Wireless Image Sensor Research Articles

Adaptive Compressive Sensing Based on Sparsity Order Estimation for Wireless Image Sensor Networks

Adaptive Compressive Sensing Based on Sparsity Order Estimation for Wireless Image Sensor Networks

High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks

High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks

Toward a Wireless Image Sensor for Real-Time Fluorescence Microscopy in Cancer Therapy.

We present a mm-sized, ultrasonically powered lensless CMOS image sensor as a progress towards wireless fluorescence microscopy. Access to biological information within the tissue has the potential to provide insights guiding diagnosis and treatment across numerous medical conditions including cancer therapy. This information, in conjunction with current clinical imaging techniques that have limitations in obtaining images continuously and lack wireless compatibility, can improve continual detection of multicell clusters deep within tissue. The proposed platform incorporates a 2.4 × 4.7 mm2 integrated circuit (IC) fabricated in TSMC 0.18 µm, a micro laser diode (µLD), a single piezoceramic and off-chip storage capacitors. The IC consists of a 36 × 40 array of capacitive trans-impedance amplifier-based pixels, wireless power management and communication via ultrasound and a laser driver all controlled by a Finite State Machine. The piezoceramic harvests energy from the acoustic waves at a depth of 2 cm to power up the IC and transfer 11.5 kbits/frame via backscattering. During Charge-Up, the off-chip capacitor stores charge to later supply a high-power 78 mW µLD during Imaging. Proof of concept of the imaging front end is shown by imaging distributions of CD8 T-cells, an indicator of the immune response to cancer, ex vivo, in the lymph nodes of a functional immune system (BL6 mice) against colorectal cancer consistent with the results of a fluorescence microscope. The overall system performance is verified by detecting 140 µm features on a USAF resolution target with 32 ms exposure time and 389 ms ultrasound backscattering.

Energy-Saving Strategy Based on Image Super-Resolution for Wireless Image Sensor Networks Assisted by Cloud

Wireless image sensor networks (WISNs) collect surveillance images, resulting in copious quantities of data requiring processing and transmission within the network. To reduce and balance energy expenditure during in-network image data processing and transmission, this study introduces an energy-saving strategy based on image super-resolution for WISNs assisted by cloud. The strategy constructs an image data processing and transmission system for WISNs, leveraging cloud infrastructure. By utilizing image quality feedback, the adaptive adjustment of image node sensing resolution is facilitated, minimizing image data transmission volume while ensuring high-quality super-resolution reconstructed image. The establishment of low-overhead, multipath transmission routes enables image nodes to transmit data in blocks, contingent upon neighboring transmission node statuses, thereby equalizing energy consumption for image data transmission. Cloud servers are employed to reconstruct high-resolution images and provide users with surveillance images that satisfy quality requisites. Empirical findings demonstrate that the proposed approach extends the lifetime of WISNs, furnishes users with superior-quality surveillance images, and markedly enhances network monitoring efficacy.

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

VisionICE: Air–Ground Integrated Intelligent Cognition Visual Enhancement System Based on a UAV

Post-disaster search and rescue is critical to disaster response and recovery efforts and is often conducted in hazardous and challenging environments. However, the existing post-disaster search and rescue operations have problems such as low efficiency, limited search range, difficulty in identifying the nature of the target, and wrong target location. Therefore, this study develops an air–ground integrated intelligent cognition visual enhancement system based on a UAV (VisionICE). The technique combines a portable AR display device, a camera-equipped helmet, and a quadcopter UAV for efficient patrols over a wide area. First, the system utilizes wireless image sensors on the UAV and helmet to capture images from the air and ground views. Using the YOLOv7 algorithm, the cloud server calculates and analyzes these visual data to accurately identify and detect targets. Lastly, the AR display device obtains real-time intelligent cognitive results. The system allows personnel to simultaneously acquire air and ground dual views and achieve brilliant cognitive results and immersive visual experiences in real time. The findings indicate that the system demonstrates significant recognition accuracy and mobility. In contrast to conventional post-disaster search and rescue operations, the system can autonomously identify and track targets of interest, addressing the difficulty of a person needing help to conduct field inspections in particular environments. At the same time, the system can issue potential threat or anomaly alerts to searchers, significantly enhancing their situational awareness capabilities.

Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

Compressive sensing (CS) technology is introduced into space optical remote sensing image acquisition stage, which could make wireless image sensor network node quickly and accurately obtain images in the case of two constraints of limited battery power and expensive sensor costs. On this basis, in order to further improve the quality of CS image reconstruction, we propose fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet) for image reconstruction. FFPL-EDRNet consists of a convolution layer and a reconstruction network. We train FFPL-EDRNet end-to-end, thus greatly simplifying the pre-processing and post-processing process and eliminating the block effect of reconstructed images. The reconstruction network is based on residual network, which introduces multi-scale feature extraction, multi-scale feature combination and multi-level feature combination. Feature fusion integrates low-level information with high-level information to reduce reconstruction error. The perceptual loss function based on pretrained InceptionV3 uses the weighted mean square error to define the loss value between the reconstructed image feature and the label image feature, which makes the reconstructed image more semantically similar to label image. In the measurement procedure, we use convolution to achieve block compression measurement, so as to obtain full image measurements. For image reconstruction, we firstly use a deconvolution layer to initially reconstruct the image and then use the residual network to refine the initial reconstructed image. The experimental results show that: in the case of measurement rates (MRs) of 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio (PSNR) = 27.502, 26.804, 24.593, 21.359 and structural similarity (SSIM) = 0.842, 0.816, 0.720, 0.568 of the reconstructed images obtained by FFPL-EDRNet. Therefore, Our FFPL-EDRNet could enhance the quality of image reconstruction.

LoRa System for Monitoring and Facial Recognition

With the increasing tendency of incorporating technology into environments and activities in everyday life, new methods are being proposed to better integrate devices and mankind. Networks designed for monitoring areas through video and image systems are being implemented in several applications.Recent studies have shown interest from both the academy and industry to integrate wireless surveillance networks with low-cost and long range transmission technologies, such as LoRa (Long Range). With the intention to explore this topic, this work presents the development of a system prototype for intelligentmonitoring as a basis to future implementation of low-cost LoRa-based Wireless Image Sensor Networks (WISN). The system developed consists of a processing unit, a data routing interface and multiple sensor nodes. Its operation is autonomous and initiated through the detection of human presence, by the sensornode, capturing an image of the individuals present at each detection. The data is transmitted through LoRa devices to the central hub, where python-based methods of facial detection and recognition are employed. Several tests were performed to both adjust the system and to verify its efficiency. The results achievedwere satisfactory, indicating the viability of the proposed low-cost LoRa-based WISN.

A Miniature Wireless Silicon-on-Insulator Image Sensor for Brain Fluorescence Imaging.

Optical recording of genetically encoded calcium indicator (GECI) allows neuroscientists to study the activity of genetically labeled neuron populations, but our current tools lack the resolution, stability and are often too invasive. Here we present the design concepts, prototypes, and preliminary measurement results of a super-miniaturized wireless image sensor built using a 32nm Silicon-on-Insulator process. SOI process is optimal for wireless applications, and we can further thin the substrate to reduce overall device thickness to ~25μm and operate the pixels using back-side illumination. The proposed device is 300μm × 300μm. Our prototype is built on a 3 × 3mm die.

An inexpensive wireless smart camera system for IoT applications based on an ARM Cortex-M7 microcontroller

This paper presents a new proposal of a low-cost and low-power wireless smart camera device, designed to be used as an inexpensive smart image acquisition edge node for Internet of Things (IoT). Taking the advantages of the ARM CortexM7 microcontrollers, a high-performance STM32F7 microcontroller is used as a main digital signal processor (DSP) to acquire and transfer images at full rate, keeping the core idle to do real-time image processing. In order to design a very compact, low-cost and low-power wireless image sensor node, the DSP is supported by an embedded 1.3 Mega Pixel CMOS camera sensor and a low consumption 2.4GHz Wi-Fi module. This setup allows a custom build-in image processing algorithm for specific IoT sensing applications or a simply setup as low-cost streaming Motion JPEG (MJPEG) wireless camera node. In this work, both configurations have been tested and analyzed. Results show that the device can acquire and process images simultaneously at a full rate (30 fps) and the MJPEG transmission reaches 7.23 fps with a QVGA resolution.

High-Efficiency Progressive Transmission and Automatic Recognition of Wildlife Monitoring Images With WISNs

Wireless image sensor networks (WISNs) are widely applied in wildlife monitoring, as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions and narrow transmission bandwidth. For the contradiction between the above limitations of WISNs and the wildlife monitoring images with high resolution and complex background, we propose a novel wildlife intelligent monitoring system. On the foundation of saliency object detection, the convolutional encoder-decoder network is utilized to realize the progressive compression transmission and restoration for wildlife monitoring images, which guarantees the transmission efficiency and quality of wildlife part. Moreover, to deal with the problems of high labor intensity, low efficiency and low recognition accuracy in classical manual sorting method, an improved Faster RCNN algorithm is proposed on the automatic recognition of wildlife images. The experimental results on our own wildlife dataset, show that the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are improved by 7.93%, 18.15% and 7.01%, 12.67% respectively on reconstruction image, when compared with the set partitioned in hierarchical tree (SPIHT) and embedded zerotree (EZW) algorithms. Compared with the traditional Faster RCNN algorithm, the recognition accuracy of six species wildlife is respectively improved by 1%, 18%, 5%, 17%, 2% and 19%, and the final mAP value reaches to 92.2% in test set increased by 10.9%, which demonstrates the proposed algorithm can ideally achieve the wildlife intelligent monitoring with WISNs.

Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks

Reducing the algorithmic complexity of image compression techniques is a major challenge in wireless image sensor networks (WISNs). Many image compression standards, such as JPEG and JPEG2000, are unsuitable for implementation in WISNs because of their high energy consumption. In this paper, a solution to this problem is proposed. It consists of a region-of-interest (ROI) based image compression using the discrete Tchebichef transform (DTT).The main idea is about compressing only the ROI instead of the whole image. The DTT is used as an alternative to the discrete cosine transform (DCT) due to its low complexity and good energy compaction. Simulation results have shown that the proposed method reduces the number of arithmetic operations, the processing/transmission energy consumption and the amount of transmitted data. The savings obtained generally exceed 50%. Furthermore, it has a competitive compression efficiency compared with the state-of-the-art image compression techniques.

Reduction of bubble-like frames using a RSS filter in wireless capsule endoscopy video

Wireless capsule endoscopy (WCE) is an advanced technique for inner visualization of the digestive tract. However, its large amount of video data collected continuously for several hours by wireless image sensors usually results in tiresome and tedious reading for physicians. To develop this technique in practice, the automatic reduction of large amounts of video data arises as a helpful alternative, which is partly implemented by detecting the non-useful frames, such as those filled with intestinal juices accompanying large bubble-like regions. Based on the bubbles’ morphology properties, a novel automatic approach for detecting the bubble-like frames in capsule video endoscopy that is based on a new ring shape selective (RSS) filter is presented. According to the shape of the bubbles’ bright ring boundary, a group of the shape probability functions and the response intensity functions produced by Hessian matrix eigenvalues are used to construct the RSS filters to enhance the boundary areas of the bubbles. Then, the bubble-like frames can be distinguished by in the bright areas after binary and morphological processing in the filtered images. The experimental results show that the uninformative bubble-like frames can be detected effectively by this approach even under non-uniform illumination conditions.

An Energy-Quality Scalable Wireless Image Sensor Node for Object-Based Video Surveillance

Wireless image sensor nodes are required to deliver better visual information of the region-of-interest (ROI) under tight energy constraints. The energy-quality scalability of the sensor node can be improved by incorporating ROI-based image processing. This paper presents an energy-quality scalable wireless image sensor node using ROI-based processing for object-based surveillance. After detecting the ROI using a low-power noise-robust method, frame images are encoded by a low-complexity ROI coding scheme with better rate-quality performance. The ROI-based processing parameters are controlled by a simple on-line rate controller to minimize the buffer requirement. We integrate the proposed approach into the wireless image sensor node, and analyze the performance assuming a base station with human operators and deep neural networks. The results show that the proposed approach provides higher quality under the available energy budget. Better energy-quality scalability yields higher energy-accuracy performance for the intelligent surveillance system with deep neural networks.

Convolutional neural network-based classification system design with compressed wireless sensor network images.

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

A proposal of low-cost and low-power embedded wireless image sensor node for IoT applications

This paper proposes the design of a low-cost, low-power and small size image acquisition device to be used in Internet of Things (IoT) applications which collects, processes and sends image data through a 2.4GHz Wi-Fi connection. The proposed processing unit is a low-cost high-performance STM32F7 microcontroller which is able to acquire and transfer images at full rate keeping the core idle to do image processing. The device is supported by a 1.3 Megapixel CMOS camera sensor, and a low consumption Wi-Fi module. This setup allows to create a very compact image sensor device with a built-in processing algorithm for a specific functionality in IoT. In this work, the device hardware architecture is described and discussed, and a comparison with alternative hardware solutions are presented.

Low-complexity low-memory energy-efficient image coding for wireless image sensor networks

ABSTRACTWireless image sensor networks are capable of sensing, processing and transmitting the image in hard-to-access regions without expensive network infrastructure and will have great contribution in Internet of Things. These networks are resource constraint systems with limited memory, energy, processing speed and bandwidth. Low computational energy and communication energy will improve the lifetime of these resource-limited networks. In this paper, an energy-efficient low-memory and low-bitrate image coding is designed exclusively for low-power camera-equipped sensor node. The performance of the proposed image coder is analysed in terms of bitrate, image quality, memory size and energy consumption. Experiments are carried out with Atmel ATmega128 processor. The experimental results show that this system consumes only 0.23% of energy consumed by true Discrete Cosine Transform-based Joint Photographic Experts Group (JPEG) standard and offers reasonable image quality suitable for visual perception at low bitrate. This system requires only 19% of memory required by standard JPEG.

LOW BITRATE HYBRID SECURED IMAGE COMPRESSION FOR WIRELESS IMAGE SENSOR NETWORK

Wireless image sensor networks are capable of sensing, processing and transmitting the visual data along with the scalar data and have attainedwide attention in sensitive applications such as visual surveillance, habitat monitoring, and ubiquitous computing. The sensor nodes in the network are resource constrained in nature. Since the image data are huge always high computational cost and energy budget are levied on the sensor nodes. The compression standards JPEG and JPEG 2000 are not feasible as they involve complex computations. To stretch out the life span of these nodes,it is required to have low complex and low bitrate image compression techniques exclusively designed for this platform. The complicated scenarioof wireless sensor network in processing and transmitting image data has been addressed by a low complex hybrid secured image compression technique using discrete wavelet transform and Bin discrete cosine transformation.

Low Complexity DCT Approximation for Image Compression in Wireless Image Sensor Networks

Energy consumption is a critical problem affecting the lifetime of wireless image sensor networks (WISNs). In such systems, images are usually compressed using JPEG standard to save energy during transmission. And since DCT transformation is the most computationally intensive part in the JPEG technique, several approximation techniques have been proposed to further decrease the energy consumption. In this paper, we propose a low-complexity DCT approximation method which is based on the combination of the rounded DCT with a pruned approach. Experimental comparison with recently proposed schemes, using Atmel Atmega128L platform, shows that our method requires less arithmetic operations, and hence less processing time and/or the energy consumption while providing better performance in terms of PSNR metric.

Adaptive compressed sensing for wireless image sensor networks

Compressed sensing (CS) based image compression can achieve a very low sampling rate, which is ideal for wireless sensor networks with respect to their energy consumption and data transmission. In this paper, an adaptive compressed sensing rate assignment algorithm that is based on the standard deviations of image blocks is proposed. Specifically, each image block is first assigned a fixed sampling rate. In addition to the fixed sampling rate, an adaptive sampling rate is then given to each block based on the standard deviation of the block. With this adaptive sampling strategy, higher sampling rates are assigned to blocks that are less compressible (e.g., blocks with complex textures are less compressible than blocks with a smooth background). The sensing matrix is constructed based on the assigned sampling rate. The fixed measurements and the adaptive measurements are concatenated to form the final measurements. Finally, the measurements are used to reconstruct the image on the decoding side. The experimental results demonstrate that the proposed algorithm can achieve image progressive transmission and improve the reconstruction quality of the images.