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In the beginning of 2020, the coronavirus (COVID-19) pandemic started to spread globally, causing panic to the lives of people around the world; many countries executed lockdown of cities or even total lockdown of the entire countries. The coronavirus disease (COVID-19) is transmitted via air droplets. In medical environments that use traditional hermetic ventilation systems, medical personnel who come in contact with patients are more susceptible to infection compared to regular staff; therefore, the air flow and air quality of hermetic negative pressure isolation wards are highly critical. For this purpose, the study proposes a full-outer-air-intake natural air-conditioning system for negative pressure isolation wards. This innovative system draws in large amounts of fresh external air to greatly improve the air exchange rate in wards; negative pressure environments can be implemented depending on requirements to solve the issue of nosocomial infections in traditional negative pressure isolation wards that draw air from within the hospital. This greatly reduces the probability of nosocomial infection and infection via air droplets; furthermore, the system’s intake and exhaust paths are completely isolated, solving the issue of air cross-contamination. Based on the results from the experiment site, this innovative system was designed and implemented based on the guidelines of hospital facilities and achieved air exchange per hour in excess of 12 times/hour, reaching a maximum of 54.5 times/hour. Indoor CO2 concentration was 576 ppm, negative pressure was −14 Pa, indoor temperature was 23.3°C, indoor humidity was 54.1%, and sensible heat exchange efficiency (ηs) was 105.88% which effectively reduced ventilation load. Therefore, this innovative full-outer-air-intake natural air-conditioning system can provide medical staff and patients with a safe and healthy environment that prevents cross-infection.

The encoding format of the 3D extension of high efficiency video coding (3D-HEVC) consists of a multiview color texture and an associated depth map. Because of the unique characteristics of the depth map, advanced coding techniques are designed for depth map coding at the expense of computational complexity. In this paper, fast algorithms are conceived to accelerate the intra coding time of the depth map based on boundary continuity. First, the proposed fast prediction unit (PU) mode decision reduces the number of conventional intra prediction modes based on calculating the total sum of squares (TSS) of the PU boundaries. Second, the proposed fast depth modeling mode (DMM) decision makes use of the variances of the boundary pixels to determine the execution of the DMM. Third, the proposed coding unit (CU) early termination algorithm decides whether to further split the current CU by utilizing the thresholds of the TSS and the rate-distortion cost (RD-cost). The experimental results show that the proposed algorithm provides better performance in terms of coding speed and bitrate than the algorithm in previous work. The coding time of the depth map is reduced by 56.08%, while the Bjøntegaard delta bitrate (BD-BR) is only increased by 0.32% for the synthesis view.

3D-HEVC (The 3D Extension of High Efficiency Video Coding), the latest international standard for 3D video coding, supports multiview plus depth 3D video format to enrich multimedia applications. For the texture coding, 3D-HEVC utilizes not only the information of temporal and spatial domains but also that of inter-view domain. However, the time consumption and complexity of 3D-HEVC also increase significantly. In this paper, fast texture coding algorithm for 3D-HEVC is proposed. We individually calculate the Pearson correlation coefficients by the rate-distortion costs (RD-costs) of coding tree units (CTUs) in the temporal, spatial and inter-view domains to analyze the correlations for independent view and dependent view. The proposed coding algorithm is based on the coding information of CTUs with higher correlations. The fast algorithm predicts and dynamically adjusts the depth range of the coding unit (CU). The prediction unit (PU) mode decision is proposed according to the complexity and partition direction of the best PU modes obtained from the highly correlated CTUs. The search range is adaptively adjusted for motion estimation. In addition, the RD-cost threshold is estimated to early terminate the CU split. Experimental results show that the proposed fast texture coding algorithm reduces the texture coding time significantly and outperforms numerous previous works.

Visual interferences caused by bad weather conditions can have a negative impact on the performance of a visual surveillance system. How can we detect the moving object in a quickly and accurately manner is a very important step for further analysis, e.g., object recognition, object tracking, event detection, or behavior analysis, in a visual surveillance system. However most of the moving object object detection systems are based on a high-level micro-processor for its highly complex algorithm. Moreover, the detection accuracy is often affected by the environment of the system used. In this paper, we proposes using an FPGA (Field Programmable Gate Array) to realize a visual surveillance system so that the real-time requirement can be achieved. Besides, the proposed system can adapted itself to a variety of environmental situations, especially for the condition of light changes, such as the existence of a shadow, raindrop, etc. As we will see in the experiment that a very good performance in terms of power consumption, memory usage, throughput, as well as the capability of adapt to interference can be achieved in the proposed system

A person stays indoors for about 85%∼90% time of his lifetime, and the need for a comfortable indoor environment is getting higher; thus, the air-conditioning dependency becomes intense too. Nowadays, residents focus on both the comfortable living environment and indoor air quality. A closed environment will become hazardous because of carbon dioxide released during respiration and toxic organic solvent vapor released from interior decoration. In order to improve the indoor air quality (IAQ), we must allow outer fresh air into the indoor space and release the dirty air out. But while taking in fresh air, the heat and factory/vehicle exhaust are also introduced. Indoor CO2, HCHO, and VOCs and outer dirty gas threaten human health badly. To solve this problem, we bring up an innovative low-power-consuming full-outer-air-intake natural air-conditioning system that completely separates intake and exhaust air, which is a solution for cross-contamination and makes mass/energy exchange by means of air and water. Design airflow exceeds 300∼500 CFM, steam evaporation mass rate reaches 3.13∼3.88 kg/hr, and heat exchange capacity becomes 1,855∼2,300 kcal/hr. The sensible heat effectiveness is 71%∼112%, and EER exceeds 14.05∼17.42 kcal/W·h. In addition, the system under design can be of positive or negative pressure status according to the user’s or work’s requirement. It creates a comfortable and healthy living environment by supplying clean and fresh outer ambient air with low power consumption.

About 7% of people’s daily time is spent in taking vehicles between office and home. Besides, with the improvement of the living standard in today’s society, people’s requirements for a comfortable environment inside the car are constantly increasing and this must rely on an effective vehicle air conditioner to maintain the comfort of the cabin environment. In general, a vehicle air conditioner uses the air-mixing mode to regulate the temperature control system. In this mode of operation, the compressor needs to work continuously, which is extremely energy consuming. The vehicle’s air conditioner is greatly affected by the inner and outer heat load, which are generated therein. Furthermore, the heat load is instantly changeable. Therefore, only when the controller can adapt to the feature of heat load, then we can find the optimal control method, thus enabling the vehicle’s air conditioner to interact with the actual heat load to supply the balanced cooling capacity and, as a result, create the most comfortable environment inside the cabin with minimum energy consumption. For this purpose, we bring up in this paper a low-energy-consumption smart vehicle air-conditioning control system to detect total heat load, which can change the vehicle’s air-conditioning capacity mode to maintain the average temperature at 25.2°C∼26.2°C and the average humidity at 46.6%∼54.4% in the cabin. When the inner heat load is stable, the rest times of the compressor can reach 16∼23 times per hour, which attains a rate of fuel saving around 21%∼28%. With the proposed architecture, the purpose of the low-energy-consumption vehicle air-conditioning system can be achieved, which, at the same time, creates a comfortable environment inside the cabin.

This paper proposes body posture recognition and turning recording system for assisting the care of bed bound patients in nursing homes. The system continuously detects the patient's body posture and records the length of time for each body posture. If the patient remains in the same body posture long enough to develop pressure ulcers, the system notifies caregivers to change the patient's body posture. The objective of recording is to provide the log of body turning for querying of patients' family members. In order to accurately detect patient's body posture, we developed a novel pressure sensing pad which contains force sensing resistor sensors. Based on the proposed pressure sensing pad, we developed a bed posture recognition module which includes a bed posture recognition algorithm. The algorithm is based on fuzzy theory. The body posture recognition algorithm can detect the patient's bed posture whether it is right lateral decubitus, left lateral decubitus, or supine. The detected information of patient's body posture can be then transmitted to the server of healthcare center by the communication module to perform the functions of recording and notification. Experimental results showed that the average posture recognition accuracy for our proposed module is 92%.

This paper presents a robotic shopping cart for a shopping mall to provide shopping assistance and information. When customers login to the shopping cart system, the information providing is first started to provide directions and recommendations of the shopping mall. In addition, the robotic shopping cart is developed to be autonomous returning to starting position while customers logout from the service screen. Finally, the purchase record of customers can be send to the centralized system of shopping mall for further analysis of various shopping behaviors.

In this paper we propose a scalable face image compression algorithm based on Principal Component Analysis (PCA) and Entropy Coding. By using PCA and some training face image patterns, we can extract the most representative eigen-image of human faces. To reduce the coding complexity as well as to achieve a higher compression ratio, only the first term of the extracted eigen-images will be used for the encoding of the human face, i.e., only the eigen-image with maximal energy strength will be selected for the encoding process. As we will see in the experiment that a good trade off between the computation complexity, compression ratio, and image quality can be achieved with the proposed algorithm.

An efficient approach to the sharpening of color images is proposed in this paper. For this, the image to be sharpened is first transformed to theHSVcolor model, and then only the channel ofValuewill be used for the process of sharpening while the other channels are left unchanged. We then apply a proposed edge detector and low-pass filter to the channel ofValueto pick out pixels around boundaries. After that, those pixels detected as around edges or boundaries are adjusted so that the boundary can be sharpened, and those nonedge pixels are kept unaltered. The increment or decrement magnitude that is to be added to those edge pixels is determined in an adaptive manner based on global statistics of the image and local statistics of the pixel to be sharpened. With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained. Finally, the adjusted channel ofValueand that ofHueandSaturationwill be integrated to get the sharpened color image. Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach.

We construct a robust and precise multi-orientation text detection system in scene images which can extensively locate possible characters with multi-information fusion. In our method, an adaptive multi-channel character grouping algorithm is first proposed to extract all possible character candidates robustly, and an AdaBoost classifier is then to properly identify character candidates as characters or non-characters. A single-link clustering with distance metric learning is thereafter used to adaptively group characters into text regions, and an effective hybrid filter with Convolution Neural Networks (CNN), AdaBoost and Bayesian classifiers is finally designed to precisely verify the extracted text regions. Our proposed technology is extensively evaluated on several public multi-orientation scene text datasets, e.g., MSRA-TD500 and USTB-SV1K, and is much better than state-of-the-art methods.