Morphological and semi-quantitative characteristics of diesel soot agglomerates emitted from commercial vehicles and a dynamometer

Diesel soot aggregates emitted from a model dynamometer and 11 on-road vehicles were segregated by a micro-orifice uniform deposit impactor （MOUDI）. The elemental contents and morphological parameters of the aggregates were then examined by scanning electron microscopy coupled with an energy dispersive spectrometer （SEM-EDS）, and combined with a fractional Brownian motion （fBm） processor. Two mode-size distributions of aggregates collected from diesel vehicles were confirmed. Mean mass concentration of 339 mg/m3 （dC/dlogdp） existed in the dominant mode （180-320 nm）. A relatively high proportion of these aggregates appeared in PM 1, accentuating the relevance regarding adverse health effects. Furthermore, the fBm processor directly parameterized the SEM images of fractal like aggregates and successfully quantified surface texture to extract Hurst coefficients （H） of the aggregates. For aggregates from vehicles equipped with a universal cylinder number, the H value was independent of engine operational conditions. A small H value existed in emitted aggregates from vehicles with a large number of cylinders. This study found that aggregate fractal dimension related to H was in the range of 1.641-1.775, which is in agreement with values reported by previous TEM-based experiments. According to EDS analysis, carbon content ranged in a high level of 30%-50% by weight for diesel soot aggregates. The presence of Na and Mg elements in these sampled aggregates indicated the likelihood that some engine enhancers composed of biofuel or surfactants were commonly used in on-road vehicles in Taiwan. In particular, the morphological H combined with carbon content detection can be useful for characterizing chain-like or cluster diesel soot aggregates in the atmosphere.

Analysis and application of error concealment tools in AVS-M decoder

Audio Video coding Standard (AVS) is the latest audio and video coding standard of China. AVS Part 7 (also known as AVS-M) targets mobility applications where error concealment is of great importance. This paper first briefly introduces the general concept of error concealment. Then two error concealment schemes are proposed and implemented on AVS-M decoder under different test conditions. Simulation results of the schemes and suggestions on how to use these tools are also provided.

Methods and Techniques of Electricity Thieving in Pakistan

The situation of electricity in Pakistan has been alarming from the last ten years. The deficiency in electricity has not only obstructed the business activities but also affected the domestic consumers, educational institutes and hospitals. Usually electrical power companies are liable for electricity shortfall and power interruption. However, electricity end consumers are also equally responsible behind strange shortfall and unusual power interruption. Frequently, the consumers use the heavy electrical equipment in their homes including heaters, geysers, irons and water motors which causes the more electricity consumption, load shedding and huge amount of bills. For escaping the huge amount of electricity bill, the consumers commit the illegal and unethical connections. The illegal usage of electrical power failed the power companies to plan schedule load shedding accordingly and the other side the damaged electricity wires or Pole Mount Transformer increased faults due to overburdening, which directly affected on extend power interruption. In addition that, responsible teams of electrical power companies cannot reach instantly to repair faults and prevent the theft. Electricity thieving is social crime committed by the consumers or meter readers which causes the electricity strange shortfall within country. This paper presents the practical demonstration about the common energy theft methods and techniques done by electricity consumers within their home and residential building. In Pakistan EPC (electrical power companies) deploy the traditional electromechanical meters for electricity consumption measurements, however, these meters do not have any real time communication. Therefore there are many easy ways to manipulate the meter reading as well as internal structural of metering system.

Statistical Analysis of Network Based Issues and Their Impact on Social Computing Practices in Pakistan

Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.

Reliability Analysis of Satellite Turntable System under Multiple Operation Modes Based on Multi-Valued Decision Diagrams

As a payload support system deployed on satellites,the turntable system is often switched among different working modes during the on-orbit operation,which can experience great state changes.In each mode,the missions to be completed are different,consecutive and non-over-lapping,from which the turntable system can be considered to be a phased-mission system(PMS).Reliability analysis for PMS has been widely studied.However,the system mode cycle characteristic has not been taken into account before.In this paper,reliability analysis method of the satellite turntable system is proposed considering its multiple operation modes and mode cycle characteristic.Firstly,the multi-valued decision diagrams(MDD)manipulation rules between two adjacent mission cycles are proposed.On this basis,MDD models for the turntable system in different states are established and the reliability is calculated using the continuous time Markov chains(CTMC)method.Finally,the comparative study is carried out to show the effectiveness of our proposed method.

Effective Capacity in Cognitive Radio Networks with Relay and Primary User Emulator

In this paper,the transmission performances are studied in cognitive radio networks with primary user emulator and relay existence.In the proposed network,the users include primary users,secondary users and primary user emulators.The decreasing access priority of the users are primary users,primary user emulators and secondary users.Different user access to the network results in different transmission effects.We impose interference power constraints on the secondary users to protect the primary users from being interfered.We also adopt the transmission mechanism that transits among more than one secondary transmitters,secondary receivers and relays.The transition models of the transmission states are proposed to describe the transmission mechanism.To investigate the transmission performances,the theory of effective capacity is adopted.The transmission performances in terms of effective capacity are expressed and demonstrated under different transmission policies.The overall effective capacity,as the overall data traffic in the cognitive radio network,is calculated.Besides,the overall effective capacity is demonstrated under different transmission strategies.The results show the greedy transmission strategy outperforms the rest of the transmission 8 policies in the overall effective capacity.For a larger number of the users,the effective capacity converges to a certain value.

WORST SUBCARRIER AVOIDING WATER-FILLING SUBCARRIER ALLOCATION SCHEME FOR OFDM-BASED CRN

Efficient and reliable subcarrier power joint allocation is served as a promising problem in cognitive OFDM-based Cognitive Radio Networks (CRN). This paper focuses on optimal subcarrier allocation for OFDM-based CRN. We mainly propose subcarrier allocation scheme denoted as Worst Subcarrier Avoiding Water-filling (WSAW), which is based on Rate Adaptive (RA) criterion and three constraints are considered in CRN. The algorithm divides the assignment procedure into two phases. The first phase is an initial subcarrier allocation based on the idea of avoiding selecting the worst subcarrier in order to maximize the transmission rate; while the second phase is an iterative adjustment process which is realized by swapping pairs of subcarriers between arbitrary users. The proposed scheme could assign subcarriers in accordance with channel coherence time. Hence, real time subcarrier allocation could be implemented. Simulation results show that, comparing with the similar existing algorithms, the proposed scheme could achieve larger capacity and a near-optimal BER performance.

Highly parallel implementation of sub-pixel interpolation for AVS HDTV decoder

In this paper, we propose an effective VLS1 architecture of sub-pixel interpolation for motion compensation in the AVS HDTV decoder. To utilize the similar arithmetical operations of 15 luma sub-pixel positions, three types of interpolation filters are proposed. A simplified multiplier is presented due to the limited range of input in the chroma interpolation process. To improve the processing throughput, a parallel and pipelined computing architecture is adopted. The simulation results show that the proposed hardware implementation can satisfy the real-time constraint for the AVS HDTV （1 920× 1 088） 30 fps decoder by operating at 108 MHz with 38.18k logic gates. Meanwhile, it costs only 216 cycles to accomplish one macroblock, which means the B frame sub-pixel interpolation can be realized by using only one set of the proposed architecture under real-time constraints.

Evidence of parameter-induced aperiodic stochastic resonance with fixed noise

Stochastic resonance （SR） is based on the cooperative effect between the stochastic dynamical system and the external forcing. As is well known, the cooperative effect is produced by adding noises. In this paper, we show the evidence that by changing the system parameters and the signal intensity, a nonlinear system in the presence of an input aperiodic signal can yield the cooperative effect, with the noise fixed. To quantify the nonlinear system output, we determine the theoretical bit error rate （BER）. By numerical simulation, the validity of the theoretical derivation is checked. Besides, we show that parameter-induced SR is more realizable than SR via adding noises, especially when the noise intensity exceeds the resonance level, or when the characteristic of the noise is not known.

AN ADAPTIVE MEASUREMENT SCHEME BASED ON COMPRESSED SENSING FOR WIDEBAND SPECTRUM DETECTION IN COGNITIVE WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

A SPARSITY AND COMPRESSION RATIO JOINT ADJUSTMENT METHOD FOR COLLABORATIVE SPECTRUM SENSING

Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.

Individual Differences in Cognitive Performance Regulated by Deep-Brain Activity during Mild Passive Hyperthermia and Neck Cooling

Hyperthermia-induced decline in cognitive performance is a moderate complication that poses challenges to the maintenance of safety. Although the underlying mechanism can be attributed to the disruption of brain networks, the propensity remains unclear. This study aimed to test the hypothesis that the extent of the alterations in cognitive performance is governed by the activity of deep brain structures, including monoaminergic neural systems. A decline in cognitive performance during mild hyperthermia and the beneficial effects of neck cooling were demonstrated using the Continuous Performance Test as a battery of cognitive tasks. Aspects of cognitive performance were characterized using the deep-brain activity (DBA) index as a neural activity parameter and the State-Trait Anxiety Inventory to assess the extent of alterations in cognitive performance as an individual measure. It was found that a higher average DBA index during tasks is essential for high cognitive performance in the heat. This beneficial effect of DBA is governed by the upper brainstem. This DBA benefit is more significant for individuals with higher average DBA indices at rest in a normal environment. Individual differences in cognitive performance in the heat are governed by differences in DBA. In addition, the beneficial effect of DBA on cognitive performance in heat only applies under conditions including neck cooling. This limited neck-cooling effect is attributed to anti-homeostatic thermoregulatory responses to cognitive tasks regulated by DBA.

Cognitive Control and Brain Network Dynamics during Word Generation Tasks Predicted Using a Novel Event-Related Deep Brain Activity Method

There is a growing interest in the diagnosis and treatment of patients with dementia and cognitive impairment at an early stage. Recent imaging studies have explored neural mechanisms underlying cognitive dysfunction based on brain network architecture and functioning. The dorsal anterior cingulate cortex (dACC) is thought to regulate large-scale intrinsic brain networks, and plays a primary role in cognitive processing with the anterior insular cortex (aIC), thus providing salience functions. Although neural mechanisms have been elucidated at the connectivity level by imaging studies, their understanding at the activity level still remains unclear because of limited time-based resolution of conventional imaging techniques. In this study, we investigated temporal activity of the dACC during word (verb) generation tasks based on our newly developed event-related deep brain activity (ER-DBA) method using occipital electroencephalogram (EEG) alpha-2 powers with a time resolution of a few hundred milliseconds. The dACC exhibited dip-like temporal waveforms indicating deactivation in an initial stage of each trial when appropriate verbs were successfully generated. By contrast, monotonous increase was observed for incorrect responses and a decrease was detected for no responses. The dip depth was correlated with the percentage of success. Additionally, the dip depth linearly increased with increasing slow component of the DBA index at rest across all subjects. These findings suggest that dACC deactivation is essential for cognitive processing, whereas its activation is required for goal-oriented behavioral outputs, such as cued speech. Such dACC functioning, represented by the dip depth, is supported by the activity of the upper brainstem region including monoaminergic neural systems.

Role of the Dorsal Anterior Cingulate Cortex in Relational Memory Formation: A Deep Brain Activity Index Study

The dorsal area of the anterior cingulate cortex (ACC) constructs the salience network associated with the anterior insular cortex. Conventional brain imaging studies, such as functional magnetic resonance imaging (fMRI), have demonstrated that relational memory formation occurs in the ACC. However, how such memory is encoded and retrieved remains unknown due to limited time resolution of conventional fMRI. This study aimed to investigate temporal dynamics of the dorsal ACC (dACC) during word-pair tasks based on a newly developed event-related deep brain activity (ER-DBA) method using occipital electroencephalogram (EEG) signal powers. The method assesses dACC activity at a temporal resolution of approximately 0.3 s beyond the conventional resolution limit. We found that transient deactivation of dACC during the presentation of the second word of each pair was essential for encoding success regardless of whether the words were related or unrelated. We also found that memory accuracy was not affected by the intervention of inter-trials until the recall trial. Taken together, these findings suggest that dACC deactivation for encoding success is accompanied with short-term potentiation essential for durability of memory. We further found that false memory formation associated with the presentation of word pairs was occasionally committed. In such cases, dACC exhibited a similar transient deactivation although false memory commission was independent of related or unrelated conditions. Our findings suggest that encoding and retrieval of associates are paralleled and that simultaneous production of associates seems to be an essential strategy for successful relational memory formation. The study was limited to the assessment of dACC activity and did not account for other regional brain activities or receptor regulation related to short-term potentiation. We detected fast behavior of dACC during relational memory formation using the novel ER-DBA method. Such temporal dynamics will be important for eliciting underlying mechanisms of memory dysfunctions.

Optimal Positions of Relay Stations for Cluster-Based Two-Hop Cellular Network

Multihop cellular networks is an exciting and a fledgling area of wireless communication which offers huge potential in terms of coverage enhancement, data-rates, power reduction, and various other quality of service improvements. However, resource allocation in MCN is an NP-hard problem. Hence, significant research needs to be done in this field in order to efficiently design the radio network. In this paper, optimal position of relay stations in a hierarchical cluster-based two-hop cellular network is investigated. Vector algebra has been used to derive general equation for carrier-to-interference ratio (C/I) of a mobile station. It has been observed that when the transmit power of base station (BS) and the gateway (GTW)/relay station (RS) are same, the RSs should be located close to mid-point of BS and the edge of the cell. However, significantly, when the transmit power of the BS is greater than that of the GTW, then the RSs should be placed closer to the edge of the cell, in order to maximize the minimum C/I at any point in the cell. This in turn results in higher modulation technique at the physical layer, and hence, a higher data-rate to all the users in the system.

Implicit Variational Inclusions and Algorithms Involving （A, η）-monotone Operators in 2-uniformly Smooth Banach Spaces

This paper deals with a new class of nonlinear set valued implicit variational inclusion problems involving （A, η）-monotone mappings in 2-uniformly smooth Banach spaces. Semi-inner product structure has been used to study the （A, η）-monotonicity. Using the generalized resolvent operator technique and the semi-inner product structure, the approximation solvability of the proposed problem is investigated. An iterative algorithm is constructed to approximate the solution of the problem. Convergence analysis of the proposed algorithm is investigated. Similar results are also investigated for variational inclusion problems involving （H, η）-monotone mappings.

Relations between Climatic Changes and High Pollution Levels in Bulgaria

One of the important consequences of the climatic changes is the potential danger of increasing the concentrations of some pollutants, which may cause damages to humans, animals and plants. Therefore, it is worthwhile to study carefully the impact of future climate changes on the high pollution levels. The major topic of the discussion in this paper is the increase of some ozone levels in Bulgaria, but several related topics are also discussed. The particular mathematical tool applied in this study is a large-scale air pollution model, the Unified Danish Eulerian Model (UNI- DEM), which was successfully used in several investigations related to potentially dangerous pollution levels in several European countries. This model is described by a non-linear system of partial differential equations, which is solved numerically by using (a) advanced numerical algorithms and (b) modern computer architectures. Moreover, (c) the code is parallelized and (d) the cache memories of the available computers are efficiently utilized. It is shown that in Bulgaria, as in the other European countries, the climatic changes will result in permanent increases of some quantities related to the ozone pollution. The important issue is that in our study the changes of the dangerous pollution levels are followed year by year. In this way, an attempt is made both to capture the effect of the interannual variations of the meteorological conditions on the levels of the ozone concentrations and to follow directly the influence of the climatic changes on the pollution levels. Moreover, the sensitivity of the pollution levels to variations of the human made (anthropogenic) and natural (biogenic) emissions is also discussed.

State-of-the-art machine learning techniques for melanoma skin cancer detection and classification:a comprehensive review

Skin cancer is among the most common and lethal cancer types,with the number of cases increasing dramatically worldwide.If not diagnosed in the nascent stages,it can lead to metastases,resulting in high mortality rates.Skin cancer can be cured if detected early.Consequently,timely and accurate diagnosis of such cancers is currently a key research objective.Various machine learning technologies have been employed in computer-aided diagnosis of skin cancer detection and malignancy classification.Machine learning is a subfield of artificial intelligence(AI)involving models and algorithms which can learn from data and generate predictions on previously unseen data.The traditional biopsy method is applied to diagnose skin cancer,which is a tedious and expensive procedure.Alternatively,machine learning algorithms for cancer diagnosis can aid in its early detection,lowering the workload of specialists while simultaneously enhancing skin lesion diagnostics.This article presented a critical review of select state-of-the-art machine learning techniques used to detect skin cancer.Several studies had been collected,and an analysis of the performance of k-nearest neighbors,support vector machine,and convolutional neural networks algorithms on benchmark datasets was conducted.The shortcomings and disadvantages of each algorithm were briefly discussed.Challenges in detecting skin cancer were highlighted and the scope for future research was proposed.

Design and fabrication of Si LED with the N-well-P^+ junction based on standard CMOS technology

A wedge shape Si LED is designed and fabricated with 0.35 μm double-grating standard CMOS technology. The device structure is based on the N-well-P+ junction. The P+ has a wedge shape and is surrounded by the N-well. The micrographs of Si LEDs' emitting and layout are captured. The I-V characteristic and spectra of the Si LED are tested. Under room temperature and backward bias, its radiant luminosity is 12 nW at 100 mA, and the wavelength of the emitting peak is located at 764 nm.