Miracle-dz/UniFAR-FTUs huggingface dataset:
Visible Light and mmWave Propagation Channel Comparison for Vehicular Communications

Future connected vehicles are expected to require fast and reliable exchange of road information to increase safety and enable cooperative driving. Currently, standardized vehicular communication technologies aim to enable basic safety message exchanges with limited bandwidth. Recently, alternative technologies, based on millimeter-wave (mmWave) and visible light spectrum are proposed as complementary vehicle-to-everything (V2X) communication schemes, provisioned to support future connected vehicles with high bandwidth and increased security. However, the understanding of channel propagation characteristics is the key to achieve reliability, due to higher path loss compared to 5.8 GHz band. In this work, we compare channel path loss characteristics of mmWave and vehicular visible light communication (VVLC) schemes to provide an overview regarding technology selection in an indoor parking garage. Path loss measurements are conducted with respect to various inter-vehicular distances, receiver angles, nearby vehicle existence, and lane occupation scenarios. Measurement results indicated path loss of 21.47 dB for VVLC from 3 m to 20 m distances. Moreover, path loss for mmWave 26.5 GHz and 38.5 GHz channels increased 12.5 dB, and 12.7 dB, respectively. Nearby vehicles are shown to decrease path loss of 26.5 GHz and 38.5 GHz signals up to 9.78 dB, and 9.56 dB, respectively, whereas VVLC channel path loss decreases 0.4 dB at the same scenario. Channel frequency response (CFR) measurements indicated frequency flat behavior of VVLC channels while mmWave channel exhibits frequency selectivity induced dispersion due to parking garage structure. Obstructed line-of-sight (OLoS) measurements further reveal that blocking vehicle interrupts VVLC signals while selecting a favorable antenna location leads up to 30 dB less path loss for mmWave signals.
Feature Pyramid SSD: Outdoor Object Detection Algorithm for Blind People

To benefit the blind by using advanced deep learning techniques, we establish a new outdoor object detection dataset, BLIND. Different from PASCAL VOC, the scales of objects in our dataset are quite various because of different distances between objects and the camera. The characteristic of the BLIND dataset requires a high ability of scale invariance for the object detector, which classical SSD isn’t adequate. We propose a novel object detector named Feature Pyramid SSD (FPSSD) focusing on BLIND, applying feature fusion strategies to classical SSD. FPSSD achieves 75.4% mean Average Precision (mAP) on BLIND, surpassing classical SSD by 1.7%. Extensive experimental results and analyses demonstrate the necessity to establish the BLIND dataset and validate the effectiveness of the proposed FPSSD object detection algorithm for the blind people.
23.4 A 28W -108.9dB/-102.2dB THD/THD+N Hybrid $\Delta\Sigma-$-PWM Class-D Audio Amplifier with 91% Peak Efficiency and Reduced EMI Emission

Class-D amplifiers are often used in high-power audio applications due to their high power efficiency. They typically employ pulse-width modulation (PWM) at a fixed carrier frequency, which may cause electromagnetic interference (EMI). Setting this frequency fPWM) below the AM band (535 to 1605kHz) helps mitigate this, but its harmonics still contain substantial energy and must be filtered out by bulky LC filters with low cut-off frequencies (fc = 20 to 40 kHz), significantly increasing system cost and size. Stability considerations also constrain the amplifier's unity-gain frequency to be $< \mathrm{f}_{\mathrm{PWM}}/\pi$ [1], compromising the audio-band loop gain required to suppress output-stage nonlinearity. Setting fPWM above the AM band helps increase fc and allows a higher loop gain [2]. However, this results in narrower pulses at higher power levels (higher modulation index), which cannot be faithfully produced by the output stage, thus exacerbating its non-linearity. Delta-sigma modulation ($\Delta\Sigma$ M) has fixed pulse widths and does not suffer from these narrow-pulse artefacts. However, the out-of-band noise of 1bit modulators then requires larger LC filters. Moreover, high-order loop filters must be used to achieve sufficient SQNR, which then require additional techniques to maintain stability as the modulation range approaches 100% [3].
Motion Compensation for Highly Squinted SAR data based on Azimuth Nonlinear Chirp Scaling Algorithm

In order to accommodate the motion errors caused by the actual trajectory, a lot of motion compensation (MOCO) methods have been developed. However, the typical two-step MOCO does not meet well-focused image requirements with the increasing of squint angle, higher order motion errors caused by acceleration need to be considered. In this paper, an extended azimuth non-linear chirp scaling (ANCS) algorithm is proposed to solve the problem, a real-time range distance expression containing acceleration is proposed. An accurate form of the signal spectrum is obtained by the method of series reversion and Extended Chirp Scaling algorithm (ECS) is applied to deal with the range focusing. Non-linear chirp scaling operation is proposed to deal with the azimuth focusing. The simulation results indicate that the extended ANCS algorithm achieves more accurate focus results than ECS algorithm.
Investigation of Grid Connected Solar PV Hosting Capacity in LV Distribution Networks

The grid connected solar PV systems have been significantly increased in the recent past mainly due to government policy initiatives around the globe for renewable energy developments. Diverse technical merits, comparatively simple installations and declining costs have influenced an unprecedented growth of solar PV. However, the technical challenges arising in distribution networks have to be systematically addressed in order to increase the solar PV hosting capacity (maximum solar PV penetration level without violating any operating constraint's) of LV distribution networks. This paper presents solar PV hosting capacity evaluation for a selected LV distribution network incorporating overvoltage issue and further the paper elaborates the effects of some operational changes and network upgrades for the enhancement of solar PV hosting capacity of the selected network.
14.2 A 65nm 24.7µJ/Frame 12.3mW Activation-Similarity-Aware Convolutional Neural Network Video Processor Using Hybrid Precision, Inter-Frame Data Reuse and Mixed-Bit-Width Difference-Frame Data Codec

Convolutional Neural Networks (CNNs) have become widely used in image signal processing, such as tracking, classification and post-processing. Modern CNNs use millions of weights and activations, leading to critical challenges for both computation and data transmission. Video applications, such as autopilot and surveillance cameras, have to process a large number of sequential images/frames within limited time, making the situation even worse. As shown in Fig. 14.2. 1, adjacent activation frames of typical video applications are similar to each other most of the time, providing an opportunity to reduce both computing and data transmission complexity significantly.
ResNet Based Classification of Female Menstrual Circle from Pulse Signal

The regularity of the menstrual cycle can reflect the physiological health of women. Nowadays, more and more women are paying attention to the problems of their own physiological period. However, there are few researches on this subject in the academic field, and most of them are explored from the perspective of ECG. The collection equipment based on ECG has the high cost. In this paper, we attempt to identify the characteristics of menstrual cycle in healthy female college students from the perspective of pulse. We extract the effective time frequency characteristics through wavelet transform, and make them as input of the residual neural network then, so as to recognizing the characteristics of different physiological stages. We have collected 120 different female pulse once and tracked the pulse of a female volunteer for 3 months in order to imitate the traditional Chinese medicine and the family-based “personal doctor”. From the two aspects, we train and predict the next round of women’s physiological period. The accuracy reached 66.7% and 81.8%, respectively.
Web-Shaped Model for Head Pose Estimation: An Approach for Best Exemplar Selection

Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. In this respect, the selection of the frames with the best face orientation can allow triggering recognition only on these, therefore decreasing the possibility of errors. This paper proposes a novel approach to head pose estimation for smart cities and video surveillance scenarios, aiming at this goal. The method relies on a cascade of two models: the first one predicts the positions of 68 well-known face landmarks; the second one applies a web-shaped model over the detected landmarks, to associate each of them to a specific face sector. The method can work on detected faces at a reasonable distance and with a resolution that is supported by several present devices. Results of experiments executed over some classical pose estimation benchmarks, namely Point ’04, Biwi, and AFLW datasets show good performance in terms of both pose estimation and computing time. Further results refer to noisy images that are typical of the addressed settings. Finally, examples demonstrate the selection of the best frames from videos captured in video surveillance conditions.
Poor Sleep Health in American Adults: Implications for Screening in Civil Aviation

Background: Poor sleep health is increasingly prevalent in the United States (US) adult population and contributes to fatigue. Fatigue is a significant safety hazard within civilian aviation, yet data regarding the prevalence of inadequate sleep in this population is sparse. Are there demographic characteristics which put individual at risk of not obtaining adequate sleep which should trigger screening during the medical certification process? Methods: This cross-sectional study used National Health Interview Survey 2014-2016 data from US adults aged 18 to 65 to observe the relationship of demographic, socioeconomic, health, and behavioral characteristics with reported inadequate sleep duration and quality (trouble falling and staying asleep, waking not rested, and use of medication for sleep). A multivariable regression model was used to generate adjusted prevalence ratios for each of the five sleep outcomes. Results: Of the 76,347 cases included, 34% did not meet sleep duration recommendations, and poor sleep quality outcomes were reported in 12.5% to 46%. In the full model, weak statistically significant associations were found between all characteristics and at least one sleep outcome, yet few associations were strong enough to be clinically relevant. The strongest associations for poor sleep were seen with poor/fair health status, joint pain, and experiencing serious psychological distress. Conclusions: Sleep is governed by complex relationships, but no particular sociodemographic group was identified to be at higher risk of reporting inadequate sleep measures. It is suggested to consider routine screening of all aviation personnel for sleep health during the medical certification process.
28.2 A 51dB-SNR 120Hz-Scan-Rate 32×18 Segmented-VCOM LCD In-Cell Touch-Display-Driver IC with 96-Channel Compact Shunt-Sensing Self-Capacitance Analog Front-End

Driven by strong market demand, various research approaches for in-cell touch display implementation have been undertaken in recent years, with several being successfully delivered to the industry and market. As a result, in-cell touch-display solutions that offer low-profile formfactor, low module cost, and simplified supply chain have extensively replaced conventional add-on touch panels. Among these, in-cell touch liquid-crystal display (LCD) using segmented-VCOM electrodes, also known as advanced in-cell touch (AIT) LCD [1] running on touch-display-driver IC (TDD), has emerged as one of the best-performing solutions in the market.
Endothelial keratoplasty for corneal decompensation leaded by a dexamethasone implant dislocation in anterior chamber

Background: Dexamethasone intravitreal implant (DEX) largely showed his safety and efficacy for the treatment of cases ofmacular edema. Even if uncommon, delivery dislocation in anterior chamber has been described in Literaure as complication of theinjection procedure, leading to irreversible endothelial cell loss in the majority of cases.We report a case of a 66-year-old man with pain and vision loss in his left eye. The anamnesis revealed a recent intravitrealinjection of DEX implant for a persistent cystoid macular edema related to central retinal vein occlusion. Anterior segmentexamination showed corneal edema and the rod implant adherent to corneal endothelium. A large peripheral iridectomy wasevident with retroillumination and IOL appeared good centered in the bag. The implant was removed but corneal decompensationwas irreversible. One month later, an endothelial keratoplasty was successfully performed restoring corneal transparency.DEX intravitreal implant can migrate from vitreous cavity to anterior chamber and lead to irreversible corneal decompensation bymechanical and chemical toxicity on corneal endothelium. Removeal of the implant is necessary to avoid total endothelialdecompensation. Despite this, in some cases endothelial keratoplasty had to be performed.
Hybrid IP/SDN Routing for Inter-Data Center Communications

Internet Service Providers (ISPs) and dedicated inter-Data Center Wide Area Networks have been exploring Software-Defined Networking (SDN) features to achieve a high utilization of the available resources. This work proposes a scalable hybrid IP/SDN routing model, and optimization procedures fostered by Evolutionary Computation algorithms, to achieve near optimal network resources utilization under changing traffic requirements.
16.6 An 800MHz-BW VCO-Based Continuous-Time Pipelined ADC with Inherent Anti-Aliasing and On-Chip Digital Reconstruction Filter

Modern wireless communication systems operating at tens of GHz have opened a near-GHz contiguous RF BW for various applications. Further, spectral efficiency has often necessitated such applications to use MIMO and beamforming, resulting in systems that are highly integrated and require tight power budgets. The ADC, being an indispensable element of these systems, has been thus pushed to digitize ever-increasing BWs (>500MHz) with low power dissipation and area, in an integration-friendly manner. Continuous-time (CT) ΔΣ ADCs have traditionally been used in integrated receiver applications because their oversampling and inherent anti-aliasing ease the frequency planning and make on-chip active filtering possible. Designing these CT ΔΣ ADCs for BW specifications of more than 500MHz, however, is challenging. Digitizing near-GHz RF BW with a typical oversampling ratio (OSR) of ~16 requires a very high sampling frequency with prohibitive power dissipation even in advanced process nodes. Using discrete-time (DT) ADCs is a possibility, but the ADC will still need to be oversampled to ease on-chip filtering and to prevent unwanted mixer terms from aliasing in-band. Thus, the power consumption and area penalties can be prohibitive.
25.4 A Scalable 20GHz On-Die Power-Supply Noise Analyzer with Compressed Sensing

Power-supply noise (PSN) is a key consideration that determines the performance, as well as functionality of ICs, especially for modern SoCs with significantly increased scale, level of integration, and sophisticated voltage and power controls. Externally measured PSN is inevitably distorted by packaging and other factors; therefore, on-die measurement of PSN is highly desirable with large bandwidth, high accuracy, high frequency resolution and reasonable measurement time, which are extremely challenging to achieve design objectives. A sample/hold-based approach with direct voltage comparison provides limited resolution under noisy supplies, preventing it from spectral measurement [1]. Time-domain comparison with VCO-based quantizers have been proposed with improved resolution and scalability. However, the fundamental tradeoff between bandwidth and accuracy has limited the high-frequency performance of S/H-based measurements, with bandwidth often limited to 10GHz [2]. Subsampling-and-averaging-based approaches relax the bandwidth constraints, often requiring PSN-uncorrelated sampling clocks [3]. In addition, the accuracy and frequency resolution of averaging-based measurements are fundamentally determined by the number of measurement points, resulting in lengthy measurement time, e.g. taking tens of minutes to achieve 20GHz BW with 10MHz frequency resolution [4].
A Review on Cellular Network Infrastructure Evolution: Challenges and Opportunities

Last few decades have witnessed tremendous growth in terrestrial communication. This has happened with improved technologies at both the physical layer as well as network layer level. This paper surveys some of the key technological advancements implemented towards the fulfilment of ever increasing demands. Basically, this paper provides a comprehensive review of the applications and challenges associated with different generation of communication system and technological enhancement towards their fulfilment. In addition, we discuss main features of next generation cloud radio area network architecture along with the cooperative transmission schemes. Further, it is shown that Cloud Radio Access Network (CRAN) is the most suitable alternative for next generation architecture.
A Collective Study of Data Mining Techniques for the Big Health Data available from the Electronic Health Records

The healthcare industry has witnessed a huge evolution in producing enormous amount of medical data that gave birth to the research in multiple fields. Efforts were done and are being done by the researchers in exploring the medical data in order to extract useful knowledge by applying the technical innovations. Electronic Health Record systems (EHRs) are the repositories of data which is the digitized format of the storing the medical data. Electronic health data is the fastest growing data diversity. Technological advancements in health care information, digitalizing health records has resulted in rapid growth of healthcare sector. Healthcare sector manages huge amount of data and need to analyse the data available and provides a better solution which makes better decision. One of the challenges is how to discover useful and important information effectively among the massive amount of data available through the data mining techniques. Data Mining plays a major role in advancing and developing the new techniques which works effectively for the big data available in healthcare. This paper reviews different predictive and descriptive data mining techniques that had been applied to voluminous health record in aid of decision making process.
Human Brain Tumor Detection Using CPW Fed UWB Vivaldi Antenna

The major finding of this work is to detect the presence of tumor in the human brain by using the proposed coplanar wave guide (CPW) fed ultra wide band (UWB) Vivaldi antenna. A six layered healthy human brain model with respective tissue properties has been designed and then a tumor having radius of 5 mm has been inserted inside the healthy human brain. The radiation properties of the CPW fed UWB Vivaldi antenna have been analyzed for both the healthy brain model and tumor induced brain model. The specific absorption rate (SAR) is higher for tumor induced brain model compared to a healthy brain model. All others important performance parameters of the proposed antenna such as return loss, BW, gain, directivity, VSWR, electric field intensity have also been analyzed for both healthy brain and tumor induced brain model. Finally it is seen that the proposed antenna can be a good candidate for human brain tumor detection.
21.1 A Fully Integrated Genetic Variant Discovery SoC for Next-Generation Sequencing

Next-generation sequencing (NGS) is now indispensable for genetics research and biomedical applications, such as disease analysis and evolution tracking [1]. However, it still takes up to a couple of days to analyze all genetic mutations (variants) of a human genome, which consists of 3 billion nucleotides, through GPU acceleration. Fig. 21.1. 1 shows an overview of NGS and the data analysis workflow. The NGS technology enables sequencing hundreds of millions of DNA segments, anchored and amplified on a microarray, in parallel. In each sequencing cycle, the nucleotides (A, T, C, G) are individually detected by their unique fluorescence labels and DNA segments can then be constructed as short reads. The NGS data analysis workflow consists of Preprocessing, Short-Read Mapping (including Exact Matching and Inexact Matching), Haplotype Calling, and Variant Calling [2]. Short reads are first mapped to a reference DNA and further used to assemble the genome of the DNA sample. Preprocessing is essential for constructing the data structure for indexing the reference DNA. In Short-Read Mapping, a seeding-and-extension scheme is applied to perform both Exact and Inexact Matching. The equal-length sub-sequences (seeds) of the short reads are used to find the exact locations on the reference DNA. Then, the seeds are extended to identify the most-likely locations through global alignment, allowing mismatches and insertions/deletions [2]. Next, in Haplotype Calling, the reads mapped to a specific region are assembled to reconstruct the paternal and maternal genomes (i.e. haplotypes) of the DNA sample. Finally, in Variant Calling, the assembled haplotypes are used to determine the variants between the reference DNA and the sample DNA. The outputs of Variant Calling indicate the location and likelihood of each variant. Dedicated VLSI solutions have been developed for acceleration, but only Suffix-Array (SA) Sorting for Preprocessing and Exact Matching for Short-Read Mapping were realized on silicon [3]. This work presents a fully integrated SoC for the entire NGS data analysis process.
Sensor Selection Method for Target Tracking based on Hybrid Binary Whale Optimization Algorithm in Wireless Sensor Networks

Due to various resource limitations of wireless sensor networks, selecting the appropriate sensors to participate in target tracking is necessary. The real measurement data effects the target tracking accuracy, so this paper proposes a hybrid binary whale optimization algorithm (HBWOA). The proposed algorithm is used to solve the sensor selection model constructed using the actual measurement data and conditional posterior Cramer-Rao lower bound (CPCRLB). The proposed algorithm adopts the position update principle of V-shaped function, proposes nonlinear dynamic adaptive convergence factor to adaptively adjust the exploration and exploitation phase of algorithm, and proposes dynamic disturbance weight enhances the searchability in the exploration stage, which improves the convergence performance and the accuracy of algorithm search in the exploitation phase, avoids local optimization and improves the accuracy of algorithm search in the exploitation phase. Experimental simulations show that the proposed algorithm has better performance on target tracking problem than binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), binary salp swarm algorithm (BSSA), and The traditional binary whale optimization algorithm (BWOA_s), it has a better balance between the exploration phase and the exploitation phase.