Miracle-dz/UniFAR-FTUs huggingface dataset:
A 15-nW 14-ppm/°C 1.18 V startup-less bandgap-based voltage regulator

We present an ultra-low-power band-gap reference-based regulator and a unique method of generating a proportional-to-absolute-temperature (PTAT) voltage and a PTAT current. The PTAT current is used to generate a BJT complementary-to-absolute-temperature (CTAT) voltage. This CTAT voltage and PTAT voltage are added to generate a regulated output voltage in the negative feedback loop to drive the load. The output of the proposed regulator is temperature stable from −20° C to 85° C with a mean variation of less than 20 ppm/° C. The proposed technique eliminates the need for an additional start-up circuitry, which is mandatory for conventional architectures. The 1.18 V output voltage regulator, designed in a 180-nm CMOS process, has a simulated line regulation of 0.1 %/V with a power consumption of 15 nW at room temperature. The proposed regulator can drive the load from 0 to 30 mA.
Multi-Class ECG Signal Processing and Classification using CWT based on various Deep Neural Networks

The basic functioning of heart can be read through Electrocardiogram (ECG) Signal, this signal gives an idea whether the functioning of heart is normal or abnormal and type abnormality can also be identified, which helps to diagnose the patients in time. This work investigates a deep-learning model using 2DCNN to classify various category of ECG signal. This proposed CNN model is trained and tested to classify three different classes of heart arrhythmia such as cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythms (NSR). The time domain ECG signal is preprocessed and further it is transformed in to time-frequency scalogram by utilizing continuous wavelet transform (CWT), these scalogram is remodeled and saved as RGB images with necessary dimensions. Later these converted RGB images are fed to the input of various 2DCNN models such as alexnet, vgg16, squeezenet and googlenet to classify arrhythmia type. ECG Recordings from MIT BIH database were chosen and used for training and testing dataset. The performance of proposed scheme is evaluated on various CNN networks, a reasonable classification accuracy of 99.33 % was acheived by alex net.
Need of Trusted Security for Sustainability of IoT-BIG Data Application Deployments

Adoption of wireless and sensor technology Internet of Things (IoT). , small and powerful sensor devices increasing in every domain in the urge of providing smart information and communication technology enabled solutions. Sustainability with emerging digital transformation and underlying seamless connected infrastructure needs to address security and trust as key challenges for deployment and shaping up a new working paradigm. In the vision of pervasive communication where mobile applications are growing immensely with the potential of resource-restricted, low cost, high performance, and seamless user centric solutions without the need of a fixed infrastructure. However, resource-restrictions, seamless devices interaction and the varying performance of devices will make it difficult to ensure end-to-end security and trust. Artificial Intelligence, deep learning, machine learning are other popular technologies emerging for intelligent decision making, proliferation and managing the data in computing environment. Security assurance is an expression of a high-level organizational security policy to deal with trust to enhance the technology adaption and security risks. Owing the highly dynamic and heterogeneous network with inherent open connectivity, secure and trusted communications, protecting mobile devices, preserving privacy and determining the security assurance are still thrust research areas. To understand the technology sustainability, security objectives must be equally important as the functional objectives. In this chapter we explore the different IoT and Big Data opportunities, challenges and threats to represent explorative and descriptive analysis of security intimidations. This study reveals various security challenges and opportunities in current age of digital transformation with a big technological shift where sustainability of these fast moving technologies deployments as another global concern. Here we propose a Trusted Security assurance by Policy Design and Deployment Auditing Model. In the winding up possible solutions inspired by the well-known quote ‘Practice makes man perfect’, security best practice gaps has been identified and recommend enforcing of policy to enhance the significant adaptation of secure and trustworthy applications where information is pervading.
Design and Development of Automated Guided Vehicle with Line Follower Concept using IR

This research article illustrates the development of an automated guided vehicle as an application of automation in manufacturing sector for warehouse manipulation. This effort had a considerable influence on the industrial sector in terms of contemporary technical achievements. It has the ability to outwit physical labour and control traffic flow in the warehouse and workspace. A robotic system can be built to operate in a complicated environment and along different pathways across the industrial facility. The purpose is to study an industrial compatible design of a user friendly robotic vehicle or AGV. Most industries come across numerous difficulties when it comes to material handling and this proposed robotic design may eliminate all inaccuracies and serve as an asset to any industry. When it comes to designing the vehicle, it is crucial that the chosen equipment have all necessary features and specifications so that it can be assembled efficiently. The AGV can adapt to the working environment and precisely move in an industry warehouse. This paper also highlights a method of line following as the basic principle of operation. A most suitable plan for path tracking is discussed which is formulated based on its drive and turning mechanisms. The embedded system should be developed in such a way that the hardware components are effectively integrated with the software. It consists of a microcontroller and proximity sensors for efficient navigation. The prototype is sophisticated with minimum mechanical and logistic errors, capable of carry loads weighing up to 200kg in pieces or compact lightweight components. The robot follows the instructions and is deployed to bring the raw materials on a specified path to the production line in minimal time. It is capable of detecting and responding autonomously to impediments in its path (persons or objects), in order to ensure that no mistakes or damage occur during transit.
Reduction of Quadrant Glitch: An Experimental Validation on XY Positioning Table

The glitch effect phenomenon is one of the important control problems for ball screw driven industrial systems. There are various practical methods proposed in the literature to compensate for quadrant glitches e.g. model and friction identification, linear/nonlinear control algorithms combined with observer or filter structures. This study aims to present experimentally the effects of observer-based control structures on quadrant errors in ball screw mechanisms. To this end, combinations of various controller and velocity/disturbance observer algorithms are applied to a ball screw driven XY positioning table for a circular motion and experimental results are presented.
Mismatch Shaping for Binary-Coded DAC

Mismatch errors in digital-to-analog converters (DACs) can be shaped using various dynamic matching techniques such as data-weighted averaging. However, these techniques become exponentially complex for high-resolution DACs, since they often require conversion from binary coded signals to thermometer coded ones before applying such a scheme. Furthermore, after conversion, it requires N-stage element shuffling circuitry for an N-bit DAC. In this paper, we present a mismatch shaping scheme based on zero mean error encoding technique, which can be applied directly to binary coded signals, without the need for binary to thermometer decoding and element shuffling circuitry.
Depletion Based Digital and Analogue Circuits with n-Channel IGZO Thin Film Transistors

In this work, both analogue and digital depletion-mode single channel transistor circuits are presented and are simulated using an n-channel IGZO technology with $\boldsymbol{V}_{\boldsymbol{TH}}= -\mathrm{0}. \mathbf{87}\mathbf{V}$. A logic family is introduced, suppressing the need for an additional voltage level and level restoring circuitry. Furthermore, in the analogue domain, a depletion current mirror topology is presented with demonstrated small current error. Finally, the current mirror is used in the design of an OpAmp, achieving a simulated open-loop gain of 45 dB, CMRR of 58 dB, unity-gain frequency of 444 kHz and a phase margin of 71 degrees.
Python based Memristor Model Library for Variability Analysis

Variability analysis is an essential step in the design of robust memristive systems. The memristor devices often are faced with device to device, and cycle to cycle variability, which can be analysed using different types of memristor model parameters. Different memristor devices fit best in behaviour to different memristor models, and there is no universal model that fits all memristor-like devices. Therefore, it is important to develop a library of memristor models to cross validate, fit and analyse the behavior of realistic devices connecting memristive systems implementation. In this paper, we propose a library that contains different models of memristor, that can be programmed, and analysed for studying variability, and sensitivity of model parameters. The focus is on those parameters that are practically relevant to the design of realistic memristive systems. This library provides the options for parameter tuning, noise analysis and device fitting to like the right models to the right devices.
A Two-Phase Linear-Exponential Incremental ADC with Second-order Noise Coupling

This paper presents a two-phase linear-exponential incremental analog-to-digital converter (IADC) with using second-order noise coupling (NC). In the first phase, it works as a first-order IADC. Then the second-order NC path is activated in the second phase to significantly expedite the accumulation speed. Moreover, during the second phase, the integrator is disabled to achieve a large maximum stable amplitude (MSA). Simulations demonstrated that the proposed architecture could achieve a higher signal-to-quantization-noise ratio (SQNR) while avoiding the noise penalty and keeping the high effectiveness of data weighting averaging (DWA) compared with the prior art with using first-order NC. Mathematical analysis and further simulation results are presented to confirm the theory of the proposed structure.
Development of Propeller-type Rotary LED Proto-transmitter for Underwater Visible Light Communication

To improve the operational efficiency of remotely operated vehicles (ROVs) used to investigate ocean resources, this paper focuses on underwater visible light communication using a camera as a receiver (i.e., underwater image sensor communication: UISC). UISC can realize wireless communication underwater, where wireless communication using radio waves is difficult. However, due to the limited area of the ROV, it is hard to increase the number of transmitter LEDs installed on the ROV, resulting in a lower communication speed. We have developed a propeller-type rotary LED transmitter (P-RTx) to solve this problem, assuming that the LEDs are attached to the ROV's thruster. P-RTx is a device that increases communication speed by rotating a blinking LED that transmits data and utilizing the receiver's ability to capture their blinking lights as multiple afterimages. As a result of experiments using a prototype P-RTx, we have achieved error-free communications up to a communication distance of 3.0 m at a communication speed of 3.24 kbps, 72 times faster than the conventional method.
Attitudes towards Client-Side Scanning for CSAM, Terrorism, Drug Trafficking, Drug Use and Tax Evasion in Germany

In recent years, there have been a rising number of legislative efforts and proposed technical measures to weaken privacy-preserving technology, with the stated goal of countering serious crimes like child abuse. One of these proposed measures is Client-Side Scanning (CSS). CSS has been hotly debated both in the context of Apple stating their intention to deploy it in 2021 as well as EU legislation being proposed in 2022. Both sides of the argument state that they are working in the best interests of the people. To shed some light on this, we conducted a survey with a representative sample of German citizens. We investigated the general acceptance of CSS vs cloud-based scanning for different types of crimes and analyzed how trust in the German government and companies such as Google and Apple influenced our participants’ views. We found that, by and large, the majority of participants were willing to accept CSS measures to combat serious crimes such as child abuse or terrorism, but support dropped significantly for other illegal activities. However, the majority of participants who supported CSS were also worried about potential abuse, with only 20% stating that they were not concerned. These results suggest that many of our participants would be willing to have their devices scanned and accept some risks in the hope of aiding law enforcement. In our analysis, we argue that there are good reasons to not see this as a carte blanche for the introduction of CSS but as a call to action for the S&P community. More research is needed into how a population’s desire to prevent serious crime online can be achieved while mitigating the risks to privacy and society.
Extreme Learning Machine: A Comprehensive Survey of Theories & Algorithms

The Extreme Learning Machine (ELM), a quick and effective approach for training single-hidden-layer feedforward neural networks, is thoroughly reviewed in this paper. The objective is to provide a systematic review and analysis of the various theories, algorithms, and applications of ELM, which have not been adequately covered in the literature. The study examines the theoretical foundations of ELM, provides an overview of various ELM algorithms, analyzes the applications of ELM in different domains, and identifies future research directions and challenges in the field of ELM. The findings suggest that ELM is a promising and effective learning algorithm with many benefits as improved learning speed, good generalisation performance, and low complexity. The implications of this research for practice and research are significant. However, more empirical studies are necessary to evaluate its performance in real-world applications, and further research is required to assess its effectiveness in different domains. Future work should focus on exploring the potential of ELM in deep learning, enhancing the interpretability of ELM models, and developing new algorithms that can improve the learning efficiency of ELM. Overall, this survey provides a valuable reference for researchers and practitioners to better understand the potential and limitations of the ELM algorithm.
A Deep Learning based Multi-edge-type decoding algorithm for 5G NR LDPC codes

Low-density parity-check(LDPC) code has been selected as the channel coding method by 5G NR because of its excellent error-correcting performance. To further improve the performance of LDPC decoding, this paper proposes a neural normalized min-sum(NNMS) algorithm based on multi-edge-type(MET). Based on the LLR convergence analysis of the protograph matrix of 5G NR, the base matrix is divided into several independent regions. Each part is assigned a unique scaling factor at different iterations. To verify the effectiveness of the proposed algorithm, We use two parity-check matrixes(PCM) derived from different base graphs in simulations. The results show that the proposed algorithm performs at most 0.45dB better than BP, 0.37dB better than NMS, and 0.25dB better than OMS, respectively, when the frame error rate (FER) is at 1$0^{-5}$ level over additive white Gaussian noise (AWGN) channels using BPSK modulation.
Classification of ECG Anomaly with Dynamically-biased LSTM for Continuous Cardiac Monitoring

This paper presents an electrocardiogram (ECG) signal classification model based on dynamically-biased Long Short-Term Memory (DB-LSTM) network. Compared to conventional LSTM networks, DB-LSTM introduces a set of parameters $C$ which save the previous time-step cell gate states of the unit cell. Hence, more feature information is preserved and a smaller size network is required for the classification task. Comprehensive simulations using MIT-BIH ECG datasets show that this model can perform ECG feature classification with shorter time window, faster training convergence while achieving comparable training and classification accuracy with much lower weigh resolution. Compared to the other state-of- art ECG analysis algorithms, this model only requires 4 layers, and it achieved 96.74% accuracy when weights are truncated from FP32 to INT4 with only 2.4% accuracy degradation. Implemented on Xilinx Artix-7 FPGA, the proposed design is estimated to consume only 40μW dynamic power, which is a promising candidate for resource constrained edge devices.
Thermal-Induced Multi-State Memristors for Neuromorphic Engineering

With the rapidly evolving internet of things (IoT) era, the ever-rising demand for data transfer and storage has put a knotty problem on conventional computers, known as the von Neumann bottleneck and memory wall problem. Slow scaling of CMOS transistors due to physical and economical limitations further exacerbates the situation. It is only logical to mimic what has been known so far as the most energy-efficient system, the human brain. The brain-inspired neuromorphic computing systems compute and store the data locally, which dramatically reduces area and energy consumption. In this work, we demonstrate thermal-induced multi-state memristors for neuromorphic engineering applications. We show that in a neural network that uses a memristor-spintronic nano oscillator connection to implement the synapse-neuron pair, with increased temperature, the total power consumption could be reduced by more than 50 % without degrading the output power of a spintronic-based neuron.
Brauer-Picard groups and pointed braided tensor categories

Tensor categories are ubiquitous in areas of mathematics involving algebraic structures. They appear, also, in other fields, such as mathematical physics (conformal field theory) and theoretical computer science (quantum computation). The study of tensor categories is, thus, a useful undertaking. Two classes of tensor categories arise naturally in this study. One consists of group-graded extensions and another of pointed tensor categories. Understanding the former involves knowledge of the Brauer-Picard group of a tensor category, while results about pointed Hopf algebras provide insights into the structure of the latter. This work consists of two main parts. In the first one we compute the Brauer-Picard group of a class of symmetric non-semisimple finite tensor categories by studying a canonical action on a vector space. In the second one we use results from the theory of Hopf algebras to prove an equivalence between the groupoid of pointed braided finite tensor categories admitting a fiber functor and a groupoid of metric quadruples.
LDPC and ultra-massive multi-input multi-output for secrecy in 6G

Sixth generation (6G) wireless networks are a promising technology for meeting the extreme demands of 2030 of high data rates, ultra-low latency, and ultra-reliability. However, supporting the services of 6G networks requires advanced security mechanisms. In addition, 6G has a rapid and simple means of accessing the channel because of its broadcast transmission method, which makes it prone to eavesdropping attacks. Physical-layer security (PLS) is predicted to be a suitable method of achieving network security, in which the characteristics and advantages of wireless networks are exploited. PLS technologies include wiretap code designs, artificial noise injection (jamming), multi-input multi-output (MIMO)(beamforming), and physical-layer authentication. This paper proposes an erasure corrector code, namely a low-density parity check (LDPC), and ultra-massive MIMO (UMMIMO) to fix this issue. The simulation results show that our solution guarantees data secrecy, even against eavesdroppers with many antennas and considerable decoding resources, while reducing power consumption, consequently supporting secure, green transmission.
Cloud-based Smart Parking System using Internet of Things

This research aims to design a smart parking system using cloud and Internet of Things (IoT) technologies to improve the process of finding parking spaces in urban areas. The proposed system utilizes sensors and microcontrollers in each parking space to provide real-time data to users through a mobile application. The primary objective of our research is to address issues, such as time-consuming manual searches for parking spaces and reduce traffic congestion. This research suggests a system of reliable smart ultra-sonic sensors connected to a microcontroller for easy and remote parking automation. Users can select a parking space by clicking on the available slot in the mobile application. Additionally, the mobile application informs users about empty spots. This smart parking system can be implemented on both small and large-scale models, and the results suggest that it effectively replicates traditional automobile parking, with the added convenience of a mobile application, in urban areas.
A 120GHz Receiver with $1/f$ Noise Mitigation Technique for Near-Field IoT

This paper presents a 120 GHz receiver for Near-Field IoT sensors, without the conventional LNA and mixers building blocks. A new technique for the reduction of the low-frequency 1/f noise is also presented. The technique is used to improve the sensitivity of the 120GHz receiver with a data rate of 100kb/s, by lowering the 1/f noise corner frequency. The technique involves the complementary switching of two MOSFET transistors in order to effectively act as a single MOSFET transistor. It also involves the isolation of the drain terminals of these identical MOSFET transistors in order to eliminate any electric field from the complementary transistors which may affect the de-trapping process. The complementary switching in the literature has only been applied to low-frequency circuits with the highest operating frequency being 120 kHz. The proposed technique leads to a 9dB reduction in the drain current noise at frequencies up to 100 kHz. Realized in a 65nm CMOS LPE technology from GlobalFoundries™, the receiver has a measured sensitivity of −46dBm, power consumption of $\mathbf{52}\ \boldsymbol{\mu} \mathbf{W}$, and energy efficiency of 5.2pJ/bit. The occupied on-chip area is 0.56mm2.
O3NMS: An Out-Of-Order-Based Low-Latency Accelerator for Non-Maximum Suppression

Non-maximum suppression (NMS) is an important post-processing method to eliminate overlapping bounding boxes in object detection neural networks. Suffering from quadratic computational complexity and frequent memory access, NMS has become a bottleneck of detection latency. To deal with this problem, we propose out-of-order NMS (O3NMS), a hardware- software co-optimization approach to reduce latency as well as area overhead of NMS accelerator. In order to reduce startup latency, we devise the O3NMS algorithm that removes pre-sort operation. To efficiently support O3NMS algorithm, we design a specialized hardware accelerator. Our design has been implemented in both Xilinx FPGA and SIMC 40nm technology. Experiments demonstrate O3NMS accelerator achieves 2.51 x speedup as well as 37 % reduction in FPGA source utilization compared with the state-of-the-art (SOTA) NMS accelerator.