Miracle-dz/UniFAR-FTUs huggingface dataset:
An Inductor-Less Bipolar-MOSFET (BiFET) Dual Band Low-Noise Amplifier (LNA) with Power Gain Peaking

This This paper proposes SiGe heterojunction Bipolar cascoded with a mosFET(BiFET) LNA with no inductor, which is capable of concurrently operating at 60 GHz and 120 GHz bands. The LNA utilizes a simple 03-transmission lines TLs impedance transformer to match input impedance $\mathbf{Z}_{\mathbf{I}\mathbf{N}}$ to the optimum noise resistance $\mathbf{R}_{\mathbf{ORT}}$ for minimum noise and $\mathbf{Z}_{\mathbf{OUT}}$ to $\pmb{ 50\Omega}$ at the output for power matching at both the bands. In the LNA circuit SiGe HBT $(\mathbf{Q}_{\pmb{1}})$ is used in the 1st stage and MOSFET $(\mathbf{M}_{\pmb{1}})$ in the 2ndstage to utilize best of the SiGe HBT (i.e. low noise) and MOSFET (i.e. high linearity) with inter-stage transmission line TL3 to compensate for signal loss at high frequency (More specifically @120GHz). The transmission line used at the gate $\mathbf{TL}_{\pmb{4}}$ of $\mathbf{M}_{\pmb{1}}$ in the proposed LNA is used to achieve power gain peaking. By representing $\mathbf{TL}_{\pmb{3}}$ and $\mathbf{TL}_{\pmb{4}}$ with corresponding lumped inductors $\mathbf{L}_{\pmb{3}}$ and $\mathbf{L}_{\pmb{4}}$, respectively in small-signal model of the LNA, signal loss due to parasitic capacitance $\mathbf{C}_{\mathbf{P}}$ at the collector node of $\mathbf{Q}_{\pmb{1}}$ at high frequency is analytically analyzed. Theoretically, predicted S-parameters of the LNA using small-signal model were found to overestimate the simulation results due to its simplified formulation. The Keysight ADS EM co-simulated transfer gain $\mathbf{S}_{\pmb{21}}=\pmb{18.6}\mathbf{dB}$ @60GHz and 18.2dB @120GHz, respectively has been realized. The proposed LNA provides 3-dB bandwidth of $\pmb{> 6.0}\mathbf{GHz}$ at both the bands and $\mathbf{NF}=\pmb{5.5}\mathbf{dB}$ and 8.6 dB @60GHz and 120GHz, respectively with input third-order intercept $\mathbf{IIP}_{\pmb{3}} > +\pmb{4.0}\mathbf{dBm}$ at both the bands and consume dc power of 8.64mW. The proposed LNA layout occupies a chip area of $\pmb{0.6}\mathbf{mm}^{\pmb{2}}$. A state-of-art new Figure-of-Merit (FoM) accounting chip area as well as involving key performances like $\mathbf{S}_{\pmb{21}}$, noise factor F, $\mathbf{IIP}_{\pmb{3}}$ and $\mathbf{P}_{\mathbf{DC}}$ was found to be significantly higher for the proposed SiGe BiCMOS LNA than previously reported experimental results of LNA using the same technology.
Investigation of CRAH Bypass for Air-Cooled Data Centers using Computational Fluid Dynamics

Computer room air handling (CRAH) bypass (BP) method utilizes fan-equipped perforated tiles to force or induce room air through relatively lower flow resistance than that in CRAH units, as CRAH fans operate at lower speed. Increasing the BP airflow rate reduces the total fan power and increases the chiller power since the CRAH unit needs to provide colder air when a certain fraction of warm room air is allowed to mix with cold CRAH airflow. Simplified airflow network and thermodynamic modeling tools addressed this optimization problem and indicated cooling energy savings potential via CRAH BP method for enclosed aisle (EA) data centers. These tools assume uniform air temperature at the server inlets, even though recent experimental and computational studies indicate temperature non-uniformities at higher fractions of BP flow. This study addresses energy implications of the uniform temperature assumption of the reduced order modeling tools on the cooling energy saving potential of EA data centers by using computational fluid dynamics (CFD) modeling. Moreover, existing literature does not thoroughly investigate the application of CRAH BP in the open aisle (OA) data centers, which requires CFD models to resolve complex airflow patterns due to recirculation of hot air into the cold aisle and leakage of cold air into the hot aisle. This study utilizes CFD to investigate both induced and forced CRAH BP methods in a representative quadrant of a 1MW data center with and without aisle containment.
Identification of Lantana Camara Distribution Using Convolutional Neural Networks

Lantana camara is an exotic invasive plant that has been a major threat to the biodiversity of several areas around the world. This paper presents a novel methodology to identify the distribution of Lantana camara flowers in aerial images. The proposed model uses aerial images as inputs and the model consist of three stages. The first step is the detection of possible flower patches in the aerial images using Local Binary Patterns mechanism. The second step is the recognition of Lantana camara flowers from the localized flower patches through a classification process using a Convolutional Neural Network (CNN). The third step is the marking the presence of the flowers of Lantana camara in the original image. Achieved sensitivity rate of this study is 40.71%. The proposed model succeeded in identifying Lantana camara distribution by identifying the presence of Lantana camara flowers in aerial images.
Constellation Learning-Based Signal Detection for Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.
Finite-Cardinality Single-RF Differential Space-Time Modulation for Improving the Diversity-Throughput Tradeoff

The matrix-based differential encoding invoked by Differential Space-Time Modulation (DSTM) typically results in an infinite-cardinality of arbitrary signals, despite the fact that the transmit antennas (TAs) can only radiate a limited number of patterns. As a remedy, the recently developed differential spatial modulation (DSM) is capable of avoiding this problem by conceiving a beneficial sparse signal matrix design, which also facilitates low-complexity single-RF signal transmission. Inspired by this development, the differential space-time block code using index shift keying (DSTBC-ISK) further introduces a beneficial diversity gain without compromising the DSM’s appealingly low transceiver complexity. However, the DSTBC-ISK’s performance advantage tends to diminish as the throughput increases, especially when an increased number of Receive Antennas (RAs) is used. By contrast, the classic Differential Group Code (DGC) that actively maximizes its diversity gain for different multiple-input-multiple-output (MIMO) system setups is capable of achieving a superior performance, but its detection complexity grows exponentially with the throughput. Against this background, we propose the differential space-time shift keying using Diagonal Algebraic Space-Time scheme, which is the first DSTM that is capable of achieving the DGC’s superior diversity gain at high throughputs without compromising the DSM’s low transceiver complexity. As a further advance, we also conceive a new differential space-time shift keying using Threaded Algebraic Space-Time arrangement, which is capable of achieving an even further improved diversity gain at a substantially reduced signal detection complexity compared to the best DGCs. Furthermore, in order to strike a practical tradeoff, we develop a generic multi-element and multi-level-ring Amplitude Phase Shift Keying design, and we also arrange for multiple reduced-size DSTM sub-blocks to be transmitted in a permuted manner, which exhibits an improved diversity-throughput tradeoff.
The Influence of Peak Current and ECR on the Transmission Performance of High Power Connectors during Faults

Electrical connectors are widely used in industrial environments and for renewable energies. For these purposes, their respective short circuit current carrying capability has to be verified. It is commonly suggested that the thermal stress for contact systems with contact elements is more critical during AC faults than during DC faults because of higher current peaks. Recent findings show the opposite behavior depending on peak current and ECR (electrical contact resistance). This suggests that a deeper look into the effect of current peaks has to be done. This paper explains how the peak current influences the thermal stress for a model contact system during short circuits. The thermal stress is evaluated by numeric calculations and compared to measurements of various load cases. Different types of degradation are linked to the corresponding pulse forms. In contradiction to usual considerations, the transmission performance improves by testing with maximum peak current in some load cases. Therefore AC testing with low peak currents or DC testing can be more critical for contact systems with contact elements. This behavior is highly significant for the dimensioning and type testing of contact systems. Additionally, it is also shown how calculations can predict the short circuit current carrying capability.
Mode Shift Keying for Reconfigurable MIMO Antennas: Performance Analysis and Antenna Design

Space-shift-keying (SSK) and spatial modulation (SM) enable multiple antenna transmission systems to convey information on antenna indices. While SSK/SM helps reduce the number of radio frequency (RF) chains, large numbers of antennas and low spatial correlations are required to achieve high data rates. This work investigates the use and design of multifunctional reconfigurable antennas (MRAs) for SSK/SM based transmission where a single-element MRA generates large numbers of modes. To enhance legacy SSK/SM performance while reducing RF hardware complexity, we propose single- and multi-carrier antenna mode-shift keying (MoSK) and mode modulation (MoM) schemes facilitated by MRAs. Based on an error probability analysis, we determine criteria for MRA design and mode set selection suitable for MoSK/MoM. We also develop two MRA designs and investigate their performances over Rayleigh fading channels. We argue that by creating MRA modes with low pattern correlations, channel correlations can be reduced to improve the detection performance. Extensive simulations demonstrate that MoSK/MoM performance exceeds that of SSK/SM along with significant complexity reduction. For instance, a single-carrier MoSK/MoM using a single MRA with 8 modes achieves about 2 dB gain compared to legacy SSK/SM requiring 8 antennas, and by multi-carrier MoSK/MoM using 4 subcarriers, an MRA with 32 modes can attain an error rate performance comparable to this single-carrier system.
Verifying Haze Dispersion from Peatland Fires using Classified Landsat 8

Haze dispersion from peatland fire that influence human health can be recognized by analyzing haze trajectory pattern. Previous studies have successfully analyzed haze dispersion as the result of the simulation using HYSPLIT. However the haze dispersion have not verified and compared with the real situation. This study aims to verify haze dispersion from peatland fires in Riau Province using Landsat 8. The Landsat 8 was classified using the C5.0 decision tree algorithm. There were eight classes used in the classification which are cloud, smoke/haze, water body, shadow, built up area, burning area, burned area, and vegetation. The best classification model has the accuracy of 97.58% and kappa coefficient of 0.97. This study has successfully verified 58 haze locations in the study area. As many 98.28% of those locations were found on smoke/haze and cloud. In addition, about 41.38% of 58 haze points are located at height and pressure that are harmful for human health.
Fast Motion Estimation Algorithm with Efficient Memory Access for HEVC Hardware Encoders

The encoding process in the HEVC standard is several times more complex than the previous standards. Since motion estimation is responsible for most of this complexity, the new Test Zone (TZ) search is usually adopted as the fast search algorithm, to alleviate the complexity. However, the TZ search requires a high rate of access to the off-chip memory, which contributes heavily to the total consumed encoding power. In this paper we demonstrate that the process of finding the best starting search point in this algorithm, does not allow effective reduction of memory access in hardware encoders. As a solution, a new fast motion estimation algorithm is proposed which estimates a proper single starting search point, in addition to an adaptively reduced search range, based on available information from the coded neighboring blocks. The experimental results show that this algorithm on average can reduce the required memory access for ME by ~78% and reduce the integer ME time by ~70%, with only 1.1% Bjontegaard Delta (BD) Rate.
Inner Attention Based bi-LSTMs with Indexing for non-Factoid Question Answering

In this paper, we focussed on non-factoid question answering problem using a bidirectional LSTM with an inner attention mechanism and indexing for better accuracy. Non factoid QA is an important task and can be significantly applied in constructing useful knowledge bases and extracting valuable information. The advantage of using Deep Learning frameworks in solving these kind of problems is that it does not require any feature engineering and other linguistic tools. The proposed approach is to extend a LSTM (Long Short Term Memory) model in two directions, one with a Convolutional layer and other with an inner attention mechanism, proposed by Bingning Wang, et al., to the LSTMs, to generate answer representations in accordance with the question. On top of this Deep Learning model we used an information retrieval model based on indexing to generate answers and improve the accuracy. The proposed methodology showed an improvement in accuracy over the referred model and respective baselines and also with respect to the answer lengths used. The models are tested with two non factoid QA data sets: TREC-QA and InsuranceQA.
The Determinant Factors of Individual Performance from Task Technology Fit and IS Success Model perspectives: a case of Public Procurement Plan Information System (SIRUP)

As an effort to shorten supply chain and improve the efficiency, effectiveness, and transparency of the procurement process, Indonesian government created a public procurement plan information system (SIRUP) as a part of e-Procurement used by government institutions in Indonesia. This research was conducted to analyze the determinant factors of SIRUP implementation in Indonesia and its impact to procurement personnel performance by using modified research model from DeLone & McLean theory, Task Technology Fit (TTF) and Technology Acceptance Model (TAM). The research model was tested using Partial Least Square Structural Equation Modeling (PLS-SEM) approach with SmartPLS 3.0 software. The result of this research with 239 respondents shows that system quality, information quality, service quality, usage, user satisfaction, subjective norm, task characteristic and task technology fit become factors influencing SIRUP implementation toward procurement personnel performance. However, only task technology fit and user satisfaction dominantly affect the performance of procurement personnel. In the end, this research could be a feedback to Policy Institute for Procurement of Goods/Services (LKPP) as a main stakeholder of SIRUP in order to improve their system and service quality.
Robust Feature Spaces from Pre-Trained Deep Network Layers for Skin Lesion Classification

The incidence of skin cancer in the world population is a public health concern, and the first diagnosis takes into account the appearance of lesions on skin. In this context, automated methods to aid the screening for malign lesions can be an important tool. However, the efficiency of developed methods depends directly on the quality of the generated feature space which may vary when considering different image datasets and sources. We present a detailed study of feature spaces obtained from deep convolutional networks (CNNs), using the benchmark PH2 dataset, considering three CNN architectures, as well as investigating different layers, impact of dimensionality reduction, use of colour quantisation and noise addition. Our results show that, features have discriminative capability comparable to competing methods with balanced accuracy 94%, and 95% with noise injection. Additionally, we present a study of fine-tuning and generalisation across image quantisation and noise levels, contributing to the discussion of learning features from deep networks and offering a guideline for future works.
The Capacity of Private Information Retrieval from Byzantine and Colluding Databases

We consider the problem of single-round private information retrieval (PIR) from <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> replicated databases. We consider the case when <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> databases are outdated (unsynchronized), or even worse, adversarial (Byzantine), and therefore, can return incorrect answers. In the PIR problem with Byzantine databases (BPIR), a user wishes to retrieve a specific message from a set of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> messages with zero-error, irrespective of the actions performed by the Byzantine databases. We consider the <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>-privacy constraint in this paper, where any <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> databases can collude, and exchange the queries submitted by the user. We derive the information-theoretic capacity of this problem, which is the maximum number of <italic>correct symbols</italic> that can be retrieved privately (under the <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>-privacy constraint) for every symbol of the downloaded data. We determine the exact BPIR capacity to be <inline-formula> <tex-math notation="LaTeX">$C=(N-2B)/N \cdot (1-T/(N-2B))/(1-(T/(N-2B))^{M})$ </tex-math></inline-formula>, if <inline-formula> <tex-math notation="LaTeX">$2B+T < N$ </tex-math></inline-formula>. This capacity expression shows that the effect of Byzantine databases on the retrieval rate is equivalent to removing <inline-formula> <tex-math notation="LaTeX">$2B$ </tex-math></inline-formula> databases from the system, with a penalty factor of <inline-formula> <tex-math notation="LaTeX">$(N-2B)/N$ </tex-math></inline-formula>, which signifies that even though the number of databases needed for PIR is effectively <inline-formula> <tex-math notation="LaTeX">$N-2B$ </tex-math></inline-formula>, the user still needs to access the entire <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> databases. The result shows that for the unsynchronized PIR problem, if the user does not have any knowledge about the fraction of the messages that are mis-synchronized, the single-round capacity is the same as the BPIR capacity. Our achievable scheme extends the optimal achievable scheme for the robust PIR (RPIR) problem to correct the <italic>errors</italic> introduced by the Byzantine databases as opposed to <italic>erasures</italic> in the RPIR problem. Our converse proof uses the idea of the cut-set bound in the network coding problem against adversarial nodes.
Cooperative Localization Algorithm Based on Hybrid Topology Architecture for Multiple Mobile Robot System

With the development of robotics, the multiple mobile robot system (MMRS) has been an attention-getting subject in recent years and has been widely used in military, civilian and other fields. Cooperative localization is the key technology of the MMRS. And observability analysis for MMRS and nonlinear problem are the foremost problems to cooperative localization. In order to enhance the accuracy of cooperative localization for MMRS, a novel cooperative localization algorithm based on hybrid topology architecture under the framework of extended Kalman filter (EKF) is proposed in this paper. In this novel algorithm, a hybrid topology architecture based on the relative position measurement graph method is adopted to enhance the usage efficiency of observations, improving the cooperative localization accuracy. Meanwhile, EKF is used in this novel cooperative localization algorithm to solve the nonlinearity of the MMRS. Finally, a real dataset is used to verify the availability and superiority of this novel algorithm.
Spin Transport in Poly-Acene Films and the Derivative Films by Using the Spin Pumping

The spin transport in thermally evaporated tetracene films and 6,13-bis(triisopropylsilylethynyl) pentacene (TIPS-pentacene) films by using the spin-pumping-induced spin injection method was challenged at room temperature. The spin transport in the TIPS-pentacene films was demonstrated, while the spin transport in tetracene films was not succeeded. For tetracene films, reconstruction of the prepared films was occurred due to the low vapor pressure and it might be caused by the deformation of the ferromagnetic material as the spin injector.
A design of CMOS Operational Amplifier in TMEC 0.8ìm technology

This paper proposes a basic two stage CMOS operational amplifier (opamp) design in TMEC $0.8 \mu \mathrm {m}$ technology. The designkits of TMEC technology was supported on Tanner EDA v15.1 tools. The opamp operates on a single supply of 5V. Simulation results show a unity gain frequency of $4\mathrm {M}\mathrm {H}\mathrm {z}$, a gain of 65-dB with $65^{\mathrm {o}}$ phase margin, and power consumption of $582\mu \mathrm {W}$. The basic opamp was implemented for instrumentation amplifier. The gain of instrumentation amplifier was varied by $\mathrm {R}_{\mathrm {g}}$ form $ 1-\Omega $ to $ 400-\mathrm {k}\Omega $ for $60 \mathrm {d}\mathrm {B}$ to $0 \mathrm {d}\mathrm {B},$ respectively. The post layout simulation of instrumentation amplifier was varied by $\mathrm {R}_{\mathrm {g}}$ form $ 1-\Omega $ to $ 350-\mathrm {k}\Omega $ for $43.7 \mathrm {d}\mathrm {B}$ to $0 \mathrm {d}\mathrm {B}$, respectively. The objective of this paper is to verify TMEC's design kits using an opamp as design example.
Assembly of Components on Inkjet-Printed Silver Structures by Soldering

Digital printing technologies are well suited for structured deposition of functional materials such as nanoparticular metal inks on different substrates. Inkjet printing as a maskless and direct writing technology is very suitable for the manufacturing of low-cost devices and packages, e.g., for highly integrated sensor systems. The availability of printable materials is ever expanding and enables new capabilities in many applications. Currently, nanoparticular silver inks are very popular for inkjet printing of conductive paths. However, at present, the availability of reliable technologies for the assembly of electrical components is a challenge. In this paper, the soldering of chip resistors on inkjet-printed silver structures was investigated.