Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Feature Extraction and Classification Methods used in CAD System

Computer Aided Diagnosis (CAD) system provides significant benefit to the medical industry by automatically detecting various diseases like cancer, tumor, fracture etc. Different medical images like X-ray, Computed Tomography (CT), Magnetic Resonance Image (MRI) etc act as input for CAD systems depending upon the type of disease. Categorization into whether disease/abnormality exists or not in that medical image acts as an output. Feature extraction and classification are the two the most important steps in designing CAD system. Accuracy of the CAD system depends upon the method used for feature extraction along with classification from the medical images. In the present study, review of various feature extraction and classification methods is done which are proposed by researchers for the CAD system in the past few years. Primary focus of the study is to discuss the advantages and limitations of different CAD system which are proposed for detecting cancer and tumor.
16.3 A 28nm 384kb 6T-SRAM Computation-in-Memory Macro with 8b Precision for AI Edge Chips

Recent SRAM-based computation-in-memory (CIM) macros enable mid-to-high precision multiply-and-accumulate (MAC) operations with improved energy efficiency using ultra-small/small capacity (0.4-8KB) memory devices. However, advanced CIM-based edge-AI chips favor multiple mid/large capacity SRAM-CIM macros: with high input (IN) and weight (W) precision to reduce the frequency of data reloads from external DRAM, and to avoid the need for additional SRAM buffers or ultra-large on-chip weight buffers. However, enlarging memory capacity and throughput increases the delay parasitics on WLs and BLs, and the number of parallel computing elements; resulting in longer compute latency (tAC), lower energy-efficiency (EF), degraded signal margin, and larger fluctuations in power consumption across data-patterns (see Fig. 16.3.1). Recent SRAM-CIM macros tend to not use in-lab SRAM cells, with a logic-based layout, in favor of foundry provided compact-layout 8T [2], 3, [5] or 6T cells with local-computing cells (LCCs) [4], [6] to reduce the cell-array area and facilitate manufacturing. This paper presents a SRAM-CIM structure using (1) a segmented-BL charge-sharing (SBCS) scheme for MAC operations, with low energy consumption and a consistently high signal margin across MAC values (MACV); (2) An new LCC cell, called a source-injection local-multiplication cell (SILMC), to support the SBCS scheme with a consistent signal margin against transistor process variation; and (3) A prioritized-hybrid-ADC (Ph-ADC) to achieve a small area and power overhead for analog readout. A 28nm 384kb SRAM-CIM macro was fabricated using a foundry compact-6T cell with support for MAC operations with 16 accumulations of 8b-inputs and 8b-weights with near-full precision output (20b). This macro achieves a 7.2ns tAC and a 22.75TOPS/W EF for 8b-MAC operations with an FoM (IN-precision × W-precision × output-ratio × output-channel × EF/tAC) 6× higher than prior work.
Channel Charting for Pilot Reuse in mMTC with Spatially Correlated MIMO Channels

Massive multiple-input multiple-output (MIMO) systems and pilot reuse are essential ingredients for internet of things (IoT) networks and massive machine-type communications (mMTC). The large number of devices and the limited amount of resources (time, frequency, and power) preclude the allocation of orthogonal pilot sequences for users. We propose a pilot reuse strategy based on channel charting to deal with the pilot contamination in massive MIMO systems with spatially correlated channels. In particular, we use channel charting as a means to extract angular domain information from channel covariance matrices and assign orthogonal pilots to users with overlapping angle of arrival intervals. The simulation results show that the proposed pilot reuse method significantly improves the channel estimation performance and the symbol error rate as compared to existing schemes.
Charging Schedule Optimization Method for Electric Buses with PV Installed at Bus Stations: Sensitivity Analysis of PV Capacity based on Real City Data

It has been discussed that electric vehicles should be utilized effectively as movable storage resources. In this research, public electric buses are focused on, which are expected to have controllability with low uncertainty but limitation caused by the rigid bus operation schedule. Recently we reported an analysis of the actual bus operation data and proposed a charging schedule optimization method. The purpose of this research is to create an optimization method that maximizes two indicators related to the benefits of the bus company. Moreover, the sensitivity of the photovoltaic capacity installed at the bus stations is analyzed. An optimization method was created by mixed integer linear programming, and sensitivity analysis simulation was conducted using real city data. This study is based on a realistic simulation with actual bus operation data in a target city. As a result, we confirmed the 107kWh (-15.3%) surplus reduction by the proposed optimization method and the trade-off relationship between the two indicators.
Autism Spectrum Self-Stimulatory Behaviors Classification Using Explainable Temporal Coherency Deep Features and SVM Classifier

Autism spectrum disorder is a very common disorder. An early diagnosis of autism is essential for the prognosis of this disorder. The common diagnosis method utilizes behavioural cues of autistic children. Doctors require years of clinical training to acquire the ability to capture these behavioural cues (such as self–stimulatory behaviours). In recent years, the advancement of deep learning algorithms and hardware enabled the use of artificial intelligence technology to automatically capture self-stimulatory behaviours. Using this technique, the work efficacy of doctors can be improved. However, the field of self-stimulatory behaviours research still lacks large annotated data to train the model. Therefore, the application of unsupervised machine learning methods is adopted. Meanwhile, it is often difficult to obtain good classification results using unlabelled data, further research to train a model that can obtain good classification results and at the same time being practical will be valuable. Nevertheless, in the area of machine learning, the interpretability of the created model has to be vital as well. Hence, we have employed the Layer-wise Relevance Propagation (LRP) method to explain the proposed model. In this article, the major innovation is utilizing the temporal coherency between adjacent frames as free supervision and setting a global discriminative margin to extract slow-changing discriminative self-stimulatory behaviours features. Extensive evaluation of the extracted features has proven the effectiveness of those features. Firstly, the extracted features are classified by the k-means method to show the classification of self-stimulation behaviours in a completely unsupervised way. Then, the conditional entropy method is used to evaluate the effectiveness of features. Secondly, we have obtained the state-of-the-art results by combining the unsupervised TCDN method with optimised supervised learning methods (such as SVM, k-NN, Discriminant). These state-of-the-art results prove the effectiveness of the slow-changing discriminative self-stimulatory behaviours features.
High Capacity and Miniaturized Flexible Chipless RFID Tag Using Modified Complementary Split Ring Resonator

This paper aims to produce a high data capacity and miniaturized flexible chipless RFID tag based on the frequency signature using the Modified Complementary Split Ring Resonator (MCSRR). The proposed 19 bits chipless RFID tag using the frequency shifting technique consists of five slotted overlaying MCSRR with Different Width (MCSRR with DW) structures and the dimension of 48 mm <inline-formula> <tex-math notation="LaTeX">$\times48$ </tex-math></inline-formula> mm. The structure is designed by using a flexible (Polyethylene Terephthalate <bold>)</bold> PET substrate with permittivity of 0.2. The operating frequency is between 0.9 GHz and 2.7 GHz. The advancement of slotted overlaying MCSRR with DW structures has successfully miniaturized the chipless RFID tag structure to about <inline-formula> <tex-math notation="LaTeX">$107~{\mathrm {mm}}^{2}$ </tex-math></inline-formula>/ bit, <inline-formula> <tex-math notation="LaTeX">$0.02\lambda _{mid}^{2}/\mathrm {bit}$ </tex-math></inline-formula> and 0.09 GHz/bit by maximizing the number of resonators in a limited space and minimizing the frequency separation between the resonators. The omnidirectional tag antenna is incorporated with the proposed MCSRR structure using the retransmission measurement method. The log-periodic antenna with a gain of 5–7 dBi is used for this measurement to improve the range distance between tag and reader system. Based on the retransmission measurement involving the antenna tag, the 19 bits chipless RFID tag which consists of five MCSRR with DW structures can be detected with a maximum range distance of 30 cm and a power transmitted level of 30 dBm.
Click Chemistry of Alkyne–Azide Cycloaddition using Nanostructured Copper Catalysts

With increasing advances on nanotechnology, the nanostructured copper catalysts have attracted much attention in alkyne–azide click reaction in the last few years owing to their large surface area, tunable morphology and sustainable catalytic activities. Supported copper nanoparticles are most widely employed. The shape, size and dispersity of supported copper nanoparticles are highly controlled by the used supports, as these will determine the catalytic activity of the heterogeneous click reaction. On the other hand, nanoporous coppers with specific three‐dimensional open pore network structure was reported most recently, which can be fabricated with a tunable ligament or pore size without any supports, exhibiting high catalytic activity and reusability. This Minireview describes the state of the art with respect to the fabrication and catalytic application of both supported copper nanoparticles and nanoporous copper in click reaction.
Investigating the Impact of Imprecise Computation in Memristive Memory Arrays on the Classification Performance of Deep Fully Connected Neural Networks

Hardware implementation of deep neural networks (DNNs) using digital hardware accelerators suffers from the inefficiency of the frequent transfer of network parameters from external memory, such as dynamic random access memory (DRAM), to the processing unit. One attractive solution to this problem is to replace the von Neumann architecture of digital systems with the concept called in-memory computation, where the computations are performed with lower precision but inside the memory array that eliminates the need to transfer the network parameters around. However, reducing the precision of computation can degrade the performance of DNN. In this article, we study the effect of using imprecise circuits, such as analog vector-by-matrix multipliers implemented with memristive crossbars, on the classification performance of fully connected neural networks and show that how by increasing the capacity of the network, we can increase the tolerance of the network to hardware defects.
Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors

Modern antenna systems are designed to meet stringent performance requirements pertinent to both their electrical and field properties. The objectives typically stay in conflict with each other. As the simultaneous improvement of all performance parameters is rarely possible, compromise solutions have to be sought. The most comprehensive information about available design trade-offs can be obtained through multi-objective optimization (MO), typically in the form of a Pareto set. Notwithstanding, MO is a numerically challenging task, in a large part due to high CPU cost of evaluating the antenna properties, normally carried out through full-wave electromagnetic (EM) analysis. Surrogate-assisted procedures can mitigate the cost issue to a certain extent but construction of reliable metamodels is hindered by the curse of dimensionality, and often highly nonlinear antenna characteristics. This work proposes an alternative approach to MO of antennas. The major contribution of our work consists in establishing a deterministic machine learning procedure, which involves sequential generation of Pareto-optimal designs based on the knowledge gathered so far in the process (specifically, by triangulation of the already obtained Pareto set), and local surrogate-assisted refinement procedures. Our methodology allows for rendering uniformly-distributed Pareto designs at the cost of a few hundreds of antenna EM simulations, as demonstrated by means of three verification case studies. Benchmarking against state-of-the-art MO techniques is provided as well.
Inverse Design of a CMOS-compatible Mode Exchange Device Based on Adjoint and Level Set Methods

We have designed a CMOS-compatible TE<inf>0</inf> and TE<inf>1</inf> mode exchange device based on adjoint and level set inverse design method. The mode conversion efficiencies are both above 93% for TE<inf>0</inf>-TE<inf>1</inf> and TE<inf>1</inf>-TE<inf>0</inf> process. © 2020 The Author(s)
A Practical Way to Improve Automatic Phonetic Segmentation Performance

Automatic phonetic segmentation is a fundamental task for many applications. Segmentation systems highly rely upon the acoustic-phonetic relationship. However, the phonemes’ realization varies in continuous speech. As a consequence, segmentation systems usually suffer from such variation, which includes the intra-phone dissimilarity and the inter-phone similarity in terms of acoustic properties. In this paper, We conducted experiments following the classic GMM-HMM framework to address these issues. In the baseline setup, we found the top error comes from diphthong /oy/ and boundary of glide-to-vowel respectively, which suggested the influence of the above variation on segmentation results. Here, we present our approaches to improve automatic phonetic segmentation performance. First, we modeled the intra-phone dissimilarity using GMM with model selection at the state-level. Second, we utilized the context-dependent models to handle the inter-phone similarity due to coarticulation effect. The two approaches are coupled with the objective to improve segmentation accuracy. Experimental results demonstrated the effectiveness for the aforementioned top error. In addition, we also took the phones’ duration into account for the HMM topology design. The segmentation accuracy was further improved to 91.32% within 20ms on the TIMIT corpus after combining the above refinements, which has a relative error reduction of 3.34% compared to the raw GMM-HMM segmentation in [1].
Attitude Adaptive Estimation With Smartphone Classification for Pedestrian Navigation

Accurate attitude for wearable devices and smartphones is needed for many applications. The major challenge is to cope with the acceleration resulting from the user or smartphone dynamics. To that end, a two-stage adaptive complementary filter for attitude estimation is proposed. Upon identifying the smartphone location on the user using a deep learning approach, the accelerometers weights in each axis are adjusted according to an optimized gain map. To evaluate the benefits of the proposed approach it is compared to commonly used algorithms both in simulation and experiments.
Estimation of Power System Inertia Under Normal Operating Conditions

We propose a method for estimating the inertia of a power system from the ambient frequency and active power signals measured by phasor measurement units (PMUs). In this method, a dynamic model relating the active power deviations to the frequency deviations is first identified using the N4SID algorithm. Then, the inertia of an individual generator or an electrical area is extracted from the step response of the identified model using the swing equation. Compared to conventional methods that use transient signals, the proposed method can estimate the inertia of a power system in real time under normal operating conditions. The effectiveness of the proposed method is validated in the IEEE 39-bus system.
Bonds of Constrained Systems and Their Characteristics

A constrained system is a set of words satisfying some constraints on the appearance of subwords, and the study on constrained systems is the core of the study on constrained coding. The notion of constrained coding has been often applied in data storage media to reduce the likelihood of errors.Given data sequences satisfying some constraints, it is not always possible to directly concatenate the data sequences so that the resulting sequence also satisfies the constraints. In this paper, we define a bond to be a sequence that can concatenate any allowed data sequences without violating the constraints, and present a necessary and sufficient condition on the existence of a bond. We also discuss on the length of a bond and the complexity of finding a bond, together with some examples of bonds.
A More Efficient Single-Phase Single-Inductor Electrolytic Capacitor-Less Converter with Power Decoupling and Automatic PFC Capability

Reducing power transmission is a new trend to increasing the whole efficiency. Meanwhile, the presence or absence of electrolytic capacitor (E-cap) is another important criterion for judging the performance of LED drivers. Based on this, a new single-stage AC/DC converter is proposed in this paper, which demands fewer power semiconductors compared to previous works due to the magnetic integration technology and the time-division multiplexing of devices, thus lower cost and higher efficiency is achieved. And, the power flow of decoupling circuit is further optimized, where most of the energy is designed to transfer directly to the load, reducing the intermediate link and simultaneously employing the film capacitor. Besides, a simple automatic PFC scheme is presented, where near perfect power factor is obtained. Finally, all the superior performances of proposed LED driver are verified under PSIM simulation platform.
12-Port Hybrid MIMO Antenna Array for 5G Smartphone Applications

A hybrid twelve-antenna array operating in the 3.3-3.6 GHz and 4.8-5.0 GHz band for multiple input multiple output (MIMO) applications in 5G smartphones is proposed. The proposed hybrid antenna array elements are symmetrically placed along the long edges of the smartphone, and they are composed of two inverted-F antennas (IFA) with different structural parameters. Simulated antenna isolation is better than -14.5 dB without any decoupling structures. Moreover, simulated radiation patterns, antenna efficiencies, and envelop correlation coefficients are also given in the paper.
A K-Band 12.1-to-16.6GHz Subsampling ADPLL with 47.3fsrms Jitter Based on a Stochastic Flash TDC and Coupled Dual-Core DCO in 16nm FinFET CMOS

The demand for massive MIMO, digital beamforming, and increased bandwidth communication dramatically increases the complexity of the remote radio head in future cellular base-stations. This complexity issue can be addressed by RF transceiver SoCs incorporating multiple direct RF radio transceivers, which have clear advantages over conventional architectures and are enabled by high-performance digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). However, the sampling clock for these high-performance data converters must fulfill very stringent in-band and out-of-band spectral mask requirements, which are hard to meet with RF frequency synthesizers integrated on the SoC. All digital PLLs (ADPLLs) utilize the advantages of scaled nodes and rely on advanced digital signal-processing techniques to boost performance. Sub-sampling ADPLLs (SS-ADPLL) enable the usage of an energy-efficient time-to-digital converter (TDC) since the TDC noise is multiplied by a smaller factor [1]. This work presents a sub-sampling ADPLL with stochastic-flash TDC (SFTDC) and coupled dual-core DCO achieving 47.3fsrms jitter performance in 16nm FinFET CMOS.
Spectrometric Detection of Organic Compounds and Toxicity of Ethanolic Leaves Extracts of Prosopis Juliflora

Spectrometric detection of saponins, tannins and alkaloids in Prosopis juliflora using Fourier Transform Infrared (FTIR) spectroscopy is reported in this paper. Crude dry plant powders and corresponding ethanolic extracts were mixed with potassium bromide (KBr) powder and compressed to a thin pellet for infrared examination. The plant powders, ethanolic extracts and leaves ethanolic extract (LEE) fractions exhibited characteristic infrared spectra due to various functional groups –OH, -C=O, C-H, and C=C absorptions. Phytochemical analysis confirmed the presence of saponins, tannins and alkaloids in the tested specimens. Prosopis juliflora whole plant parts were used as a reference sample. Toxicity and safety levels of P. juliflora were also investigated. Acute and sub-acute toxicity of P. juliflora ethanolic leaves extract was evaluated using Swiss albino rats. All clinical signs and symptoms were recorded within 24 hours. These results indicated that direct detection of bioactive compounds was possible by infrared analysis. Toxicity symptoms were moderately observed and post mortem did not show any major gross effects on the internal organs. Keywords: Prosopis juliflora , infrared examination, potassium bromide, acute and sub-acute toxicity, clinical signs, doses, symptoms.
3D-LIDAR Based Object Detection and Tracking on the Edge of IoT for Railway Level Crossing

Object detection is an essential technology for surveillance systems, particularly in areas with a high risk of accidents such as railway level crossings. To prevent future collisions, the system must detect and track any object that passes through the monitored area with high accuracy, and this process must be performed fulfilling real-time specifications. In this work, an edge IoT HW platform implementation capable of detecting and tracking objects in a railway level crossing scenario is proposed. The response of the system has to be calculated and sent from the proposed IoT platform to the train, so as to trigger a warning action to avoid a possible collision. The system uses a low-resolution 3D 16-channel LIDAR as a sensor that provides an accurate point cloud map with a large amount of data. The element used to process the information is a custom embedded edge platform with low computing resources and low-power consumption. This processing element is located as close as possible to the sensor, where data is generated to improve latency, privacy, and avoid bandwidth limitations, compared to performing processing in the cloud. Additionally, lightweight object detection and tracking algorithm is proposed in this work to process a large amount of information provided by the LIDAR, allowing to reach real-time specifications. The proposed method is validated quantitatively by carrying out implementation on a car road, emulating a railway level crossing.
Synthesis of single silica nanotubes in the presence of citric acid

Citric acid has been employed as a structural modifier for the first time to synthesize single silica nanotubes via a sol–gel process at room temperature. XRD results reveal the amorphous nature of the silica framework. SEM and TEM images show that the nanotubes were 0.5–20 µm in length, and 50–500 nm in width. TG-DTA and FTIR results indicate that the possible interaction between citric acid molecules and inorganic species should be very weak. The structural direction of ammonium citrate crystals under appropriate synthetic conditions was argued to explain the formation of particular morphologies.