Deep convolutional neural networks (CNNs) have reformed numerous fields, comprising computer vision and natural language processing. Nevertheless, CNNs' computational complexity frequently raises obstacles to utilization on resource-constrained devices. Different optimization strategies have been proposed in response to this issue, comprising quantization, knowledge distillation, and pruning. This paper familiarizes a novel pruning method, Feature Map Importance Pruning (FMIP), designed to optimize the performance of deep CNNs while reducing computational costs. The FMIP computes the importance of feature maps within convolutional layers according to the area of their activation values, facilitating a systematic approach to pruning decisions. By detecting and eliminating the redundant feature maps, FMIP can considerably cut the number of parameters and FLOPs in a CNN model without conceding accuracy. This is chiefly advantageous for using CNNs on devices with limited memory and computational power. We evaluated FMIP on various CNN architectures, including VGG16 and ResNet50, using datasets such as CIFAR-10 and ImageNet. Our experiments demonstrated significant model compression while preserving high accuracy. Specifically, FMIP achieved the following results: VGG16 with CIFAR-10 demonstrated an 81.9% reduction in parameters and a 48.6% reduction in FLOPs, with an accuracy of 93.33%. ResNet50 with ImageNet achieved an 83.11% reduction in parameters and a 71.55% reduction in FLOPs, with an accuracy of 92.03%.

The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.

Fixed-wing UAVs (FW-UAVs) are empowered to handle diverse civilian and military missions, but sensor failure scenarios are constantly rising. Rapid advancement in deep learning methods currently proposes state-of-the-art solutions for fault detection of UAVs. However, most recent deep learning-based detection models suffer from model size, high computational complexity, and high-power consumption, which are challenging for small-sized FW-UAVs with limited battery backup and computational power. Therefore, to overcome these problems, this article introduces a lightweight CNN model built on prior work combined with the LSTM-AM framework to obtain accurate fault detection of FW-UAVs with low power consumption and fast computations. First, lightweight CNN architecture aims to minimize computational complexity while maintaining high accuracy in fault detection. The LSTM model merged with Attention Mechanism (AM), allows the architecture to obtain temporal dependencies and concentrate on essential features for enhanced fault detection accuracy. The combined version of lightweight CNN, LSTM, and AM commits to more reliable and efficient fault detection in FW-UAV applications, improving UAV drones’ overall performance and safety.

With the development of autonomous driving, there has been considerable attention on 3D object detection using LiDAR. Pillar-based LiDAR point cloud detection algorithms are extensively employed in the industry due to their simple structure and high real-time performance. Nevertheless, the pillar-based detection network suffers from significant loss of 3D coordinate information during the feature degradation and extraction process. In the paper, we introduce a novel framework with high performance, termed EFNet. The EFNet uses the Enhancing Pillar Feature Module (EPFM) to provide more accurate representations of features from two directions: pillar internal space and pillar external space. Additionally, the Head Up Module (HUM) is utilized in the detection head to integrate multi-scale information and enhance the network’s information perception ability. The EFNet achieves impressive results on the nuScenes datasets, namely, 53.3% NDS and 42.4% mAP. Compared to the baseline PointPillars, EFNet improves 8% NDS and 11.9% mAP. The results demonstrate that the proposed framework can effectively improve the network’s accuracy while ensuring deployability.

Several deep learning algorithms have shown amazing performance for existing object detection tasks, but recognizing darker objects is the largest challenge. Moreover, those techniques struggled to detect or had a slow recognition rate, resulting in significant performance losses. As a result, an improved and accurate detection approach is required to address the above difficulty. The whole study proposes a combination of spiked and normal convolution layers as an energy-efficient and reliable object detector model. The proposed model is split into two sections. The first section is developed as a feature extractor, which utilizes pre-trained VGG16, and the second section of the proposal structure is the combination of spiked and normal Convolutional layers to detect the bounding boxes of images. We drew a pre-trained model for classifying detected objects. With state of the art Python libraries, spike layers can be trained efficiently. The proposed spike convolutional object detector (SCOD) has been evaluated on VOC and Ex-Dark datasets. SCOD reached 66.01% and 41.25% mAP for detecting 20 different objects in the VOC-12 and 12 objects in the Ex-Dark dataset. SCOD uses 14 Giga FLOPS for its forward path calculations. Experimental results indicated superior performance compared to Tiny YOLO, Spike YOLO, YOLO-LITE, Tinier YOLO and Center of loc+Xception based on mAP for the VOC dataset.

The fundamental criteria for industrial manipulator applications are vibration free and smooth motion with minimum time. This paper investigates the trajectory tracking and vibration control of rotary flexible joint manipulator with parametric uncertainties. Firstly, the dynamic modeling via Euler Lagrange equation for a single link flexible joint manipulator is discussed. Secondly, for the execution of smooth motion between two points, bounded and continuous jerk trajectory is developed and implemented. In addition, the prospective strategy uses the concatenation of fifth-order polynomials to provide a smooth trajectory between two-way points. In the planned algorithm, user can independently define the position, velocity, acceleration and jerk values at both initial and final positions. The feature of user-defined parameters gives the versatility to the suggested algorithm for generating trajectories for diverse applications of robotic manipulators. Moreover, the planned scheme is easy to implement and computationally efficient. In the last, the performance of the presented scheme is examined by comparison with cubic splines and a linear segment with parabolic blends (LSPB) techniques. Generated trajectories were evaluated successfully by carrying multiple experiments on QUANSER’s flexible joint manipulator.

The rapid progress of convolutional neural networks (CNNs) in multiple applications of practical implementation is generally hindered by an upsurge in network size and computational complexity.Currently, engineers focus on reducing these problems through compressing the CNNs by pruning filters and their weights.In this paper, we present a fresh and easy-to-use pruning approach that reduces the model size by eliminating complete filters and filter weights based on the sparse group LASSO (Least Absolute Shrinkage and Selection Operator) method across the convolutional layers.More precisely, it regulates the sparsity at the feature level and the group level.During the process of pruning, the unnecessary filters with their weights eliminate directly without sacrificing accuracy in the test, resulting in much compact and slimmer architectures.We experimentally compute the effectiveness of our methodology with various state-of-art CNN models on various benchmark data sets.Mainly, CIFAR-10 data sets applied on VGG-16 model and reduce the parameters approx.96.1% and saved approx.83.55% float-point-operations (FLOPs) without sacrificing accuracy and have obtained development in state-of-art.

Convolutional neural networks (CNNs) are quickly evolving, which usually results in a surge of computational cost and model size. In this article, we present a correlation-based filter pruning (CFP) approach to train more reliable CNN models. Unlike several available filter pruning methodologies, our presented approach eliminates useless filters according to the volume of information available in their related feature maps. We apply correlation to compute the duplication of information carried in the feature maps and created a feature selection scheme to obtain pruning approaches. Pruning and fine-tuning are cycled many times, producing slim and denser networks with similar accuracy to the original unpruned model. We practically calculate the success of our technique with various state-of-art CNN models on many standard datasets. Specifically, for ResNet-50 on ImageNet, our approach eliminates 44.6% filter weights and saves 51.6% Float-Point-Operations (FLOPs) with 0.5% accuracy gain and obtained state-of-art performance.

We report an all-dielectric optical amplitude modulator based on guided-mode resonance. This design consists of a silicon nitride nanograting placed on indium-tin-oxide (ITO) integrated silicon-on-insulator platform. By leveraging the electro-tunable epsilon-near-zero effect of ITO, we achieve ~ 15 dB modulation depth at 1550 nm wavelength.

Improving solar cell efficiency is crucial to meet the ever-growing global energy demand. Here, we present a wide-angle and polarization-independent design of broadband plasmonic metamaterial absorbers. We report 96.9% average absorption over 400-1100 nm wavelengths. An integrated design of broadband absorbers into photovoltaic-modules could significantly enhance solar cell efficiency.

We report an epsilon-near-zero (ENZ) material based polarization-insensitive electro-tunable metamaterial absorber, which comprises a patterned-metal–insulator–metal based nanostructure. The numerical simulations show that coupling of electrically-induced ENZ-mode to the gap-plasmon resonance-mode enables more than 80% average absorbance over 1500–1800 nm wavelength regime and ~10-dB modulation-depth at 1550 nm.

Crowd counting plays a significant role in crowd monitoring and management, which suffers from various challenges, especially in crowd-scale variations and background interference issues. Therefore, we propose a method named depth and edge auxiliary learning for still image crowd density estimation to cope with crowd-scale variations and background interference problems simultaneously. The proposed multi-task framework contains three sub-tasks including the crowd head edge regression, the crowd density map regression and the relative depth map regression. The crowd head edge regression task outputs distinctive crowd head edge features to distinguish crowd from complex background. The relative depth map regression task perceives crowd-scale variations and outputs multi-scale crowd features. Moreover, we design an efficient fusion strategy to fuse the above information and make the crowd density map regression generate high-quality crowd density maps. Various experiments were conducted on four main-stream datasets to verify the effectiveness and portability of our method. Experimental results indicate that our method can achieve competitive performance compared with other superior approaches. In addition, our proposed method improves the counting accuracy of the baseline network by 15.6%.

In this article, an autonomous carrier landing problem of an aircraft is addressed by developing an autonomous carrier landing system (ACLS) composed of previewable guidance and control systems. In the guidance system, an appropriate touchdown point is estimated by predicting the seakeeping motion of the deck by unscented Kalman filtering technique, which is then utilized to adjust the reference glide path and produce an effective deck motion compensation, indispensable for a safe landing. The adaptive preview control (APC) scheme is proposed, which utilizes future reference information. The feedback and feedforward adaptive gains are derived through the Lyapunov stability theorem ensuring better tracking response and disturbance rejection. Hence, the asymptotic stability of the closed‐loop system is guaranteed. The simulation results depict better performance of the proposed ACLS in the presence of deck fluctuations and airwake disturbance compared with PID and LMI based preview control schemes.

Convolution neural network (CNN) widely used for the application of computer vision, such applications would be beneficial for CNN if their workload could be reduced. In this paper, we evaluate CNN's arithmetic properties (Matrix Multiplications and Additions) and proposed a Strassen algorithm with the help of Pan's result to minimize their workload of computation. That algorithm needs the least workspace and it has great computational accuracy. Matrix multiplication (MM) is the most fundamental computational operation, and the performance of Matrix multiplication (MM) depends on different factors. Moreover, that is a very flexible and robust structure. Here we are focusing on the number of element-wise multiplications and addition, the effects of this research on modern computations.

The blistering progress of convolutional neural networks (CNNs) in numerous applications of the real-world usually obstruct by a surge in network volume and computational cost. Recently, researchers concentrate on eliminating these issues by compressing the CNN models, such as pruning filters and weights. In comparison with the technique of pruning weights, the technique of pruning filters doesn’t effect in sparse connectivity patterns. In this article, we have proposed a fresh new technique to estimate the significance of filters. More precisely, we combined L1-norm with capped L1-norm to represent the amount of information extracted by the filter and control regularization. In the process of pruning, the insignificant filters remove directly without any loss in the test accuracy, providing much slimmer and compact models with comparable accuracy and this process is iterated a few times. To validate the effectiveness of our algorithm. We experimentally determine the usefulness of our approach with several advanced CNN models on numerous standard data sets. Particularly, data sets CIFAR-10 is used on VGG-16 and prunes 92.7% parameters with float-point-operations (FLOPs) reduction of 75.8% without loss of accuracy and has achieved advancement in state-of-art.

The robotic arm is a kind of manipulator, used to carry out most difficult tasks, which is the need of today's world and its applications are in vast fields. It can be used for many purposes such as to pick and drop and to work in the environment where a human cannot work i.e. harmful chemicals, high temperature and so on. One needs some skilled personnel to make that robotic arm functional as per requirement and for making some amendments in its functionality, one has to reprogram its parameters which needs an expert. A robotic arm that can learn from its mimicry done by a human which can be easily taught to perform certain operation as per its application by an unskilled person. We have designed a prototype of the robotic arm which can work according to the way one teaches furthermore, robotic arm does not need a skilled person, the mimicry feedback will be recorded in a processing unit i.e. microcontroller (Arduino) and on a single command of play that robotic arm will work continuously to perform that specific task.

Due to the fast-growing industry of intelligent vehicles the advanced driver assistance system (ADAS) has engrossed a lot of attention of the scholars. One of the biggest hurdles for new autonomous vehicles is to detect curvy lanes, multiple lanes, and lanes with a lot of discontinuity and noise. The purpose of this paper is to analyze the possibilities of image processing techniques for a computer vision application focusing on the problem of lane detection to enable traffic safety and driving comfort. The proposed algorithm is a combination of two sub-algorithms. The first sub-algorithm called Fuzzy Noise Reduction Filter (FNPF); removes the noise and smoothen the sequences of images received by the camera. While the second sub-algorithm aims to detect lane in normal as well as challenging scenarios by applying the concept of Hough Transform (HT) with a capable region of interest. The novelty of the proposed research study is the tracking of the lanes under inclement weather and challenging lightening conditions with improved computational time. The result achieved through our proposed algorithm is satisfactory in video sequences captured on several road types and under very challenging lighting and weather conditions.

This paper attempts to clarify the concept and applications of Li-Fi technology. The current Wi-Fi network use Radio Frequency waves, but the usage of the available RF spectrum is limited. Therefore a new technology, Li-Fi has come into picture. Li-Fi is a recently developed technology. This paper explains how array of LEDs are used to transmit data in the visible light spectrum. This technology has advantages like security, increased accessible spectrum, low latency efficiency and much higher speed as compared to Wi- Fi. The aim of this research paper is to design a Li-Fi transceiver using Arduino which is able to transmit and receive data in binary format. The software coding is done in Arduino- Uno platform. Successful transmission and reception of data(alphanumeric) has been done.

The rapid development of convolutional neural networks (CNNs) is usually accompanied by an increase in model volume and computational cost. In this paper, we propose an entropy-based filter pruning (EFP) method to learn more efficient CNNs. Different from many existing filter pruning approaches, our proposed method prunes unimportant filters based on the amount of information carried by their corresponding feature maps. We employ entropy to measure the information contained in the feature maps and design features selection module to formulate pruning strategies. Pruning and fine-tuning are iterated several times, yielding thin and more compact models with comparable accuracy. We empirically demonstrate the effectiveness of our method with many advanced CNNs on several benchmark datasets. Notably, for VGG-16 on CIFAR-10, our EFP method prunes 92.9% parameters and reduces 76% float-point-operations (FLOPs) without accuracy loss, which has advanced the state-of-the-art.