSTM Deep Learning model is proposed for understanding user usage patterns and time series based forecasting of slice utilization, active users, resource usage and workload. The Concept of Self-Healing Networks for better Quality of Experience (QoE) and fault detection, anomaly detection diagnosis is discussed.
Forestry Crane Automation using Learning-based Visual Grasping Point Prediction

This paper presents an approach to automate the log-grasping of a forestry crane. A common hydraulic actuated log-crane is converted into a robotic device by retrofitting it with various sensors yielding perception of internal and environmental states. The approach uses a learning-based visual grasp detection. Once a suitable grasping candidate is determined, the crane starts its kinematic controlled operation. The system’s design process is based on a real-sim-real transfer to avoid possibly harmful, to humans and itself, crane behavior. Firstly, the grasping position prediction network is trained with real-world images. Secondly, an accurate simulation model of the crane, including photo-realistic synthetic images, is established. Note that in simulation, the prediction network trained on real-world data can be used without re-training. The simulation is used to design and verify the crane’s control- and the path planning scheme. In this stage, potentially dangerous maneuvers or insufficient quality of sensory information become visible. Thirdly, the elaborated closed-loop system configuration is transferred to the real-world forestry crane. The pick and place capabilities are verified in simulation as well as experimentally. A comparison shows that simulation and real-world scenarios perform equally well, validating the proposed real-sim-real design procedure.1
ACCEPTED FROM OPEN CALL

successful partnership to enhance network issues such as energy consumption, resilience, and dependable applications. Furthermore, there could be no Internet of Drones (IoD) without reliable and efficient drone coordination in the airspace. This necessity makes IoD an architecture responsible for managing drone communication and mobility. The airspace organization is crucial in combining IoD and Terrestrial Networks in a smart city scenario, as intelligent drone positioning can promote and facilitate integration with networks on the ground. Given these challenges, this work discusses airspace organization, describes possible partnership ways between IoD and TNs
Tracking Coverage Artefacts for Periodic Signals using Sequence-based Abstractions

Periodic signals and recurrent events are very common in Analog and Mixed-Signal (AMS) behaviours. Tracking attributes related to frequency and recurrent behaviours is an integral part of the verification coverage plan for AMS designs. It is straight-forward to validate periodic artefacts in a signal, such as frequency or duty cycle, when a reference value is provided. From a CAD perspective, the difficulty arises when a reference is not provided, and more so when a static reference does not exist. In this paper, we propose a novel methodology to compute various artefacts of a periodic signal algorithmically in absence of prior information regarding the periodic nature of the signal. We propose to encode the signal using a window-based partitioned sequence of literals and then operate over this sequence to glean out the relevant periodic artefacts. Our experiments performed on waveforms collected from industrial test cases and medical benchmarks demonstrate the efficacy of our proposal.
The Mechanism and Path of the Cross-Market Innovation for Catching-Up Firms: A Case Study on New Chinese Electric Vehicle OEMs

Mainstream technology catching-up literature only focuses on how latecomers to assimilate the acquired technology from the markets of the developed countries, which emphasize the latecomers’ innovation activities only focusing on their domestic markets, paying little attention to their R&D and innovation activities in the markets of the developed countries, and to reverse back to markets of their home countries. This study discovers the mechanism and path of cross-market innovation based on multiple case analysis on the Chinese new electric vehicle (EV) OEMs. These new EV players directly go to the USA to innovate on top of acquiring new technologies and overcoming the latecomer disadvantage, quickly to become technologically leading position in some new technology innovations, such as drive motor control units, battery management systems, autonomous drive, connected vehicle, big data analytics, V2X and share based business model, in the EV space. The phenomenon of innovative paths by these EV OEMs provide the base to explore the underlined innovation model for latecomers, Cross-Market Innovation (CMI), to catch up and even to lead in both home country and developed country.
Ellipsoid-Like Herbaceous Tibetan Medicinal Materials Recognition Using DenseNet with Attention and LBP-Encoded Gabor Features

Tibetan medicine materials play an important role in Tibetan culture. Some types of Tibetan medicinal materials are similar in shape and color, but have completely different medicinal properties and functions. Wrong use of such medicinal materials may produce poisonousness, delay treatment, and bring incalculable consequences to patients. Existing recognition methods of medicinal materials with similar shapes and colors mainly rely on manual appraisal through observation, touch, taste, and smell. In this paper, we propose a deep learning based method for recognizing ellipsoid-like herbaceous Tibetan medicinal materials. We establish an image dataset of ellipsoid-like herbaceous Tibetan medicinal materials, which contains 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. These medicinal materials are highly similar in shape and color with each other. To cope with the complex background and highly similar shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we use DenseNet with Attention and LBP-Encoded Gabor Features to recognize the ellipsoid-like herbaceous Tibetan medicinal materials images. The experimental results show that our method can achieve 92.38% accuracy for the 18 types of Ellipsoid-like herbaceous Tibetan medicinal materials and 90.13% accuracy for two highly similar medicinal materials.
Interaction-based Caching in Content-Centric Networking

CCN improves the hit performance, the content retrieval latency, and the bandwidth consumption of the network by increasing the availability of contents by in-network caching of them. To mitigate the resulting content redundancy issue, besides content replacement policies, deterministic and probabilistic caching strategies are developed to temporarily and spatially limit content replication. However, the entangling of content-related characteristics with topology-related specifics to jointly optimize caching requires further research. Inspired by Quantum Field Theory, we present a caching theory that interprets the interplay between the entities involved in caching as interaction. In the Interaction-based Caching (IC), clients of contents play the role of sources of gravitational fields, which couple to the demanded contents through intermediary force-carrying request quanta. The gravity on contents is then transformed to a graph-theoretical metric that enables the unification of the properties and states of the interacting entities with the network topology in coupling terms that describe the interactions. The multidimensional matrix structure of the coupling terms allows the differentiation between individual clients and contents, making the conduct of the kinematics of contents and requests in the network possible. This control gives rise to various policies for network management and prioritization of content clients and content producers. As a heterogeneous, implicitly cooperative, on-path as well as off-path caching strategy, the IC significantly increases the caching performance, especially concerning cache hit ratio, path length contraction, content retrieval latency, resource utilization, and service portfolio. We evaluated the performance of the IC model in a series of scenarios based on multiple simulation models with different parameterizations.
The Effect on the Innovation Performance of ICT Venture Companies by the Types of Government Support Focusing on Funding and R&D Support

Today, ICT venture companies play a key role in the national economy, but they are having a lot of difficulties in their growth due to rapid changes in external environments and limitations in internal resources. To solve this problem, the government is pursuing various support policies, but its effectiveness is still being questioned. Therefore, this study aims to examine the effect of each type of government support on the innovation performance of ICT venture companies.For the empirical analysis, the ICT venture panel survey data provided by Korea Information Society Development Institute was used, and the innovation performance of ICT venture companies was measured by the number of patent applications. The reason is that, above all else, it is important to continuously create the innovation performance in order to secure the competitiveness of ICT venture companies. In addition, the types of government support were analyzed by dividing them into funding and R&D support. Finally, the propensity score matching was applied to solve the problem of selection bias and to determine the pure effect of government support.As a result of the analysis, it was found that funding and R&D support had a positive effect on the innovation performance of ICT venture companies. This suggests that the necessary support for ICT venture companies should be provided first, rather than promoting various support policies. The results of this study can be used as basic data when implementing government support policies.
Hardware-aware Partitioning of Convolutional Neural Network Inference for Embedded AI Applications

Embedded image processing applications like multicamera-based object detection or semantic segmentation are often based on Convolutional Neural Networks (CNNs) to provide precise and reliable results. The deployment of CNNs in embedded systems, however, imposes additional constraints such as latency restrictions and limited energy consumption in the sensor platform. These requirements have to be considered during hardware/software co-design of embedded Artifical Intelligence (AI) applications. In addition, the transmission of uncompressed image data from the sensor to a central edge node requires large bandwidth on the link, which must also be taken into account during the design phase.Therefore, we present a simulation toolchain for fast evaluation of hardware-aware CNN partitioning for embedded AI applications. This approach explores an efficient workload distribution between sensor nodes and a central edge node. Neither processing all layers close to the sensor nor transmitting all uncompressed raw data to the edge node is an optimal solution for each use case. Hence, our proposed simulation toolchain evaluates power and performance metrics for each reasonable partitioning point in a CNN. In contrast to the state of the art, our approach does not only consider the neural network architecture. In the evaluation, our simulation toolchain additionally takes into account hardware components such as special accelerators and memories that are implemented in the sensor node.Exemplary, we show the simulation results for three commonly used CNNs in embedded systems. Thereby, we identify advantageous partitioning points regarding inference latency and energy consumption. With the support of the toolchain, we are able to identify three beneficial partitioning points for FCN ResNet-50 and two for GoogLeNet as well as for SqueezeNet V1.1.
Exploring Patterns of Academic-Industrial Collaboration for Digital Transformation Research: A Bibliometric-Enhanced Topic Modeling Method

Interactions between industry and academia provide inspiration for knowledge fusion, and more importantly serve as a stimulus for both basic and applied research, creating impact and potential opportunities. From the perspective of text mining and co-authorship network analysis, this paper aims to explore the patterns of academic-industrial collaboration using publication data. We propose a bibliometric-enhanced topic modeling method to profile the core constituents of industry and academia collaborative hotspots in digital transformation research, using a 10-year publication dataset from 2009 to 2018 extracted from Web of Science. We then examine interactions and distinctions between topics authored by only academic researchers and having industrial collaboration to further develop a comprehensive understanding of the content and driving force of industrial engagement. The empirical insights of this paper provide a detailed picture of academic-industrial linkages, which potentially can be used to lead academics to engage with industry, and assist innovation management and problem-solving in digital transformation research and practice.
A Compact Broadband Power Amplifier Covering 23-39 GHz for 5G Mobile Communication

This paper presents a two-stage broadband power amplifier (PA) for fifth-generation (5G) mobile communication. For simultaneous broadband performance in small-signal gain, saturated output power $(\mathrm{P}_{\text{sat}})$ and 1-dB compression output power $(P_{\text{laB}})$, a leakage inductor and a shunt capacitor are added in output matching network and a weakly-coupled transformer is adopted in inter-stage matching network. Implemented in 65 nm CMOS process, this PA achieves peak gain of 18. dB with 3-dB small-signal gain bandwidth from 23 to 39GHz(51.6%), 1-dB $P_{\text{sat}}$ bandwidth from 23.9 to 38.1 GHz(45.8%) and I-dB