Miracle-dz/UniFAR-FTUs huggingface dataset:
A 125 MS/s 10.4 ENOB 10.1 fJ/Conv-Step Multi-Comparator SAR ADC with Comparator Noise Scaling in 65nm CMOS

Traditional SAR ADC employs a single comparator for all comparisons in the binary-search (BS) algorithm. Since the noise present in each comparison contributes differently to the overall ADC noise, that approach generally leads to sub-optimal performance in terms of the noise/power trade-off, as the comparator is sized for the worst-case LSB comparison. This paper presents a multi-comparator SAR ADC featuring noise scaling on the comparators. Since the comparator noise impact increases exponentially from MSB to LSB, we exponentially scale the comparators noise voltage upward from the LSB (which requires low noise) to the MSB for saving power consumption. Furthermore, the MSB comparators are designed for higher bandwidth (and thus worse noise) compared to the LSB comparators to facilitate high conversion speed. These techniques are demonstrated by a single channel, 12-bit SAR design with 5 different comparators. A background calibration technique is proposed to alleviate comparator offset mismatch. The 65 nm CMOS ADC runs up to 125MS/s achieving SNDR/SFDR of 64.4/75.1dB above Nyquist frequency and consumes 1.7mW from 1.2 V supply and has an FoM of 10.1 fJ/conv-step. To our best knowledge, this is the first reported single channel> 10 ENOB SAR ADC to achieve> 100 MS/s conversion speed.
Hybrid Approach Mixing Mathematical Programming and Machine Learning Techniques for Thermal Grid Systems

We focus on thermal grid systems which aim to control a number of air conditioning systems as a unit for achieving highly energy-efficient buildings. In order to realize real-time control of them, we propose hybrid systems in which artificial neural networks are involved and it is trained by using mathematical programming techniques determining the outputs of the air conditioning machines in multi-periods. Through some numerical experiments with the actual settings of the thermal grid systems, the potential of our proposed method is examined.
Validation of stereo vision based liver surface reconstruction for image guided surgery

Image guided surgery systems aim to provide navigation to surgeons in order to improve accuracy and safety of the procedures. Through stereo reconstruction algorithms, it is possible to generate 3D surfaces intra-operatively by means of a laparoscopic stereo-camera. This study aims to setup a simulation system and quantitatively validate a recent proposed reconstruction algorithm with application to laparoscopic liver resection surgery. The intra-operative surface will be used to guide model to patient registration. This will also improve accuracy of the navigation by correcting intra-operative deformations of the liver, such as those due to pneumoperitoneum. The accuracy results of the reconstruction method was found to be $3.7\pm 0.8$ mm in terms of Hausdorff distance. The validation therefore indicates the feasibility and accuracy of the surface reconstruction method.
Suppression of Common and Differential Component of Crosstalk Coupling onto the Differential Line and Field analysis of the Crosstalk

When a high speed signal flows in the differential line or single ended line, that runs parallel to the differential line of interest, it induces crosstalk. Crosstalk induced common and differential noise cannot be canceled by the virtue of differential signaling as mode conversion happens in the differential line. This induced crosstalk will cause EMI/EMC issues. A novel method is proposed to suppress these components by defected ground plane (DGP) structure. DGP structure consists of two ground strips and this design can be optimized for the given data rate of the signal. The proposed design suppresses the common and differential component of the crosstalk better than conventional differential line. The field analysis shows the reduction of field density around the differential line by the proposed design and in other words reduced crosstalk. SNR has been improved from 12.298dB to 18.36dB by the proposed design in the presence of high speed single ended trace.
An Embodied Learning Game Using Kinect and Labanotation for Analysis and Visualization of Dance Kinesiology

In this paper, we present an educational framework for analysis and visualization of dance kinesiology based on Labanotation and embodied learning concepts. The low-cost Kinect sensor is employed to extract skeletal data which are then processed and transformed geometrically. In the sequel, they are analyzed based on the Labanotation system to characterize the posture of the human limbs. Two modules have been developed. The first module serves for recording, analyzing and visualizing body movements. The second module is an application in which the user is required to perform with his upper limbs, a sequence of gestures given by the system in the form of Labanotation symbols. Dance notation consists of a set of symbols and rules for recording dance (or movement in general), in a similar way that music notation records music. The fact that Labanotation is recognized as one of the most widely used and accurate notation systems for recording dance highlights the proposed framework's applicability and potential as an educational visualization tool.
Autonomous Steer Actuation for an urban Quadricycle

In recent years, autonomous driving has become one of the most important and challenging topic in automotive industry. When dealing with autonomous car most of the attention is focused on high level trajectory planning algorithms and less attention is posed on the actuation level. Nevertheless, the design of a proper actuation system, in order to adapt a commercial vehicle for autonomous driving, is a non trivial task. This paper presents a detailed procedure that can be adopted to make the steering system of a commercial vehicle suitable for both full autonomous driving and remote driving. In particular, it concerns the implementation of actuators and sensors on the vehicle which allow the driver taking control on the car in each moment if required. Furthermore, the steering and control system models are described: they have been validated by means of experimental campaign.
MPoWS: Merged Proof of Ownership and Storage for Block Level Deduplication in Cloud Storage

With the rapid increase in amount of data that is being generated everyday, the requirement for cloud storage is also increasing. This fact makes deduplication an immediate necessity in order to achieve efficient utilization of storage on cloud servers. Client side deduplication detection also results in proper utilization of network resources which is considerably more costly than off site storage. But this fact has also lead to rise in spoofing based attacks where identity imitation is used to facilitate either faulty or unauthenticated retrieval of data. Hence the requirement for creation of validation protocols for both the clients and the storage servers in which both the players have to establish their identity. In our work, we have proposed a scheme called Merged Proof of Ownership and Storage (MPoWS) which fulfills the requirement of mutual validation for both clients and storage servers. The scheme works well with block level deduplication where relatively large files have to be uploaded to servers and checked for duplication by using different block tags. The protocol uses a randomized check approach by virtue of which it becomes difficult to predict which particular block will be validated thus ensuring improved security.
Incentive Mechanism Design for Crowdsourcing-Based Indoor Localization

Wi-Fi-based indoor localization system needs to construct a radio map by site surveys. The process of site surveys is time-consuming and crowdsourcing is one feasible option to tackle this issue. Meanwhile, privacy protection has drawn concerns from both industry and academia. In this paper, we propose two incentive mechanisms to stimulate mobile users (MUs) to contribute indoor trajectory data for crowdsourcing-based indoor localization with differential privacy to prevent MUs’ privacy leakage. The first mechanism considers fixed reward for MUs and incomplete information, where each MU’s sensitivity level of the data privacy is unknown to the crowdsourcing platform (CP). The interaction between MUs and CP is formulated into a two-stage Stackelberg game to maximize MUs’ utility and CP’s profit. The second mechanism jointly considers the variable reward for MUs and assumes CP knows each MUs’ sensitivity level of the data privacy. A demand function is used to model the relationship among CP, MUs, and service customer. The optimization problem of maximizing CP’s profit is studied to show the impact of the price fluctuation. Comprehensive simulations are presented to evaluate the performance of the proposed mechanisms and show some insights of the crowdsourced indoor localization incentive mechanism with privacy protection.
A Virtual Nose as a Rest-Frame - The Impact on Simulator Sickness and Game Experience

This paper presents an experiment measuring the impact of a virtual nose on simulator sickness and game experience in a virtual reality game presented on an Oculus Rift DK 2. Furthermore, the presented study investigated in the significance of the rest-frame's salience, for it to reduce simulator sickness. The results of our study indicated that the usage of a virtual nose reduces simulator sickness while it does not affect the game experience. Further results showed no significant impact of the rest-frame's salience, neither on simulator sickness nor on the game experience. In sum, the outcome of the study supports the thesis that a rest-frame in form of a virtual nose can be used in virtual reality applications to reduce simulator sickness.
On the Local Minima of the Empirical Risk

Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex non-smooth losses (such as modern deep networks), the population risk is generally significantly more well behaved from an optimization point of view than the empirical risk. In particular, sampling can create many spurious local minima. We consider a general framework which aims to optimize a smooth nonconvex function $F$ (population risk) given only access to an approximation $f$ (empirical risk) that is pointwise close to $F$ (i.e., $\norm{F-f}_{\infty} \le \nu$). Our objective is to find the $\epsilon$-approximate local minima of the underlying function $F$ while avoiding the shallow local minima---arising because of the tolerance $\nu$---which exist only in $f$. We propose a simple algorithm based on stochastic gradient descent (SGD) on a smoothed version of $f$ that is guaranteed to achieve our goal as long as $\nu \le O(\epsilon^{1.5}/d)$. We also provide an almost matching lower bound showing that our algorithm achieves optimal error tolerance $\nu$ among all algorithms making a polynomial number of queries of $f$. As a concrete example, we show that our results can be directly used to give sample complexities for learning a ReLU unit.
ORB-SLAM2 with 6DOF Motion

ORB-SLAM2 is an excellent keyframe-based SLAM with six degrees of freedom(6DOF), it can only triangulate 6DOF image frames for localization and mapping, and has no ability to process 3DOF(degrees of freedom) image frames which are not satisfied with the requirement of triangulation. However, ORB-SLAM2 both receive 3DOF and 6DOF image frames and can't distinguish them, that it wrongly triangulates 3DOF keyframes, resulting processing more useless and redundant keyframes. To address this problem, we add a new 6DOF keyframe selection module into the local thread of ORB-SLAM2 to filter out the 3DOF keyframe candidates in this paper. Experiments demonstrate the improved ORB-SLAM2 can process less keyframes, while keeping good location ability and very low absolute camera trajectory RMSE according to qualitative and quantitative evaluations.
A Novel Multi-Unit Out-Rotor Homopolar Inductor Machine for Flywheel Energy Storage System

The homopolar inductor machine (HIM) is of particular interest in the field of flywheel energy storage system, where it has the potential to significantly reduce self-discharge associated with magnetic losses. However, the conventional HIM suffers from low power and torque density due to its unipolar air-gap flux density. Besides, the air-gap flux density of HIM is limited by the cross section of rotor, and the rotor diameter is limited by the maximum tip speed of the rotor. Thus, the maximum effective length and diameter of rotor should be within a certain range, which constrains the output power and stored energy of the HIM. To solve these problems, a novel multi-unit out-rotor HIM (MO-HIM) with bipolar air-gap flux density is proposed in this paper. First, the structure and operating principle of MO-HIM is illustrated. Then, the characteristics of the proposed machine are analyzed, indicating that MO-HIM can be regarded as a superposition of multiple unit machines. Finally, to verify the superior performances of the proposed MO-HIM, it is compared with a unipolar out-rotor HIM with multi-segment stator cores in terms of air-gap flux density, back electromotive force, and output torque. Results show that the proposed MO-HIM has larger fundamental air-gap flux density, higher power, and torque density.
Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks

Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction, and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least 5 to 15 times faster than baseline methods.
LoRa Parameter Choice for Minimal Energy Usage

LoRa (Long Range) is a radio technology that facilitates low power communications over a large distance. The long range of communication means that a simple single hop transmission to a centralised gateway is sufficient for most applications. To supply this performance the LoRa physical layer offers a range of transmission parameters to tailor the capabilities for different applications. With over 6000 parameter combinations it is difficult to select the most efficient combination. Using experimental measurements and simulations we show how to find a parameter set with minimal energy usage for a given situation. Using data from real world deployments we also show that using payload replication with data-aware compression can improve data delivery ratios by 16 to 25% at no or minimal extra energy cost. For energy-sensitive applications low energy LoRa settings and forward error correction give better results than settings that maximise the packet reception ratio.
Contextual Topic Modeling For Dialog Systems

Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems. In this work, we focus on context-aware topic classification methods for identifying topics in free-form human-chatbot dialogs. We extend previous work on neural topic classification and unsupervised topic keyword detection by incorporating conversational context and dialog act features. On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances. We show that topical metrics such as topical depth is highly correlated with dialog evaluation metrics such as coherence and engagement implying that conversational topic models can predict user satisfaction. Our work for detecting conversation topics and keywords can be used to guide chatbots towards coherent dialog.
Implementation and Analysis of Single Precision Floating Point Multiplication Using Vedic and Canonic Signed Digit Algorithm

Multipliers are the most vital blocks in many digital applications, i.e. digital filters, Digital Signal Processors (DSPs). Further, multipliers are considered as crucial elements in Floating Point(FP) arithmetic operations which are widely used in the real time signal processing applications. The design of FP multiplier is relatively complex than integer multipliers. In floating point multiplication so far, many multiplier designs like array, Wallace and booth are embedded to improve the performance of the design. From the literature, it is inferred that no multiplier design provides satisfied performance with respect to all important performance metrics such as area, delay and power. Hence, in this paper, the design and analysis of single precision floating point multiplication with Vedic multiplier is presented by considering modified 2×1 multiplexers and modified 4:2 compressors in order to overcome the drawbacks in the existing multiplier techniques. To achieve further improvement in the performance of single precision floating point multiplication with Vedic multiplier, Canonic Signed Digit CSD technique is incorporated in that floating-point multiplication to improve the hardware efficiency by using minimum number of adders (or subtractor) blocks. Moreover, this paper examines the optimized design methods of CSD multipliers by using different techniques which are used in the above-mentioned multiplier using Vedic. Further, the performance analysis of single precision floating point multiplier using Vedic and CSD is analyzed in terms of area and delay and also compared with that of different existing techniques. From the simulation results, it is observed that single precision floating point multiplication with CSD multiplier gives better performance than the floating-point multiplier using Vedic with and without modified techniques. All the blocks involved for floating point multiplication are coded with Verilog and synthesized using Xilinx ISE Simulator.
SAR Automatic Target Recognition Using Transfer Learning Approach

In this paper we propose a new approach for Synthetic Aperture Radar (SAR) automatic target recognition (ATR). One of the main obstacles in SAR ATR is the limited availability of datasets that are used for training. In this paper, a deep learning approach is employed for ATR. The proposed scheme is based on employing a pre-trained convolutional neural network (CNNs) as transfer learning. A pre-trained CNN namely AlexNet is utilized as a feature extractor whereas the output features are used to train a multiclass support vector machine (SVM) classifier. The effectiveness of the proposed framework is verified on a public database where the final result using three target classes attain an accuracy of 99.4%.
Security-Constrained Optimal Power Flow via Cross-Entropy Method

This paper proposes a new optimization tool based on the cross-entropy (CE) method to assess security-constrained optimal power flow (SCOPF) solutions. First, the corresponding SCOPF stochastic problem is defined so that the optimum solution is interpreted as a rare event to be reached by a random search. Second, the CE method solves this new problem efficiently by making adaptive changes to the probability density function according to the Kullback–Leibler distance, creating a sequence of density functions that guides the search in the direction of the theoretically degenerate density at the optimal point. Different types of density functions are tested in order to cope with discrete variables present in the SCOPF problem. Two test systems, namely the IEEE 57 bus and the IEEE 300 bus, are used to evaluate the effectiveness of the proposed method in terms of solution quality and computational effort. Comparisons carried out with reference algorithms in the literature demonstrate that the CE method is capable of finding better solutions for the SCOPF problem with fewer evaluations.
Blind Identification of SFBC-OFDM Signals Using Subspace Decompositions and Random Matrix Theory

Blind signal identification has important applications in both civilian and military communications. Previous investigations on blind identification of space-frequency block codes (SFBCs) only considered identifying Alamouti and spatial multiplexing transmission schemes. In this paper, we propose a novel algorithm to identify SFBCs by analyzing discriminating features for different SFBCs, calculated by separating the signal subspace and noise subspace of the received signals at different adjacent OFDM subcarriers. Relying on random matrix theory, this algorithm utilizes a serial hypothesis test to determine the decision boundary according to the maximum eigenvalue in the noise subspace. Then, a decision tree of a special distance metric is employed for decision making. The proposed algorithm does not require prior knowledge of the signal parameters such as the number of transmit antennas, channel coefficients, modulation mode, and noise power. Simulation results verify the viability of the proposed algorithm for a reduced observation period with an acceptable computational complexity.
Gradient Descent Using Stochastic Circuits for Efficient Training of Learning Machines

Gradient descent (GD) is a widely used optimization algorithm in machine learning. In this paper, a novel stochastic computing GD circuit (SC-GDC) is proposed by encoding the gradient information in stochastic sequences. Inspired by the structure of a neuron, a stochastic integrator is used to optimize the weights in a learning machine by its “inhibitory” and “excitatory” inputs. Specifically, two AND (or XNOR) gates for the unipolar representation (or the bipolar representation) and one stochastic integrator are, respectively, used to implement the multiplications and accumulations in a GD algorithm. Thus, the SC-GDC is very area- and power-efficient. As per the formulation of the proposed SC-GDC, it provides unbiased estimate of the optimized weights in a learning algorithm. The proposed SC-GDC is then used to implement a least-mean-square algorithm and a softmax regression. With a similar accuracy, the proposed design achieves more than <inline-formula> <tex-math notation="LaTeX">$30 \times $ </tex-math></inline-formula> improvement in throughput per area (TPA) and consumes less than 13% of the energy per training sample, compared with a fixed-point implementation. Moreover, a signed SC-GDC is proposed for training complex neural networks (NNs). It is shown that for a 784-128-128-10 fully connected NN, the signed SC-GDC produces a similar training result with its fixed-point counterpart, while achieving more than 90% energy saving and 82% reduction in training time with more than <inline-formula> <tex-math notation="LaTeX">$50 \times $ </tex-math></inline-formula> improvement in TPA.