Abstract: Decentralized eco-sanitation system is a promising and sustainable mode comparing to the century-old centralized conventional sanitation system. The decentralized concept relies on an environmentally and economically sound management of water, nutrient and energy fluxes. Source-separation systems for urban waste management collect different solid waste and wastewater streams separately to facilitate the recovery of valuable resources from wastewater (energy, nutrients). A resource recovery centre constituted for 20,000 people will act as the functional unit for the treatment of urban waste of a high-density population community, like Singapore. The decentralized system includes urine treatment, faeces and food waste co-digestion, and horticultural waste and organic fraction of municipal solid waste treatment in composting plants. A design model is developed to estimate the input and output in terms of materials and energy. The inputs of urine (yellow water, YW) and faeces (brown water, BW) are calculated by considering the daily mean production of urine and faeces by humans and the water consumption of no-mix vacuum toilet (0.2 and 1 L flushing water for urine and faeces, respectively). The food waste (FW) production is estimated to be 150 g wet weight/person/day. The YW is collected and discharged by gravity into tank. It was found that two days are required for urine hydrolysis and struvite precipitation. The maximum nitrogen (N) and phosphorus (P) recovery are 150-266 kg/day and 20-70 kg/day, respectively. In contrast, BW and FW are mixed for co-digestion in a thermophilic acidification tank and later a decentralized/centralized methanogenic reactor is used for biogas production. It is determined that 6.16-15.67 m3/h methane is produced which is equivalent to 0.07-0.19 kWh/ca/day. The digestion residues are treated with horticultural waste and organic fraction of municipal waste in co-composting plants.
Abstract: Application of nanoparticles as additives in membrane
synthesis for improving the resistance of membranes against fouling
has triggered recent interest in new membrane types. However, most
nanoparticle-enhanced membranes suffer from the tradeoff between
permeability and selectivity. In this study, nano-WS2 was explored as
the additive in membrane synthesis by non-solvent induced phase
separation. Blended PES-WS2 flat-sheet membranes with the
incorporation of ultra-low concentrations of nanoparticles (from 0.025
to 0.25%, WS2/PES ratio) were manufactured and investigated in
terms of permeability, fouling resistance and solute rejection.
Remarkably, a significant enhancement in the permeability was
observed as a result of the incorporation of ultra-low fractions of
nano-WS2 to the membrane structure. Optimal permeability values
were obtained for modified membranes with 0.10%
nanoparticle/polymer concentration ratios. Furthermore, fouling
resistance and solute rejection were significantly improved by the
incorporation of nanoparticles into the membrane matrix. Specifically,
fouling resistance of modified membrane can increase by around 50%.
Abstract: By employing BS (Base Station) cooperation we can
increase substantially the spectral efficiency and capacity of cellular
systems. The signals received at each BS are sent to a central unit that
performs the separation of the different MT (Mobile Terminal) using
the same physical channel. However, we need accurate sampling and
quantization of those signals so as to reduce the backhaul
communication requirements.
In this paper we consider the optimization of the quantizers for BS
cooperation systems. Four different quantizer types are analyzed and
optimized to allow better SQNR (Signal-to-Quantization Noise
Ratio) and BER (Bit Error Rate) performance.
Abstract: This paper presents the source extraction system which can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.
Abstract: In the last few years, three multivariate spectral
analysis techniques namely, Principal Component Analysis (PCA),
Independent Component Analysis (ICA) and Non-negative Matrix
Factorization (NMF) have emerged as effective tools for oscillation
detection and isolation. While the first method is used in determining
the number of oscillatory sources, the latter two methods
are used to identify source signatures by formulating the detection
problem as a source identification problem in the spectral domain.
In this paper, we present a critical drawback of the underlying linear
(mixing) model which strongly limits the ability of the associated
source separation methods to determine the number of sources
and/or identify the physical source signatures. It is shown that the
assumed mixing model is only valid if each unit of the process gives
equal weighting (all-pass filter) to all oscillatory components in its
inputs. This is in contrast to the fact that each unit, in general, acts
as a filter with non-uniform frequency response. Thus, the model
can only facilitate correct identification of a source with a single
frequency component, which is again unrealistic. To overcome
this deficiency, an iterative post-processing algorithm that correctly
identifies the physical source(s) is developed. An additional issue
with the existing methods is that they lack a procedure to pre-screen
non-oscillatory/noisy measurements which obscure the identification
of oscillatory sources. In this regard, a pre-screening procedure
is prescribed based on the notion of sparseness index to eliminate
the noisy and non-oscillatory measurements from the data set used
for analysis.