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The use of video streaming is constantly increasing. High-resolution video requires resources on both the sender and the receiver side. Many compression techniques can be utilized to compress the video and simultaneously maintain quality. The main goal of this paper is to provide an overview of video streaming and QoE. This paper describes the basic concepts and discusses existing methodologies to measure QoE. Subjective, objective, and video compression technologies are discussed. This review paper gathers the codec implementation developed by MPEG, Google, and Apple. This paper outlines the challenges and future research directions that should be considered in the measurement and assessment of the quality of experience for video services.

The use of video streaming is constantly increasing. High-resolution video requires resources on both the sender and the receiver side. Many compression techniques can be utilized to compress the video and simultaneously maintain quality. The main goal of this paper is to provide an overview of video streaming and QoE. This paper describes the basic concepts and discusses existing methodologies to measure QoE. Subjective, objective, and video compression technologies are discussed. This review paper gathers the codec implementation developed by MPEG, Google, and Apple. This paper outlines the challenges and future research directions that should be considered in the measurement and assessment of the quality of experience for video services. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach for accurate pose estimation in ship unloaders, improving grasping accuracy by reconstructing 3D coordinates. A convolutional neural network optimizes depth map prediction from RGB images, further enhanced by a conditional generative adversarial network to refine quality. Evaluation of simulated ship unloading tasks showed over 90% grasping success rate, outperforming baseline methods. This research offers valuable insights into advanced visual perception and deep learning for next-generation automated cargo handling. [ABSTRACT FROM AUTHOR]

Abstract Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.

Purpose: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods: This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results: To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion: Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal tracking performance based on the codec, colored noise, and bandwidth constraints in the network time‐delay systems. Choosing the optimal two‐degree‐of‐freedom compensator and the dominant expression of the system optimal tracking performance is obtained after Youla parameterization by using the coprime factorizations method and spectral decomposition technology. The result shows that the tracking performance of the system is determined by the nonminimum phase zero and unstable pole of the controlled plant. Moreover, codec, colored noise, and bandwidth constraints can also significantly impact the performance. Finally, two simulation examples prove the accuracy of the theory. [ABSTRACT FROM AUTHOR]

Currently, the multiprotocol label switching (MPLS) standard is extremely prevalent. By exploiting the features provided by MPLS technology, a range of services, including IP telephony, have enhanced their overall performance. However, due to the size of the packet header, the IP telephony service consumes a significant portion of the MPLS network's available bandwidth. For instance, in IP telephony over MPLS networks, the packet header might account for as much as 80% of lost time and bandwidth. Designers working on IP telephony are making substantial efforts to address this issue. This study contributes to current efforts by proposing a novel approach called Tel-MPLS, which involves IP telephony over MPLS. TelMPLS approach uses the superfluous fields in the IP telephony packet's header to retain the packet data, therefore lowering or zeroing the IP telephony packet's payload. Tel-MPLS is an approach that significantly reduces the bandwidth of IP telephony MPLS networks. According to the findings, the Tel-MPLS approach is capable of reducing the amount of bandwidth that is lost by 12% when using the G.729 codec. [ABSTRACT FROM AUTHOR]

We present in this article a detection system of ultrasounographic signal (USG) dedicated to the measurement of parietal rheology in human carotid artery. The aim of this practical work is to develop an ultrasonic method capable of visualizing the localized velocity profile of the carotid artery wall. The device produced makes it possible to take the USG signal from carotid wall of the human neck. It comprises an ultrasonic contact probe applied during the measurement (sensor), a signal shaping circuit, an acquisition and digitization interface in conjunction with an RS232 serial communication port and a local computer terminal hosting a graphical user interface (GUI) implemented in Visual Basic integrated development environment enabling the display, archiving and the digital processing of parietal ultrasonic signal. The aim of this study is to reconnoitering the possibility of using ultrasound for a continuous and non-invasive measurement of the arteries parietal rheology. In this work, a sensor of ultrasound is used to measure the movement and the speed of the carotid artery wall, an image of the arteries parietal rheology. [ABSTRACT FROM AUTHOR]

Vehicle Detection algorithms run on deep neural networks. But one problem arises, when the vehicle scale keeps on changing then we may get false detection or even sometimes no detection at all, especially when the object size is tiny. Then algorithms like CNN, fast-RCNN, and faster-RCNN have a high probability of missed detection. To tackle this situation YOLOv3 algorithm is being used. In the codec module, a multi-level feature pyramid is added to resolve multi-scale vehicle detection problems. The experiment was carried out with the KITTI dataset and it showed high accuracy in several environments including tiny vehicle objects. YOLOv3 was able to meet the application demand, especially in traffic surveillance Systems. [ABSTRACT FROM AUTHOR]

The existing architectures used in the multiparty audio conferencing systems are typically categorized as either centralized or decentralized. These architectures expose a trade-off between processing latency and system capacity, namely the number of participants. This paper proposes a multiparty audio conferencing system for mobile users to improve the processing latency and system capacity. Instead of using the pure centralized or decentralized architecture, the proposed system adopts a novel cooperation-based architecture, in which only some participants are selected as the central controllers to deal with the tasks such as acoustic echo cancellation, encoding, decoding, mixing and de-mixing. The proposed system also uses a buffer reordering scheme to solve the problems of network jitters and out-of-order packets. This study analyzed the processing latency of the multiparty audio conferencing systems using the existing and proposed architectures. We also implemented these systems on diverse mobile platforms to compare the processing latency and number of participants. Performance evaluation results confirmed that the proposed cooperation-based architecture can not only reduce the processing latency but also support more participants, compared to the existing architectures. [ABSTRACT FROM AUTHOR]

An efficient Reed-Solomon redundant arrays of independent disks 6 (RS-RAID 6) exclusive OR (XOR) accelerator with a scaling scheme is proposed in this paper. The accelerator first considers the hardware working characteristic to reduce the complexity of codec computation. To increase efficiency, the parameter calculation of the 8-bit Galois field [GF(8)] is converted to a GF(2) calculation, where two additions, one multiplication, and one division are optimized in every 8 bits of calculation. A new scaling scheme is also proposed to guarantee the input/output (I/O) balance. The proposed Reed-Solomon (RS) codec hardware architecture is fully verified at the Real Time Logistics (RTL) level and synthesized with a GF 12 nm technology library to compare the gate cost and input/output operations per second (IOPS) performance with those of other classic solutions. Separate simulations are performed for different codec algorithms and scaling schemes. The IOPS performance of the proposed RS-RAID 6 XOR accelerator is improved by a factor of 2-3 at the cost of 47% more gates. [ABSTRACT FROM AUTHOR]

Nowadays, as the vivid visual enjoyment requiring in mobile display devices exploding everywhere, enormous bandwidth pressure lands in all high-speed digital transmission interfaces, such as HDMI, MIPI, DP, eDP etc. A low latency in pixels' time, flexible implementation for circuits, and high performance in visual quality video stream encoder and decoder (FHP-Codec) is proposed to relax the stress. The image structural features are analyzed, which directs an accurate 1D or 2D prediction reducing the redundancy utmost compared with DSC by 10~20% benefit in entropy, without obvious increments in circuit area and power consumption. According to the benchmark in Kodak image suite, FHP-Codec shows a considerable cost-performance ratio for display driver application fields. [ABSTRACT FROM AUTHOR]

The demand for the telecommunication services, such as IP telephony, has increased dramatically during the COVID-19 pandemic lockdown. IP telephony should be enhanced to provide the expected quality. One of the issues that should be investigated in IP telephony is bandwidth utilization. IP telephony produces very small speech samples attached to a large packet header. The header of the IP telephony consumes a considerable share of the bandwidth allotted to the IP telephony. This wastes the network's bandwidth and influences the IP telephony quality. This paper proposes a mechanism (called Smallerize) that reduces the bandwidth consumed by both the speech sample and the header. This is achieved by assembling numerous IP telephony packets in one header and use the header's fields to carry the speech sample. Several metrics have been used to measure the achievement Smallerize mechanism. The number of calls has been increased by 245.1% compared to the typical mechanism. The bandwidth saving has also reached 68% with the G.28 codec. Therefore, Smallerize is a possible mechanism to enhance bandwidth utilization of the IP telephony. [ABSTRACT FROM AUTHOR]

IP Phone merupakan salah perangkat pada Voice over Internet Protocol (VoIP) yang dapat menyalurkan suara menggunakan IP. Berbagai tipe IP Phone bermunculan dengan harga dan fitur yang bervariasi, sehingga menimbulkan pertanyaan terkait bagaimana perbandingan kualitas suara pada IP Phone yang dihadirkan oleh berbagai tipe tersebut, mengingat kualitas suara dalam telekomunikasi sangat penting agar tidak menimbulkan kesalahfahaman saat telekomunikasi berlangsung. Terdapat dua tipe IP Phone pada sistem yang sudah diregistrasikan dengan server sehingga dapat melakukan panggilan dengan range frekuensi 300Hz s/d 3400Hz, kemudian dari komunikasi antar IP Phone terdapat bentuk gelombang sinyal informasi input dan output, respon amplitudo suara terhadap perubahan frekuensi suara serta nilai delay dari pergeseran fasa menggunakan alat ukur Tranmission Test Set dan Oscilloscope yang digunakan untuk mengetahui karakter kualitas suara dalam komunikasi antar IP Phone sesuai hasil uji parameter. Hasil pengujian sitem dapat disimpulkan bahwa bentuk gelombang sinyal informasi input dan output pada frekuensi suara memiliki bentuk gelombang sinus, namun gelombang output pada IP Phone tipe A frekuensi 300Hz s/d 1100Hz dan IP Phone tipe B frekuensi 300Hz s/d 1300Hz mengalami distorsi, selanjutnya dari hasil nilai Peak to Peak gelombang diketahui adanya penguatan tegangan yaitu semakin besar nilai frekuensi pada input maka akan semakin kecil nilai penguatan tegangan yang dihasilkan. Nilai respon amplitudo terhadap perubahan frekuensi suara dengan nilai amplitudo input -23.5 dBm pada kedua IP Phone memiliki penguatan daya yang semakin kecil ketika nilai frekuensi suara semakin tinggi. Nilai delay yang didapatkan dari pergeseran gelombang input dan output antar IP Phone yaitu kurang dari 150 ms.

Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models. [ABSTRACT FROM AUTHOR]

• A novel fuzzy adaptive network predicts greenhouse climate over long periods. • Fuzzy preprocessing and adaptive normalization mitigate data volatility impact. • The model shows superior long-term predictions for greenhouse environment. To achieve accurate medium- and long-term predictions of greenhouse temperature and humidity, this study proposes a fuzzy adaptive normalized encoding and decoding network. Initially, a fuzzy preprocessing method is applied to temperature, humidity, and weather prediction data from the Internet of Things, reducing the impact of volatile fluctuations on prediction accuracy. Additionally, a fusion of the fuzzy module with an adaptive normalization technique is introduced to prevent issues like neural saturation and gradient vanishing in deep networks. The framework integrates a Codec model with the adaptive normalization module, incorporating attention normalization to enhance long-term data dependencies and improve predictive performance. The experimental dataset includes hourly temperature and humidity readings collected throughout 2020 from a smart greenhouse in Weifang, Shandong, China, which serves as the training data. Evaluation conducted on data from January 1 to February 15, 2021, shows that the proposed model outperformed all comparative approaches, particularly excelling in long-term greenhouse environment predictions. In terms of RMSE, the AN-FSANC model improved temperature prediction accuracy by 30.8 %, 24.6 %, 29.6 %, 35.3 %, 35.1 %, 16.7 %, 25.3 %, and 24.9 % compared to LSTM, GRU, BiLSTM, BiGRU, Attention-LSTM, Attention-GRU, SAN+LSTM, and AN-SANC, respectively. The model also achieved the highest performance in temperature and humidity predictions, as demonstrated by R2 and MAPE metrics, providing valuable support for agricultural applications. [ABSTRACT FROM AUTHOR]

In a video call there are several components, such as IP phone or softphone, CODEC, and server. Selection of softphone and CODEC is a consideration in building a video communication network because it will affect the quality of video call. This research compare the quality of video calls based on softphone application and CODEC combination. The quality measured by QoS, PSNR, and MOS parameters. Softphone applications examined in this research are Blink, Zoiper, MicroSIP, PortGo, Linphone, and X-Lite. CODEC examined in this research are H.264, VP8, H.263+, and H.263. Each softphone application will be combined with a CODEC that is native to the softphone. There are nine combinations of softphone application and CODEC. Based on the research results, CODEC H.264 has the best performance when paired with the Blink softphone application. CODEC VP8 has the best performance when paired with the Zoiper softphone application. The H.263+ CODEC has the best performance when paired with the PortGo softphone application. The H.263 CODEC and X-Lite softphone applications have the worst test results but still get “good” grades when tested using QoS, PSNR, and MOS parameters.

Adaptive streaming over HTTP is the dominant video streaming technology for more than a decade. HTTP Adaptive Streaming (HAS) systems provide a framework which enables clients to adapt quality with respect to network fluctuations during streaming, hence to optimize the perceived quality on the client side. Recently, network assistance is integrated with HAS in order to improve underlying network conditions and to provide network-related information to the clients. The performance of HAS systems can be further enhanced if the characteristics of the streamed video are considered. In this paper, we propose a HAS system architecture where Software Defined Networking (SDN) technology is utilized for assisting clients to select the most appropriate video codec and bitrate under the constraint of current network conditions as well as routing the video packet over the appropriate paths. In the proposed architecture, layered video is used, where each additional layer improves the quality. The controller estimates the packet loss probability by taking video codec characteristics, the bitrates of the layers and network capacity into account. Based on these estimations, the controller selects the appropriate codec type and video quality for the clients and manage the network. Simulation results show that the performance of the video streaming architecture can be improved significantly when codec, quality and path selection are jointly considered, and combined with SDN flexibility and advantageous.

The usage of internet protocol for voice communication is widely used and more efficient rather than an analog signal. However, there is no security guaranteed on IP-based voice communication. The voice payload can be easily tapped or even manipulated. In the case of improving the security aspect, communication quality should be also considered. VoIP requires sufficient bandwidth to get proper communication quality. The ITU-T released a standard unit of communication quality, known as Mean Opinion Score (MOS) which is made from the subjective judgments of some individuals. However, MOS method takes time and is expensive. In this research, we measure VoIP communication which is secured by using VPN and build a tool for analyzing the voice packet between communication peers. The tool has capabilities to measure delay, jitter, and packet loss. Since VoIP has a QoS standard by ITU-T, the usage of VPN for security purpose needs to be considered. The sound quality might be decreased due to the addition of header for tunneling method, as well as the additional delay when the encryption processing is carried out. We used 3 types of codec: a-Law, GSM, and iLBC which will be passed on 4 types of bandwidth (256, 128, 64, 32 kbps) through the UDP-based VPN that use 3 types of encryption method (3-DES, Blowfish, AES).

Aims: Restrictions during the COVID‐19 crisis will have impacted on opportunities to be active. We aimed to (a) quantify the impact of COVID‐19 restrictions on accelerometer‐assessed physical activity and sleep in people with type 2 diabetes and (b) identify predictors of physical activity during COVID‐19 restrictions. Methods: Participants were from the UK Chronotype of Patients with type 2 diabetes and Effect on Glycaemic Control (CODEC) observational study. Participants wore an accelerometer on their wrist for 8 days before and during COVID‐19 restrictions. Accelerometer outcomes included the following: overall physical activity, moderate‐to‐vigorous physical activity (MVPA), time spent inactive, days/week with ≥30‐minute continuous MVPA and sleep. Predictors of change in physical activity taken pre‐COVID included the following: age, sex, ethnicity, body mass index (BMI), socio‐economic status and medical history. Results: In all, 165 participants (age (mean±S.D = 64.2 ± 8.3 years, BMI=31.4 ± 5.4 kg/m2, 45% women) were included. During restrictions, overall physical activity was lower by 1.7 mg (~800 steps/day) and inactive time 21.9 minutes/day higher, but time in MVPA and sleep did not statistically significantly change. In contrast, the percentage of people with ≥1 day/week with ≥30‐minute continuous MVPA was higher (34% cf. 24%). Consistent predictors of lower physical activity and/or higher inactive time were higher BMI and/or being a woman. Being older and/or from ethnic minorities groups was associated with higher inactive time. Conclusions: Overall physical activity, but not MVPA, was lower in adults with type 2 diabetes during COVID‐19 restrictions. Women and individuals who were heavier, older, inactive and/or from ethnic minority groups were most at risk of lower physical activity during restrictions. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult for traffic monitoring videos to detect multi-scale vehicle targets, especially small vehicle targets in complex scenarios, a codec-based vehicle detection algorithm is proposed. This algorithm is based on YOLOv3. In order to solve the multi-scale vehicle target detection problem, a new multi-level feature pyramid structure added with the codec module to detect vehicle targets of different scales. The experimental results on the KITTI dataset and UA-DETRAC dataset confirm that the algorithm in this paper has achieved good detection results for vehicle targets in various environments and at various scales in the surveillance video, especially for small vehicle targets, which can better meet the actual application demand.

This article seeks to define Digital Theology, first by exploring the development of the CODEC Research Centre at Durham University – perhaps the only centre developed to explore Digital Theology. The aims of the centre and some of its projects are explored leading to a discussion of CODEC’s place within Digital Humanities. The article concludes with a focus on different aspects and definitions of Digital Theology.

Joint Photographic Experts Group (JPEG) XS is a new International Standard from the JPEG Committee (formally known as ISO/International Electrotechnical Commission (IEC) JTC1/SC29/WG1). It defines an interoperable, visually lossless low-latency lightweight image coding that can be used for mezzanine compression within any AV market. Among the targeted use cases, one can cite video transport over professional video links (serial digital interface (SDI), internet protocol (IP), and Ethernet), real-time video storage, memory buffers, omnidirectional video capture and rendering, and sensor compression (for example, in cameras and the automotive industry). The core coding system is composed of an optional color transform, a wavelet transform, and a novel entropy encoder, processing groups of coefficients by coding their magnitude level and packing the magnitude refinement. Such a design allows for visually transparent quality at moderate compression ratios, scalable end-to-end latency that ranges from less than one line to a maximum of 32 lines of the image, and a low-complexity real-time implementation in application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), central processing unit (CPU), and graphics processing unit (GPU). This article details the key features of this new standard and the profiles and formats that have been defined so far for the various applications. It also gives a technical description of the core coding system. Finally, the latest performance evaluation results of recent implementations of the standard are presented, followed by the current status of the ongoing standardization process and future milestones. [ABSTRACT FROM AUTHOR]

This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity. [ABSTRACT FROM AUTHOR]

Telecommunication technology serves several fields in the world. One of the most significant fields is the emergency services to provide a fast connection between the case, the vehicle and emergency treatment office. This paper is a part of a long-term project to design a reliable communication system service to be used for emergency services of a specific city. The hardware devices of this system are intended to work within an mm waves frequency. In the current research, as a starting point, an exhaustive study accomplished to pave the way to the main goal of the project. The system uses OFDM technology to improve the performance of the system. Other requirements for error correction are also included in the model such as convolutional, hamming coding and interleaving. The system development is supported by a Matlab interface software to simulate the job of an IoT real-time network covering both vehicles and the control centres. [ABSTRACT FROM AUTHOR]

In the last few years, satellite on-board data handling bandwidth requirements grew significantly, as well as production volume of these systems. A series of different protocols currently try to answer this need. In particular, the European Space Agency developed an open protocol solution: SpaceFibre. The SpaceFibre protocol can sustain a line rate of 6.25 Gb/s per lane (up to 16 lanes). It offers advanced and flexible Quality-of-Service features, as well as Fault Detection Isolation and Recovery services. The protocol structure has been developed so that full hardware implementation of its core layers is straightforward, granting high performances at low price in terms of complexity and power consumption, one of the most stringent requirements in space applications. In this paper, a FPGA implementation on both rad-hardened (RTAX2000, RTG4, Virtex-5) and commercial (ZYNQ 7000) devices of the SpaceFibre CODEC is presented together with its verification environment and a hardware validation set-up. Particular attention is given to the trade-off between resources utilisation, power consumption and CODEC configurations, in order to enable future system adopters to efficiently explore the design space. • Satellite on-board data-handling bandwidth is increasing up to tens of Gbps. • SpaceFibre is the upcoming protocol promoted by ESA to handle such requirement. • SpaceFibre can sustain 6.25gbps per lane (up to 16 lanes in parallel). • It offers advanced quality of service, fault detection isolation and recovery. • SpaceFibre codec development with performance analysis is carried out. [ABSTRACT FROM AUTHOR]

This paper proposes two major novelties. First, we provide a mathematical framework for hardware resource efficient IP core-based image compression and decompression (CODEC). The framework includes CODEC functions that are capable of determining the pixel intensities of a compressed gray scale image using significantly lesser hardware resources. Digital pixel values of the original image are fed as an input to the functions of proposed IP framework and compressed digital pixel values of compressed image generated. Similarly, digital pixel values of the compressed image are fed into other functions of the proposed framework for image decompression. Second, the second novelty is using the derived IP functions to propose designs of reusable IP cores for complete Haar wavelet transformation (HWT)-based lossy image CODEC. Testing of images from various data sets (NASA, medical applications, and so on) in terms of hardware resources, image quality, and compression efficiency have indicated that the proposed IP core framework was successful in achieving hardware efficient CODEC compared with JPEG and conventional HWT CODECs.

With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge amounts of traffic on the network, to solve this problem, the deception method is required, which appears unencrypted but a confidential stream. In this paper, we propose the new deception method that utilizes standard NAL unit rules of video codec, where the unpromised device shows the cover video and the promised device shows the secret video for deceptive security. This method allows a low encryption cost and the stream to dodge entropy-based sniffing scan attacks. The proposed stream shows that successful decoding using five standard decoders and processing performance was 61% faster than the conventional encryption method in the test signal conformance set. In addition, a network encrypted stream scan method the HEDGE showed classification results that our stream is similar to a compressed video.

VoIP traffic classification plays a major role towards network policy enforcements. Characterization of VoIP media traffic is based on codec behaviour. With the introduction of variable bit rate codecs, coding, compression and encryption present different complexities with respect to the classification of VoIP traffic. The randomness tests do not extend directly to classification of compressed and encrypted VoIP traffic. The paper examines the applicability of randomness tests to encrypted and unencrypted VoIP traffic with constant bit rate and variable bit rate codecs. A novel method Construction-by-Selection that constructs a test sequence from partial payload data of VoIP media session is proposed in this paper. The results based on experimentations on this method show that such construction exhibit randomness and hence allows differentiation of encrypted VoIP media traffic from unencrypted VoIP media traffic even in the case of variable bit rate codecs. [ABSTRACT FROM AUTHOR]

This research proposes an enhanced measurement method for VoIP quality assessment which provides an improvement to accuracy and reliability. To improve the objective measurement tool called the simplified E-model for the selected codec, G.729, it has been enhanced by utilizing a model of Mean Opinion Score (MOS), called a subjective MOS prediction model, based on native Thai users who use the Thai-tonal language. Then, the different results from the simplified E-model and subjective MOS prediction model were used to create the Bias function, before adding to the simplified E-model. Finally, it has been found that the outputs from the enhanced simplified E-model for the G.729 codec show better accuracy when compared to the original simplified E-model, specially, after the enhanced model has been evaluated with 4 test sets. The major contribution of this enhancement is that errors are reduced by 58.87% when compared to the generic simplified E-model. That means the enhanced simplified E-model as proposed in this study can provide improvement beyond the original simplified one significantly. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive fast mode decision (AFMD) algorithm for intra prediction in high efficiency video coding (HEVC) using hierarchical and skip methods. AFMD consists of two phases: an adaptive hierarchical mode decision and an adaptive rate-distortion optimization. The proposed AFMD focuses mainly on reducing the number of prediction modes, and thus it can decrease computation time. For rough mode decision and rate-distortion optimization, AFMD computes candidate modes in a way that includes only those intra modes that are most promising. In our experiments, AFMD shows times saving of 41.5% and 40.58% compared to the HEVC test model (HM) reference software, versions 14.0 and 16.0. In addition, the proposed algorithm increases the Bjontegaard delta bitrate by only about 1.00% and 0.93% compared to HM 14.0 and HM 16.0. [ABSTRACT FROM AUTHOR]

The increase in high-quality video consumption requires increasingly efficient video coding algorithms. Versatile video coding (VVC) is the current state-of-the-art video coding standard. Compared to the previous video standard, high efficiency video coding (HEVC), VVC demands approximately 50% higher video compression while maintaining the same quality and significantly increasing the computational complexity. In this study, coarse-grain profiling of a VVC decoder over two different platforms was performed: One platform was based on a high-performance general purpose processor (HGPP), and the other platform was based on an embedded general purpose processor (EGPP). For the most intensive computational modules, fine-grain profiling was also performed. The results allowed the identification of the most intensive computational modules necessary to carry out subsequent acceleration processes. Additionally, the correlation between the performance of each module on both platforms was determined to identify the influence of the hardware architecture.

Real-time human movement inertial measurement unit (IMU) signals are central to many emerging medical and technological applications, yet few techniques have been proposed to process and represent this information modality in an efficient manner. In this paper, we explore methods for the lossless compression of human movement IMU data and compute compression ratios as compared with traditional representation formats on a public corpus of human movement IMU signals for walking, running, sitting, standing, and biking human movement activities. Delta coding was the highest performing compression method which compressed walking, running, and biking data by a factor of 10 and compressed sitting and standing data by a factor of 18 relative to the original CSV formats. Furthermore, delta encoding was shown to approach the a posteriori optimal linear compression level. All methods were implemented and released as open source C code using fixed point computation which can be integrated into a variety of computational platforms. These results could serve to inform and enable human movement data compression in a variety of emerging medical and technological applications.

The Motion Picture Experts Group is working on requirements for a new video codec, expected to be completed by 2020. The future-generation codec is anticipated to provide a 50% bitrate reduction compared with the High Efficiency Video Coding (HEVC) main profile for the same perceptual quality. Although this is sufficient for certain use cases, it may justify a future video coding standard for other use cases that require bitrate reductions higher than 50%. This paper focuses on target applications for the new video codec that includes 8K, virtual reality (VR) in 3DoF and 6DoF, the state of IP networks, and better compression through "Network Distributed Video Coding" (NDVC). The author elaborates on how new techniques leveraging content-aware encoding, elastic encoding, machine-learning techniques, and pre- and post-processing pairing can lead to factor four compression efficiency. This paper also compares different approaches including HEVC, HEVC enhanced with NDVC techniques, and the Joint Video Exploration Team codec compared with NDVC techniques. [ABSTRACT FROM AUTHOR]

Voice over Internet Protocol (VoIP) uses packet switching to transmit voice, video, data and chat services. VoIP applications depend upon various speech codecs which vary in speech quality, bandwidth and computational requirements. The key challenges for improving the VoIP Quality of Service (QoS) and Quality of Experience (QoE) is to use best codec by considering network traffic and selecting the optimal hardware. Hardware Calibration is a mechanism to aid service providers by selecting a suitable hardware for Call Manager to transmit different real-time based applications. The paper focuses on proposing a computational model which suggests the most suitable hardware for a specific load handling requirement. The selection of processor is done by taking into consideration the effect of different codecs when it takes calls per second as input and gives output as whichever hardware is recommended for Call Manager to handle different types of applications like Peer-To-Peer (P2P) application, Back-To-Back (BTB) application, enabling voice logging, etc. The emulations demonstrate that the proposed model is suitable for selection of hardware which provides better QoS and QoE for the transmission VoIP based real-time applications. Further, a multi-criteria decision-making method based on Analytic Hierarchy Process (AHP) is also proposed to decide which hardware can be chosen as Call Manager. The results obtained from AHP are used to validate the outcome of the proposed computational model. The computational model is applicable only when the tests are run on particular systems and it suggests the best hardware among them for the particular offered load. However, to suggest the best configuration corresponding to the input load from the possible configurations available in the market, benchmarking is performed. A common scale model for hardware benchmark estimation particularly has been proposed for different scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a method for generating coded video bit streams requiring less decoding energy than conventionally coded bit streams. To this end, we propose extending the standard rate-distortion optimization approach to also consider the decoding energy. In the encoder, the decoding energy is estimated during runtime using a feature-based energy model. These energy estimates are then used to calculate decoding-energy-rate-distortion costs that are minimized by the encoder. This ultimately leads to optimal tradeoffs between these three parameters. Therefore, we introduce the mathematical theory for describing decoding-energy-rate-distortion optimization and the proposed encoder algorithm is explained in detail. For rate-energy control, a new encoder parameter is introduced. Finally, measurements of the software decoding process for HEVC-coded bit streams are performed. Results show that this approach can lead to up to 30% of decoding energy reduction at a constant visual objective quality when accepting a bit rate increase at the same order of magnitude. [ABSTRACT FROM AUTHOR]

IP video traffic on the network continues to grow. To account for a future traffic of a million minutes of videos per second, it is important to minimize the traffic each video generates. This is a performance comparison of the HEVC, AV1, and xvc (in fast mode) video CODECs with the focus on minimizing network traffic for common use cases, such as video conferences and social media video streaming, these being used, where less than optimal videos are sent. The purpose is to identify which CODEC gives the best quality at the lowest bit rate. This is done by running a test bench with multiple videos of different qualities, encoding and decoding each video with each CODEC. The study shows that xvc (fast mode) and AV1 (double-pass) has similar quality performance on the dataset and has a noticeable improvement compared to HEVC (double-pass) when it comes to less optimal video quality. This research work was conducted in the context of the course II2202 Research Methodology and Scientific Writing at KTH Royal Institute of Technology, Sweden [ABSTRACT FROM AUTHOR]

This paper proposes a method of transmitting video streaming data based on downsampling-upsampling pyramidal decomposition. By implementing an octal tree decomposition of the frame cubes, prior to transforming them into hypercubes, the algorithm manages to increase the granularity of the transmitted data. In this sense, the communication relies on a series of smaller hypercubes, as opposed to a single hypercube containing the entire, undivided frames form a sequence. This translates into increased adaptability to the variations of the transmitting channel’s bandwidth.

The admission control is required to maintain the established route between the source and the destination in the wireless network. To maintain the stability of the route, the wireless channel parameters has to be adopted appropriately. Thus, this study analyzes the wireless access medium parameter through direct coordination (DCF) and point-to-point coordination method. The packets are fragmented in DCF and contention free period interval are adjusted to study the QoS parameters for various VOIP codec using OPNET simulation tool. The result shows that packet fragmentation to 256 bytes and contention free period for 20 ms improves QoS for G.729 for Voice traffic. [ABSTRACT FROM AUTHOR]

This paper presents an implementation of hardware/software co-design for high efficiency advanced-audio-coding (HE-AAC) audio decoder. The decoder system is partitioned into software and hardware part throughout the computation analysis. In our design strategy, the bitstream parser and lower complexity part are performed by software solution, and the higher complexity part is computed by hardware solution. As in the dedicated hardware, four units are developed to cope with IMDCT, analysis quadrature mirror filterbank (AQMF), HF generator and envelope adjuster and synthesis quadrature mirror filterbank (SQMF). To support the various types of transformation-based functions in HE-AAC decoding, we manipulate it based on the decomposition of common radix-2 FFT method. The hardware part is designed as an intellectual property (IP) by TSMC 90 nm library. As a cost-effective design, it consumes about 150 K gates and executes at a very low operation frequency with 1.75 MHz. The power consumption is only 7.69 mW with some low power design considerations. Moreover we construct the overall system including the wrapper design and embedded platform as a system on a programmable chip (SOPC) platform. With this design approach, over 91.26 % processor-based loading can be saved and substituted by this hardware IP. [ABSTRACT FROM AUTHOR]

Mobile and wireless technologies provide Voice over Internet Protocol (VoIP) service to the users with the help of internet. However, there are some limitations with respect to bandwidth, delay, jitter, etc. While it meets congestion and heavy load on the network, it drastically consumes and wastes the resources unnecessarily. Likewise, Mobile Ad-hoc Networks (MANETs) is a part of ad hoc network to provide VoIP communication among the users without the aid of any internet facilities. Since the nodes are mobile in MANETs, it faces many challenges like dynamic topology, limited bandwidth and battery power in order to provide Quality of Service (QoS). This paper proposes a CBT-VoIP technique for reducing the VoIP transmission delay. Ultimately, it helps to improve QoS in MANETs. [ABSTRACT FROM AUTHOR]

Data communication networks are a vital component of any modern society. They are used extensively in numerous applications, including financial transactions, social interactions, education, national security and commerce. In particular, both wired and wireless devices are capable of performing a plethora of advanced functions that support a range of services, such as voice telephony, web browsing, streaming multimedia and data transfer [1]. With the rapid evolution of microelectronics, wireless transceivers are becoming more versatile, powerful and portable. In the wireless technology the major issue is the issue of spectrum utilization. The utilization of spectrum has increased to its maximum level. To tackle this issue the technology named cognitive radio was introduced to the rescue. The cognitive radio is an emerging technology that facilitate dynamic spectrum access in wireless networks. The cognitive radio is capable of expediently using the obtainable portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic access of the accessible channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. In this paper we are focusing the different traffic types such video and voice. In the voice we have different codec and in the video application we have different model types from which we need to choose the best for the optimum results. [ABSTRACT FROM AUTHOR]

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